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Remote Sensing
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Land Surface Phenology and Seasonality: Novel Approaches and Applications
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Land Surface Phenology and Seasonality: Novel Approaches and Applications

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (28 February 2017) | Viewed by 116517

Special Issue Editors

Dr. Geoffrey M. Henebry

E-Mail Website
Guest Editor
Dept of Geography, Environment, and Spatial Sciences & Center for Global Change and Earth Observations, Michigan State University, East Lansing, MI 48824, USA
Interests: land surface phenology; ecological remote sensing; grasslands; croplands; urban areas; land cover/land use change
Dr. Forrest M. Hoffman

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Guest Editor
Climate Change Science Institute, Computational Earth Sciences Group, Oak Ridge National Laboratory, Oak Ridge, TN 37831-6301, USA
Interests: vegetation and soil dynamics; global Earth system modeling; global carbon cycle modeling; terrestrial and marine biogeochemistry; model benchmarking and model–data integration; high performance computational science; large scale Earth system data analytics and machine learning; remote sensing; ecological modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Jitendra Kumar

E-Mail Website
Guest Editor
Climate Change Science Institute, Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
Interests: remote sensing; landscape ecology; forest ecology; hydrology; clustering; classification; feature extraction; machine learning; data mining; algorithms; parallel and distributed computing
Dr. Xiaoyang Zhang

E-Mail Website
Guest Editor
Geospatial Sciences Center of Excellence, South Dakota State University, Brookings, SD 57007, USA
Interests: biomass burning emissions; burned area; fire seasonality; climate change; real-time monitoring; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid pace of land surface phenology (LSP) monitoring and modeling has positioned the field to make significant advances in the coming decade. The primary lesson to be learned from the past 15 years of LSP research is that there is no single approach to LSP that fits all situations. The community is starting to explore approaches to LSP monitoring and modeling that embrace suites of sensors and algorithms toward developing biome-tuned LSP models. Land surface seasonality (LSS) is a recent concept that could be used in tandem with LSPs to tackle biome-specific monitoring and modeling. For example, the seasonality of soil freeze/thaw is a key transition in ecosystem processes and one that can be monitored effectively using microwave frequencies with passive and active sensors.

Although most of the LSP literature has focused on green-up dynamics, it is necessary to move beyond a focus on spring. Recent work on the dynamics of autumnal senescence has demonstrated some novel approaches, but there is much more to explore in monitoring and modeling the processes of canopy coloring, nutrient retranslocation, drying, and foliage abscission.

Much of the LSP literature has focused on optical imagery and on very few vegetation indices. It is time to explore the possibilities of incorporating multiple remote sensing modalities beyond the visible to near infrared end of the spectrum.

Many LSP studies have focused on natural landscapes and ecosystems, but we should also leverage our understanding of human-managed systems, whether in croplands or urbanized areas, to advance LSP monitoring and modeling.

Very little research has been done to date on the influence of LSPs and LSSs on the spatial structure of surface characteristics and vice versa. A few field studies have shown how the spatial pattern of reflectance changes during the growing season. Spatial analyses of image time series have revealed characteristic seasonal patterns in reflectance, emittance, and backscattering that can enable the detection and evaluation of change. With the increased accessibility of the Landsat archive, this avenue of LSP research could be very fruitful area in the coming decade.

Cross-calibration of LSP metrics with other indicators of phenology has been studied since the launch of ERTS (Landsat-1) in 1972. More recent efforts to cross-calibrate estimates of phenophases have found a tendency for LSP timings to be early relative to a suite of bioclimatic. Which sorts of data constitute appropriate reference sets for ground-level phenological observations remains an open question with multiple regional solutions tuned to specific vegetation assemblages the most likely answer. However, it is clear that the community needs coordinated observations across multiple scales to link landscape heterogeneity to pixel variability. The use of flux tower observations and "phenocams" for cross-calibration are critical, but there is another source of finer spatial resolution remote sensing data that promises a rich source for cross-calibration efforts, viz., the global Landsat data record.

Validation of land surface products is the proverbial "elephant in the room". Note that we say land surface products and not land surface phenology products. The challenge facing the remote sensing community is larger than validation of just LSPs. The Land Product Validation Subgroup (LPVS) of the Committee on Earth Observation Systems (CEOS) Working Group on Calibration and Validation (WGCV) has been active in a number of areas (http://lpvs.gsfc.nasa.gov), including land surface phenology (http://lpvs.gsfc.nasa.gov/pheno_home.html). Despite an effort to self-organize, progress in bringing the LSP community together to engage in validation exercises has been slow, compared to what has been accomplished for leaf area index (LAI) retrievals. This situation is due, in large part, to a lack of funding for a validation campaign, but it is also attributable to (a) the relative scale-invariance of intensive variables like vegetation indices, (b) the sensitivity of vegetation indices to sensor band centers and bandwidths, (c) the lack of sharply defined phenometrics, and (d) the various ways to generate phenometrics from image time series.

We invite you to submit articles concerning your recent research in modeling and/or measuring and monitoring land surface phenologies and seasonalities with respect to the following topics:

  • Beyond NDVI and EVI: using narrowband spectral indices to capture phenophase transitions
  • Beyond VNIR: using longwave sensors (active and passive) to capture phenophase transitions
  • LSPs from solar induced fluorescence (SIF)
  • LSPs in croplands
  • LSPs in grasslands, savannas, and shrublands
  • LSPs in tropical ecosystems, including croplands
  • LSPs in and around cities
  • LSPs in mountain ecosystems
  • LSPs at high latitudes
  • LSPs in arid ecosystems
  • LSPs in lotic, lentic, estuarine, and marine ecosystems
  • LSPs and spatial and spatio-temporal patterning
  • Cross-calibration of LSPs
  • Validation of LSPs

Authors are required to check and follow the specific Instructions to Authors, https://www.mdpi.com/journal/remotesensing/instructions.

Dr. Geoffrey M. Henebry
Dr. Forrest M. Hoffman
Dr. Jitendra Kumar
Dr. Xiaoyang Zhang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Published Papers (14 papers)

