Team Members:
Person Name | Person role on project | Affiliation |
---|---|---|
Mark Cochrane | Principal Investigator | University of Maryland, Cambridge, United States |
Andrew Elmore | Co-Investigator | University of Maryland Center for Environmental Science (UMCES), Frostburg, |
Xin Zhang | Co-Investigator | University of Maryland Center for Environmental Science (UMCES), Frostburg, USA |
Jing Zhao | Postdoc Researcher | University of Maryland Center for Environmental Science (UMCES), Frostburg, USA |
Janice Lee | Collaborator |
Indonesia’s tropical peatlands are essential for stabilizing global climate and conserving biodiversity. These peatlands cover an estimated 200,000 km2, are biodiversity hotspots, and comprise a 57 Gt carbon reservoir. In recent years, these forested ecosystems have been rapidly degraded through unsustainable logging, drainage, conversion to agriculture and wildfire, leaving only 6% of peatlands unaffected by development. Due to rapid expansion and poor predictability, smallholder oil palm plantations are of particular interest. Indonesia’s oil palm has expanded from 70,000 ha in 1961 to 11.8 million ha in 2016, due to strong global demand for vegetable oil and biofuel. It is now the largest producer in the world. Smallholder farmers manage half the oil palm area in Indonesia, with a greater expansion rate (11%) than large-scale plantations (5%). The economic and policy forces driving oil palm expansion are evolving, but include both local and global factors.
The key objectives of this proposal are to understand historical and projected impacts of smallholder oil palm agriculture on Indonesian tropical peatlands and related impacts on aspects of landscape sustainability. To achieve these objectives, we propose to monitor and characterize the land-use and land-cover changes (LCLUC) in this landscape, to identify major drivers and impacts of those changes, and to use development and conservation scenarios to estimate likely landscape outcomes. The project would employ recently developed techniques to perform continuous change detection from dense stacks of medium resolution remote sensing data (e.g., Landsat and Sentinel). Resulting time series of forest canopy disturbance will be used in conjunction with LiDAR (airborne and NASA Global Ecosystem Dynamics Investigation data), to generate maps of canopy height in existing oil palm agriculture. Based on these products and spatial analysis, we will characterize land use transitions in the following categories from 2000 to present: (1) transition from peat forest to smallholding oil palm; (2) transition from other land uses (e.g., abandoned land) to smallholding oil palm; (3) transition from smallholding oil palm to large industrial estates. The impact of land use transitions will be assessed across a wide range of parameters, including environmental parameters such as carbon emissions, nitrogen loading, and forest cover loss, but also economic factors such as profitability and employment. We will develop statistical models to identify key drivers for each transition. Proposed drivers to evaluate include vegetation type, fire frequency, peat depth, climate conditions, oil palm processing infrastructure, market prices, and demographic characteristics of the labor population. These drivers and ecological impacts will be evaluated synthetically in a coupled assessment model that will be used to develop a decision-support tool capable of projecting future LCLUC transitions and their impacts for potential market, policy, disturbance, and climate scenarios.
The proposed research is directly responsive to the LCLUC call for proposals addressing Land-Use Transitions in Asia, specifically focusing on the critical, but poorly understood smallholder land use dynamics surrounding oil palm expansion in Indonesian peatlands. We will make effective use of NASA observing systems (Landsat and GEDI observations) to refine and expand existing techniques for estimating canopy height across landscapes. For oil palm, this relates directly to age, maturity and likely rotation periods. Our remote sensing products, together with ancillary inputs on socioeconomic drivers of land transitions, will allow us to assess the landscape impact of agricultural production, develop land use transition models, and provide new understanding of how future LCLUC transitions will affect this dynamic and important coupled human-environmental system.