Assessing Global Forest Land-Use Change by Object-Based Image Analysis
"> Figure 1
<p>The global, degree grid framework, including the Canadian NFI plot scheme. FAO processing methodology was applied to sites (>11,000) in the boreal, temperate and sub-tropical climatic domains (in black) (From [<a href="#B1-remotesensing-08-00678" class="html-bibr">1</a>]).</p> "> Figure 2
<p>(From [<a href="#B1-remotesensing-08-00678" class="html-bibr">1</a>]) A multi-date segmentation for a 20 km × 20 km site in the boreal climatic domain. Landsat imagery from 1990, 2000, 2005 are combined into a single data stack and segmented. The segments (white outlines in image on right) capture areas of spectral similarity as well as areas of change over time. The Landsat image on the right is a colour composite of Landsat band 5 from 1990 (red), 2000 (blue) and 2005 (green). Changes in tree cover between time periods are evident.</p> "> Figure 3
<p>The multi-date processing scheme over the same area as in <a href="#remotesensing-08-00678-f002" class="html-fig">Figure 2</a> for year 1990 (top row), year 2000 (middle row) and year 2005 (bottom row) the following: (<b>A</b>) the 20 × 20 km Landsat imagery in colour composite b3,4,5; (<b>B</b>) the potential training objects (grey) for year 2000; (<b>C</b>) the training dataset after class labelling, where Dark green = Tree Cover, Orange = Other Wooded Land, and Pink = Herbaceous; (<b>D</b>) the final automated land cover classification, where Dark green = Tree Cover, Orange = Other Wooded Land, and Pink = Herbaceous; (<b>E</b>) the final, expert revised land cover classification of the central 10 × 10 km (corresponding to white box in D), where Dark green = Tree Cover, Light green = Tree Cover Mosaic, Orange = Other Wooded Land, and Pink = Herbaceous; and (<b>F</b>) the corresponding final, expert revised land use classification, where Dark green = Forest, Orange = Other Wooded Land, and Pink = Other Land Use.</p> "> Figure 4
<p>(<b>Left</b>) A comparison of the tree cover area obtained automatically for level-1 segmentation (x-axis) and the 5 ha MMU (y-axis); and (<b>Right</b>) a comparison of the tree cover area obtained automatically at the 5 ha MMU (x-axis) and the expert reviewed-revised tree cover at the 5 ha MMU (y-axis). The dotted line represents the slope of the line formed by the linear regression. The solid line represents the one-to-one line.</p> "> Figure 5
<p>The amount of change detected between 1990–2000 using IR-MAD and increasing change thresholds (<b>columns</b>) aggregated to level-1 and 5 ha MMU segments (<b>rows</b>) for a 20 × 20 km sample site. Columns left to right represent thresholds of 0.91, 0.95 and 0.99 of the IR-MAD change likelihood layer, respectively. Rows top to bottom represent pixel-level, level-1 image segments and 5 ha MMU segments, respectively. Changed areas are in black. Non-changed areas are white.</p> "> Figure 6
<p>(<b>Left</b>) The mean area detected as change (Y-axis) over all samples using pixel-based (triangles), level-1 segments (+) and level-2 segments (circles) at increasing IR-MAD thresholds (X-axis). (<b>Centre</b>) The difference in the total area detected as change (Y-axis) between pixel and level-2 segments plotted as a function of the change segments’ landscape shape index (X-axis). (<b>Right</b>) The difference in the total area detected as change (Y-axis) between pixel and level-2 segments plotted as a function of the mean change patch area (X-axis).</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Image Segmentation
2.2. Automated Image Classification
2.3. Neural Network Land Cover Classification
2.4. Multivariate Alteration Detection for Land-Cover Change
2.5. Assigning Class Labels to 1990 and 2005 Time Periods
2.6. Land Cover vs. Land Use
3. Results and Discussion
3.1. OBIA, Minimum Mapping Units and Land Cover Classification
3.2. OBIA, Minimum Mapping Units and Change Detection
3.3. Landscape Metrics and Change Detection
3.4. Automated Land-Cover Classification and Change Detection Results
