The expansion of tree-based boom crops in mainland Southeast Asia: 2001 to 2014

Hurni, Kaspar; Fox, Jefferson

doi:10.1080/1747423x.2018.1499830

Cited by 73 publications

(67 citation statements)

References 45 publications

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“…The main drivers of land cover change include expansion of agriculture and plantation estates, extraction of natural resources [5], infrastructure development, and small and large-scale logging. The underlying drivers of land cover change include population and economic dynamics, often intensified by weak governance [6,7]. As a consequence, wildlife, hydrological and ecological functions, and local communities that rely on forest resources are experiencing significant negative impacts.…”

Section: Introductionmentioning

confidence: 99%

“…It has been determined that an essential factor for accurate mapping of rubber plantations in SE Asian ecosystems is the availability of data at two crucial phenological periods: defoliation (leaf off) and new leaf emergence (leaf on), which distinguishes deciduous rubber trees from other vegetation. Many approaches consist of using optical satellite data [6,[17][18][19][20][21] to detect rubber plantations. However, due to the persistent cloud cover in tropical regions, it is difficult to acquire sufficient high resolution satellite imagery which in turn compromises spatial resolution, as coarser satellites such as MODIS are frequently used.…”

Section: Introductionmentioning

confidence: 99%

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Mapping Plantations in Myanmar by Fusing Landsat-8, Sentinel-2 and Sentinel-1 Data along with Systematic Error Quantification

Poortinga¹,

Tenneson²,

Shapiro

et al. 2019

Remote Sensing

122

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Forests in Southeast Asia are experiencing some of the highest rates of deforestation and degradation in the world, with natural forest species being replaced by cropland and plantation monoculture. In this work, we have developed an innovative method to accurately map rubber and palm oil plantations using fusion of Landsat-8, Sentinel 1 and 2. We applied cloud and shadow masking, bidirectional reflectance distribution function (BRDF), atmospheric and topographic corrections to the optical imagery and a speckle filter and harmonics for Synthetic Aperture Radar (SAR) data. In this workflow, we created yearly composites for all sensors and combined the data into a single composite. A series of covariates were calculated from optical bands and sampled using reference data of the land cover classes including surface water, forest, urban and built-up, cropland, rubber, palm oil and mangrove. This training dataset was used to create biophysical probability layers (primitives) for each class. These primitives were then used to create land cover and probability maps in a decision tree logic and Monte-Carlo simulations. Validation showed good overall accuracy (84%) for the years 2017 and 2018. Filtering for validation points with high error estimates improved the accuracy up to 91%. We demonstrated and concluded that error quantification is an essential step in land cover classification and land cover change detection. Our overall analysis supports and presents a path for improving present assessments for sustainable supply chain analyses and associated recommendations.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mapping Plantations in Myanmar by Fusing Landsat-8, Sentinel-2 and Sentinel-1 Data along with Systematic Error Quantification

Poortinga¹,

Tenneson²,

Shapiro

et al. 2019

Remote Sensing

122

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“…Mapping and linking ELCs to deforestation in Cambodia have previously been studied by e.g. Davis et al (2015), Hurni and Fox (2018), Grogan et al (2019), but there is a limited understanding of how land-use and landcover changes have affected carbon pools (i.e. carbon stored in above-and below-ground vegetation, soil, litter, and harvested products) on a national and subnational scale, as well as how carbon loss rates have varied over the past three decades in different types of land-use areas in Cambodia (e.g.…”

Section: Aim Of Studymentioning

confidence: 99%

Foreign demand for agricultural commodities drives virtual carbon exports from Cambodia

Johansson

Olin

Seaquist

2020

Environ. Res. Lett.

