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

Ning, Chunying; Subedi, Rajan; Lu, Hao

doi:10.3390/su15086957

Cited by 13 publications

(4 citation statements)

References 58 publications

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“…An analysis of the land use/land cover using Landsat imagery from 2020 revealed that the Chitwan area consists of grassland (1.73%), barren areas (1.76%), riverine forests (1.93%), water bodies (1.97%), developed areas (4.13%), Sal-dominated forests (15.4%), croplands (28.13%), and mixed forests (44.95%). Comparing land cover changes between 2000 and 2020, there was an overall increase in Sal-dominated forests (7.6%), developed areas (31.34%), and mixed forests (37.46%), and a decrease in riverine forests (11.29%), barren areas (20.03%), croplands (29.87%), and grasslands (49.71%) [ 31 , 32 ]. A significant portion of the forested land in the district is the Chitwan National Park, which is protected as a habitat for various wildlife and other ecological benefits, and attracts a large number of tourists [ 33 ].…”

Section: Methodsmentioning

confidence: 99%

Comparative analysis of soil organic matter fractions, lability, stability ratios, and carbon management index in various land use types within bharatpur catchment, Chitwan District, Nepal

Murindangabo,

Kopecký,

Hoang

et al. 2023

Carbon Balance Manage

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Background Land use and land cover changes have a significant impact on the dynamics of soil organic matter (SOM) and its fractions, as well as on overall soil health. This study conducted in Bharatpur Catchment, Chitwan District, Nepal, aimed to assess and quantify variations in total soil organic matter (TSOMC), labile organic matter fraction (CL), stable organic matter fraction (CS), stability ratio (SR), and carbon management index (CMI) across seven land use types: pastureland, forestland, fruit orchards, small-scale conventional agricultural land, large-scale conventional agricultural land, large-scale alternative fallow and conventional agricultural land, and organic farming agricultural land. The study also explored the potential use of the Carbon Management Index (CMI) and stability ratio (SR) as indicators of soil degradation or improvement in response to land use changes. Results The findings revealed significant differences in mean values of TSOMC, CL, and CS among the different land use types. Forestland and organic farming exhibited significantly higher TSOMC (3.24%, 3.12%) compared to fruit orchard lands (2.62%), small scale conventional farming (2.22%), alternative fallow and conventional farming (2.06%), large scale conventional farming (1.84%) and pastureland (1.20%). Organic farming and Forestland also had significantly higher CL (1.85%, 1.84%) and CS (1.27%, 1.39%) compared to all other land use types. Forest and organic farming lands showed higher CMI values, while pastures and forests exhibited higher SR values compared to the rest of the land use types. Conclusions This study highlights the influence of various land use types on soil organic matter pools and demonstrates the potential of CMI and SR as indicators for assessing soil degradation or improvement in response to land use and land cover changes.

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Section: Methodsmentioning

confidence: 99%

Comparative analysis of soil organic matter fractions, lability, stability ratios, and carbon management index in various land use types within bharatpur catchment, Chitwan District, Nepal

Murindangabo,

Kopecký,

Hoang

et al. 2023

Carbon Balance Manage

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“…Different LUCC trends of diverse land types are obtained in macrolevel research, which helps provide essential evidence of driving mechanisms. Causing tremendous decrease in vegetation of natural landscape, urban sprawl and expansion of unplanned built-up areas are considered the biggest threat to other land types, especially in mountain valleys and basins or other ecologically fragile regions [17][18][19]. In regions where the urbanization level is already high, interactions and changes between natural land types are much more obvious.…”

Section: Introductionmentioning

confidence: 99%

A Two-Layer SD-ANN-CA Model Framework for Multi-Typed Land Use and Land Cover Change Prediction under Constraints: Case Study of Ya’an City Area, Western China

