Richer histories for more relevant policies: 42 years of tree cover loss and gain in Southeast Sulawesi, Indonesia

Abstract

Understandings of contemporary forest cover loss are critical for policy but have come at the expense of long-term, multidirectional analyses of land cover change. This is a critical gap given (i) profound reconfigurations in land use and land control over the past several decades and (ii) evidence of widespread 'woodland resurgence' throughout the tropics. In this study, we argue that recent advancements within the field of land change science provide new opportunities to address this gap. In turn, we suggest that multidecadal and multidirectional analyses of land cover change can facilitate richer social analyses of land cover change and more relevant conservation policies and practice. Our argument is grounded in a case study from Southeast Sulawesi, Indonesia. Using a novel analytical platform, Google Earth Engine, and open access to high-quality Landsat data, we map land cover change in Southeast Sulawesi, Indonesia, from 1972 to 2014. We find that tree cover loss constitutes the single largest net change over the period 1972-2014 but that gross rates of tree cover gain were three times higher than gross loss rates from 1972 to 1995 and equivalent to loss rates from 1995 to 2014. We suggest the smallholder tree crop economy likely produced both forest loss and Imperata grassland restoration in this region. This case points to the need to expand rather than collapse the baselines used to study carbon and biodiversity change in tropical regions. It also demonstrates the possible utility of applying such methods to other regions.

Flooding and Land Use Change in Southeast Sulawesi, Indonesia

“…The study village, Lawonua, and surrounding floodplains in Southeast Sulawesi, Indonesia (Figure 1), are characterized by a wet-dry climate and encompass a variety of habitats, including peat swamp, lowland and montane forest, grasslands, and diverse agricultural lands [31,32]. The region is also shaped by the flow of the Konawe'eha River, which stretches from the northwestern reaches of the province into the southwest, flowing from Bulu Brama mountain in the Mekongga mountain range through Kolaka Timur, Konawe Selatan, and Konawe districts.…”

Using Google Earth Engine to Map Complex Shade-Grown Coffee Landscapes in Northern Nicaragua

“…Following the application of cloud-and shadow-masking techniques, a median pixel value was taken across all observations spanning the three-year study period. Elsewhere, analysts used this approach to produce relatively gap-free image mosaics, even in areas of persistent cloud coverage [34,58,59]. Though analysts previously used shorter durations of time to construct image mosaics (given challenges linked to inter-annual land use and cover changes, e.g., References [58,59]), a three-year period was necessary to achieve relatively gap-free image mosaics given particularly high cloud coverage in the study area.…”

“…GEE, for example, hosts the entire Landsat archive, provides tools and an application program interface for summoning, processing, and mosaicking this imagery, and runs all analyses in parallel across many machines in Google's cloud-based processing platform [32]. As prior work has illustrated, these developments enable analyses at high computational efficiency even when relying on multi-temporal image mosaics [23,33,34]. The authors of [33], for example, used >650,000 Landsat scenes and >1 million central processing unit (CPU) hours to produce annual maps of tree cover gain and loss, performing these computations in just several days.…”

Changes in food access by mestizo communities associated with deforestation and agrobiodiversity loss in Ucayali, Peruvian Amazon

“…If national policy makers aim to promote sustainable and inclusive development, thereby ensuring food security while protecting ecosystem services, adopting a strategy based on the expansion of agricultural land and specialisation, leading to increased deforestation, does not seem appropriate. More in-depth assessments of the multiple impacts caused by changes in the trajectories of productive systems are certainly needed (Kelley et al 2017) while few long-term analyses have been conducted on the changes in dietary diversity and agrobiodiversity over time (Jones 2017).…”