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Rapid deforestation is a major sustainability challenge, partly as the loss of carbon sinks exacerbates global climate change. In Cambodia, more than 13% of the total land area has been contracted out to foreign and domestic agribusinesses in the form of economic land concessions, causing rapid large-scale land use change and deforestation. Additionally, the distant drivers of local and global environmental change often remain invisible. Here, we identify hotspots of carbon loss between 1987-2017 using the dynamic global vegetation model LPJ-GUESS and by comparing past and present land use and land cover. We also link global consumption and production patterns to their environmental effects in Cambodia by mapping the countries to which land-use embedded carbon are exported. We find that natural forests have decreased from 54%-21% between 1987 and 2017, mainly for the expansion of farmland and orchards, translating into 300 million tons of carbon lost, with loss rates over twice as high within economic land concessions. China is the largest importer of embedded carbon, mainly for rubber and sugarcane from Chinese agribusinesses. Cambodian investors have also negatively affected carbon pools through export-oriented products like rubber. The combined understanding of environmental change and trade flows makes it possible to identify distant drivers of deforestation, which is important for crafting more environmentally and socially responsible policies on national and transnational scales.

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“…Remote sensing is a powerful and efficient tool to extract rubber plantations from the surrounding landscape. A number of studies have used optical satellite data to detect rubber plantations, mostly relying on spectral signatures like NDVI, EVI, LSWI, and SWIR1 of optical images to delineate rubber plantations [6,7]. However, rubber plantations in Mainland Southeast Asia grow in tropical rainforest areas with complex ecosystems, characterized by high vegetation coverage and less seasonal variation.…”

Section: Introductionmentioning

confidence: 99%

Effect of Textural Features in Remote Sensed Data on Rubber Plantation Extraction at Different Levels of Spatial Resolution

Zhang

Huang

et al. 2020

Forests

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The expansion of rubber (Hevea brasiliensis) plantations has been a critical driver for the rapid transformation of tropical forests, especially in Thailand. Rubber plantation mapping provides basic information for surveying resources, updating forest subplot information, logging, and managing the forest. However, due to the diversity of stand structure, complexity of the forest growth environment, and the similarity of spectral characteristics between rubber trees and natural forests, it is difficult to discriminate rubber plantation from natural forest using only spectral information. This study evaluated the validity of textural features for rubber plantation recognition at different spatial resolutions using GaoFen-1 (GF-1), Sentinel-2, and Landsat 8 optical data. C-band Sentinel-1 10 m imagery was first used to map forests (including both rubber plantations and natural forests) and non-forests, then the pixels identified as forests in the Sentinel-1 imagery were compared with GF-1, Sentinel-2, and Landsat 8 images to separate rubber plantations and natural forest using two different approaches: a method based on spectral information characteristics only and a method combining spectral and textural features. In addition, we extracted textural features of different window sizes (3 × 3 to 31 × 31) and analyzed the influence of window size on the separability of rubber plantations and natural forests. Our major findings include: (1) the suitable texture extraction window sizes of GF-1, Sentinel-2, and Landsat 8 are 31 × 31, 11 × 11 to 15 × 15, and 3 × 3 to 7 × 7, respectively; (2) correlation (COR) is a robust textural feature in remote sensing images with different resolutions; and (3) compared with classification by spectral information only, the producer’s accuracy of rubber plantations based on GF-1, Sentinel-2, and Landsat 8 was improved by 8.04%, 9.44%, and 8.74%, respectively, and the user’s accuracy was increased by 4.63%, 4.54%, and 6.75%, respectively, when the textural features were introduced. These results demonstrate that the method combining textural features has great potential in delineating rubber plantations.

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The expansion of tree-based boom crops in mainland Southeast Asia: 2001 to 2014

Cited by 73 publications

References 45 publications

Mapping Plantations in Myanmar by Fusing Landsat-8, Sentinel-2 and Sentinel-1 Data along with Systematic Error Quantification

Mapping Plantations in Myanmar by Fusing Landsat-8, Sentinel-2 and Sentinel-1 Data along with Systematic Error Quantification

Foreign demand for agricultural commodities drives virtual carbon exports from Cambodia

Effect of Textural Features in Remote Sensed Data on Rubber Plantation Extraction at Different Levels of Spatial Resolution

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