3.5. Effect of Land Cover or Land Use Definition on Forest Area Calculations
4. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
Abbreviations
OBIA | Object-based Image Analysis |
FAO | United Nations Food and Agriculture Organization |
JRC | Joint Research Centre of the European Commissions |
USGS | United States Geological Survey |
GLS | Global Land Survey |
MMU | Minimum Mapping Unit |
IR-MAD | Iteratively Re-weighted Multivariate Alteration Detection |
MODIS | Moderate Resolution Imaging Spectroradiometer |
ANN | Artificial Neural Network |
REDD | Reducing Emissions from Deforestation and Degradation |
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Land Cover Class | Segment Characteristic |
---|---|
Water | Segment proportion of Global Water Mask > 0.7 |
Tree | Mean segment VCF% tree cover > 11 |
Other Wooded Land | Mean segment VCF% tree cover < 11 AND Globcover Class = Shrub |
Herbaceous | Mean segment VCF% tree cover < 11 AND Globcover Class = Herbaceous ORLandsat Band 5 DN > 120 |
Bare ground | Mean segment Landsat Band 2 DN > 200 AND Landsat Band 5 DN > 200 |
Cloud | Mean segment Landsat Band 4 DN + Band 5 DN + Band 7 DN > 500 |
Level 1 LC | Percent Composition | Level-2 LC |
---|---|---|
Tree cover | ≥30 | Tree cover |
Other wooded land | ≥70 | Other wooded land |
Other land cover | ≥70 | Other land cover |
Water | ≥70 | Water |
Tree | Wooded | Other | Water | No Data | Total | User’s | |
Tree | 6957 | 802 | 1599 | 61 | 33 | 9451 | 0.74 |
Wooded | 564 | 1609 | 2336 | 10 | 2 | 4521 | 0.36 |
Other | 580 | 793 | 12,684 | 28 | 21 | 14,107 | 0.90 |
Water | 17 | 24 | 92 | 705 | 3 | 840 | 0.84 |
No Data | 1 | 3 | 40 | 2 | 103 | 149 | 0.69 |
Total | 8120 | 3230 | 16,751 | 805 | 162 | 29,069 | |
Producer’s | 0.86 | 0.50 | 0.76 | 0.87 | 0.63 | 0.76 |
1990–2000 | Tree-Tree | Tree-Other | Other-Tree | Other-Other | Total | User’s |
Tree–Tree | 4447 | 63 | 33 | 1391 | 5934 | 0.75 |
Tree–Other | 107 | 211 | 2 | 602 | 922 | 0.23 |
Other–Tree | 117 | 1 | 138 | 372 | 628 | 0.22 |
Other–Other | 214 | 28 | 17 | 16,014 | 16,273 | 0.98 |
Total | 4885 | 303 | 190 | 18,379 | 23,757 | |
Producer’s | 0.91 | 0.70 | 0.73 | 0.87 | 0.88 | |
2000–2005 | Tree-Tree | Tree-Other | Other-Tree | Other-Other | Total | User’s |
Tree–Tree | 4185 | 49 | 37 | 1449 | 5720 | 0.73 |
Tree–Other | 69 | 226 | 1 | 327 | 623 | 0.36 |
Other–Tree | 119 | 2 | 100 | 665 | 886 | 0.11 |
Other–Other | 182 | 19 | 9 | 15,393 | 15,603 | 0.99 |
Total | 4555 | 296 | 147 | 17,834 | 22,832 | |
Producer’s | 0.92 | 0.76 | 0.68 | 0.86 | 0.87 |
Continent/Country | Climatic Domain | Forest Land-Use (Proportion) | Tree Cover (Proportion) |
---|---|---|---|
Canada | boreal | 0.72 | 0.69 |
Europe | boreal | 0.69 | 0.69 |
Africa | subtropical | 0.04 | 0.04 |
Europe (ex. Russian Fed.) | subtropical | 0.20 | 0.20 |
North and Central America | subtropical | 0.23 | 0.25 |
Oceania | subtropical | 0.07 | 0.07 |
South America | subtropical | 0.11 | 0.11 |
Asia | temperate | 0.63 | 0.61 |
Canada | temperate | 0.62 | 0.59 |
Europe (ex. Russian Fed.) | temperate | 0.33 | 0.33 |
North and Central America | temperate | 0.29 | 0.32 |
Oceania | temperate | 0.51 | 0.51 |
South America | temperate | 0.16 | 0.16 |
Africa | tropical | 0.20 | 0.16 |
Asia | tropical | 0.25 | 0.27 |
North and Central America | tropical | 0.46 | 0.46 |
Oceania | tropical | 0.18 | 0.18 |
South America | tropical | 0.54 | 0.55 |
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Lindquist, E.J.; D’Annunzio, R. Assessing Global Forest Land-Use Change by Object-Based Image Analysis. Remote Sens. 2016, 8, 678. https://doi.org/10.3390/rs8080678
Lindquist EJ, D’Annunzio R. Assessing Global Forest Land-Use Change by Object-Based Image Analysis. Remote Sensing. 2016; 8(8):678. https://doi.org/10.3390/rs8080678
Chicago/Turabian StyleLindquist, Erik J., and Rémi D’Annunzio. 2016. "Assessing Global Forest Land-Use Change by Object-Based Image Analysis" Remote Sensing 8, no. 8: 678. https://doi.org/10.3390/rs8080678