Explicitly Identifying the Desertification Change in CMREC Area Based on Multisource Remote Data
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Materials
2.3. Methods
2.3.1. Flowchart of Technical Process
2.3.2. Indicators
2.3.3. Classification and Accuracy Assessment Methods
2.3.4. Quantitative Assessment of the Driving Mechanisms
3. Results
3.1. Quality Assessment
3.2. Change Patterns of Desertification
3.3. Impacts of Human Activities and Climate Change on Desertification
4. Discussion
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Data | Data Sources | Spatial Resolution | Temporal Resolution/Data Acquisition Dates | Data ID 1 |
---|---|---|---|---|
Landsat | http://landsat.usgs.gov/ | 30 m | 16 days/2000, 2015 | LANDSAT/LT05/C01/T1_SR; LANDSAT/LE07/C01/T1_SR; LANDSAT/LC08/C01/T1_SR |
SRTM3 | http://dwtkns.com/srtm30m/ | 30 m | —/— | USGS/SRTMGL1_003 |
LST | https://lpdaac.usgs.gov/products/mod11a2v006/ | 1000 m | 8 days/2000-2015 | MODIS/006/MOD11A2 |
ESA LC | http://maps.elie.ucl.ac.be/CCI/viewer/download.php | 300 m | 1 year/2000, 2015 | — |
MCD12Q1 | https://lpdaac.usgs.gov/products/mcd12q1v006/ | 500 m | 1 year/2000, 2015 | MODIS/006/MCD12Q1 |
Sentinel-2 | https://earth.esa.int/web/sentinel/user-guides/sentinel-2-msi/product-types/level-1c | 10 m | 5 days/2015 | COPERNICUS/S2 |
VCF | https://lpdaac.usgs.gov/products/gfcc30tcv003/ | 30 m | —/2000, 2005, 2010, 2015 | GLCF/GLS_TCC |
Sand Content | https://www.openlandmap.org/ | 250 m | —/— | OpenLandMap/SOL/SOL_SAND-WFRACTION_USDA-3A1A1A_M/v02 |
Global Flux Tower | https://daac.ornl.gov/ | — | —/2000, 2015 | — |
NPP | https://lpdaac.usgs.gov/products/mod17a3hv006/ | 500 m | 1 year/2000-2015 | MODIS/006/MOD17A3H; |
WorldClim Rainfall | http://www.climatologylab.org/terraclimate.html | 2.5 arc minutes | 1 month/2000-2015 | IDAHO_EPSCOR/TERRACLIMATE |
PML | — | 500 m | 8 days/2000-2015 | projects/pml_evapotranspiration/PML/OUTPUT/PML_V1_8day |
GDP | http://sedac.ciesin.columbia.edu/data/collection/gpw-v4 | 30 arc-seconds | —/2000, 2005, 2010, 2015 | — |
GLDAS | http://ldas.gsfc.nasa.gov/gldas/ | 0.25 arc degrees | 3 h/2000–2015 | ASA/GLDAS/V021/NOAH/G025/T3H |
Desertification Status | Visual Interpretation | Land Cover Characteristics | Vegetation Coverage (%) |
---|---|---|---|
Non-desertification | Forests, cropland, and high coverage rate of grassland | >65 | |
Slight | Vegetation degradation; the growth of the original plant species was affected | 50–65 | |
Moderate | Degenerated plants, low shrub, and sand mounds | 10–50 | |
High | The vegetation land starts to convert into wildland in some areas; the grass is mixed with sandy plants in the grassland area | 1–10 | |
Severe | Bare land, sandy land, and the Gobi desert | <1 |
Spectral Indices | Equation 1 |
---|---|
NDVI | |
FVC | |
MSAVI | |
NDWI | |
TGSI | |
Albedo | |
BSI |
Model’s Name | Model Mechanisms |
---|---|
Classification and regression tree (CART) | The target variables (desertification degrees) and the test variables (desertification indicators) of the training sample set can be divided into two groups to form a binary tree model. |