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Research

9337 KiB  
Article
Response of Land Surface Phenology to Variation in Tree Cover during Green-Up and Senescence Periods in the Semi-Arid Savanna of Southern Africa
by Moses A. Cho, Abel Ramoelo and Luthando Dziba
Remote Sens. 2017, 9(7), 689; https://doi.org/10.3390/rs9070689 - 4 Jul 2017
Cited by 32 | Viewed by 5122
Abstract
Understanding the spatio-temporal dynamics of land surface phenology is important to understanding changes in landscape ecological processes of semi-arid savannas in Southern Africa. The aim of the study was to determine the influence of variation in tree cover percentage on land surface [...] Read more.
Understanding the spatio-temporal dynamics of land surface phenology is important to understanding changes in landscape ecological processes of semi-arid savannas in Southern Africa. The aim of the study was to determine the influence of variation in tree cover percentage on land surface phenological response in the semi-arid savanna of Southern Africa. Various land surface phenological metrics for the green-up and senescing periods of the vegetation were retrieved from leaf index area (LAI) seasonal time series (2001 to 2015) maps for a study region in South Africa. Tree cover (%) data for 100 randomly selected polygons grouped into three tree cover classes, low (<20%, n = 44), medium (20–40%, n = 22) and high (>40%, n = 34), were used to determine the influence of varying tree cover (%) on the phenological metrics by means of the t-test. The differences in the means between tree cover classes were statistically significant (t-test p < 0.05) for the senescence period metrics but not for the green-up period metrics. The categorical data results were supported by regression results involving tree cover and the various phenological metrics, where tree cover (%) explained 40% of the variance in day of the year at end of growing season compared to 3% for the start of the growing season. An analysis of the impact of rainfall on the land surface phenological metrics showed that rainfall influences the green-up period metrics but not the senescence period metrics. Quantifying the contribution of tree cover to the day of the year at end of growing season could be important in the assessment of the spatial variability of a savanna ecological process such as the risk of fire spread with time. Full article
(This article belongs to the Special Issue Land Surface Phenology and Seasonality: Novel Approaches and Applications)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Biomes of South Africa (<b>a</b>) (Mucina and Rutherford, 2006); and mean annual precipitation (<b>b</b>) (Schulze, 2007). One hundred random plots selected to analyse spatial and temporal patterns in land surface phenological metrics (<b>c</b>).</p> Full article ">Figure 2
<p>Tree cover (%) estimation using classification of high resolution Google Earth imagery (<b>a</b>) into tree and background classes (<b>b</b>). The polygon boundary is indicated in black on image (<b>a</b>).</p> Full article ">Figure 3
<p>Sample time series of two spectral indices extracted from MODIS imagery of the study area, (<b>a</b>) the Normalised Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), and (<b>b</b>) leaf area index (LAI) derived from the red and near-infrared bands.</p> Full article ">Figure 4
<p>Leaf area index map of South Africa (Julian day 73) generated by inversion of PROSAIL radiative transfer model on MODIS imagery.</p> Full article ">Figure 5
<p>Land surface phenological metrics resulting from fitting an inverted Gaussian model to the LAI austral year curve on the green-up and senescence periods of the vegetated landscape. SGS, MGR, PGS, MSR and EGS denote dates corresponding to start, maximum growing rate, peak growth, maximum senescing rate and end of growing season, respectively.</p> Full article ">Figure 6
<p>Fourteen-year median maps of the various phenological metrics: (<b>a</b>–<b>e</b>) the day of the austral year at start (SGS), maximum growing rate (MGR), peak growth (PGS), maximum senescing rate (MSR) and end of growing season (EGS), respectively. The austral year used in the study ranges from 4 July (Julian Day 185) of one year to 26 June (Day 177) of the following year.</p> Full article ">Figure 7
<p>Differences in land surface phenology between three tree cover (%) classes (low, medium and high) for day of the year at: (<b>a</b>) start of growing season; (<b>b</b>) maximum growing rate; (<b>c</b>) peak growth; (<b>d</b>) maximum senescing rate; and (<b>e</b>) end of growing season. The Austral used in the study ranges from 4 July (Julian Day 185) of one year to 26 June (Day 177) of the following year.</p> Full article ">Figure 8
<p>Linear regression analyses between tree cover (%) and landscape phenological metrics for day of the year at: (<b>a</b>) start of growing season; (<b>b</b>) maximum growing rate; (<b>c</b>) peak production; (<b>d</b>) maximum senescing rate; and (<b>e</b>) end of growing season. The growing season used in the study ranges from 4 July (Julian Day 185) of one year to 26 June (Day 177) of the following year.</p> Full article ">Figure 9
<p>Model used to map tree cover (%) in the study area.</p> Full article ">Figure 10
<p>Tree cover (%) derived from the day of the austral year at maximum senescing rate (a land surface phenological metric).</p> Full article ">Figure 11
<p>Inter-annual variability in land surface phenological metrics. Vertical bars denote ±95% confidence interval. SGS, MGR, PGS, MSR and EGS denote day of year at Start of growing season, maximum growing rate, peak growth, maximum senescing rate and end of growing season, respectively.</p> Full article ">Figure 12
<p>Yearly deviation of July-September mean rainfall (5 meteorological stations) from the 13 year (2002–2014) mean (<b>a</b>); and yearly deviation of start of growing season date for 100 randomly selected polygons in the study area (<b>b</b>).</p> Full article ">
7888 KiB  
Article
Comparing Passive Microwave with Visible-To-Near-Infrared Phenometrics in Croplands of Northern Eurasia
by Woubet G. Alemu and Geoffrey M. Henebry
Remote Sens. 2017, 9(6), 613; https://doi.org/10.3390/rs9060613 - 15 Jun 2017
Cited by 8 | Viewed by 6588
Abstract
Planting and harvesting times drive cropland phenology. There are few datasets that derive explicit phenological metrics, and these datasets use the visible to near infrared (VNIR) spectrum. Many different methods have been used to derive phenometrics such as Start of Season (SOS) and [...] Read more.
Planting and harvesting times drive cropland phenology. There are few datasets that derive explicit phenological metrics, and these datasets use the visible to near infrared (VNIR) spectrum. Many different methods have been used to derive phenometrics such as Start of Season (SOS) and End of Season (EOS), leading to differing results. This discrepancy is partly due to spatial and temporal compositing of the VNIR satellite data to minimize data gaps resulting from cloud cover, atmospheric aerosols, and solar illumination constraints. Phenometrics derived from the downward Convex Quadratic model (CxQ) include Peak Height (PH) and Thermal Time to Peak (TTP), which are more consistent than SOS and EOS because they are minimally affected by snow and frost and other non-vegetation related issues. Here, we have determined PH using the vegetation optical depth (VOD) in three microwave frequencies (6.925, 10.65 and 18.7 GHz) and accumulated growing degree-days derived from AMSR-E (Advanced Microwave Scanning Radiometer on EOS) data at a spatial resolution of 25 km. We focus on 50 AMSR-E cropland pixels in the major grain production areas of Northern Eurasia (Ukraine, southwestern Russia, and northern Kazakhstan) for 2003–2010. We compared the land surface phenologies of AMSR-E VOD and MODIS NDVI data. VOD time series tracked cropland seasonal dynamics similar to that recorded by the NDVI. The coefficients of determination for the CxQ model fit of the NDVI data were high for all sites (0.78 < R2 < 0.99). The 10.65 GHz VOD (VOD1065GHz) achieved the best linear regression fit (R2 = 0.84) with lowest standard error (SEE = 0.128); it is therefore recommended for microwave VOD studies of cropland land surface phenology. Based on an Analysis of Covariance (ANCOVA) analysis, the slopes from the linear regression fit were not significantly different by microwave frequency, whereas the intercepts were significantly different, given the different magnitudes of the VODs. PHs for NDVI and VOD were highly correlated. Despite their strong correspondence, there was generally a lag of AMSR-E PH VOD10.65GHz by about two weeks compared to MODIS peak greenness. To evaluate the utility of the PH determination based on maximum value, we correlated the CxQ derived and maximum value determined PHs of NDVI and found that they were highly correlated with R2 of 0.87, but with a one-week bias. Considering the one-week bias between the two methods, we find that PH of VOD10.65GHz lags PH of NDVI by three weeks. We conclude, therefore, that maximum-value based PH of VOD can be a complementary phenometric for the CxQ model derived PH NDVI, especially in cloud and aerosol obscured regions of the world. Full article
(This article belongs to the Special Issue Land Surface Phenology and Seasonality: Novel Approaches and Applications)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Land cover stability in Ukraine (UA), southern Russia (RU), and northern Kazakhstan (KZ) as revealed by International Geosphere Biosphere Programme (IGBP) global land cover classification scheme MODIS 0.05° cropland (IGBP class 12) land cover products for 2003–2010. For details on how the land cover stability analysis was conducted, please refer to [<a href="#B22-remotesensing-09-00613" class="html-bibr">22</a>,<a href="#B34-remotesensing-09-00613" class="html-bibr">34</a>]. In this figure, Yellow = stable core area; Magenta = unstable peripheral areas; Black = no occurrence of the given land cover class. Overlaid are the 49 specific cropland and one mixed forest (site 50—most northern site) study sites. The AMSR-E pixels are numbered from lower to higher latitude. Modified from [<a href="#B22-remotesensing-09-00613" class="html-bibr">22</a>]. Site names, latitudinal and longitudinal coordinates for all the study sites can be found in <a href="#remotesensing-09-00613-t003" class="html-table">Table A1</a>.</p> Full article ">Figure 2
<p>Average cropland (purple), and grassland (orange) cover in percent for 2003–2010 from the MODIS IGBP Land Cover Type 1 Percent Product at ~5.6 km spatial resolution MCD12C1 [<a href="#B27-remotesensing-09-00613" class="html-bibr">27</a>], and tree cover fraction (%) in 2010 from the Landsat ETM+ 30 m resolution Global Land Cover product [<a href="#B33-remotesensing-09-00613" class="html-bibr">33</a>]. X-axis indicates study sites numbered from lower (1) to higher (50) latitudes. <a href="#remotesensing-09-00613-f001" class="html-fig">Figure 1</a> shows the latitudinal distributions of the study sites and countries in which they are found. Sites are sorted from largest (left) to smallest (right) cropland cover percent. Site 50 is a Mixed Forest site, in Mari El, RU. Note that summation of land cover proportions can exceed 100% at some sites, because the tree cover data source is different and at a different scale from the other two land cover classes. Cropland cover in some sites might be larger than what is presented here, since the MODIS land cover data has a separate class named Cropland/Natural Vegetation Mosaic (CNVM, IGBP class 14), which is not illustrated here to avoid overlap with the tree cover data. Land cover values &gt;5% are labeled on the respective bars. These MODIS land cover data are also used in [<a href="#B22-remotesensing-09-00613" class="html-bibr">22</a>].</p> Full article ">Figure 3
<p>Example of the filtering and temporal alignment of the vegetation optical depth (VOD) data with MODIS data. The unfiltered VOD time series appears in the black circles, the eight-day forward moving average filtered VOD data appears at the blue circles, and the MODIS NBAR NDVI appears in blue pluses.</p> Full article ">Figure 4
<p>VOD time series for the three microwave frequencies: green pluses = 18.7; purple circles = 10.65; orange triangles = 6.925 GHz; and blue diamonds = NDVI. Selected series arranged from south to north of our study region: (<b>a</b>) Stavropol, RU, site 2; (<b>b</b>) Kirovohrad, UA, site 15; (<b>c</b>) Petropavlovsk 3, KZ, site 42; and (<b>d</b>) Kazan, RU, site 49; and (<b>e</b>) Mari El, RU, site 50 (MFO site included for contrast). Note that the VOD range for the MFO site is higher (0.2–1.9) than the range for the cropland sites (0.2–1.6).</p> Full article ">Figure 5
<p>VODs in three microwave frequencies (green squares = 18.7, purple circles = 10.65, and orange triangles = 6.925 GHz) and NDVI (blue diamonds) interannual variability of a southern study site (Simferopol, UA, 45.6°N) for 2003–2010. The 6.925 GHz vegetation transmissivity was missing from the source dataset in 2004. Seasonal patterns changed from bimodal (2003–2004) to unimodal (2005–2009) and back to bimodal (2010).</p> Full article ">Figure 6
<p>Average NDVI (2003–2010) as a function of AGDD for cropland sites that encompass the whole latitudinal range: (<b>a</b>) 48, RU; (<b>b</b>) 28, KZ; (<b>c</b>) 11, UA; and (<b>d</b>) 1, RU. Coefficients of determination (R<sup>2</sup>) for these sites ranged from 0.88 to 0.97.</p> Full article ">Figure 7
<p>Scatterplot for TTP<sub>NDVI</sub> as a function of latitude fitted with a convex quadratic model. There is a significant positive relationship (R<sup>2</sup> = 0.50): as latitude increased, TTP<sub>NDVI</sub> increased in lower latitudes to certain limit and then declined in the northernmost study sites (54–56°N latitude).</p> Full article ">Figure 8
<p>Scatterplot for: PH<sub>NDVI</sub> minus NDVI at half-TTP (NAHT) as a function of latitude (<b>a</b>); and PH<sub>NDVI</sub> as a function of latitude (<b>b</b>). Both are fitted with linear trend line yielding strong correspondence for the former (R<sup>2</sup> = 0.81), but no clear correspondence for the latter (R<sup>2</sup> = 0.07).</p> Full article ">Figure 9
<p>Annual phenometrics of NAHT as a function of PH<sub>NDVI</sub>: (<b>a</b>) for all study sites; and (<b>b</b>) selected study sites for 2003–2010. Sites are numbered according to their latitudinal position from south (1) to north (50) (<a href="#remotesensing-09-00613-f001" class="html-fig">Figure 1</a>).</p> Full article ">Figure 10
<p>Scatter plot of the maximum value approach determined PH VODs and NDVI as a function of their corresponding TTP. Note the magnitude of the VODs and NDVI PHs; VODs PH lagged their counterpart NDVI PH; and also the lag among the VODs PH relative to their microwave frequency.</p> Full article ">Figure 11
<p>Scatterplots and linear regression fits of the CxQ model derived PH<sub>NDVI</sub> and maximum value determined PH<sub>VOD</sub> at three microwave frequencies (6.925 GHz (orange triangles), 10.65 GHz (purple circles), and 18.7 GHz (green plus)) for 2003–2010. The PH linear fits for two datasets were high with coefficients of determination of 0.77, 0.84, and 0.78 for the 6.925, 10.65, and 18.7 GHz frequencies, respectively.</p> Full article ">Figure 12
<p>Scatterplots of: (<b>a</b>) the PH<sub>VOD</sub> determined by maximum value and the PH<sub>NDVI</sub> from the CxQ model as a function of their corresponding TTP; and (<b>b</b>) PH<sub>VOD</sub> lags relative to their corresponding PH<sub>NDVI</sub> as a function of the respective TTP VODs. Details can be found in <a href="#remotesensing-09-00613-t003" class="html-table">Table A1</a>.</p> Full article ">Figure 13
<p>Scatterplot and linear fit between the PH<sub>NDVI</sub> derived from the CxQ model and the PH<sub>NDVI</sub> determined by the maximum-value method from the same MODIS dataset for 2003–2010. The linear fit for the PH<sub>NDVI</sub> from the two approaches of the same dataset was high with R<sup>2</sup> = 0.86.</p> Full article ">Figure 14
<p>VOD and NDVI plots for sample sites: (<b>a</b>) Mykolayiv, UA; and (<b>b</b>) Voronezh, RU, affected by the 2007 and 2010 Ukrainian and Russian heatwaves, respectively. In both plots, purple circle represents VOD at 10.65 GHz and blue diamond represents NDVI plots average (2003–2010) excluding the respective heatwave years with relative maximum and minimum error bars. The red circle and orange diamond plots represent heatwave year VODs and NDVIs, respectively, for both sites. Note the PH in both the average and heatwave affected years. Note also the shapes and magnitudes of the time series in the heatwave years relative to the other year’s average.</p> Full article ">Figure 15
<p>Scatter plots of: (<b>a</b>) NDVI at half-TTP as a function of PH<sub>NDVI</sub> derived from a CxQ model for the 2010 heatwave affecting Russia and Kazakhstan; and (<b>b</b>) the 2010 data and the average data for the rest of the years (2003–2009). Note in (<b>a</b>) the position of the 2010 phenometrics presented by red diamonds relative to the other years in their respective sites. Note also the 2010 marker for the upper-right corner, which is for the MFO site in Mari El, Russia.</p> Full article ">
35673 KiB  
Article
Impacts of Thermal Time on Land Surface Phenology in Urban Areas
by Cole Krehbiel, Xiaoyang Zhang and Geoffrey M. Henebry
Remote Sens. 2017, 9(5), 499; https://doi.org/10.3390/rs9050499 - 18 May 2017
Cited by 23 | Viewed by 8230
Abstract
Urban areas alter local atmospheric conditions by modifying surface albedo and consequently the surface radiation and energy balances, releasing waste heat from anthropogenic uses, and increasing atmospheric aerosols, all of which combine to increase temperatures in cities, especially overnight, compared with surrounding rural [...] Read more.
Urban areas alter local atmospheric conditions by modifying surface albedo and consequently the surface radiation and energy balances, releasing waste heat from anthropogenic uses, and increasing atmospheric aerosols, all of which combine to increase temperatures in cities, especially overnight, compared with surrounding rural areas, resulting in a phenomenon called the “urban heat island” effect. Recent rapid urbanization of the planet has generated calls for remote sensing research related to the impacts of urban areas and urbanization on the natural environment. Spatially extensive, high spatial resolution data products are needed to capture phenological patterns in regions with heterogeneous land cover and external drivers such as cities, which are comprised of a mixture of land cover/land uses and experience microclimatic influences. Here we use the 30 m normalized difference vegetation index (NDVI) product from the Web-Enabled Landsat Data (WELD) project to analyze the impacts of urban areas and their surface heat islands on the seasonal development of the vegetated land surface along an urban–rural gradient for 19 cities located in the Upper Midwest of the United States. We fit NDVI observations from 2003–2012 as a quadratic function of thermal time as accumulated growing degree-days (AGDD) calculated from the Moderate-resolution Imaging Spectroradiometer (MODIS) 1 km land surface temperature product to model decadal land surface phenology metrics at 30 m spatial resolution. In general, duration of growing season (measured in AGDD) in green core areas is equivalent to duration of growing season in urban extent areas, but significantly longer than duration of growing season in areas outside of the urban extent. We found an exponential relationship in the difference of duration of growing season between urban and surrounding rural areas as a function of distance from urban core areas for perennial vegetation, with an average magnitude of 669 AGDD (base 0 °C) and the influence of urban areas extending greater than 11 km from urban core areas. At the regional scale, relative change in duration of growing season does not appear to be significantly related to total area of urban extent, population, or latitude. The distance and magnitude that urban areas exert influence on vegetation in and near cities is relatively uniform. Full article
(This article belongs to the Special Issue Land Surface Phenology and Seasonality: Novel Approaches and Applications)
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Figure 1

