Geospatial Analysis of Land Use Land Cover Change Modeling in Phewa Lake Watershed of Nepal by Using GEOMOD Model

Regmi, Raju Raj; Saha, S. K.; Subedi, Dinesh

doi:10.3126/hj.v6i0.18363

Cited by 42 publications

(45 citation statements)

References 13 publications

(11 reference statements)

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“…According to the authors, the MLP-MC model gave superior results for projected scenario simulation than CA-Markov model. On the other hand, Regmi et al [8] compared CA-Markov and GEOMOD models to analyse and model the LULC dynamics in the Phewa lake watershed in Nepal. They found that CA-Markov chains were quite good as an operational model in projecting future LULC scenario.…”

Section: Background and Analysis Of The Literaturementioning

confidence: 99%

“…The CA-Markov model is one of the commonly used models among many LULC modelling tools and techniques, which models both spatial and temporal changes [8,31]. CA-Markov model combines cellular automata and Markov chain to predict the LULCC trends and characteristics over time [32].…”

Section: Simulation Of Lulc Change Using Ca-markov Modelmentioning

confidence: 99%

“…Accordingly, agricultural land is most vulnerable to those changes under conflict and political forces [6] in addition to the human habitat itself. Furthermore, analysing, modelling and understanding the transformation of LULCC is significant for several planning and management activities [7,8]. Observing past LULCC assists in understanding the trends of changes and futuristic extrapolations.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Land Use/Land Cover Changes Using a CA-Markov Model under Two Different Scenarios

2018

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Multi-temporal Landsat images from Landsat 5 Thematic Mapper (TM) acquired in 1993, 1998, 2003 and 2008 and Landsat 8 Operational Land Imager (OLI) from 2017, are used for analysing and predicting the spatio-temporal distributions of land use/land cover (LULC) categories in the Halgurd-Sakran Core Zone (HSCZ) of the National Park in the Kurdistan region of Iraq. The aim of this article was to explore the LULC dynamics in the HSCZ to assess where LULC changes are expected to occur under two different business-as-usual (BAU) assumptions. Two scenarios have been assumed in the present study. The first scenario, addresses the BAU assumption to show what would happen if the past trend in 1993–1998–2003 has continued until 2023 under continuing the United Nations (UN) sanctions against Iraq and particularly Kurdistan region, which extended from 1990 to 2003. Whereas, the second scenario represents the BAU assumption to show what would happen if the past trend in 2003–2008–2017 has to continue until 2023, viz. after the end of UN sanctions. Future land use changes are simulated to the year 2023 using a Cellular Automata (CA)-Markov chain model under two different scenarios (Iraq under siege and Iraq after siege). Four LULC classes were classified from Landsat using Random Forest (RF). Their accuracy was evaluated using κ and overall accuracy. The CA-Markov chain method in TerrSet is applied based on the past trends of the land use changes from 1993 to 1998 for the first scenario and from 2003 to 2008 for the second scenario. Based on this model, predicted land use maps for the 2023 are generated. Changes between two BAU scenarios under two different conditions have been quantitatively as well as spatially analysed. Overall, the results suggest a trend towards stable and homogeneous areas in the next 6 years as shown in the second scenario. This situation will have positive implication on the park.

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Section: Background and Analysis Of The Literaturementioning

confidence: 99%

Section: Simulation Of Lulc Change Using Ca-markov Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predicting Land Use/Land Cover Changes Using a CA-Markov Model under Two Different Scenarios

2018

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“…The stratified random sampling method was used in this study to carry out the accuracy assessment of the classified data in Google Earth as well as in base imagery in ArcGIS. Google Earth is a freeware that provides high-resolution satellite imageries which act as an important reference for the validation of classification outputs [54] and has been widely used in many studies [29,36,[55][56][57][58][59][60]. A total of 901, 905 and 890 stratified random points were generated in the classified image of 2000, 2010 and 2017, respectively, and were verified in Google Earth imageries.…”

Section: Accuracy Assessmentmentioning

confidence: 99%

Assessment of Ecosystem Services Value Based on Land Use and Land Cover Changes in the Transboundary Karnali River Basin, Central Himalayas

