Rapid Land-Cover and Land-Use Change in the Indo-Malaysian Region over the Last Thirty-Four Years Based on AVHRR NDVI Data. Issue 8 (11th November 2022)

• Record Type: Journal Article
• Authors: He, Yaqian; Chipman, Jonathan; Siegert, Noel; Mankin, Justin S.
• Is Part Of: Annals of the American Association of Geographers. Volume 112:Issue 8(2022)
• Page Start: 2131
• Page End: 2151
• Publication Date: 2022-11-11
• Subjects: GIMMS NDVI3g -- Indo-Malaysia -- land cover and land use -- temporal convolutional neural network -- transition; GIMMS NDVI3g -- 印度—马来西亚 -- 土地覆盖和土地利用 -- 时域卷积神经网络 -- 转化; cobertura y uso del suelo -- GIMMS NDVI3g -- Indo-Malasia -- red neural convolucional temporal -- transición; Geography -- Periodicals; Environmental sciences -- Periodicals; Geography; Electronic journals; Periodicals; 550
• DOI: 10.1080/24694452.2022.2077168
• Languages: English
• ISSNs: 2469-4452

Abstract

Abstract : The Indo-Malaysian region is a hot spot of rapid land-cover and land-use change (LCLUC) with little consensus about the rates and magnitudes of such change. Here we use temporal convolutional neural networks (TempCNNs) to generate a spatiotemporally consistent LCLUC data set for nearly thirty-five years (1982–2015), validated against two reference data sets with over 80 percent accuracy, better than other LCLUC products for the region. Our results both confirm and complicate estimates from earlier work that relied on decadal, rather than interannual, changes in regional land cover. We find forests decrease in mainland and maritime Southeast Asia and increase in South China and South Asia. Consistent with geographic theory about the drivers of land-use change, we find cropland expansion is a driving force for deforestation in mainland Southeast Asia with savannas playing a superior role, suggesting widespread forest degradation in this region. In contrast to earlier work and theory, we find that South China's increasing forest cover comes principally from savanna (rather than cropland) conversion. The explicit interannual LCLUC patterns, rates, and transitions identified in this study provide a valuable data source for studies of land-use theory, environmental and climate changes, and regional land-use policy evaluations.