Figure 1
<p>(<b>a</b>) 2011 National Land Cover Database Land Cover Type over the Upper Midwest region of the United States showing the 19 selected study cities in purple and corresponding region of interest in cyan. (<b>b</b>) MODIS Land Surface Temperature-derived decadal (2003–2012) mean annual accumulated growing degree-days (AGDD) over the Upper Midwest showing the southwest (shades of red; higher AGDD) to northeast (shades of blue; lower AGDD) gradient of thermal time in the region. Additional information on the urban areas can be found in <a href="#remotesensing-09-00499-t001" class="html-table">Table 1</a>.</p> Full article ">Figure 2
<p>(<b>a</b>) Example of land cover type (LCT) classification (derived from the 2011 National Land Cover Database LCT product) over Sioux Falls, SD. “Water”, “Barren”, and “Change” pixels (white) were excluded from the analyses; (<b>b</b>) Example of the four urban spatial subregions used in the analysis including: urban extent (UE), urban core areas (UCAs), green core areas (GCAs), and areas outside of the UE over Sioux Falls, SD.</p> Full article ">Figure 3
<p>Processing outline for MODIS Land Surface Temperature (LST) to accumulated growing degree-days (AGDD) algorithm that converts MODIS LST 8-day composites into annual time series of AGDD (adapted from [<a href="#B40-remotesensing-09-00499" class="html-bibr">40</a>]).</p> Full article ">Figure 4
<p>Quadratic land surface phenology model fit to the 2003–2012 time series of Web-Enabled Landsat Data Normalized Difference Vegetation Index NDVI vs. MODIS Land Surface Temperature-derived accumulated growing degree-days for an example of perennial forest (green) and annual cropland (orange) pixels selected from Omaha, NE. In grey are the phenometrics derived from the model.</p> Full article ">Figure 5
<p>Example of exponential trend model fit to change in duration of growing season (ΔDGS<sub>AGDD</sub>) as a function of distance from nearest urban core area for Omaha-Council Bluffs, NE-IA. The grey diamonds show where the exponential model reaches 95% of asymptotic values, used to calculate the magnitude of ΔDGS<sub>AGDD</sub> and the distance at which urban effects become insignificant. In blue is the model fit to strictly perennial vegetation land cover types and in green annual croplands are included.</p> Full article ">Figure 6
<p>Results from equivalence tests between group means of duration of growing season (DGS<sub>AGDD</sub>). DGS<sub>AGDD</sub> is equivalent between green core areas (green) and urban extent (UE) areas (tan), but significantly lower in areas outside of the UE (brown) for 17 of 19 cities.</p> Full article ">Figure 7
<p>Duration of growing season (DGS<sub>AGDD</sub>) for nine study cities within the greater Minneapolis-St. Paul, MN-WI region. Water is masked (blue) and pixels with quadratic land surface phenology model fit &lt;0.5 are in black.</p> Full article ">Figure 8
<p>Land cover type (LCT) classification scheme for nine study cities within the greater Minneapolis-St. Paul, MN-WI region demonstrating regional differences in dominant LCT between the intensely cultivated regions in the southwest (brown) and increasingly forest/herbaceous LCTs (green/yellow) to the north and east, with the large metropolitan area of Minneapolis-St. Paul (grey) lying between the two regions.</p> Full article ">Figure 9
<p>Exponential trend model fit to difference in duration of growing season (ΔDGS<sub>AGDD</sub>) as a function of distance from nearest UCA for four selected cities. Differences in ΔDGS<sub>AGDD</sub> calculated with croplands (green) and without croplands (blue) are evident, particularly in the predominantly agricultural areas surrounding Omaha-Council Bluffs, NE-IA, and Des Moines, IA, compared to rural Rochester, MN, and Minneapolis-St. Paul, MN-WI, where forests and herbaceous land covers are more widely distributed. The grey diamonds show where the exponential model reaches 95% of asymptotic values, used to calculate the magnitude of ΔDGS<sub>AGDD</sub> and the distance at which urban effects become insignificant.</p> Full article ">Figure 10
<p>Difference in duration of growing season (ΔDGS<sub>AGDD</sub>) in terms of: (<b>a</b>) accumulated growing degree-days (AGDD); (<b>b</b>) calendar days; and (<b>c</b>) percentage of mean DGS<sub>AGDD</sub> for results from model fit with (orange) and without (blue) croplands. Notice that ΔDGS is significantly related to latitude in terms of: total AGDD (<b>a</b>); but not relative (%) ΔDGS (<b>c</b>).</p> Full article ">Figure 11
<p>Examples of linear regression model fit to PH<sub>NDVI</sub> vs. Half-TTP<sub>NDVI</sub> for four selected cities. Note the large variation in Half-TTP<sub>NDVI</sub> for croplands (yellow) and positive linear relationships seen in the three perennial vegetation land cover types.</p> Full article ">
10240 KiB  
Article
Evaluation of the Plant Phenology Index (PPI), NDVI and EVI for Start-of-Season Trend Analysis of the Northern Hemisphere Boreal Zone
by Paulina Karkauskaite, Torbern Tagesson and Rasmus Fensholt
Remote Sens. 2017, 9(5), 485; https://doi.org/10.3390/rs9050485 - 16 May 2017
Cited by 112 | Viewed by 12669
Abstract
Satellite remote sensing of plant phenology provides an important indicator of climate change. However, start of the growing season (SOS) estimates in Northern Hemisphere boreal forest areas are known to be challenged by the presence of seasonal snow cover and limited seasonality in [...] Read more.
Satellite remote sensing of plant phenology provides an important indicator of climate change. However, start of the growing season (SOS) estimates in Northern Hemisphere boreal forest areas are known to be challenged by the presence of seasonal snow cover and limited seasonality in the greenness signal for evergreen needleleaf forests, which can both bias and impede trend estimates of SOS. The newly developed Plant Phenology Index (PPI) was specifically designed to overcome both problems. Here we use Moderate Resolution Imaging Spectroradiometer (MODIS) data (2000–2014) to analyze the ability of PPI for estimating start of season (SOS) in boreal regions of the Northern Hemisphere, in comparison to two other widely applied indices for SOS retrieval: the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI). Satellite-based SOS is evaluated against gross primary production (GPP)-retrieved SOS derived from a network of flux tower observations in boreal areas (a total of 81 site-years analyzed). Spatiotemporal relationships between SOS derived from PPI, EVI and NDVI are furthermore studied for different boreal land cover types and regions. The overall correlation between SOS derived from VIs and ground measurements was rather low, but PPI performed significantly better (r = 0.50, p < 0.01) than EVI and NDVI which both showed a very poor correlation (r = 0.11, p = 0. 16 and r = 0.08, p = 0.24). PPI, EVI and NDVI overall produce similar trends in SOS for the Northern Hemisphere showing an advance in SOS towards earlier dates (0.28, 0.23 and 0.26 days/year), but a pronounced difference in trend estimates between PPI and EVI/NDVI is observed for different land cover types. Deciduous needleleaf forest is characterized by the largest advance in SOS when considering all indices, yet PPI showed less dramatic changes as compared to EVI/NDVI (0.47 days/year as compared to 0.62 and 0.74). PPI SOS trends were found to be higher for deciduous broadleaf forests and savannas (0.54 and 0.56 days/year). Taken together, the findings of this study suggest improved performance of PPI over NDVI and EVI in retrieval of SOS in boreal regions and precautions must be taken when interpreting spatio-temporal patterns of SOS from the latter two indices. Full article
(This article belongs to the Special Issue Land Surface Phenology and Seasonality: Novel Approaches and Applications)
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<p>(<b>A</b>) boreal zone of the Northern hemisphere delineation based on the Terrestrial Ecoregions of the World (TEOW) dataset [<a href="#B51-remotesensing-09-00485" class="html-bibr">51</a>]; (<b>B</b>) land cover classes based on The International Geosphere–Biosphere Programme (IGBP), derived from MODIS Land Cover Type products (MCD12Q1) (Land Processes Distributed Active Archive Center (LP DAAC), <a href="http://lpdaac.usgs.gov" target="_blank">lpdaac.usgs.gov</a>).</p> Full article ">Figure 1 Cont.
<p>(<b>A</b>) boreal zone of the Northern hemisphere delineation based on the Terrestrial Ecoregions of the World (TEOW) dataset [<a href="#B51-remotesensing-09-00485" class="html-bibr">51</a>]; (<b>B</b>) land cover classes based on The International Geosphere–Biosphere Programme (IGBP), derived from MODIS Land Cover Type products (MCD12Q1) (Land Processes Distributed Active Archive Center (LP DAAC), <a href="http://lpdaac.usgs.gov" target="_blank">lpdaac.usgs.gov</a>).</p> Full article ">Figure 2
<p>Vegetation index start of season (SOS) evaluation against gross primary production (GPP) SOS derived for the flux tower sites (<a href="#remotesensing-09-00485-f001" class="html-fig">Figure 1</a>) (<span class="html-italic">n</span> = 81) for (<b>A</b>) the Plant Phenology Index (PPI); (<b>B</b>) the Enhanced Vegetation Index (EVI); and (<b>C</b>) the Normalized Difference Vegetation Index (NDVI); (<b>D</b>) seasonality (2000–2015) of PPI, EVI and NDVI for the pixels used in evaluation against in situ GPP-SOS (average values for all sites shown). Time series is split into three periods for improved readability.</p> Full article ">Figure 3
<p>(<b>A</b>) per pixel average PPI SOS (2000–2014). (<b>B</b>) relative difference in PPI and NDVI SOS and (<b>C</b>) relative difference in PPI and EVI SOS (2000–2014). Water bodies and pixels of forest loss are masked.</p> Full article ">Figure 3 Cont.
<p>(<b>A</b>) per pixel average PPI SOS (2000–2014). (<b>B</b>) relative difference in PPI and NDVI SOS and (<b>C</b>) relative difference in PPI and EVI SOS (2000–2014). Water bodies and pixels of forest loss are masked.</p> Full article ">Figure 4
<p>Per pixel trend of VI SOS (2000–2014). (<b>A</b>) PPI SOS significant pixels; (<b>B</b>) PPI SOS all pixels; (<b>C</b>) EVI SOS significant pixels; (<b>D</b>) EVI SOS all pixels; (<b>E</b>) NDVI SOS significant pixels; (<b>F</b>) NDVI SOS all pixels.</p> Full article ">
8998 KiB  
Article
Characterizing Land Cover Impacts on the Responses of Land Surface Phenology to the Rainy Season in the Congo Basin
by Dong Yan, Xiaoyang Zhang, Yunyue Yu and Wei Guo
Remote Sens. 2017, 9(5), 461; https://doi.org/10.3390/rs9050461 - 9 May 2017
Cited by 17 | Viewed by 6038
Abstract
Knowledge of how rainfall seasonality affects land surface phenology has important implications on understanding ecosystem resilience to future climate change in the Congo Basin. We studied the impacts of land cover on the response of the canopy greenness cycle (CGC) to the rainy [...] Read more.
