Integrating Analytical Frameworks to Investigate Land-Cover Regime Shifts in Dynamic Landscapes

Abstract

Regime shifts—rapid long-term transitions between stable states—are well documented in ecology but remain controversial and understudied in land use and land cover change (LUCC). In particular, uncertainty surrounds the prevalence and causes of regime shifts at the landscape level. We studied LUCC dynamics in the Tanintharyi Region (Myanmar), which contains one of the last remaining significant contiguous forest areas in Southeast Asia but was heavily deforested between 1992–2015. By combining remote sensing methods and a literature review of historical processes leading to LUCC, we identified a regime shift from a forest-oriented state to an agricultural-oriented state between 1997–2004. The regime shift was triggered by a confluence of complex political and economic conditions within Myanmar, notably the ceasefires between various ethnic groups and the military government, coupled with its enhanced business relations with Thailand and China. Government policies and foreign direct investment enabling the establishment of large-scale agro-industrial concessions reinforced the new agriculture-oriented regime and prevented reversion to the original forest-dominated regime. Our approach of integrating complementary analytical frameworks to identify and understand land-cover regime shifts can help policymakers to preempt future regime shifts in Tanintharyi, and can be applied to the study of land change in other regions.

Improved estimates of mangrove cover and change reveal catastrophic deforestation in Myanmar

“…A recent and revised Landsatderived estimate of mangrove cover and change for Myanmar highlighted a growing mangrove deforestation crisis, and demonstrated the need to develop ground-up datasets and avoid sub-setting global datasets for national-level mangrove estimates (Estoque et al 2018). Other studies have demonstrated the importance of reporting gross land cover statistics when evaluating change dynamics, including mangroves (Thomas et al 2017, Estoque et al 2018, Gaw et al 2018, De Alban et al 2019. This is because gross land cover change estimates provide essential information on transitions among land cover classes unavailable through net change studies, leading to a more robust analysis of the drivers of land cover change (Pontius et al 2004, Aldwaik and, which is especially important for relatively dynamic mangrove communities since they are amenable to rapid deforestation (loss) but can also rapidly regenerate (gain) when biophysical conditions are appropriate (Lewis 2005, Loon et al 2016.…”

“…and raster(Hijmans et al 2019) packages in R v.3.4 (https://rproject.org) (R Core Team 2016) and a Microsoft Excel Macro spreadsheet (https://sites.google.com/ site/intensityanalysis) (Aldwaik and Pontius 2012) (SM 1.7). Intensity Analysis has been extensively applied to detect systematic transitions and dominant signals of land change, thus providing a basis for identifying the proximate causes and underlying drivers of change(Pontius et al 2004, Braimoh 2006, Aldwaik and Pontius 2012, Huang et al 2012, Teixeira et al 2014, De Alban et al 2019. Systematic transitions are a two-sided land cover change relationship, and can be targeted or avoided in nature…”

Local Perspectives on Ecosystem Service Trade-Offs in a Forest Frontier Landscape in Myanmar

“…However, in the past 20 years, the area has increasingly become a hotspot for large-scale land acquisitions for oil palm or rubber on the one hand and conservation activities on the other, frequently hand-in-hand [21]. In addition to a major deforestation period between 1997 and 2004 [25], a government-supported rubber boom that began in 2007 has been motivating private investors and smallholder farmers to grow rubber plantations on former forest and mixed plantation land [26]. Outdated and incoherent land classification systems fail to account for the customary land use rights of local communities [27].…”

Assessing the Importance of Tree Cover Threshold for Forest Cover Mapping Derived from Global Forest Cover in Myanmar

“…This software enabled us to interpret the land cover of the sampled area with plot layout design through imageries with varying spatial and temporal resolutions within Google Earth, Bing Maps, and Google Earth Engine [47]; it geo-synchronized the views of the ground situation at each sample within different imageries (Figure 2a-c). Previous studies used Collect Earth for various purposes, including ground truth data collection for accuracy assessment, land cover change analysis, and vegetation survey, for analyses at global scale [48] and specific regions of interest [49][50][51][52][53][54][55][56].…”