Optical Remote Sensing

Assessments of agriculture at the national scale require the best possible information on the distribution of cropland and the management of that cropland. Official cropland statistics for China contain much information on the distribution of crop types, but are known to underestimate total cropland area and are generally at coarse spatial resolution. Remote sensing products can provide moderate to fine spatial resolution estimates of cropland location and extent, but usually supply little information on crop type or management. We combined 1990 county‐scale agricultural census statistics on total cropland area and sown area of 17 major crops with a fine‐resolution land‐cover map derived from 1995‐96 optical remote sensing (Landsat Thematic Mapper) data to generate 0.5° resolution maps of the distribution of 47 different single‐ and multi‐crop rotations in mainland China. We estimated that, of 1.3‐million km2 of cropland, approximately 60% was single‐cropped, 30% was double‐cropped, and 10% was triple‐cropped. Total sown or planted area was 2.0‐million km2.

This paper outlines the first part of a series of research studies to investigate the potential and approaches for using optical remote sensing to assess vegetation water content. It first analyzes why most methods used as approximations of vegetation water content (such as vegetation stress indices, estimation of degree of curing and chlorophyll content) are not suitable for retrieving water

In the face of increased land use pressures and climate change, floodplains in the Lower Amazon region are increasingly vulnerable. A detailed land-cover map of the floodplain is needed to assess the impacts of increased land use pressures. Assessing land use and land-cover changes requires accurate representation of important land covers, a persistent challenge in the complex and dynamic environment of the Amazon River floodplains. This study maps detailed floodplain vegetation for a 663 square kilometer area of the Lower Amazon River floodplain using Quickbird 2 scenes (28 October, 2005) and a cotemporaneous Landsat 5 TM scene (29 October, 2005). Sub-pixel analysis data products are incorporated into a hierarchical object-based classification of (semi-) natural floodplain vegetation, as defined by the United Nations Food and Agricultural Organization’s Land-cover classification System (DiGregorio 2005). Within (semi-)natural floodplain vegetation, the land-cover classes of interest include forest, open woodland, closed shrubland, and open shrubland. As part of the detailed floodplain vegetation classification, we mapped the extent of woody vegetation (86% overall accuracy). Within areas mapped as woody vegetation, we classified the amount of vegetation cover (94% overall accuracy) and dominant woody vegetation lifeforms (90% overall accuracy). The vegetation cover and dominant woody lifeform classifications utilize the spectral information and spatial relationships between meaningful image objects. The resulting detailed floodplain vegetation classification reveals a dominance of closed and open shrubs within a complex distribution of vegetation structure and dominant lifeforms. Woody vegetation is distributed as 18% (29,879 m2) of forest, 13% (23,448 m2) of open woodland, 20% (34,616 m2) of closed shrub, 26% (46,291 m2) of open shrub, and 23% (40,490 m2) of herbaceous cover. Improved identification of detailed floodplain vegetation provides a basis for studying localized land-cover changes, improved estimates of ecological contributions for specific floodplain habitats, and evaluating the impacts of household land use decisions.

The objective of this paper is to review research conducted over the past decade on the application of multi-temporal remote sensing for monitoring changes of Arctic tundra lands. Emphasis is placed on results from the National Science Foundation Land–Air–Ice Interactions (LAII) program and on optical remote sensing techniques. Case studies demonstrate that ground-level sensors on stationary or moving track platforms

