TY - JOUR

T1 - Monitoring Annual Forest Cover Fraction Change During 2000-2020 in China's Han River Basin Using Time-Series MODIS NDVI, VCF and Spatio-Temporal Regression

AU - Zhong, Xinyan

AU - Du, Yun

AU - Wang, Xia

AU - Li, Xiaodong

AU - Zhao, Wenqiong

AU - Zhang, Yihang

AU - Atkinson, Peter M.

PY - 2024/6/30

Y1 - 2024/6/30

N2 - While it is crucial to monitor the spatio-temporal dynamics of forests at the subpixel scale, most available nonlinear methods are used to predict forest cover fraction maps only at the acquisition time of the training samples and are, thus, unable to estimate time-series forest cover fraction beyond the acquisition time. Based on MODIS NDVI, VCF and Landsat tree canopy height data, we developed a spatio-temporal regression (STR) method to estimate annual forest cover fraction maps during 2000–2020 on China's Han River Basin. Results obtained by the proposed STR method achieved significantly higher accuracy (R 2 = 0.897, RMSE = 0.1364, MAE = 0.077) than that obtained by a traditional nonlinear regression method. Moreover, the STR exhibits increased accuracy when using training samples from both 2000 and 2020 compared to those using training samples solely from either 2000 or 2020. We also introduced Landsat tree cover maps in 2005, 2010, and 2015 as reference data to verify the effectiveness of the STR method. The STR method was employed to produce annual forest cover fraction maps from 2000 to 2020. The outcomes revealed a distinct overall recovery trend of forest cover at both the pixel and subpixel scales in China's Han River Basin during 2000–2020, notably around the vicinity of the Danjiangkou reservoir area. In general, the STR method proposed in this research is superior in accurately tracking time-series high-intensity and low-intensity forest cover fraction changes in a complex forest ecosystem, which is composed of various forest types in a subtropical monsoon climate.

AB - While it is crucial to monitor the spatio-temporal dynamics of forests at the subpixel scale, most available nonlinear methods are used to predict forest cover fraction maps only at the acquisition time of the training samples and are, thus, unable to estimate time-series forest cover fraction beyond the acquisition time. Based on MODIS NDVI, VCF and Landsat tree canopy height data, we developed a spatio-temporal regression (STR) method to estimate annual forest cover fraction maps during 2000–2020 on China's Han River Basin. Results obtained by the proposed STR method achieved significantly higher accuracy (R 2 = 0.897, RMSE = 0.1364, MAE = 0.077) than that obtained by a traditional nonlinear regression method. Moreover, the STR exhibits increased accuracy when using training samples from both 2000 and 2020 compared to those using training samples solely from either 2000 or 2020. We also introduced Landsat tree cover maps in 2005, 2010, and 2015 as reference data to verify the effectiveness of the STR method. The STR method was employed to produce annual forest cover fraction maps from 2000 to 2020. The outcomes revealed a distinct overall recovery trend of forest cover at both the pixel and subpixel scales in China's Han River Basin during 2000–2020, notably around the vicinity of the Danjiangkou reservoir area. In general, the STR method proposed in this research is superior in accurately tracking time-series high-intensity and low-intensity forest cover fraction changes in a complex forest ecosystem, which is composed of various forest types in a subtropical monsoon climate.

U2 - 10.1109/jstars.2024.3417302

DO - 10.1109/jstars.2024.3417302

M3 - Journal article

VL - 17

SP - 12092

EP - 12111

JO - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

JF - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

SN - 1939-1404

ER -