A combined impact assessment of climate and land use/land cover change in an Eastern Himalayan watershed in northeast India.

Delineating the degree of genetic divergence within Assam lemon (Citrus limon ‘Assam lemon’) accessions in Assam, India

The Himalayan forests are vulnerable to climate change leading to disturb the current flow of critical ecosystem services to the dependent population. Present study attempts to evaluate the current vulnerability status of major forest types in Nagaland, India due to climate change along with identifying the hot spots for mitigating the vulnerability of the forests. Vulnerability of mixed moist deciduous, pine, wet hill and wet temperate forests were evaluated using the IPCC framework. Local Moran's I analysis, hot spot analysis and a histogram analysis of the vulnerability were made in QGIS. Field data such as Shannon-Weiner Index and Importance Value Index along with secondary data such as edaphic factors, vegetation factors and climatic variables were used for vulnerability analysis. Analysis results that mixed moist deciduous forests had the highest area in under high-high clusters, hot spots and was highly vulnerable. The result suggests that very dense or moderately dense forest cover were less vulnerable than low density forests in the region. The study provides baseline information along with identification of vulnerable hot spot forests for future climate change policy and management of resources at the division level. The study suggests strengthening the less dense forests along with increasing the biodiversity through suitable strategy for mitigating the vulnerability. 

Climate Change Ecological Vulnerability and Hotspot Analysis of Himalayan Forests in North-Eastern Region, India

The United Nations Convention to Combat Desertification (UNCCD) launched the Land Degradation Neutrality (LDN) initiative to cut the extent of land degradation. Its Good Practice Guidance (GPG) document for Sustainable Development Goal (SDG) Indicator 15.3.1 provides the roadmap for assessing land degradation. Three subindicators namely, (i) land cover change, (ii) land productivity, and (iii) soil organic carbon (SOC) content are used to determine the soil health. A particular land area is regarded as degraded if one or more of these subindicators show signs of degradation. This method is known as the one-out-all-out (1OAO) approach. UNCCD signatory countries report the extent of degraded land in an effort to track and reduce this problem. India's commitment is to restore 26 million hectares of degraded land by 2030. As a case study, we wanted to assess the land degradation status of Nagaland, a northeastern state of India because of recent degradation reports. Trends.Earth application and various Earth observation data from 2001 to 2015 were used to compute this change. Results suggest that almost 66% of total land areas have remained stable, whereas 17.22% of the land has shown signs of degradation. However, with 16.78% of overall improvement seems to have counterbalanced this loss. Overall land degradation status of the state is found to be stable at least during the period of 2001 to 2015. 

Land degradation evaluation based on SDG indicators in the eastern Himalayan region: A case study from Nagaland state

Timely and accurate estimation of acreage and production of horticulture crops is necessary for deciding how much, where and when to export these commodities in the national and global markets. Remote sensing has been one of the methods adopted, in addition to conventional sampling methods, for improving the estimates. Parametric image classification algorithms have been used by many researchers for identification and area estimation of horticulture crops. But these algorithms result in several unclassified pixels leading to over/underestimates. This study has been undertaken to estimate the area of two horticulture crops (i.e., mango and coconut) of Hesaraghatta hobli of Bengaluru urban district using Convolutional Neural Network (CNN) on Google Colaband Random Forest (RF) algorithms on Google Earth Engine (GEE). Remotely sensed data acquired by the Multi-Spectral Instrument (MSI) onboard Sentiel-2A satellite was used. Spectral signatures of horticulture crops and other associated cover types have been generated to identify the cover types and for selecting appropriate band combinations. Two different band combinations were used for area estimation of selected horticulture crops: i) Near-InfraRed (NIR), Red, and Green all three having a spatial resolution of 10 m, ii) Red edge-3, Short-Wave InfraRed1 (SWIR1) and Short-Wave InfraRed2 (SWIR2) having 20 m spatial resolution. Area estimates of horticulture crops and associated cover types were validated with respect to ground truth and statistical reports from Karnataka State Directorate of Horticulture (KSDH). It was found that the CNN model performed better than RF using NIR, Red, and Green band combination with an overall accuracy of 84%, but it failed to give similar accuracies with Red edge 3, SWIR1, and SWIR2 band combination. We attempted transfer learning using the trained CNN model at two different study areas far away from the study area and found encouraging results.

Area Estimation of Mango and Coconut Crops using Machine Learning in Hesaraghatta Hobli of Bengaluru Urban District, Karnataka

Applications of Advanced Geospatial Technology for Expansion of Area under Horticultural Crops in North Eastern Region of India

. Land use is a dynamic phenomenon, changing with time and space. Land use/land cover (LULC) information and its periodic changes has become crucial to carry out the prediction to the dynamical change of land use. This study analyzed the spatial and temporal changes in land use that has taken place in Nagaland over the last 10 years (2005–2016). It has been observed that the dynamic change in land use and land cover has been mainly caused by the traditional practice of jhum cultivation, also known as shifting cultivation in Nagaland, which constitute about 81% of the total agricultural land. As a result, there is a decline in forest area by 593.87 km2 (5.66%) from 2005–2006 to 2015–2016. Concurrently, abandoned jhum land and scrubland has increased by 11.72% and 24.89%, demonstrating the decreased in jhum/ shifting cultivation cycle. The loss of forest in the last decade was attributed to ever increasing population, putting pressure on demand of jhum/ shifting cultivation and other anthropogenic activities. The degradation of forest is ever increasing, which calls for intervention of appropriate technology and holistic approach to address this issue.

/pdf/evaluation-of-spatio-temporal-aspects-of-land-use-and-land-3nrr1i2ue5.pdf

Evaluation of spatio-temporal aspects of land use and land cover changes in nagaland, north-east, india

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Applications of Advanced Geospatial Technology for Expansion of Area under Horticultural Crops in North Eastern Region of India

Evaluation of spatio-temporal aspects of land use and land cover changes in nagaland, north-east, india