This paper discusses the bathymetric mapping technologies by means of satellite remote sensing (RS) with special emphasis on bathymetry derivation models, methods, accuracies, advantages, limitations, and comparisons. Traditionally, bathymetry can be mapped using echo sounding sounders. However, this method is constrained by its inefficiency in shallow waters and very high operating logistic costs. In comparison, RS technologies present efficient and cost-effective means of mapping bathymetry over remote and broad areas. RS of bathymetry can be categorised into two broad classes: active RS and passive RS. Active RS methods are based on active satellite sensors, which emit artificial radiation to study the earth surface or atmospheric features, e.g. light detection and ranging (LIDAR), polarimetric synthetic aperture radar (SAR), altimeters, etc. Passive RS methods are based on passive satellite sensors, which detect sunlight (natural source of light) radiation reflected from the earth and thermal radiation in the visible and infrared portion of the electromagnetic spectrum, e.g. multispectral or optical satellite sensors. Bathymetric methods can also be categorised as imaging methods and non-imaging methods. The non-imaging method is elucidated by laser scanners or LIDAR, which measures the distance between the sensor and the water surface or the ocean floor using a single wave pulse or double waves. On the other hand, imaging methods approximate the water depth based on the pixel values or digital numbers (DN) (representing reflectance or backscatter) of an image. Imaging methods make use of the visible and/or near infrared (NIR) and microwave radiation. Imaging methods are implemented with either analytical modelling or empirical modelling, or by a blend of both. This paper presents the development of bathymetric mapping technology by using RS, and discusses the state-of-the-art bathymetry derivation methods/algorithms and their implications in practical applications.

Recently, thermal properties of the landscaped rooftops and walls have attracted the interest of researchers because of the potential to minimize energy consumption in urban areas and to aid summer-time thermal control. For this reason the creation of a plant-based shade for walls or above buildings is highly important. In this paper we evaluate using Lygodium japonicum, one of the many ferns and fern allies traditionally used in Japanese gardening, as a component of thermal-buffering green walls. Lygodium japonicum, the only climbing fern species in Japan, is fast-growing, adheres easily to walls and has a climbing nature. A simple thermal analysis of the sun-shading effect of Lygodium canopy suggested that local surface temperature above the ceramic tiles placed on the rooftop of a building can be buffered (lowered in daytime and maintained relatively warm at night) by the presence of leafy climbing ferns covering the tiles, possibly due to the reflection and absorbance of solar radiation. Furthermore, the presence of the plants may also slow the night-time release of heat from the building surface. Because plants installed on tall walls or on the tops of buildings are not easily accessed for manual care, we performed a real-time routine monitoring and control of plant growth status using various optical sensors that could be automated and monitored remotely for large-scale applications. For this purpose, the optical properties of a L. japonicum canopy under solar incident light have been determined. In order to evaluate the natural shading and growing properties of a green canopy, the incident solar radiation spectrum (J), leaf canopy-filtered light spectrum (transmittance, T) and leaf-reflectivity spectrum (R) were measured. By reading the reflectivity spectrum, concomitant chlorophyll fluorescence signals (F) from Lygodium leaves were also detected at 760 nm, which corresponds to the O2-A Fraunhofer line. Our data suggests that the daily change in photosynthetic status (P) can be traced by monitoring the change in relative F in relation to the estimated heat loss (H) and measured J, R, and T using a series of practical equations designed to roughly estimate the gross photosynthetic response within the plant canopy. Using our equations, the photosynthetic capacity in the plant canopy structure could be simply simulated and predictable by optical sensors.

Archaeological remote sensing is not a novel discipline. Indeed, there is already a suite of geoscientific techniques that are regularly used by practitioners in the field, according to standards and best practice guidelines. However, (i) the technological development of sensors for data capture; (ii) the accessibility of new remote sensing and Earth Observation data; and (iii) the awareness that a combination of different techniques can lead to retrieval of diverse and complementary information to characterize landscapes and objects of archaeological value and significance, are currently three triggers stimulating advances in methodologies for data acquisition, signal processing, and the integration and fusion of extracted information. The Special Issue "Remote Sensing and Geosciences for Archaeology" therefore presents a collection of scientific contributions that provides a sample of the state-of-the-art and forefront research in this field. Site discovery, understanding of cultural landscapes, augmented knowledge of heritage, condition assessment, and conservation are the main research and practice targets that the papers published in this Special Issue aim to address.

Satellite technologies are increasingly used to track looting in remote and inaccessible archaeological sites and assess damage to heritage. Evidence gathered in our study proves a growing user uptake of these technologies, beyond the specialist remote sensing community, but also that a more synergistic use of optical and radar data is required. The advantages of such an approach to satellite monitoring are demonstrated on Apamea, Syria. Current limitations and future perspectives are outlined, as an entry point to a comprehensive review published by the authors in the referenced journal article, that the readers are encouraged to refer to for a more in-depth and specialist discussion.

