A Texture-Based Land Cover Classification for the Delineation of a Shifting Cultivation Landscape in the Lao PDR Using Landscape Metrics

Assessment of forest fragmentation and ecological dynamics in Western Himalayan Region over three decades (1990-2020)

A spatial assessment of temporal forest cover changes is essential for effective forest conservation and management practices. This study analyzes changes in forest cover and the evolution of forest spatial configuration using Landsat satellite imagery over the past three decades (1990-2020) in Azad Jammu and Kashmir (AJK), Pakistan. To achieve the objectives, landscape metrics and forest fragmentation analyses were applied. Additionally, a pattern-based spatial analysis was conducted to examine forest cover changes in the study area. Overall, the forest cover change from 1990 to 2020 was 74 km2 (- 1.75%), with an average annual forest cover change rate of - 2.5 km2 (- 0.06%) for the entire study period. A gradual decline in forest cover was observed between 1990 and 2020, with the most significant decline of - 29.92 km2 from 2000 to 2010. The forest fragmentation analysis reveals that the core forest areas (> 500 acres) are increasingly being divided into smaller (< 250 acres) and medium-sized (250-500 acres) patches. Landscape metrics at the class level show that, with a few exceptions, the overall forests in AJK remain connected and aggregated. Based on forest cover and ecoregions in the region, the pattern-based spatial and dissimilarity analysis identifies forest hotspots (areas of gains or losses) and clusters. To conserve, restore, and monitor the forests in the study area, decision-making and policy-making institutions can establish conservation priorities. They can also implement a more robust incentive-based UN-REDD + mechanism. This would involve formulating management strategies aimed at curbing forest loss and reducing forest degradation.

Improvement in Satellite Image-Based Land Cover Classification with Landscape Metrics

The use of an object-based image analysis (OBIA) method has recently become quite common for classifying high-resolution remote-sensed images. However, despite OBIA’s segmentation being equally useful for analysing medium-resolution images, it is not used for them as often. This study aims to analyse the effect of landscape metrics that have not yet been used in image classification to provide additional information for land cover mapping to improve the thematic accuracy of satellite image-based land cover mapping. To this end, multispectral satellite images taken by Landsat 8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI) during three different seasons in 2017 were analysed. The images were segmented, and based on these segments, four patch-level landscape metrics (mean patch size, total edge, mean shape index and fractal dimension) were calculated. A random forest classifier was applied for classification, and the Coordination of Information on the Environment Land Cover (CLC) 2018 database was used as reference data. According to the results, landscape metrics both with and without segmentation can significantly improve the overall accuracy of the classification over classification based on spectral values. The highest overall accuracy was achieved using all data (i.e., spectral values, segmentation, and metrics).

Farms or Forests? Understanding and Mapping Shifting Cultivation Using the Case Study of West Garo Hills, India

Attempts to study shifting cultivation landscapes are fundamentally impeded by the difficulty in mapping and distinguishing shifting cultivation, settled farms and forests. There are foundational challenges in defining shifting cultivation and its constituent land-covers and land-uses, conceptualizing a suitable mapping framework, and identifying consequent methodological specifications. Our objective is to present a rigorous methodological framework and mapping protocol, couple it with extensive fieldwork and use them to undertake a two-season Landsat image analysis to map the forest-agriculture frontier of West Garo Hills district, Meghalaya, in Northeast India. We achieve an overall accuracy of ~80% and find that shifting cultivation is the most extensive land-use, followed by tree plantations and old-growth forest confined to only a few locations. We have also found that commercial plantation extent is positively correlated with shortened fallow periods and high land-use intensities. Our findings are in sharp contrast to various official reports and studies, including from the Forest Survey of India, the Wastelands Atlas of India and state government statistics that show the landscape as primarily forested with only small fractions under shifting cultivation, a consequence of the lack of clear definitions and poor understanding of what constitutes shifting cultivation and forest. Our results call for an attentive revision of India’s official land-use mapping protocols, and have wider significance for remote sensing-based mapping in other shifting cultivation landscapes.

A synoptic land change assessment of Ethiopia's Rainfed Agricultural Area for evidence-based agricultural ecosystem management

This paper demonstrates synoptic ways of presenting and characterizing land change processes across Ethiopia's large, complex Rainfed Agricultural Area (RAA). We translated pixel-level detected changes into neighbourhood-level changes that are useful to decision-makers. First, we identified pixel-level changes without and with type/direction of change, based on land cover maps from the years 1986 and 2010. For type-/direction-based characterization, we sorted observed transitions into four categories of prominent land change processes ("forest degradation", "deforestation", "afforestation", and "no change"). Adopting appropriate window sizes for identified ecoregions in the study area, we ran a focal statistics summation operator separately on the two change rasters (with/without consideration of direction of change). The results obtained by applying the approach can be described in relative terms as well as qualitative terms, using ranges of change values that can be further classified using qualitative terms, i.e. ranging from "no change" to "high/substantial change". Our non-directional change assessment result showed that approximately 6% of the RAA is characterized by substantial change, whereas 40% appears stable ("no change"). Based on the directional-change assessment results, 3% of deforestation, 4% of forest degradation, and 3% of revegetation processes were found to constitute "high/substantial change". The types and intensity of landscape transformations display distinct spatial patterns linked to agro-ecological belts and socio-economic dynamics. Minimal reverse changes were observed on some severely degraded lands in the highlands, but the overall per cent cover remains relatively small. Overall, vegetation degradation still exceeds regeneration by more than half a per cent. Relatively lower altitudes and middle altitudes exhibit higher transformation. The presented approach and resulting outputs can provide planners and decision-makers with a synoptic view of land change processes. It can support policy formulation of sustainable land management and rehabilitation activities of the agricultural ecosystem at national and regional scales.

A global view of shifting cultivation: Recent, current, and future extent

Mosaic landscapes under shifting cultivation, with their dynamic mix of managed and natural land covers, often fall through the cracks in remote sensing–based land cover and land use classifications, as these are unable to adequately capture such landscapes’ dynamic nature and complex spectral and spatial signatures. But information about such landscapes is urgently needed to improve the outcomes of global earth system modelling and large-scale carbon and greenhouse gas accounting. This study combines existing global Landsat-based deforestation data covering the years 2000 to 2014 with very high-resolution satellite imagery to visually detect the specific spatio-temporal pattern of shifting cultivation at a one-degree cell resolution worldwide. The accuracy levels of our classification were high with an overall accuracy above 87%. We estimate the current global extent of shifting cultivation and compare it to other current global mapping endeavors as well as results of literature searches. Based on an expert survey, we make a first attempt at estimating past trends as well as possible future trends in the global distribution of shifting cultivation until the end of the 21^st century. With 62% of the investigated one-degree cells in the humid and sub-humid tropics currently showing signs of shifting cultivation—the majority in the Americas (41%) and Africa (37%)—this form of cultivation remains widespread, and it would be wrong to speak of its general global demise in the last decades. We estimate that shifting cultivation landscapes currently cover roughly 280 million hectares worldwide, including both cultivated fields and fallows. While only an approximation, this estimate is clearly smaller than the areas mentioned in the literature which range up to 1,000 million hectares. Based on our expert survey and historical trends we estimate a possible strong decrease in shifting cultivation over the next decades, raising issues of livelihood security and resilience among people currently depending on shifting cultivation.