TY - JOUR
T1 - Assessing long-term trends in vegetation cover change in the Xilin River Basin
T2 - Potential for monitoring grassland degradation and restoration
AU - Zhou, Yajun
AU - Batelaan, Okke
AU - Guan, Huade
AU - Liu, Tingxi
AU - Duan, Limin
AU - Wang, Yixuan
AU - Li, Xia
PY - 2024/1/1
Y1 - 2024/1/1
N2 - Under the influence of climate change and human activities, the problem of grassland degradation is becoming increasingly severe. Detection of changes in vegetation cover is crucial for a better understanding of the interaction between humans and ecosystems. This study maps changes in vegetation cover using the Google Earth Engine (GEE). We used 36 years of Landsat satellite imagery (1985–2020) in the Xilin River Basin, China, to classify grassland conditions and validated the results with field observation data. The overall classification of the model accuracy assessment was 83.3%. The Dynamic Reference Vegetation Cover Method (DRCM) was adopted to remove the effect of interannual variation of rainfall, allowing to focus on the impact of human activities on vegetation cover changes. The results identify five categories of vegetation cover changes: significantly increased, potentially increased, stable, potentially decreased, and significantly decreased. The reference level is derived from the most persistent land surface coverage across different grassland types and all years. Overall, 9.3% of the study area had a significant increase in vegetation cover, 14.2% a potential increase, 48.6% of the area showed a stable vegetation condition, 9.8% showed a potential decrease, and 18.1% a significant decrease in vegetation cover. The largest proportion of combined potential and significant reduction was 35.2% for desert grassland, where the vegetation faced the most severe reduction. This study will provide a basis for identifying grassland degradation and developing scientific management policies.
AB - Under the influence of climate change and human activities, the problem of grassland degradation is becoming increasingly severe. Detection of changes in vegetation cover is crucial for a better understanding of the interaction between humans and ecosystems. This study maps changes in vegetation cover using the Google Earth Engine (GEE). We used 36 years of Landsat satellite imagery (1985–2020) in the Xilin River Basin, China, to classify grassland conditions and validated the results with field observation data. The overall classification of the model accuracy assessment was 83.3%. The Dynamic Reference Vegetation Cover Method (DRCM) was adopted to remove the effect of interannual variation of rainfall, allowing to focus on the impact of human activities on vegetation cover changes. The results identify five categories of vegetation cover changes: significantly increased, potentially increased, stable, potentially decreased, and significantly decreased. The reference level is derived from the most persistent land surface coverage across different grassland types and all years. Overall, 9.3% of the study area had a significant increase in vegetation cover, 14.2% a potential increase, 48.6% of the area showed a stable vegetation condition, 9.8% showed a potential decrease, and 18.1% a significant decrease in vegetation cover. The largest proportion of combined potential and significant reduction was 35.2% for desert grassland, where the vegetation faced the most severe reduction. This study will provide a basis for identifying grassland degradation and developing scientific management policies.
KW - Drought index
KW - Google earth engine
KW - Landsat
KW - Vegetation coverage
KW - Xilin river basin
UR - http://www.scopus.com/inward/record.url?scp=85178498603&partnerID=8YFLogxK
U2 - 10.1016/j.jenvman.2023.119579
DO - 10.1016/j.jenvman.2023.119579
M3 - Article
C2 - 37976643
AN - SCOPUS:85178498603
SN - 0301-4797
VL - 349
JO - Journal of Environmental Management
JF - Journal of Environmental Management
M1 - 119579
ER -