Knowledge of how rainfall seasonality affects land surface phenology has important implications on understanding ecosystem resilience to future climate change in the Congo Basin. We studied the impacts of land cover on the response of the canopy greenness cycle (CGC) to the rainy season in the Congo Basin on a yearly basis during 2006–2013. Specifically, we retrieved CGC from the time series of two-band enhanced vegetation index (EVI2) acquired by the Spinning Enhanced Visible and Infrared Imager (SEVIRI). We then detected yearly onset (ORS) and end (ERS) of the rainy season using a modified Climatological Anomalous Accumulation (CAA) method based on the daily rainfall time series provided by the Tropical Rainfall Measurement Mission. We further examined the timing differences between CGC and the rainy season across different types of land cover, and investigated the relationship between spatial variations in CGC and rainy season timing. Results show that the rainy season in the equatorial Congo Basin was regulated by a distinct bimodal rainfall regime. The spatial variation in the rainy season timing presented distinct latitudinal gradients whereas the variation in CGC timing was relatively small. Moreover, the inter-annual variation in the rainy season timing could exceed 40 days whereas it was predominantly less than 20 days for CGC timing. The response of CGC to the rainy season varied with land cover. The lead time of CGC onset prior to ORS was longer in tropical woodlands and forests, whereas it became relatively short in grasslands and shrublands. Further, the spatial variation in CGC onset had a stronger correlation with that of ORS in grasslands and shrublands than in tropical woodlands and forests. In contrast, the lag of CGC end behind ERS was widespread across the Congo Basin, which was longer in grasslands and shrublands than that in tropical woodlands and forests. However, no significant relationship was identified between spatial variations in ERS and CGC end. Full article
(This article belongs to the Special Issue Land Surface Phenology and Seasonality: Novel Approaches and Applications)
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<p>A diagram illustrating the procedures for analyzing canopy greenness cycle, rainfall seasonality, and land cover impacts. EVI2: Two-band Enhanced Vegetation Index, CGC: Canopy greenness cycle, CAA: Climatological anomalous accumulation.</p> Full article ">Figure 2
<p>Land cover types in the Congo Basin extracted from the Global Land Cover 2000 product. Black dots in three grid cells represent deciduous woodland, rainforest and grassland, respectively, which were used to illustrate the analyses of CGC and the rainy season in detail. The dashed lines divide the study area into three sub-basins: Northern Congo Basin (NCB), Eastern Congo Basin (ECB), and Southern Congo Basin (SCB).</p> Full article ">Figure 3
<p>An illustration for determining climatological onset of rainy season (ORS) and end of rainy season (ERS) using the Climatological Anomalous Accumulation (CAA) method. The CAA time series at the deciduous woodland grid cell in NCB (<b>a</b>), the rainforest grid cell in ECB (<b>b</b>) and the grassland grid cell in SCB (<b>c</b>). Grid cells 1 and 3 have a unimodal rainfall regime, whereas grid cell 2 has a bimodal rainfall regime.</p> Full article ">Figure 4
<p>A comparison of the cumulative distribution function for CGC onset lead time (<b>a</b>) and CGC end lag time (<b>b</b>) between grassland and shrubland (GRS) and deciduous forest and savanna mosaic (DFS) land cover groups. The Y-axis represents the percentage of grid cells. The X-axis in (<b>a</b>) represents the number of days that CGC onset precedes ORS, whereas the X-axis in (<b>b</b>) indicates the number of days that CGC end lags behind ERS.</p> Full article ">Figure 5
<p>Inter-annual variations in the rainy season and CGC timing at the deciduous woodland grid cell in NCB (<b>a</b>), the rainforest grid cell in ECB (<b>b</b>) and the grassland grid cell in SCB (<b>c</b>). Black and blue lines represent the reconstructed EVI2 temporal trajectory and the anomalous rainfall accumulation, respectively. Daily rainfall is shown as black bars. Green and orange triangles represent CGC onset and end, respectively. Green and orange circles represent the rainy season onset and end, respectively. The onset and end dates are labeled next to the corresponding symbols using a MM/DD format. The left Y-axis indicates anomalous rainfall accumulation. The first Y-axis on the right represents daily rainfall, whereas the second Y-axis displays EVI2.</p> Full article ">Figure 6
<p>The mean value and standard deviation of the rainy season and CGC timings during 2006–2013 across the Congo Basin. Mean value of rainy season timing (<b>a</b>–<b>d</b>); mean value of CGC timings (<b>e</b>–<b>h</b>); standard deviation of the rainy season timing (<b>i</b>–<b>l</b>); standard deviation of CGC timing (<b>m</b>–<b>p</b>). Note that for areas with two rainy seasons (or CGCs) in ECB, the first rainy season and CGC begin in the first cycle ((<b>a</b>) and (<b>e</b>)) and end in the second cycle ((<b>c</b>) and (<b>g</b>)), whereas the second rainy season and CGC begin in the second cycle ((<b>b</b>) and (<b>f</b>)) and end in the first cycle ((<b>d</b>) and (<b>h</b>)). The gray area indicates that the specific rainy season or CGC metric was detected less than four times during 2006–2013.</p> Full article ">Figure 7
<p>Inter-comparison of cumulative distribution functions (CDF) of the timing differences between CGC and the rainy season, among the four land cover groups. Orange, dark red, green and dark green curves represent the CDF for grassland &amp; shrubland, deciduous woodland &amp; forest, deciduous forest &amp; savanna mosaic and tropical rainforests, respectively. X-axis in (<b>a</b>–<b>h</b>) represents CGC onset lead time (i.e., CGC onset–ORS) whereas X-axis in (<b>i</b>–<b>p</b>) represents CGC end lag time (i.e., CGC end–ERS). For each land cover group, Y-axis specifies the proportion of grid cells with a timing difference, up to the difference specified on the X-axis.</p> Full article ">Figure 8
<p>Regression between spatial variation in ORS and CGC onset during 2006–2013 for grassland &amp; shrubland (<b>the top row</b>), deciduous woodland (<b>the middle row</b>) and deciduous forest (<b>the bottom row</b>). X-axis represents ORS, whereas Y-axis represent CGC onset. The number of samples (N) used in each regression analysis is indicated at the upper left corner of a panel, whereas the R-squared value (R<sup>2</sup>) and residual standard error (RSE) are indicated at the lower right corner. The solid black lines represent the fitted regression lines, whereas the dashed black lines represent the 1:1 lines. Lighter shade indicates higher point density. The regressions shown in this figure are all significant at <span class="html-italic">p</span> &lt; 0.001</p> Full article ">
5777 KiB  
Article
Spring and Autumn Phenological Variability across Environmental Gradients of Great Smoky Mountains National Park, USA
by Steven P. Norman, William W. Hargrove and William M. Christie
Remote Sens. 2017, 9(5), 407; https://doi.org/10.3390/rs9050407 - 26 Apr 2017
Cited by 26 | Viewed by 8236
Abstract
Mountainous regions experience complex phenological behavior along climatic, vegetational and topographic gradients. In this paper, we use a MODIS time series of the Normalized Difference Vegetation Index (NDVI) to understand the causes of variations in spring and autumn timing from 2000 to 2015, [...] Read more.
Mountainous regions experience complex phenological behavior along climatic, vegetational and topographic gradients. In this paper, we use a MODIS time series of the Normalized Difference Vegetation Index (NDVI) to understand the causes of variations in spring and autumn timing from 2000 to 2015, for a landscape renowned for its biological diversity. By filtering for cover type, topography and disturbance history, we achieved an improved understanding of the effects of seasonal weather variation on land surface phenology (LSP). Elevational effects were greatest in spring and were more important than site moisture effects. The spring and autumn NDVI of deciduous forests were found to increase in response to antecedent warm temperatures, with evidence of possible cross-seasonal lag effects, including possible accelerated green-up after cold Januarys and early brown-down following warm springs. Areas that were disturbed by the hemlock woolly adelgid and a severe tornado showed a weaker sensitivity to cross-year temperature and precipitation variation, while low severity wildland fire had no discernable effect. Use of ancillary datasets to filter for disturbance and vegetation type improves our understanding of vegetation’s phenological responsiveness to climate dynamics across complex environmental gradients. Full article
(This article belongs to the Special Issue Land Surface Phenology and Seasonality: Novel Approaches and Applications)
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<p>Location of the Great Smoky Mountains National Park study area with respect to the Southeastern USA. Dots show the location of the Smokylook (A) and Smokypurchase (B) PhenoCams used in this study.</p> Full article ">Figure 2
<p>Variation in mean cumulative growing degree days (GDD), cumulative chilling degree days (CDD), precipitation (PCP), daylength (DAYL), and NDVI for all vegetation types expressed as Percent Completion—the percentage of the respective maximum 8-day period values, 2000–2015, for Great Smoky Mountains National Park.</p> Full article ">Figure 3
<p>The influence of elevation and aspect on spring and autumn land surface phenology for deciduous forests of Great Smoky Mountains National Park, 2000–2015. For each similarly colored and dated pair, the mean for mesic cells is shown by a solid line while xeric cells are represented by dashed lines.</p> Full article ">Figure 4
<p>Mean variation in the timing and progression of spring and autumn, 2000–2015, for deciduous forests below 1515 m (5000 ft.) in Great Smoky Mountains National Park.</p> Full article ">Figure 5
<p>Correlation between spring (<b>A</b>) and autumn (<b>B</b>) NDVI and greenness captured by the Smokylook (dotted line, open circles) and Smokypurchase (dashed line, crosses) PhenoCam. NDVI values are the average for all deciduous forests in the Park within a 100 m elevation band centered on the PhenoCam elevation.</p> Full article ">Figure 6
<p>Distribution of 24-day time-lagged correlations of growing degree days (GDD; (<b>A</b>,<b>D</b>)), chilling degree days (CDD; (<b>B</b>,<b>E</b>)) and precipitation (PCP; (<b>C</b>,<b>F</b>)) with spring (<b>A</b>–<b>C</b>) and autumn (<b>D</b>–<b>F</b>) NDVI across the Park, 2000–2015. Arrows represent the fixed spring (16 May) and autumn NDVI (31 October) periods being correlated with antecedent weather that goes backward in time to the left. Two correlation box plots are shown for each date with medians that are connected by a blue line wherein the left member shows pure hardwood forests (<span class="html-italic">N</span> = 17,456 MODIS cells) and the right shows conifer and mixed forests (<span class="html-italic">N</span> = 15,876 MODIS cells). Box plots show the 99th, 75th, 50th, 25th and 1st percentiles for each date-cover type combination. For general guidance, dashed red lines show critical values for 95% confidence intervals with 15 degrees of freedom (years).</p> Full article ">Figure 7
<p>Correlations of mid-spring and mid-autumn NDVI with antecedent weather: (<b>A</b>) Correlation of spring NDVI with growing degree days (GDD); (<b>B</b>) spring NDVI with chilling degree days (CDD); (<b>C</b>) spring NDVI with precipitation (PCP); (<b>D</b>) autumn NDVI with autumn GDD; (<b>E</b>) autumn NDVI with autumn CDD; and (<b>F</b>) autumn NDVI with autumn PCP. Spring weather variables are lagged by two eight-day periods and the autumn variables are lagged by four periods compared to NDVI (see <a href="#remotesensing-09-00407-f006" class="html-fig">Figure 6</a> for context).</p> Full article ">Figure 8
<p>Relative mean spring and autumn NDVI for pure hardwood forests between 610 and 1220 m (2000–4000 ft.) elevation, 2000–2015, compared to cumulative growing degree days (GCC) and cumulative precipitation in mm (PCP). Larger diameter circles had higher mean NDVI for: 16 May (<b>A</b>); or 31 October (<b>B</b>). Weather measures accumulate from: 18 February to 16 May (<b>A</b>); or 28 July to 23 October (<b>B</b>).</p> Full article ">Figure 9
<p>Areas of known blowdown, fire and insect-induced tree mortality within Great Smoky Mountains National Park, 2000–2015.</p> Full article ">
11108 KiB  
Article
Investigation of Urbanization Effects on Land Surface Phenology in Northeast China during 2001–2015