Support vector machine (SVM) | In the feature space of the training dataset, SVM is based on the kernel function to find the support vector with a large distinguishing function and construct the classifier, so as to maximize the distance of desertification degrees in the sample. |
Random forest (RF) | Based on the statistical theory, RF uses a bootstrap sampling method to extract multiple sample sets from the original sample set and then adopts a decision tree for each sample set, which can combine multiple decision trees for prediction, and finally obtains the prediction results through voting. |
Albedo-NDVI | Utilizing the negative correlation between Albedo and NDVI, the normalized NDVI and Albedo values are constructed into the Albedo-NDVI feature space scatter diagram, and then the linear relationship between them is determined to attain the desertification difference index (DDI) 1. |
Desertification Status | Scenarios | Sp | SH | Definition |
---|---|---|---|---|
reversion | Scenario 1 | >0 | >0 | The driving force of climate change is 100% |
Scenario 2 | <0 | <0 | The driving force of human activities is 100% | |
Scenario 3 | >0 | <0 | The driving force of desertification reversion partly results from climate change (i.e., ), while the driving force of human activities is the remaining part of the total. | |
Scenario 4 | <0 | >0 | The scenario is defined as an “Error” area where the actual vegetation growth is getting better, but the impact of climate change and human activities are causing more land degradation. | |
Expansion | Scenario 1 | <0 | <0 | The driving force of climate change is 100% |
Scenario 2 | >0 | >0 | The driving force of human activities is 100% | |
Scenario 3 | <0 | >0 | The driving force of desertification expansion partly results from climate change (i.e., ), while the driving force of human activities is the remaining part of the total. | |
Scenario 4 | >0 | <0 | The scenario is defined as an “Error” area where the actual vegetation status is degrading, but the impact of climate change and human activities benefited better vegetation growth. |
Algorithms | Non-Desertification | Slight | Moderate | High | Severe | OA | Kappa | |||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
PA | UA | PA | UA | PA | UA | PA | UA | PA | UA | |||
CART | 95.7 | 95.5 | 58.6 | 77.5 | 70 | 77.5 | 75 | 37.5 | 83.4 | 85.7 | 85 | 0.754 |
SVM | 82.2 | 86.3 | 17.1 | 21.2 | 40 | 75 | 75 | 18.1 | 10 | 75 | 58.75 | 0.396 |
RF | 88.5 | 92 | 34.2 | 39.3 | 50 | 62.5 | 100 | 41.4 | 90 | 100 | 75 | 0.602 |
Albedo-NDVI | 87.5 | 91.9 | 0 | 0 | 100 | 50 | 100 | 100 | 100 | 100 | 85 | 0.722 |
Severe | High | Moderate | Slight | Non-Desertification | |||
---|---|---|---|---|---|---|---|
Total | 2000 | Area | 538,358 | 441,703 | 168,064 | 719,685 | 4,557,603 |
Percentage | 8.24 | 6.76 | 2.57 | 11.01 | 69.73 | ||
2015 | Area | 465,269 | 398,478 | 154,157 | 947,999 | 4,431,883 | |
Percentage | 7.12 | 6.10 | 2.36 | 14.50 | 67.81 | ||
2000–2015 | Area | −73,089 | −43,225 | −13,907 | 228,314 | −125,720 | |
