Shifting cultivation induced burn area dynamics using ensemble approach in Northeast India

Das, Pulakesh; Barik, Saroj Kanta; Mudi, Sujoy; Jagadish, Buddolla; Sarkar, Sudeep; Joshi, S. R.; Adhikari, Dibyendu; Behera, Soumit K.; Sarma, Kiranmay; Srivastava, Prashant K.; Chauhan, Puneet Singh

doi:10.1016/j.tfp.2021.100183

Cited by 10 publications

(8 citation statements)

References 30 publications

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“…In the aspect of swidden area, RdNBR_4 also showed the highest accuracy (81.33%), followed by dNBR_2 (77.67%) and by dNDVI_3 (73.55%). In previous studies, dNBR-and RdNBR-based classifications showed a higher degree of accuracy in their research results (Miller and Thode 2007;Rozario et al 2018;Miller et al 2009) and NDVI and NDVI difference also provided a great performance in capturing burned areas in Northeast India (Das et al 2022).…”

Section: Remotely Sensed Data Indices Thresholds and Models For Detec...mentioning

confidence: 84%

“…Using remote sensing, burned areas can be mapped with various image classification methods, inclusive of visual analysis, single channel density slicing, multitemporal thresholding of vegetation indices, principal component analysis, regression modelling, supervised and unsupervised classification, and spectral mixture analysis. Recent studies identified burned forest areas of shifting cultivation using a threshold value method with great overall accuracy (Das et al 2021;Das et al 2022;. A range of spectral indices such as the Normalized Burn Ratio (NBR), the difference in the Normalized Burn Ratio between pre-and post-fire images (dNBR), and the Normalized Difference Vegetation Index (NDVI) are commonly used for producing burned area maps (Lentile et al 2006;Miller and Thode 2007;Rozario et al 2018).…”

Section: Introductionmentioning

confidence: 99%

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Assessing swidden land use in Myanmar by decision tree-based detection method using landsat imagery

et al. 2022

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Swidden agriculture is a common land use found in the mountainous regions, especially in Southeast Asia. In Myanmar, the swidden agriculture has been practicing as an important livelihood strategy of millions of people, mainly by the ethnic groups. However, the extent of swidden agriculture in Myanmar is still in question. Therefore, we attempted to detect swidden patches and estimate the swidden extent in Myanmar using free available Landsat images on Google Earth Engine in combination with a decision tree-based plot detection method. We applied the commonly used indices such as dNBR, RdNBR, and dNDVI, statistically tested their threshold values to select the most appropriate combination of the indices and thresholds for the detection of swidden, and assessed the accuracy of each set of index and thresholds using ground truth data and visual interpretation of sample points outside the test site. The results showed that dNBR together with RdNBR, slope and elevation demonstrated higher accuracy (84.25%) compared to an all-index combination (dNBR, RdNBR, dNDVI, slope, and elevation). Using the best-fit pair, we estimated the extent of swidden at national level. The resulting map showed that the total extent of swidden in Myanmar was about 0.1 million ha in 2016, which is much smaller than other previously reported figures. Also, swidden patches were mostly observed in Shan State, followed by Chin State. In this way, this study primarily estimated the total extent of swidden area in Myanmar at national level and proved that the use of a decision tree-based detection method with appropriate vegetation indices and thresholds is highly applicable to the estimation of swidden extent on a regional basis. Also, as Myanmar is the largest country in mainland Southeast Asia in area with a great majority of the population living in rural areas, and many in the mountains, its land resources are of great relevance to the people’s livelihoods and thereby the nation’s progress. Therefore, this study will contribute to sustainable land management planning on both regional and national scale.

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Section: Remotely Sensed Data Indices Thresholds and Models For Detec...mentioning

confidence: 84%

Section: Introductionmentioning

confidence: 99%

Assessing swidden land use in Myanmar by decision tree-based detection method using landsat imagery

et al. 2022

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“…The NDVI and DVI change layers were created by subtracting defoliation (leaf-off condition) from refoliation (leaf-on condition), ensuring higher positive values for RP layers than for other features. However, the opposite change, i.e., subtracting refoliation from defoliation, could misclassify RPs with deforestation or shifting cultivation in northeastern India [26,27]. This approach ensured accurate differentiation of RPs from natural or anthropogenic deforestation, such as shifting cultivation.…”