by Rui Yao, Lunche Wang, Xin Huang, Xian Guo, Zigeng Niu and Hongfu Liu
Remote Sens. 2017, 9(1), 66; https://doi.org/10.3390/rs9010066 - 12 Jan 2017
Cited by 54 | Viewed by 7411
Abstract
The urbanization effects on land surface phenology (LSP) have been investigated by many studies, but few studies have focused on the temporal variations of urbanization effects on LSP. In this study, we used the Moderate-resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI), MODIS [...] Read more.
The urbanization effects on land surface phenology (LSP) have been investigated by many studies, but few studies have focused on the temporal variations of urbanization effects on LSP. In this study, we used the Moderate-resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI), MODIS Land Surface Temperature (LST) data and China’s Land Use/Cover Datasets (CLUDs) to investigate the temporal variations of urban heat island intensity (UHII) and urbanization effects on LSP in Northeast China during 2001–2015. LST and phenology differences between urban and rural areas represented the urban heat island intensity and urbanization effects on LSP, respectively. A Mann–Kendall nonparametric test and Sen’s slope were used to evaluate the trends of urbanization effects on LSP and urban heat island intensity. The results indicated that the average LSP during 2001–2015 was characterized by high spatial heterogeneity. The start of the growing season (SOS) in old urban areas had become earlier and earlier compared to rural areas, and the differences in SOS between urbanized areas and rural areas changed greatly during 2001–2015 (?0.79 days/year, p < 0.01). Meanwhile, the length of the growing season (LOS) in urban and adjacent areas had become increasingly longer than rural areas, especially in urbanized areas (0.92 days/year, p < 0.01), but the differences in the end of the growing season (EOS) between urban and adjacent areas did not change significantly. Next, the UHII increased in spring and autumn during the whole study period. Moreover, the correlation analysis indicated that the increasing urban heat island intensity in spring contributed greatly to the increases of urbanization effects on SOS, but the increasing urban heat island intensity in autumn did not lead to the increases of urbanization effects on EOS in Northeast China. Full article
(This article belongs to the Special Issue Land Surface Phenology and Seasonality: Novel Approaches and Applications)
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<p>The location and the CLUD (2015) of the study area.</p> Full article ">Figure 2
<p>The CLUDs in 2000, 2005, 2010, and 2015, and the schematic diagram of four areas (old urban areas (OUAs), urbanized areas, 0–2 km buffer and 2–5 km buffer), an example of Changchun city.</p> Full article ">Figure 3
<p>The average start of the growing season (SOS) (<b>a</b>) and end of the growing season (EOS) (<b>b</b>) in Northeast China during 2001–2015.</p> Full article ">Figure 4
<p>Interannual variations of (<b>a</b>) ∆SOS; (<b>b</b>) ∆EOS; (<b>c</b>) ∆LOS (length of the growing) four regions in Northeast China during 2001–2015. Dashed lines represent the linear trends.</p> Full article ">Figure 5
<p>Interannual variations of ∆T (°C) (<b>a</b>) in spring and (<b>b</b>) autumn in four regions during 2001–2015. Dashed lines represent the linear trends of OUAs, urbanized areas, 0–2 km buffer, and 2–5 km buffer, respectively.</p> Full article ">Figure 6
<p>The relationships between (<b>a</b>) ∆T1 in spring and ∆SOS1; (<b>b</b>) ∆T2 in spring and ∆SOS2; (<b>c</b>) ∆T3 in spring and ∆SOS3; and (<b>d</b>) ∆T4 in spring and ∆SOS4. Each dot represents each year during 2001–2015.</p> Full article ">Figure 7
<p>The relationships between (<b>a</b>) ∆T1 in autumn and ∆EOS1; (<b>b</b>) ∆T2 in autumn and ∆EOS2; (<b>c</b>) ∆T3 in autumn and ∆EOS3; and (<b>d</b>) ∆T4 (°C) in autumn and ∆EOS4. Each dot represents each year during 2001–2015.</p> Full article ">
42438 KiB  
Article
Spatiotemporal Variability of Land Surface Phenology in China from 2001–2014
by Zhaohui Luo and Shixiao Yu
Remote Sens. 2017, 9(1), 65; https://doi.org/10.3390/rs9010065 - 12 Jan 2017
Cited by 43 | Viewed by 6321
Abstract
Land surface phenology is a highly sensitive and simple indicator of vegetation dynamics and climate change. However, few studies on spatiotemporal distribution patterns and trends in land surface phenology across different climate and vegetation types in China have been conducted since 2000, a [...] Read more.
Land surface phenology is a highly sensitive and simple indicator of vegetation dynamics and climate change. However, few studies on spatiotemporal distribution patterns and trends in land surface phenology across different climate and vegetation types in China have been conducted since 2000, a period during which China has experienced remarkably strong El Niño events. In addition, even fewer studies have focused on changes of the end of season (EOS) and length of season (LOS) despite their importance. In this study, we used four methods to reconstruct Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) dataset and chose the best smoothing result to estimate land surface phenology. Then, the phenophase trends were analyzed via the Mann-Kendall method. We aimed to assess whether trends in land surface phenology have continued since 2000 in China at both national and regional levels. We also sought to determine whether trends in land surface phenology in subtropical or high altitude areas are the same as those observed in high latitude areas and whether those trends are uniform among different vegetation types. The result indicated that the start of season (SOS) was progressively delayed with increasing latitude and altitude. In contrast, EOS exhibited an opposite trend in its spatial distribution, and LOS showed clear spatial patterns over this region that decreased from south to north and from east to west at a national scale. The trend of SOS was advanced at a national level, while the trend in Southern China and the Tibetan Plateau was opposite to that in Northern China. The transaction zone of the SOS within Northern China and Southern China occurred approximately between 31.4°N and 35.2°N. The trend in EOS and LOS were delayed and extended, respectively, at both national and regional levels except that of LOS in the Tibetan Plateau, which was shortened by delayed SOS onset more than by delayed EOS onset. The absolute magnitude of SOS was decreased after 2000 compared with previous studies, and the phenophase trends are species specific. Full article
(This article belongs to the Special Issue Land Surface Phenology and Seasonality: Novel Approaches and Applications)
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<p>Vegetation types (DNF, deciduous needle-leaf forest; ENF, evergreen needle-leaf forest; EBF, evergreen broadleaf forest; DBF, deciduous broadleaf forest; and GM, grassland and meadow) across the three sub-region divisions (<b>a</b>) and a map showing provinces of China (<b>b</b>).</p> Full article ">Figure 2
<p>Average of start of season (<b>a</b>), end of season (<b>b</b>), and length of season (<b>c</b>) with the corresponding standard deviations within China between 2001 and 2014 (<b>d</b>–<b>f</b>).</p> Full article ">Figure 3
<p>Comparison of the ground-observed start of season (SOS; (<b>a</b>)) and end of season (EOS; (<b>b</b>)) with the corresponding satellite based SOS and EOS values.</p> Full article ">Figure 4
<p>Trends in start of season (SOS; (<b>a</b>)), end of season (EOS; (<b>b</b>)), and length of season (LOS; (<b>c</b>)) within China between 2001 and 2014. A positive trend indicates that SOS and EOS were delayed while LOS was extended; in contrast, negative trend indicates that SOS and EOS occurred earlier, while LOS was shortened; individual pixels are shown with significant (<span class="html-italic">p</span> &lt; 0.05) and very significant (<span class="html-italic">p</span> &lt; 0.01) trends for SOS (<b>d</b>), EOS (<b>e</b>), and LOS (<b>f</b>). (DVS, delayed very significantly; DS, delayed significantly; AVS, advanced very significantly; AS, advanced significantly). The count distributions of phenology trends for SOS (<b>g</b>), EOS (<b>h</b>), and LOS (<b>i</b>) are also shown.</p> Full article ">Figure 5
<p>Ten lines that were buffered to acquire change rate of SOS during 2001–2014 in China.</p> Full article ">Figure 6
<p>Transition zone exhibiting a change in start of season (SOS) in China from 2001 to 2014.</p> Full article ">
5868 KiB  
Article
Evaluation of a Phenology-Dependent Response Method for Estimating Leaf Area Index of Rice Across Climate Gradients
by Bora Lee, Hyojung Kwon, Akira Miyata, Steve Lindner and John Tenhunen
Remote Sens. 2017, 9(1), 20; https://doi.org/10.3390/rs9010020 - 29 Dec 2016
Cited by 18 | Viewed by 6927
Abstract
Accurate estimate of the seasonal leaf area index (LAI) in croplands is required for understanding not only intra- and inter-annual crop development, but also crop management. Lack of consideration in different growth phases in the relationship between LAI and vegetation indices (VI) often [...] Read more.
Accurate estimate of the seasonal leaf area index (LAI) in croplands is required for understanding not only intra- and inter-annual crop development, but also crop management. Lack of consideration in different growth phases in the relationship between LAI and vegetation indices (VI) often results in unsatisfactory estimation in the seasonal course of LAI. In this study, we partitioned the growing season into two phases separated by maximum VI ( VI max ) and applied the general regression model to the data gained from two phases. As an alternative method to capture the influence of seasonal phenological development on the LAI-VI relationship, we developed a consistent development curve method and compared its performance with the general regression approaches. We used the Normalized Difference VI (NDVI) and the Enhanced VI (EVI) from the rice paddy sites in Asia (South Korea and Japan) and Europe (Spain) to examine its applicability across different climate conditions and management cycles. When the general regression method was used, separating the season into two phases resulted in no better estimation than the estimation obtained with the entire season observation due to an abrupt change in seasonal LAI occurring during the transition between the before and after VI max . The consistent development curve method reproduced the seasonal patterns of LAI from both NDVI and EVI across all sites better than the general regression method. Despite less than satisfactory estimation of a local LAI max , the consistent development curve method demonstrates improvement in estimating the seasonal course of LAI. The method can aid in providing accurate seasonal LAI as an input into ecological process-based models. Full article
(This article belongs to the Special Issue Land Surface Phenology and Seasonality: Novel Approaches and Applications)
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<p>Seasonal variation of MODIS NDVI and EVI at the study sites. Closed circles indicate original NDVI and EVI, open circles indicate smoothed NDVI and EVI by the TIMESAT method, and solid lines indicate estimated daily NDVI and EVI from spline interpolation.</p> Full article ">Figure 2
<p>The relationships between LAI and NDVI (black closed circles with solid line) and between LAI and EVI (gray closed circles with solid line) using the data from the entire growing season. HK = Haean (S. Korea), MSE = Mase (Japan), and ESES2 = El Saler-Sueca (Spain).</p> Full article ">Figure 3
<p>The relationship between LAI and NDVI and between LAI and EVI using the data separated into two growth phases. HK = Haean (S. Korea), MSE = Mase (Japan), and ESES2 = El Saler-Sueca (Spain).</p> Full article ">Figure 4