Percentage | −13.58% | −9.79% | −8.27% | 31.72% | −2.76% | ||
China | 2000 | Area | 362,888 | 196,807 | 124,393 | 310,998 | 170,692 |
Percentage | 13.29 | 7.21 | 4.56 | 11.39 | 62.53 | ||
2015 | Area | 272,208 | 193,098 | 95,898 | 401,396 | 172,054 | |
Percentage | 9.93 | 7.04 | 3.50 | 14.64 | 62.75 | ||
2000–2015 | Area | −90,680 | −3709 | −28,495 | 90,398 | 1362 | |
Percentage | −24.99% | −1.88% | −22.91% | 29.07% | 0.80% | ||
Mongolia | 2000 | Area | 171,887 | 226,925 | 42,866 | 301,878 | 363,434 |
Percentage | 15.50 | 20.46 | 3.87 | 27.22 | 32.78 | ||
2015 | Area | 190,895 | 190,513 | 54,897 | 400,772 | 269,648 | |
Percentage | 17.22 | 17.18 | 4.95 | 36.14 | 24.32 | ||
2000–2015 | Area | 19,008 | −36,412 | 12,031 | 98,894 | −93,786 | |
Percentage | 11.06% | −16.05% | 28.07% | 32.76% | −25.81% | ||
Russia | 2000 | Area | 3583 | 17,971 | 804 | 106,810 | 248.73 |
Percentage | 0.13 | 0.67 | 0.03 | 3.96 | 92.20 | ||
2015 | Area | 2167 | 14,867 | 3363 | 145,831 | 244.17 | |
Percentage | 0.08 | 0.55 | 0.13 | 5.43 | 90.93 | ||
2000–2015 | Area | −1416 | −3104 | 2559 | 39,021 | −5 | |
Percentage | −39.52% | −17.27% | 318.28% | 36.53% | −1.83% |
Significant Expansion | Expansion | No Conversion | Reversion | Significant Reversion | ||
---|---|---|---|---|---|---|
Total | Area | 82,143 | 550,516 | 5,288,340 | 393,120 | 221,775 |
Percentage | 1.26 | 8.42 | 80.91 | 6.01 | 3.39 | |
China | Area | 40,218 | 194,653 | 2,107,904 | 233,211 | 153,514 |
Percentage | 1.47 | 7.13 | 77.23 | 8.54 | 5.62 | |
Mongolia | Area | 36,920 | 281,681 | 603,662 | 126,108 | 60,490 |
Percentage | 3.33 | 25.40 | 54.44 | 11.37 | 5.46 | |
Russia | Area | 5005 | 74,182 | 2,576,774 | 33,801 | 7771 |
Percentage | 0.19 | 2.75 | 95.52 | 1.25 | 0.29 |
Climate Change | Human Activities | Error | |||||
---|---|---|---|---|---|---|---|
Reversion | Expansion | Reversion | Expansion | Reversion | Expansion | ||
Total | Area | 381.16 | 8.29 | 151.60 | 396.65 | 21.24 | 164.32 |
Percentage | 68.80 | 1.46 | 27.37 | 69.68 | 3.83 | 28.87 | |
China | Area | 269.88 | 0.58 | 78.09 | 144.91 | 0.36 | 65.62 |
Percentage | 77.48 | 0.27 | 22.42 | 68.64 | 0.10 | 31.09 | |
Mongolia | Area | 97.10 | 0.01 | 70.81 | 188.27 | 0.28 | 98.62 |
Percentage | 57.73 | 0.00 | 42.10 | 65.62 | 0.16 | 34.38 | |
Russia | Area | 14.18 | 2.90 | 2.69 | 63.47 | 20.60 | 0.08 |
Percentage | 37.84 | 4.37 | 7.19 | 95.52 | 54.97 | 0.11 |
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Fan, Z.; Li, S.; Fang, H. Explicitly Identifying the Desertification Change in CMREC Area Based on Multisource Remote Data. Remote Sens. 2020, 12, 3170. https://doi.org/10.3390/rs12193170
Fan Z, Li S, Fang H. Explicitly Identifying the Desertification Change in CMREC Area Based on Multisource Remote Data. Remote Sensing. 2020; 12(19):3170. https://doi.org/10.3390/rs12193170
Chicago/Turabian StyleFan, Zemeng, Saibo Li, and Haiyan Fang. 2020. "Explicitly Identifying the Desertification Change in CMREC Area Based on Multisource Remote Data" Remote Sensing 12, no. 19: 3170. https://doi.org/10.3390/rs12193170