Spatio-temporal assessment on land use and land cover (LULC) and forest fragmentation in shifting agroecosystem landscape in Ukhrul district of Manipur, Northeast India

Shimrah, Tuisem; Lungleng, Peimi; Devi, Ahanthem Rebika; Sarma, Kiranmay; Varah, Franky; Khuman, Y. S. Chandra

doi:10.1007/s10661-021-09548-3

Cited by 13 publications

(3 citation statements)

References 22 publications

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“…The prepared land use land cover map has been validated with the Accuracy Assessment method and the Kappa co-efficient (Eq.1). The Kappa co-efficient have been calculated using the following methods (Shimrah 2022).…”

Section: Lulcmentioning

confidence: 99%

An analysis of land dynamics in relation to urban sprawl in the Guwahati city of Assam, India

Gogoi¹

2023

Ecocycles

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Urban sprawl is the growth of a settlement that occurs during urbanization. Guwahati city, located in the Kamrup Metropolitan District of Assam, India, is the hub of the northeastern states. To examine urban sprawl and the rate of population expansion during the past 30 years, research has been conducted to determine the built-up land characteristics. A temporal study using satellite images for two different years (1990 and 2020) has been carried out through GIS and remote sensing techniques, supervised classification using maximum likelihood for land use land cover preparation and raster calculator has been used to delineate NDVI and the Urban Index. The city has been divided into five buffer zones from the central growth point, and the sprawl has been analyzed for each buffer zone. The city's growth is so drastic that the built-up area has increased from 27.18% in 1990 to 47.87% in 2020. Zone-wise, the most remarkable changes have been seen in zone 2 and zone 5, up to 28.95% and 22.62%, respectively. Zone-3 counts as the minor changes in the built-up area, i.e., 6.2% due to the Ramsar site named Deeparbeel. This study will help urban planners understand the nature of sprawl and develop a feasible solution.

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Section: Lulcmentioning

confidence: 99%

An analysis of land dynamics in relation to urban sprawl in the Guwahati city of Assam, India

Gogoi¹

2023

Ecocycles

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“…Identifying regional LUCC and its drivers is crucial for land use planning and management [8,9]. In addition, LUCC has significant impacts on the ecological integrity of forests, biodiversity, and natural resources, such as urbanization, which can block the connectivity of natural vegetation and cause fragmentation [10,11]. It has been suggested that the area of global land use change in the last 60 years is four times larger than previously estimated [12].…”

Section: Introductionmentioning

confidence: 99%

Land Use/Cover Change, Fragmentation, and Driving Factors in Nepal in the Last 25 Years

Ning

Subedi

2023

Sustainability

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Because of the influence of climate change and human activities, an in-depth analysis of land use/cover change (LUCC) and its drivers in Nepal is important for local community forestry management and sustainable development. This paper analyzed the direction, magnitude, and rate of LUCCs and their spatial aggregation, as well as landscape fragmentation in Nepal, from 1995 to 2020 using the ESA/CCI (European Space Agency Climate Change Initiative) dataset. A total of 10 factors including population, socioeconomic development, climate factors, and forest management factors were selected to determine the dominant driving factors affecting LUCC in Nepal by Principal component analysis (PCA) and linear regression analysis. Our study showed that climate change, human activities, and forest management (e.g., community forestry) all influenced LUCC. In Nepal, land use/cover shifted among forest, shrub, grassland, and cropland from 1995 to 2020, mainly from forest to cropland. The most significant LUCC in recent decades has been caused by the expansion of cropland and urbanization. The area of coniferous and broadleaf forests decreased from 1995 to 2001 due to deforestation and forest degradation, and recovered gradually after 2001, which was attributed to the successful practice of community forestry in Nepal. Accelerated urbanization was also found in Nepal, and the significant expansion of construction land came mainly came from cropland. Land fragmentation in Nepal was severe and exhibited spatial aggregation characteristics. Human activities played a greater role in LUCC in Nepal than climate factors. The community forestry, GDP growth, and precipitation were positive driving factors for increases in forest area, while the development of the services sector and rising temperature were negative driving factors.

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“…The most widely used methods of LULC are MLC and RF methods. The application of MLC in RS image classification is mainly to monitor the change of vegetation coverage [6], [7] and analyze the causes of vegetation change or damage [8], [9]. However, some comparative analysis results show that the accuracy of MLC is not high compared with other classification methods in some cases [10].…”

Section: Introductionmentioning

confidence: 99%

Land Cover Classification of Resources Survey Remote Sensing Images Based on Segmentation Model

et al. 2022

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Land type survey is an important task of land resources survey and the basis of scientific management of land resources. With the increasingly prominent problems of population, resources, and environment, there is an urgent need for a fast and accurate classification method of large-scale land use and land cover based on remote sensing data. Traditional machine learning classification methods based on pixel classification achieved sufficient results and are widely used, such as maximum likelihood classification and random forests method. However, with the development of the novel technology of deep learning, in practical application, for multi-classified land resources, how to use the fast and effective classification method of low and medium resolution RS images needs further research. This paper takes the land resource classification of the Tonghe medium resolution RS dataset of the third land survey in China as an example to screen and compare traditional machine learning classification methods and semantic segmentation models FC-DenseNet56, GCN, BiSeNet, U-Net, DeepLabV3, AdapNet, and PSPNet, which aim to select the optimal feature extraction model. The results show that the classification accuracy of the U-Net model can reach 93.62%, which is more accurate and effective than traditional machine learning methods and other semantic segmentation models. It is suitable for multi-classification tasks of land cover resources in low and medium resolution RS images and shows a superior effect in practical application. Besides, the conclusion of this study can provide a demonstration for large-scale land cover resources investigation using low and medium resolution RS images.INDEX TERMS land use and land cover, semantic segmentation, multi-classification, deep learning, U-Net.

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Spatio-temporal assessment on land use and land cover (LULC) and forest fragmentation in shifting agroecosystem landscape in Ukhrul district of Manipur, Northeast India

Cited by 13 publications

References 22 publications

An analysis of land dynamics in relation to urban sprawl in the Guwahati city of Assam, India

An analysis of land dynamics in relation to urban sprawl in the Guwahati city of Assam, India

Land Use/Cover Change, Fragmentation, and Driving Factors in Nepal in the Last 25 Years

Land Cover Classification of Resources Survey Remote Sensing Images Based on Segmentation Model

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