Automated Mapping for Long-Term Analysis of Shifting Cultivation in Northeast India

Das, Pulakesh; Mudi, Sujoy; Barik, Saroj Kanta; Mishra, Deepak R.; Roy, P. S.

doi:10.3390/rs13061066

Cited by 40 publications

(23 citation statements)

References 36 publications

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“…The largest increase in tea plantations were found in the Darjeeling and then Jalpaiguri districts which were mostly attributed to the expansion of tea estates along with demography. The expansion of tea estates furthermore has an impact on deforestation and forest cover loss in this region (Prokop 2018) in addition to other processes, such as shifting cultivation (Das et al 2021), forest fires (Bar et al 2021), and climate impacts ). With respect to population dynamics, people from the neighboring states (e.g., Bihar, Sikkim, and Assam) and countries (e.g., Nepal, Bhutan, and Bangladesh) have been migrated to tea-growing regions in North Bengal, mainly for employment opportunities (Roy and Saha 2011).…”

Section: Discussionmentioning

confidence: 99%

Mapping tea plantations dynamics during 2000–2020 and monitoring biophysical attributes using multi-temporal satellite data in North Bengal (India)

Parida

Kumari

2021

Arab J Geosci

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Tea is an important cash crop, and it becomes necessary to map the spatial distribution of tea plantations. The tea industry has been expanding rapidly in the Northeast region of India, and consequently, the area under tea plantations is changing rapidly which needs periodic monitoring. In this study, satellite data such as Landsat-5 Thematic Mapper (TM) and Sentinel-2A were deployed for tea plantation identification and to analyze the dynamics of tea extent in the North Bengal district during 2000 and 2020. The result showed that the tea plantation area has increased from 1097 km 2 (2000) to 1377 km 2 (2020), depicting an increase in tea plantation area by 280 km 2 (or 25%) during the last 20 years. The highest increase in tea plantations was in Darjeeling (+137 km 2 ) and Jalpaiguri (+ 83 km 2 ) districts due to the expansion of tea estates along with the increasing population. The accuracy of tea plantation was evaluated using recall and precision with F1 score of 0.90 in 2000 and 0.92 in 2020. The tea plantation health condition was also assessed using both biophysical (LAI, EVI, FPAR) and biochemical parameters (leaf chlorophyll content). The results revealed that tea plants leaf area index (LAI) varied from 1 to 3, with healthy tea plants showed LAI > 2. Similarly, healthy tea plants depicted leaf chlorophyll content of 40 to 70 μg/cm 2 . Assessment of dynamics of tea plantation is required for promoting sustainable tea practices and safeguarding economic and social importance of people living in North Bengal.

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Section: Discussionmentioning

confidence: 99%

Mapping tea plantations dynamics during 2000–2020 and monitoring biophysical attributes using multi-temporal satellite data in North Bengal (India)

Parida

Kumari

2021

Arab J Geosci

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“…For the temperature exposure characteristic, we used moderate resolution imaging spectroradiometer (MODIS) satellite imagery on day surface temperature and normalized vegetation difference index (NDVI). The latter was included due to the moderating effects of vegetation on heat stress and other extreme events [64][65][66], expecting a positive correlation with households resilience. The remote sensing data was processed in the geoinformation software QGIS 3.10 and analyzed accordingly.…”

Section: Methodsmentioning

confidence: 99%

Quantification of Resilience Considering Different Migration Biographies: A Case Study of Pune, India

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Zhu

Karutz

2021

Land

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Urbanization proceeds globally and is often driven by migration. Simultaneously, cities face severe exposure to environmental hazards such as floods and heatwaves posing threats to millions of urban households. Consequently, fostering urban households’ resilience is imperative, yet often impeded by the lack of its accurate assessment. We developed a structural equation model to quantify households’ resilience, considering their assets, housing, and health properties. Based on a household survey (n = 1872), we calculate the resilience of households in Pune, India with and without migration biography and compare different sub-groups. We further analyze how households are exposed to and affected by floods and heatwaves. Our results show that not migration as such but the type of migration, particularly, the residence zone at the migration destination (formal urban or slum) and migration origin (urban or rural) provide insights into households’ resilience and affectedness by extreme weather events. While on average, migrants in our study have higher resilience than non-migrants, the sub-group of rural migrants living in slums score significantly lower than the respective non-migrant cohort. Further characteristics of the migration biography such as migration distance, time since arrival at the destination, and the reasons for migration contribute to households’ resilience. Consequently, the opposing generalized notions in literature of migrants either as the least resilient group or as high performers, need to be overcome as our study shows that within one city, migrants are found both at the top and the bottom of the resilience range. Thus, we recommend that policymakers include migrants’ biographies when assessing their resilience and when designing resilience improvement interventions to help the least resilient migrant groups more effectively.

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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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Automated Mapping for Long-Term Analysis of Shifting Cultivation in Northeast India

Cited by 40 publications

References 36 publications

Mapping tea plantations dynamics during 2000–2020 and monitoring biophysical attributes using multi-temporal satellite data in North Bengal (India)

Mapping tea plantations dynamics during 2000–2020 and monitoring biophysical attributes using multi-temporal satellite data in North Bengal (India)

Quantification of Resilience Considering Different Migration Biographies: A Case Study of Pune, India

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

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