Section: Rubber Plantation Mappingmentioning

confidence: 99%

“…In India, RP development is being practiced in the warm and humid tropical climate regimes of the Western Ghats (WG) and the northeast (NE) [25]. RP mapping in the biodiversity-rich WG and NE India is essential for examining the impact of anthropogenic disturbances and native landscape management practices such as shifting cultivation [26,27]. Few studies have attempted to map the extent of RPs in WG and NE India.…”

Section: Introductionmentioning

confidence: 99%

Multi-Decadal Mapping and Climate Modelling Indicates Eastward Rubber Plantation Expansion in India

et al. 2022

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Automated long-term mapping and climate niche modeling are important for developing adaptation and management strategies for rubber plantations (RP). Landsat imageries at the defoliation and refoliation stages were employed for RP mapping in the Indian state of Tripura. A decision tree classifier was applied to Landsat image-derived vegetation indices (Normalized Difference Vegetation Index and Difference Vegetation Index) for mapping RPs at two-three years intervals from 1990 to 2017. A comparison with actual plantation data indicated more than 91% mapping accuracy, with most RPs able to be identified within six years of plantation, while several patches were detected after six years of plantations. The RP patches identified in 1990 and before 2000 were used for training the Maxent species distribution model, wherein bioclimatic variables for 1960–1990 and 1970–2000 were used as predictor variables, respectively. The model-estimated suitability maps were validated using the successive plantation sites. Moreover, the RPs identified before 2017 and the Shared Socioeconomic Pathways (SSP) climate projections (SSP126 and SSP245) were used to predict the habitat suitability for 2041–2060. The past climatic changes (decrease in temperature and a minor reduction in precipitation) and identified RP patches indicated an eastward expansion in the Indian state of Tripura. The projected increase in temperature and a minor reduction in the driest quarter precipitation will contribute to more energy and sufficient water availability, which may facilitate the further eastward expansion of RPs. Systematic multi-temporal stand age mapping would help to identify less productive RP patches, and accurate monitoring could help to develop improved management practices. In addition, the existing RP patches, their expansion, and the projected habitat suitability maps could benefit resource managers in adapting climate change measures and better landscape management.

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“…Although rotational cultivation is almost the same as shifting cultivation, there are differences between both systems. In shifting cultivation, the same area is used for forestry and crop cultivation at different times (Das et al 2022). On the other hand, the rotational cultivation system can be seen as a newer stage in the evolution of farming after shifting cultivation.…”

Section: Introductionmentioning

confidence: 99%

Vegetation composition and structure across land use types in a rotational cultivation system in Meratus Mountain, South Kalimantan, Indonesia

et al. 2022

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Abstract. Rezekiah AA, Ruslan M, Kadir S, Mahmud. 2022. Vegetation composition and structure across land use types in a rotational cultivation system in Meratus Mountain, South Kalimantan, Indonesia. Biodiversitas 23: 4234-4242. The rotational cultivation system has become a local ecological knowledge of the Meratus Dayak community in South Kalimantan, Indonesia. This land management applies a cultivation system by dividing land into four types, namely pahumaan (cultivated area of annual crops), balukar anum (former cultivated field aged 3-6 years), jurungan (former field aged 7-12 years), and kabun buah (former fields aged more than 15 years). This study is aimed to investigate the floristic structure and composition of the vegetation in each land use type in the rotational cultivation system conducted by the Dayak Meratus community in three villages (i.e., Loksado, Lok Lahung and Haratai). Vegetation sampling was conducted purposively across four vegetation levels (i.e., seedlings, saplings, poles and trees) and the data was analyzed to calculate Important Value Index (IVI), Shannon-Wiener diversity index, species richness index and evenness index. The results showed that each land type had a certain vegetation structure and composition. In pahumaan, the dominant plant species were annual plants, especially crops. The balukar anum and jurungan were dominated by woody plants, while the kabun buah was dominated by fruit plants. The changes in vegetation structure and composition suggested that the succession process was in progress. The diversity index for each type of land use in the three villages was in the medium to high category. The species richness index was in the low to the high category, while the evenness index for all land uses in the three villages was at a high level. These findings suggest that traditional rotational farming activities carried out by the community did not damage the forest.

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Shifting cultivation induced burn area dynamics using ensemble approach in Northeast India

Cited by 10 publications

References 30 publications

Assessing swidden land use in Myanmar by decision tree-based detection method using landsat imagery

Assessing swidden land use in Myanmar by decision tree-based detection method using landsat imagery

Multi-Decadal Mapping and Climate Modelling Indicates Eastward Rubber Plantation Expansion in India

Vegetation composition and structure across land use types in a rotational cultivation system in Meratus Mountain, South Kalimantan, Indonesia

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