<p>The pooled relationships between LAI and NDVI and between LAI and EVI using the data from the entire growing season. The regressions for Asia includes the data from Haean and Mase (y = <math display="inline"> <semantics> <msup> <mi mathvariant="normal">e</mi> <mrow> <mo>(</mo> <mn>9</mn> <mo>.</mo> <mn>92</mn> <mi>x</mi> <mo>−</mo> <mn>6</mn> <mo>.</mo> <mn>81</mn> <mo>)</mo> </mrow> </msup> </semantics> </math> with <math display="inline"> <semantics> <msup> <mi mathvariant="normal">R</mi> <mn>2</mn> </msup> </semantics> </math> = 0.62 for NDVI and y = <math display="inline"> <semantics> <msup> <mi mathvariant="normal">e</mi> <mrow> <mo>(</mo> <mn>8</mn> <mo>.</mo> <mn>07</mn> <mi>x</mi> <mo>−</mo> <mn>3</mn> <mo>.</mo> <mn>69</mn> <mo>)</mo> </mrow> </msup> </semantics> </math> with <math display="inline"> <semantics> <msup> <mi mathvariant="normal">R</mi> <mn>2</mn> </msup> </semantics> </math> = 0.56 for EVI), while the regressions for Asia and Europe includes Haean, Mase, and El Saler-Sueca (y = <math display="inline"> <semantics> <msup> <mi mathvariant="normal">e</mi> <mrow> <mo>(</mo> <mn>8</mn> <mo>.</mo> <mn>73</mn> <mi>x</mi> <mo>−</mo> <mn>5</mn> <mo>.</mo> <mn>86</mn> <mo>)</mo> </mrow> </msup> </semantics> </math> with <math display="inline"> <semantics> <msup> <mi mathvariant="normal">R</mi> <mn>2</mn> </msup> </semantics> </math> = 0.60 for NDVI and y = <math display="inline"> <semantics> <msup> <mi mathvariant="normal">e</mi> <mrow> <mo>(</mo> <mn>7</mn> <mo>.</mo> <mn>78</mn> <mi>x</mi> <mo>−</mo> <mn>3</mn> <mo>.</mo> <mn>46</mn> <mo>)</mo> </mrow> </msup> </semantics> </math> with <math display="inline"> <semantics> <msup> <mi mathvariant="normal">R</mi> <mn>2</mn> </msup> </semantics> </math> = 0.60 for EVI).</p> Full article ">Figure 5
<p>The pooled relationships between LAI and NDVI and between LAI and EVI using the data separated into two growth phases. The regressions for Asia include the data from Haean and Mase, while the regressions for Asia and Europe include Haean, Mase, and El Saler-Sueca.</p> Full article ">Figure 6
<p>Comparison of the measured LAI (open circles with dashed line) and the estimated LAI using the data from the entire growing season (E, solid line) and the combined two growth phases (B and A, dashed line).</p> Full article ">Figure 7
<p>Consistent development curves drawn from the scaled LAI in relation to <math display="inline"> <semantics> <msub> <mi>NDVI</mi> <mi>max</mi> </msub> </semantics> </math> and <math display="inline"> <semantics> <msub> <mi>EVI</mi> <mi>max</mi> </msub> </semantics> </math>, respectively, using the generalized additive model. The data from all sites were used. Scale was calculated as the ratio of the observed LAI at each site to the average observed maximum LAI (<math display="inline"> <semantics> <msub> <mi>LAI</mi> <mi>max</mi> </msub> </semantics> </math>, 5.3 from all sites).</p> Full article ">Figure 8
<p>Comparison of the measured and the estimated LAI. Open circle with the dashed line indicating the measured LAI, whereas the solid line indicates the estimated LAI from NDVI (black line) and from EVI (gray line), respectively, using the consistent development curve method.</p> Full article ">Figure 9
<p>Validation of the consistent development curve method in estimating LAI by applying to an independent dataset from MSE, 2006, Shizukuishi, 2000, and Aso, 2003 in Japan).</p> Full article ">
7387 KiB  
Article
Characterizing Cropland Phenology in Major Grain Production Areas of Russia, Ukraine, and Kazakhstan by the Synergistic Use of Passive Microwave and Visible to Near Infrared Data
by Woubet G. Alemu and Geoffrey M. Henebry
Remote Sens. 2016, 8(12), 1016; https://doi.org/10.3390/rs8121016 - 11 Dec 2016
Cited by 10 | Viewed by 6480
Abstract
We demonstrate the synergistic use of surface air temperature retrieved from AMSR-E (Advanced Microwave Scanning Radiometer on Earth observing satellite) and two vegetation indices (VIs) from the shorter wavelengths of MODIS (MODerate resolution Imaging Spectroradiometer) to characterize cropland phenology in the major grain [...] Read more.
We demonstrate the synergistic use of surface air temperature retrieved from AMSR-E (Advanced Microwave Scanning Radiometer on Earth observing satellite) and two vegetation indices (VIs) from the shorter wavelengths of MODIS (MODerate resolution Imaging Spectroradiometer) to characterize cropland phenology in the major grain production areas of Northern Eurasia from 2003–2010. We selected 49 AMSR-E pixels across Ukraine, Russia, and Kazakhstan, based on MODIS land cover percentage data. AMSR-E air temperature growing degree-days (GDD) captures the weekly, monthly, and seasonal oscillations, and well correlated with station GDD. A convex quadratic (CxQ) model that linked thermal time measured as growing degree-days to accumulated growing degree-days (AGDD) was fitted to each pixel’s time series yielding high coefficients of determination (0.88 ? r2 ? 0.98). Deviations of observed GDD from the CxQ model predicted GDD by site corresponded to peak VI for negative residuals (period of higher latent heat flux) and low VI at beginning and end of growing season for positive residuals (periods of higher sensible heat flux). Modeled thermal time to peak, i.e., AGDD at peak GDD, showed a strong inverse linear trend with respect to latitude with r2 of 0.92 for Russia and Kazakhstan and 0.81 for Ukraine. MODIS VIs tracked similar seasonal responses in time and space and were highly correlated across the growing season with r2 > 0.95. Sites at lower latitude (?49°N) that grow winter and spring grains showed either a bimodal growing season or a shorter unimodal winter growing season with substantial inter-annual variability, whereas sites at higher latitude (?56°N) where spring grains are cultivated exhibited shorter, unimodal growing seasons. Sites between these extremes exhibited longer unimodal growing seasons. At some sites there were shifts between unimodal and bimodal patterns over the study period. Regional heat waves that devastated grain production in 2007 in Ukraine and in 2010 in Russia and Kazakhstan appear clearly anomalous. Microwave based surface air temperature data holds great promise to extend to parts of the planet where the land surface is frequently obscured by clouds, smoke, or aerosols, and where routine meteorological observations are sparse or absent. Full article
(This article belongs to the Special Issue Land Surface Phenology and Seasonality: Novel Approaches and Applications)
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<p>Land cover stability in Ukraine, southern Russia, and northern Kazakhstan as revealed by IGBP global land cover classification scheme MODIS 0.05° land cover products (resampled to AMSR-E spatial resolution: 0.25°) from 2003–2010: (<b>a</b>) crop–natural vegetation mosaic (IGBP class 14); (<b>b</b>) cropland (IGBP class 12); and (<b>c</b>) grassland (IGBP class 10). Land cover percentage from 2003–2010 displayed as red = maximum percentage, green = mean percentage, and blue = range of percentages. For legend, refer to <a href="#remotesensing-08-01016-t002" class="html-table">Table 2</a>.</p> Full article ">Figure 2
<p>Study region cropland stability map superimposed with the 49 specific AMSR-E pixels selected for this study. The AMSR-E pixels are numbered by latitude starting from the most southern site. Name for each site is their closest large settlement (cf. <a href="#remotesensing-08-01016-t003" class="html-table">Table 3</a>). Red squares are in Ukraine, cyan squares in Russia, and blue squares in Kazakhstan.</p> Full article ">Figure 3
<p>Scatter plots and linear regression fits of station GDD with satellite GDD—AMSR-E GDD (black circles) and MODIS GDD (blue diamonds)—at Simferopol, Ukraine (site 4) for 2003. The linear regression fit for the two datasets were high with r<sup>2</sup> of 0.93 for the AMSR-E GDD and r<sup>2</sup> of 0.89 for the MODIS GDD.</p> Full article ">Figure 4
<p>(<b>left</b>) Time series plot of AMSR-E (black) and station (red) daily GDD as a function of station AGDD for 2003 at Kirovohrad, Ukraine (site 15); and (<b>right</b>) linear regression fit of AMSR-E GDD with station GDD for the same dataset yielding strong correspondence with r<sup>2</sup> = 0.95. Note also the bias (intercept) and the underestimation (slope &lt; 1) of the AMSR-E GDD relative to the station GDD.</p> Full article ">Figure 5
<p>Average (2003–2010) MODIS (plus signs) and AMSR-E (circles) GDD and their fitted (solid (MODIS LST) and dashed (AMSR-E ta) lines) average GDD as a function of DOY (<b>left</b>) and AGDD (<b>right</b>) for two cropland sites at the latitudinal extremes of the study region: Cheboksary, Russia (site 48, 55.7°N; (<b>a</b>,<b>b</b>)); and Cherkessk, Russia (site 1, 44.4°N; (<b>c</b>,<b>d</b>)). N.B.: MODIS GDDs are multiplied by 8, while AMSR-E GDDs are eight-day sums.</p> Full article ">Figure 6
<p>Thermal climates as a function of latitude revealed by: (<b>a</b>) average daily GDD; and (<b>b</b>) Thermal Time to Peak (TTP<sub>GDD</sub>). Latitudes were the geographic centers of AMSR-E pixels. All 49 study sites are displayed in both figures. In (<b>b</b>), hollow circles = Russia, orange diamonds = Kazakhstan, and cyan crosses on blue background = Ukraine. Both panels show a general decrease in (<b>a</b>) GDD or (<b>b</b>) TTP as latitude increases from 44° to 56°N. The uppermost two hollow circles are the northernmost study sites (site 49, Kazan’, Russia and site 48, Cheboksary, Russia), while the lowermost two hollow circles are the southernmost study sites (site 1, Cherkessk, Russia and 2, Stavropol, Russia).</p> Full article ">Figure 7
<p>Line plots of annual observed GDD (blue), annual predicted GDD based on multi-year average model (orange) and observed GDD residuals (green) at Orenburg (RU) for: a cooler year (2003) (<b>left</b>); a close-to-average year (2009) (<b>center</b>); and a hotter year (2010) (<b>right</b>). N.B: GDDs are 8-day sums.</p> Full article ">Figure 8
<p>Average MODIS NDVI and EVI as a function of AMSR-E AGDD for Petropavlovsk 3, Kazakhstan (site 42) for 2003–2010. Note that the NDVI displays a larger dynamic range than the EVI.</p> Full article ">Figure 9
<p>(<b>a–d</b>)In 2003, lower latitude sites (Cherkessk, Russia: 44.4°N (<b>a</b>) and Simferopol, Ukraine: 45.6°N (<b>c</b>) display bimodal growing seasons, while the higher latitude sites show unimodal, shorter growing seasons (Kazan’, Russia: 56.1°N (<b>d</b>)). The middle latitude sites display a longer unimodal growing season (Odesa, Ukraine: 47.3°N (<b>b</b>)).</p> Full article ">Figure 10
<p>NDVI and EVI interannual variability in one of the southernmost study sites (Simferopol, Ukraine at 45.6°N) from 2003–2010. Whether due to changes in cultivation practice or crop failures, the VI curves change from bimodal (2003–2004) to unimodal (2005–2009) and back to bimodal (2010).</p> Full article ">Figure 11
<p>Changes in seasonal patterns by sites during 2003–2010: no change (blue circles), one change (white stars with pink borders), or two changes (red squares). Northern study sites displayed no change in seasonal pattern, while southern study sites experienced multiple changes.</p> Full article ">Figure 12
<p>(<b>a</b>–<b>f</b>) Comparison of AMSR-E AGDDta at 95% of the initial peak VIs with the MODIS AGDDlst at 95% of the initial peak VIs in 2003. The panels span a latitudinal gradient: lowest latitude (Cherkessk, Russia (<b>a</b>,<b>d</b>)), middle latitude (Sumy, Ukraine (<b>b</b>,<b>e</b>)), and highest latitude (Kazan’, Russia (<b>c</b>,<b>f</b>)) for 95% of the initial peak NDVI (<b>a–c</b>) and EVI (<b>d–f</b>). There is strong linear relationship between AGDDs from AMSR-E and MODIS at 95% of initial peak VIs for these sites in 2003 with r<sup>2</sup> ranging from 0.88 to &gt;0.99. This strong positive relationship was consistent in space and time with r<sup>2</sup> ranging from 0.60 to &gt;0.99 across all study sites and years.</p> Full article ">Figure 12 Cont.
