Abstract
Information on the distribution, area and extent of swidden agriculture landscape is necessary for implementing the program of Reducing Emissions from Deforestation and Forest Degradation (REDD), biodiversity conservation and local livelihood improvement. To our knowledge, explicit spatial maps and accurate area data on swidden agriculture remain surprisingly lacking. However, this traditional farming practice has been transforming into other profit-driven land use, like tree plantations and permanent cash agriculture. Swidden agriculture is characterized by a rotational and dynamic nature of agroforestry, with land cover changing from natural forests, newly-cleared swiddens to different-aged fallows. The Operational Land Imager (OLI) onboard the Landsat-8 satellite has visible, near-infrared and shortwave infrared bands, which are sensitive to the changes in vegetation cover, land surface moisture content and soil exposure, and therefore, four vegetation indices (VIs) were calculated, including the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Moisture Index (NDMI), the Normalized Burn Ratio (NBR) and the Soil Adjusted Vegetation Index (SAVI). In this study, we developed a multi-step threshold approach that uses a combination of thresholds of four VIs and local elevation range (LER) and applied it to detect and map newly-opened swiddens and different-aged fallows using OLI imagery acquired between 2013 and 2015. The resultant Landsat-derived swidden agriculture maps have high accuracy with an overall accuracy of 86.9% and a Kappa coefficient of 0.864. The results of this study indicated that the Landsat-based multi-step threshold algorithms could potentially be applied to monitor the long-term change pattern of swidden agriculture in montane mainland Southeast Asia since the late 1980s and also in other tropical regions, like insular Southeast Asia, South Asia, Latin America and Central Africa, where swidden agriculture is still common.
Highlights
Swidden, a term invented by the Swedish anthropologist Karl Gustav Izikowitz, is resurrected to describe the age-old slash and burn agriculture in the tropics, in the context of the global collaborative program of the Reducing Emissions from Deforestation and Forest Degradation (REDD) in the developing countries [1,2,3]
We developed algorithms for detecting and mapping swidden agriculture by comprehensively comparing the relationship of various vegetation indices (VIs) derived from Landsat Operational Land Imager (OLI) among the major land cover types, in combination with other environmental information in ArcGIS 10.2
The multi-step threshold (MST) method with various criteria of VIs was applied to detect and delineate swidden landscape consisting of newly-opened swiddens and multi-year fallows, typically less than seven years, in combination with a mountainous area mask
Summary
A term invented by the Swedish anthropologist Karl Gustav Izikowitz, is resurrected to describe the age-old slash and burn agriculture in the tropics, in the context of the global collaborative program of the Reducing Emissions from Deforestation and Forest Degradation (REDD) in the developing countries [1,2,3]. Traditional swidden systems have reduced fallow periods, and experienced rapid transformation or are changing into other land use types, such as permanent agriculture and tree-crop monocultures [5,6,7]. There is an argument that swidden agriculture will be positive in saving biodiversity [16] and necessary for buffering market fluctuation [2,8]. Apart from this debate, there is a surprising lack of accurate data on the extent and distribution of swidden in SEA [3,5,6,7]. Accurately detecting and mapping the explicit spatial information with remote sensing techniques becomes very critical for poverty alleviation [5], carbon storage and biodiversity conservation
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