Zhao,

Zhu,

Zhang

et al. 2024

Land

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Land use and land cover change (LUCC) prediction of cities in Western China requires higher accuracy in quantitative demand and spatial layout because of complex challenges in balancing relationships between urban constructions and ecological developments. Considering city-level areas and various types of land use and land cover, existing LUCC models without constraint or with only loose demand constraints were impractical in providing evidence of high accuracy and high-resolution predictions in areas facing fierce land competition. In this study, we proposed a two-layer SD-ANN-CA model to simulate and explore the LUCC trend and layout predictions for 2018, 2028, and 2038 in Ya’an City, Western China. The two-layer structure with an upper layer of the SD model and a lower layer of the ANN-CA model, as well as the advantages of all three methods of system dynamics (SD), artificial neural network (ANN), and cellular automata (CA), have allowed us to consider the macro-level demand constraints, meso-level driving factors constraints, and the micro-level spatial constraints into a unified model framework. The simulation results of the year 2018 have shown significant improvement in the accuracy of the ANN-CA model constructed in our earlier work, especially in types of forest land (error-accuracy: 0.08%), grassland (error-accuracy: 0.23%), and construction land (error-accuracy: 0.18%). The layout predictions of all six types of land use in 2028 and 2038 are then carried out to provide visual evidence support, which may improve the efficiency of planning and policy-making processes. Our work may also provide insights into new ways to combine quantitative methods into spatial methods in constructing city-level or even regional-level LUCC models with high resolution.

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“…The "International Geosphere-Biosphere Programme" (IGBP), the LUCC Program, and the Future Earth Program were implemented at the international Land 2023, 12, 1797 2 of 21 level, making significant progress in the theory, methodology, and practice of land use/land cover research [6]. The study shows that climate change, human activities, and government policies are the main causes of land cover change, with natural factors playing an important role in land cover change and the frequency and extent of human-induced land cover change escalating worldwide [7]. In addition, the emergence of new technologies and tools, in particular the application of remote sensing and GIS, has provided technical and data security for land use and land cover monitoring and assessment [8].…”

Section: Introductionmentioning

confidence: 99%

The Spatiotemporal Change in Land Cover and Discrepancies within Different Countries on the Qinghai–Tibet Plateau over a Recent 30-Year Period

Chen,

2023

Land

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The Qinghai–Tibet Plateau is a unique global natural geographical unit with a high altitude and fragile ecology, where land cover change has been affecting its regional ecological security and sustainable development. The plateau covers nine countries with different variations in climate change and human activities, which cause significant spatial variations in its land cover change. This paper uses land cover data to reveal the spatiotemporal characteristics and multi-country differences in land cover change on the Qinghai–Tibet Plateau from 1992 to 2020 by applying spatiotemporal characteristic analysis and mapping methods. The results show that grassland (65.70%), bare land (15.56%), and woodland (10.25%) are the main types of land cover on the plateau, accounting for 91.51% of the total area. The interconversion of bare land and grassland was dominant in 1992–2020, with an area share of 67.83%. The turning point year of grassland change occurred in 2015. The grassland area increased by 6312 km2 in 1992–2015, while it decreased by 14,646 km2 in the plateau in 2015–2020. The areas of woodland, cropland, and water increased by 2.77%, 5.85%, and 7.57%, respectively, and the area of built-up land increased from 299.17 km2 to 1206.29 km2. Overall, a warming and wetting trend of the climate in the Qinghai–Tibet Plateau has driven the expansion of natural vegetation and water in the central plateau within China, with its natural vegetation area increasing by 0.11%. However, the development of regional warming and drying caused local grassland degradation, where Kyrgyzstan and India within the Qinghai–Tibet Plateau experienced the most prominent vegetation degradation. Human activities are relatively frequent in the Qinghai–Tibet Plateau within China and India, causing higher growth rates of built-up land and cropland than in other countries. The establishment of reserves and effective forest management policies have led to significant increases in woodland areas in China and Nepal within the plateau, while weak forest management and limited investment in forest conservation have led to high rates of deforestation in India and Myanmar. Accelerated temperature rises and regional differences in precipitation are the main drivers of large-scale land cover change on the plateau and differences in human activities and land use policies are responsible for the dramatic and diverse localized land cover change.

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

Cited by 13 publications

References 58 publications

Comparative analysis of soil organic matter fractions, lability, stability ratios, and carbon management index in various land use types within bharatpur catchment, Chitwan District, Nepal

Comparative analysis of soil organic matter fractions, lability, stability ratios, and carbon management index in various land use types within bharatpur catchment, Chitwan District, Nepal

A Two-Layer SD-ANN-CA Model Framework for Multi-Typed Land Use and Land Cover Change Prediction under Constraints: Case Study of Ya’an City Area, Western China

The Spatiotemporal Change in Land Cover and Discrepancies within Different Countries on the Qinghai–Tibet Plateau over a Recent 30-Year Period

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