<p>(<b>a</b>–<b>f</b>) Comparison of AMSR-E AGDDta at 95% of the initial peak VIs with the MODIS AGDDlst at 95% of the initial peak VIs in 2003. The panels span a latitudinal gradient: lowest latitude (Cherkessk, Russia (<b>a</b>,<b>d</b>)), middle latitude (Sumy, Ukraine (<b>b</b>,<b>e</b>)), and highest latitude (Kazan’, Russia (<b>c</b>,<b>f</b>)) for 95% of the initial peak NDVI (<b>a–c</b>) and EVI (<b>d–f</b>). There is strong linear relationship between AGDDs from AMSR-E and MODIS at 95% of initial peak VIs for these sites in 2003 with r<sup>2</sup> ranging from 0.88 to &gt;0.99. This strong positive relationship was consistent in space and time with r<sup>2</sup> ranging from 0.60 to &gt;0.99 across all study sites and years.</p> Full article ">Figure 13
<p>Average GDD residuals and NDVI with error bars displaying maxima and minima for sites at similar latitude. (<b>a</b>) Sites 35–37 and 39–41 in Kazakhstan (NDVI—blue triangles and GDD residuals—black diamonds) and sites 34 and 42 in Kazakhstan (NDVI—green crosses and GDD residuals—gray circles); and (<b>b</b>,<b>c</b>) sites 1 and 2 in Russia (NDVI—blue diamonds and GDD residuals—black circles) for selected bimodal cropland pattern years and unimodal years, respectively.</p> Full article ">Figure 13 Cont.
<p>Average GDD residuals and NDVI with error bars displaying maxima and minima for sites at similar latitude. (<b>a</b>) Sites 35–37 and 39–41 in Kazakhstan (NDVI—blue triangles and GDD residuals—black diamonds) and sites 34 and 42 in Kazakhstan (NDVI—green crosses and GDD residuals—gray circles); and (<b>b</b>,<b>c</b>) sites 1 and 2 in Russia (NDVI—blue diamonds and GDD residuals—black circles) for selected bimodal cropland pattern years and unimodal years, respectively.</p> Full article ">Figure 14
<p>Comparison of VIs and GDD at similar places during heat wave years (Kuybuskev 2 (<b>a</b>), Russia in 2010 and Mykolayive (<b>b</b>), Ukraine in 2007) and average years (Kuybuskev 2 (<b>c</b>), Russia in 2005 and Mykolayive (<b>d</b>), Ukraine in 2009). N.B: GDDs are eight-day sums.</p> Full article ">Figure 15
<p>Sites affected by: the 2010 heat wave (blue circles), the 2007 heat wave (red squares), both the 2010 and 2007 heat waves (white stars with pink borders), and sites affected by neither the 2010 nor the 2007 heat wave (cyan triangles).</p> Full article ">
6516 KiB  
Article
Mapping Deforestation in North Korea Using Phenology-Based Multi-Index and Random Forest
by Yihua Jin, Sunyong Sung, Dong Kun Lee, Gregory S. Biging and Seunggyu Jeong
Remote Sens. 2016, 8(12), 997; https://doi.org/10.3390/rs8120997 - 3 Dec 2016
Cited by 39 | Viewed by 14183
Abstract
Phenology-based multi-index with the random forest (RF) algorithm can be used to overcome the shortcomings of traditional deforestation mapping that involves pixel-based classification, such as ISODATA or decision trees, and single images. The purpose of this study was to investigate methods to identify [...] Read more.
Phenology-based multi-index with the random forest (RF) algorithm can be used to overcome the shortcomings of traditional deforestation mapping that involves pixel-based classification, such as ISODATA or decision trees, and single images. The purpose of this study was to investigate methods to identify specific types of deforestation in North Korea, and to increase the accuracy of classification, using phenological characteristics extracted with multi-index and random forest algorithms. The mapping of deforestation area based on RF was carried out by merging phenology-based multi-indices (i.e., normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and normalized difference soil index (NDSI)) derived from MODIS (Moderate Resolution Imaging Spectroradiometer) products and topographical variables. Our results showed overall classification accuracy of 89.38%, with corresponding kappa coefficients of 0.87. In particular, for forest and farm land categories with similar phenological characteristic (e.g., paddy, plateau vegetation, unstocked forest, hillside field), this approach improved the classification accuracy in comparison with pixel-based methods and other classes. The deforestation types were identified by incorporating point data from high-resolution imagery, outcomes of image classification, and slope data. Our study demonstrated that the proposed methodology could be used for deciding on the restoration priority and monitoring the expansion of deforestation areas. Full article
(This article belongs to the Special Issue Land Surface Phenology and Seasonality: Novel Approaches and Applications)
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<p>Study site.</p> Full article ">Figure 2
<p>Raw and fitted MODIS NDVI (<b>A</b>); red reflectance (<b>B</b>); NIR reflectance (<b>C</b>); and SWIR reflectance (<b>D</b>). TIMESAT seasonality variables derived from the functions are numbered on the figure for the interval from January 2012 to December 2013.</p> Full article ">Figure 3
<p>Independent training samples (1660 points) (<b>A</b>) were collected regarding eight land cover classes in North Korea with a field survey; the 999 test points (<b>B</b>) used for the classification accuracy assessment were selected by random sampling. The displayed images are Landsat 8 OLE products.</p> Full article ">Figure 4
<p>Time series indices of MODIS NDVI, NDSI, and NDWI (derived from RED, NIR, and MIR reflectance) for paddy, field, hillside field, unstocked forest, forest, and plateau vegetation categories. Random samples of 100 points were individually extracted for each type.</p> Full article ">Figure 5
<p>Mean decrease accuracy values of overall and for each class from the RF classification using phenology-based multi-index and topographic variables.</p> Full article ">Figure 6
<p>Final forest classification map of North Korea: (<b>A</b>) land cover map in North Korea; and (<b>B</b>) distribution of deforested land in North Korea.</p> Full article ">
2579 KiB  
Article
Using Ordinary Digital Cameras in Place of Near-Infrared Sensors to Derive Vegetation Indices for Phenology Studies of High Arctic Vegetation
by Helen B. Anderson, Lennart Nilsen, Hans Tømmervik, Stein Rune Karlsen, Shin Nagai and Elisabeth J. Cooper
Remote Sens. 2016, 8(10), 847; https://doi.org/10.3390/rs8100847 - 17 Oct 2016
Cited by 65 | Viewed by 9557 | Correction
Abstract
To remotely monitor vegetation at temporal and spatial resolutions unobtainable with satellite-based systems, near remote sensing systems must be employed. To this extent we used Normalized Difference Vegetation Index NDVI sensors and normal digital cameras to monitor the greenness of six different but [...] Read more.
To remotely monitor vegetation at temporal and spatial resolutions unobtainable with satellite-based systems, near remote sensing systems must be employed. To this extent we used Normalized Difference Vegetation Index NDVI sensors and normal digital cameras to monitor the greenness of six different but common and widespread High Arctic plant species/groups (graminoid/Salix polaris; Cassiope tetragona; Luzula spp.; Dryas octopetala/S. polaris; C. tetragona/D. octopetala; graminoid/bryophyte) during an entire growing season in central Svalbard. Of the three greenness indices (2G_RBi, Channel G% and GRVI) derived from digital camera images, only GRVI showed significant correlations with NDVI in all vegetation types. The GRVI (Green-Red Vegetation Index) is calculated as (GDN ? RDN)/(GDN + RDN) where GDN is Green digital number and RDN is Red digital number. Both NDVI and GRVI successfully recorded timings of the green-up and plant growth periods and senescence in all six plant species/groups. Some differences in phenology between plant species/groups occurred: the mid-season growing period reached a sharp peak in NDVI and GRVI values where graminoids were present, but a prolonged period of higher values occurred with the other plant species/groups. In particular, plots containing C. tetragona experienced increased NDVI and GRVI values towards the end of the season. NDVI measured with active and passive sensors were strongly correlated (r > 0.70) for the same plant species/groups. Although NDVI recorded by the active sensor was consistently lower than that of the passive sensor for the same plant species/groups, differences were small and likely due to the differing light sources used. Thus, it is evident that GRVI and NDVI measured with active and passive sensors captured similar vegetation attributes of High Arctic plants. Hence, inexpensive digital cameras can be used with passive and active NDVI devices to establish a near remote sensing network for monitoring changing vegetation dynamics in the High Arctic. Full article
(This article belongs to the Special Issue Land Surface Phenology and Seasonality: Novel Approaches and Applications)
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<p>Maps of (<b>a</b>) location of the study area in Svalbard; (<b>b</b>) sensor locations in Adventdalen, Central Svalbard; (<b>c</b>) and (<b>d</b>) images of the Decagon Normalized Difference Vegetation Index (NDVI) surface reflectance sensor, hemispherical sensor and red-green-blue wavelength (RGB) camera. The black symbol in (<b>a</b>) shows the location of the study site in Svalbard and in (<b>b</b>) the locations of the six monitoring sites in Adventdalen. Rivers are indicated in blue in (<b>a</b>,<b>b</b>) and contour lines with elevations (masl) are also displayed in (<b>b</b>).</p> Full article ">Figure 2
<p>NDVI and greenness index values from six different High Arctic plant communities throughout the growing season. Readings were taken between 5 June (day of year = 156) and 30 August (day of year = 242) 2015 in: (<b>a</b>) Graminoid/<span class="html-italic">Salix polaris</span>; (<b>b</b>) <span class="html-italic">Cassiope tetragona</span>; (<b>c</b>) <span class="html-italic">Luzula</span> spp.; (<b>d</b>) <span class="html-italic">Dryas octopetala</span>/<span class="html-italic">Salix polaris</span>; (<b>e</b>) <span class="html-italic">Cassiope tetragona</span>/<span class="html-italic">Dryas octopetala</span>; and (<b>f</b>) Graminoid/bryophyte vegetation. NDVI was recorded using Decagon surface reflectance sensors (black circles) and a Trimble Greenseeker handheld sensor (open circles); the Green-Red Vegetation Index (GRVI) values (grey squares) were calculated from red and green channel data from RGB images.</p> Full article ">Figure 3
<p>RGB camera images of six High Arctic plant species/groups from Svalbard during plant green-up (10 June DOY 161), peak plant growth (19 July DOY 200) and senescence (28 August DOY 240).</p> Full article ">Figure 4
<p>Comparison of Decagon surface reflectance sensor-derived NDVI (passive) and Trimble Greenseeker-derived NDVI (active) from different vegetation types in High Arctic Svalbard. Readings were taken between 18 June (day of year = 169) and 28 August (day of year = 240) 2015 in: (<b>a</b>) Graminoid/<span class="html-italic">Salix polaris</span>; (<b>b</b>) <span class="html-italic">Cassiope tetragona</span>, and; (<b>c</b>) <span class="html-italic">Cassiope tetragona</span>/<span class="html-italic">Dryas octopetala</span> vegetation. Fitted lines from the linear models are shown. The closer points are to the dashed line (<span class="html-italic">y</span> = <span class="html-italic">x</span>), the more similar are the values recorded by the Decagon sensors and the Greenseeker device.</p> Full article ">Figure 4 Cont.