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 < R 2 < 0.99). The 10.65 GHz VOD (VOD 1065GHz) achieved the best linear regression fit (R 2 = 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 VOD 10.65GHz 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 R 2 of 0.87, but with a one-week bias. Considering the one-week bias between the two methods, we find that PH of VOD 10.65GHz 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.

Forest aboveground biomass (AGB) measurement is a direct estimator of the live carbon stock of that forest region. Increasing emission and concentration of CO2 is a global threat as it is a major cause of today’s global warming. The forest AGB is a live carbon sequester that plays a major role by absorbing atmospheric CO2. There are field-based measurement methods of AGB, but the main disadvantage is that they are primarily destructive. Several authors have undertaken AGB estimation using different remote sensing data types, but they are mostly not cost-effective for extensive study areas. We have created a cost-effective algorithm for AGB estimation using multispectral (MSS) data. In this study, Indian Remote-Sensing Satellite-P6 (IRS P6) Linear Imaging Self-Scanning Sensor-4 (LISS-IV) MSS data have been used for the analysis. The research has tried to estimate the AGB of different types of forests existing in the study area by using various vegetation indices and the gray-level co-occurrence matrix (GLCM) and created a hybrid methodology combining the vegetation indices and GLCM. Among all vegetation indices, the simple ratio (SR) highly correlates with AGB of pure deciduous and coniferous forests. In a mixed forest region, due to a mixture of two canopy stands, there is a mixture of foliage angle and optical scattering distribution. Therefore, modified simple ratio (MSR) becomes dominant in mixed forest AGB estimation. Previously there was no study to justify this GLCM texture parameter selection. In this study, we have justified the parameter selection of GLCM texture statistics. This parameter selection will help researchers choose the proper GLCM texture parameter for their study. Integration of GLCM textures with vegetation indices enhances the AGB model strength for all forest regions. The deciduous forest map gives validation R2 of 0.89 with an RMSE of 1.93 ton/pixel. The validation R2 of the Coniferous Forest map is 0.83 with an RMSE of 1.35 ton/pixel. There is a comparatively identifiable improvement in mixed forest with validation R2 of 0.96 and RMSE of 0.25 ton/pixel. This study shows AGB storage of deciduous forest has a maximum share over other forest region of Kalimpong forest.

The largest error in determining volcanic gas fluxes using ground based optical remote sensing instruments is typically the determination of the plume speed, and in the case of fixed scanning instruments also the plume height. We here present a newly developed technique capable of measuring plume height, plume speed and gas flux using one single instrument by simultaneously collecting scattered sunlight in two directions. The angle between the two measurement directions is fixed, removing the need for time consuming in-field calibrations. The plume height and gas flux is measured by traversing the plume and the plume speed is measured by performing a stationary measurement underneath the plume. The instrument was tested in a field campaign in May 2005 at Mt. Etna, Italy, where the measured results are compared to wind fields derived from a meso-scale meteorological model (MM5). The test and comparison show that the instrument is functioning and capable of estimating wind speed at the plume height.

(Authors: F. Baret and R. Vintila)
This study investigated the potential of high spatial resolution and high revisit frequency observations to monitor the LAI distribution over regional domains. It is based on the ADAM experiment during which 39 SPOT images were acquired from October 2000 and July 2001 over the study area located close to Bucharest in Romania. The study focuses on wheat crops for which intensive ground measurements were performed over the whole growing season for 40 elementary sampling units corresponding to about 30×30m². The sampling units were divided into 10 for calibration and 30 for validation.
The approach is based on the use of a semi-empirical model of LAI dynamics (MODLAI). It was first adjusted on the calibration units to smooth out local measurement uncertainties and interpolate the measurements to the dates of SPOT acquisition. Then, empirical transfer functions were derived for each date, based on multiple linear regressions. Finally, the LAI was estimated for all the validation units at the SPOT dates. MODLAI was adjusted to the retrieved LAI of the validation units, and used to interpolate the values to the date of ground measurements. The LAI values derived this way were compared to those measured directly in the field. They show a very good consistency. The potentials of this approach and its adaptation to other applications is then discussed."