<p>Comparison of Decagon surface reflectance sensor-derived NDVI (passive) and Trimble Greenseeker-derived NDVI (active) from different vegetation types in High Arctic Svalbard. Readings were taken between 18 June (day of year = 169) and 28 August (day of year = 240) 2015 in: (<b>a</b>) Graminoid/<span class="html-italic">Salix polaris</span>; (<b>b</b>) <span class="html-italic">Cassiope tetragona</span>, and; (<b>c</b>) <span class="html-italic">Cassiope tetragona</span>/<span class="html-italic">Dryas octopetala</span> vegetation. Fitted lines from the linear models are shown. The closer points are to the dashed line (<span class="html-italic">y</span> = <span class="html-italic">x</span>), the more similar are the values recorded by the Decagon sensors and the Greenseeker device.</p> Full article ">
2914 KiB  
Article
Effects of Different Methods on the Comparison between Land Surface and Ground Phenology—A Methodological Case Study from South-Western Germany
by Gourav Misra, Allan Buras and Annette Menzel
Remote Sens. 2016, 8(9), 753; https://doi.org/10.3390/rs8090753 - 13 Sep 2016
Cited by 45 | Viewed by 6563
Abstract
Several methods exist for extracting plant phenological information from time series of satellite data. However, there have been only a few successful attempts to temporarily match satellite observations (Land Surface Phenology or LSP) with ground based phenological observations (Ground Phenology or GP). The [...] Read more.
Several methods exist for extracting plant phenological information from time series of satellite data. However, there have been only a few successful attempts to temporarily match satellite observations (Land Surface Phenology or LSP) with ground based phenological observations (Ground Phenology or GP). The classical pixel to point matching problem along with the temporal and spatial resolution of remote sensing data are some of the many issues encountered. In this study, MODIS-sensor’s Normalised Differenced Vegetation Index (NDVI) time series data were smoothed using two filtering techniques for comparison. Several start of season (SOS) methods established in the literature, namely thresholds of amplitude, derivatives and delayed moving average, were tested for determination of LSP-SOS for broadleaf forests at a site in southwestern Germany using 2001–2013 time series of NDVI data. The different LSP-SOS estimates when compared with species-rich GP dataset revealed that different LSP-SOS extraction methods agree better with specific phases of GP, and the choice of data processing or smoothing strongly affects the LSP-SOS extracted. LSP methods mirroring late SOS dates, i.e., 75% amplitude and 1st derivative, indicated a better match in means and trends, and high, significant correlations of up to 0.7 with leaf unfolding and greening of late understory and broadleaf tree species. GP-SOS of early understory leaf unfolding partly were significantly correlated with earlier detecting LSP-SOS, i.e., 20% amplitude and 3rd derivative. Early understory SOS were, however, more difficult to detect from NDVI due to the lack of a high resolution land cover information. Full article
(This article belongs to the Special Issue Land Surface Phenology and Seasonality: Novel Approaches and Applications)
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Full article ">Figure 1
<p>Study area and CORINE land cover map showing the distribution of broadleaf forests. (NDVI image is for day of the year (DOY) 145 in 2001). Inset: Location of study area in Germany.</p> Full article ">Figure 2
<p>Illustration of smoothing of a pre-processed and outlier removed NDVI time series using Gaussian and Double Log functions. Note: In comparison to the Double Log smoothed NDVI, the Gaussian smoothed NDVI shows lower residuals in the winter troughs. The residuals in the non-winter period are almost similar for both the smoothing techniques.</p> Full article ">Figure 3
<p>LSP-SOS from (<b>a</b>) Gaussian and (<b>b</b>) Double Log smoothed NDVI for broadleaf pixels using various methods (spatially averaged SOS for specific years as filled-coloured circles and one standard deviation as error bars). Overall mean is the mean SOS (2001–2013), which is a temporal and spatially averaged measure of LSP-SOS. The temporal trends in days/year (right y-axis) for all pixels’ LSP-SOS are given as means and respective one standard deviation during 2001–2013. The year-to year variability in SOS reflects the different spring weather patterns.</p> Full article ">Figure 4
<p>(<b>a</b>) Mean LSP-SOS (day of year) for the broadleaf pixels in the study area; (<b>b</b>) Linear trends of LSP-SOS (days/year) for the broadleaf pixels in the study area.</p> Full article ">Figure 5
<p>Comparison of LSP-SOS from Gaussian smoothed NDVI (mean LSP-SOS as special symbols in black and one standard deviation as error bars) and various species-specific GP-SOS (as filled and coloured circles, refer to <a href="#app1-remotesensing-08-00753" class="html-app">Supplementary Table S1</a>). Numbers are given in order of increasing mean SOS. Codes for GP: HA (herbaceous annuals), HP (herbaceous perennials) and WP (woody perennials) refer to understory leaf unfolding dates; U (Conifers leaf unfolding); LU (leaf unfolding) and G (greening) of broadleaf species (see <a href="#app1-remotesensing-08-00753" class="html-app">Supplementary Table S1</a> for complete details of species-specific information).</p> Full article ">Figure 6
<p>Maps showing Spearman’s rank correlations (p &lt; 0.05, one-tailed positive) between LSP-SOS and GP-SOS for selected understory and broadleaf tree species. MS, <span class="html-italic">Myosotis sylvatica</span> (leaf unfolding); LN, <span class="html-italic">Lathyrus niger</span> (leaf unfolding); and FG(G), <span class="html-italic">Fagus sylvatica</span> (greening), with mean SOS of 70.5, 102.7 and 120.9 day of year, and species ID/No. 12, 95 and 119, respectively. Note: The mean correlations of each species GP-SOS over the study area are shown in <a href="#app1-remotesensing-08-00753" class="html-app">Figure S2 in supplement</a>. Refer to <a href="#app1-remotesensing-08-00753" class="html-app">Table S1</a> for details of GP-SOS.</p> Full article ">Figure 7
<p>Spearman’s rank correlation matrix for selected species-specific GP-SOS; the heatmap confirms that the phenology of many late understory species is highly correlated with broadleaf tree phenology. Note: Species are arranged in increasing order of their mean SOS; refer to <a href="#app1-remotesensing-08-00753" class="html-app">Supplementary Table S1</a> for details of species-specific information.</p> Full article ">Figure 8
<p>Comparison of LSP-SOS time series (day of year) obtained from spatially or regionally averaged NDVI for the broadleaf pixels in the study area (y-axis) and SOS averaged from single/individual pixels SOS (x-axis). Note: SOS time series as coloured unfilled circles and its mean as coloured crosses.</p> Full article ">Figure 9
<p>Spearman’s rank correlation coefficients between GP-SOS and selected LSP-SOS based on a regionally averaged NDVI for broadleaf pixels during 2001–2013. Region above dotted horizontal red line comprises significant correlation coefficients, p &lt; 0.05. Note: Species on the x-axis are grouped according to traits (Early Understory = leaf unfolding of early understory, Late Understory = leaf unfolding of late understory, U = leaf unfolding of conifers, LU = leaf unfolding of broadleaf species and G = greening of broadleaf species) and arranged in order of increasing mean GP-SOS; the x-labels are species ID number (see <a href="#app1-remotesensing-08-00753" class="html-app">Supplementary Table S1</a> for complete details of GP).</p> Full article ">
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Article
Land Cover Classification Based on Fused Data from GF-1 and MODIS NDVI Time Series
by Fanjie Kong, Xiaobing Li, Hong Wang, Dengfeng Xie, Xiang Li and Yunxiao Bai
Remote Sens. 2016, 8(9), 741; https://doi.org/10.3390/rs8090741 - 8 Sep 2016
Cited by 61 | Viewed by 10613
Abstract
Accurate regional and global information on land cover and its changes over time is crucial for environmental monitoring, land management, and planning. In this study, we selected Fengning County, in China’s Hebei Province, as a case study area. Using satellite data, we generated [...] Read more.
Accurate regional and global information on land cover and its changes over time is crucial for environmental monitoring, land management, and planning. In this study, we selected Fengning County, in China’s Hebei Province, as a case study area. Using satellite data, we generated fused normalized-difference vegetation index (NDVI) data with high spatial and temporal resolution by utilizing the STARFM algorithm to produce a fused GF-1 and MODIS NDVI dataset. We extracted seven phenological parameters (including the start, end, and length of the growing season, base value, mid-season date, maximum NDVI, seasonal NDVI amplitude) from a fused NDVI time-series after reconstruction using the TIMESAT software. We developed four classification scenarios based on different combinations of GF-1 spectral features, the fused NDVI time-series, and the phenological parameters. We then classified the land cover using a support vector machine and analyzed the classification accuracies. We found that the proposed method achieved satisfactory classification results, and that the combination of the fused NDVI data with the extracted phenological parameters significantly improved classification accuracy. The classification accuracy based on the composited GF-1 multi-spectral bands combined with the phenological parameters was the highest among the four scenarios, with an overall classification accuracy of 88.8% and a Kappa coefficient of 0.8714, which represent increases of 9.3 percentage points and 0.1073, respectively, compared with GF-1 spectral data alone. The producer’s and user’s accuracy for different land cover types improved, with a few exceptions, and cropland and broadleaf forest had the largest increase. Full article
(This article belongs to the Special Issue Land Surface Phenology and Seasonality: Novel Approaches and Applications)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>The geographical location of the study area, a GF-1 false- color composite image from GF-1 satellite data, and the distribution of the field survey sites in 2015.</p> Full article ">Figure 2
<p>Flowchart of the land cover classification process based on fusion of GF-1 and MODIS NDVI time-series data.</p> Full article ">Figure 3
<p>(<b>a</b>–<b>d</b>) Comparisons of the actual GF-1 NDVI images (<b>left</b>) and the corresponding fused images (<b>right</b>) on two dates.</p> Full article ">Figure 4
<p>Scatterplots between values from the actual GF-1 NDVI images and the fused NDVI images (using STARFM) on the same date. (<b>a</b>) refers to data on 25 May 2015; (<b>b</b>) refers to data on 14 September 2015. Diagonal lines represent the line <span class="html-italic">y = x</span>.</p> Full article ">Figure 5
<p>Comparison of the NDVI profiles before and after Savitzky-Golay (S-G) filtering. <span class="html-italic">x</span>-axis refers to the dates of the synthetic images.</p> Full article ">Figure 6
<p>Representative NDVI profiles of the seven main land cover types and the dynamic of temperature. X-axis refer to the dates of the synthetic images. Daily mean temperature for Fengning station (41°7′48″N, 116°22′48″E) were obtained from the China Meteorological Data Sharing and Service System (<a href="http://data.cma.cn/" target="_blank">http://data.cma.cn/</a>) and the mean values for every 16 days were calculated.</p> Full article ">Figure 7
<p>Distribution of the land cover types based on the SVM land cover classification for Scenario 4 (combination of the composited GF-1 multi-spectral bands and the phenological parameters).</p> Full article ">Figure 8
<p>The differences of producer’s accuracy between scenarios.</p> Full article ">Figure 9
<p>The differences in user’s accuracy between scenarios.</p> Full article ">
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