Shrestha

Khadka

2019

Sustainability

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Land use and land cover change (LUCC) and its spatio-temporal characteristics are essential for natural resource management and sustainable development. LUCC is one of the major factors that affect the ecosystem and the services it provides. In this study, we used remote sensing techniques and a geographical information system to extract the land cover categories based on the Object-Based Image Analysis (OBIA) technique from Landsat TM/ETM/OLI satellite images in the transboundary Karnali River Basin (KRB, China and Nepal) of central Himalayas from 2000 to 2017. Spatio-temporal integrated methodology-Tupu was used to spatially show the LUCC as well as spatial characteristics of the arisen Tupu and shrunken Tupu. In addition, the ecosystem services value (ESV) were obtained and analyzed for each land cover category. In 2017, forest covered the highest area (33.45%) followed by bare area (30.3%), shrub/grassland (18.49%), agriculture (13.12%), snow/ice (4.32%), waterbody (0.3%) and built-up area (0.04%) in the KRB. From 2000 to 2017, the areas of forest, waterbody and snow/ice have decreased by 0.59, 6.14, and 1072.1 km 2 , respectively. Meanwhile, the areas of shrub/grassland, agriculture, barren land, and built-up categories have increased by 82.21, 1.44, 991.97, and 3.11 km 2 , respectively. These changes in the land cover have led to an increase in the ESV of the basin, especially the increase in shrub/grassland, agriculture, and water bodies (in the higher elevation). The total ESV of the basin was increased by $1.59 × 10 6 from 2000 to 2017. Anthropogenic factors together with natural phenomena drive LUCC in the basin and thus the ESV. The findings of this study could facilitate the basin-level policy formulation to guide future conservation and development management interventions.Ecosystem function refers to the habitat, biological or system properties and physical processes that occur within or across the ecosystem [17]. Ecosystem services are the benefits humans reap from the ecosystem function [17][18][19] that includes provisioning, regulating, cultural and supporting services [18,20]. In addition, the ecosystem function plays an important and multi-dimensional (ecological, socio-cultural and economic) role in the human society [21]. Ecosystem services do not directly flow from nature to human well-being, rather this occurs through the interaction with other forms of capital such as human capital (presence of human being), built capital (human built environment) and social capital (their community) [22]. Generally, the structure of the ecosystem is modified due to increased land use intensity and frequent changes in the land cover types. This results in changes in the ecosystem functions and the associated services it provides [23][24][25]. In recent decades, ecosystem services have been degrading due to an ever-increasing demand for these services, leading to a substantial and largely irreversible loss of natural and biological diversity life on earth [18]. The assessment and valuation of ecosystem ser...

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“…In recent times, the problem of soil erosion proliferates due to unscientific use and overutilization of the natural resources by humans in the form of changing natural landscape to land-use. The conversion of land use, land cover (LULC) usually has an unintended consequence on the natural environment (Regmi, Saha, & Subedi, 2017), especially in the form of soil erosion (Abdulkareem, Pradhan, Sulaiman, & Jamil, 2017;Chalise, Kumar, & Kristiansen, 2019;Kumar, 2015;Ozsahin, Duru, & Eroglu, 2017). *Corresponding author, e-mail: mkumar_ias@yahoo.co.in Several studies have been conducted with the aim to understand the soil dynamics since the mid of last century and many empirical and mathematical models for estimating soil erosion have been developed (Adinarayana, Gopal Rao, Rama Krishna, Venkatachalam, & Suri, 1999;D'Ambrosio, Di Gregorio, Gabriele, & Gaudio, 2001; Morgan, R., Morgan, & Finney, 1984;Renard, Foster, Weesies, McCool, & Yoder, 1997;Shen et al, 2003;Wischmeier & Smith, 1978).…”

Section: Introductionmentioning

confidence: 99%

The impact of land use dynamics on the soil erosion in the Panchnoi River Basin, Northeast India

Jaiswal¹,

Amin²

2020

J geographical I JC

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Land use (LU) dynamics and its relation to the accelerated soil erosion phenomenon in two broad geomorphic divisions of the Panchnoi River basin of Northeast India have studied. The present study was based on the Revised Universal Soil Loss Equation (RUSLE). To measure the impact of the LU dynamics on soil erosion, the basin was divided into two broad geomorphic divisions, i.e., plain zone and hilly zone, and the rate of soil erosion has been estimated separately for both of the geomorphic divisions. It has been found that in the plain zone, LU dynamics significantly accelerated soil erosion-from 0.52 ton/ha/yr in 1990 to 0.94 ton/ha/yr in 2015. Similarly, the vegetation density decreased significantly in the mountainous and hilly zone as the mean Normalized Difference Vegetation Index (NDVI) value changed from 0.45 in 1990 to 0.35 in 2015, which accelerated soil erosion from 12.06 ton/ha/yr to 18.30 ton/ha/yr from 1990 to 2015. The study indicates that soil erosion may give rise to a severe environmental as well as economic problem in the Panchnoi river basin, which may trigger issues related to the soil fertility of the basin area.

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Geospatial Analysis of Land Use Land Cover Change Modeling in Phewa Lake Watershed of Nepal by Using GEOMOD Model

Cited by 42 publications

References 13 publications

Predicting Land Use/Land Cover Changes Using a CA-Markov Model under Two Different Scenarios

Predicting Land Use/Land Cover Changes Using a CA-Markov Model under Two Different Scenarios

Assessment of Ecosystem Services Value Based on Land Use and Land Cover Changes in the Transboundary Karnali River Basin, Central Himalayas

The impact of land use dynamics on the soil erosion in the Panchnoi River Basin, Northeast India

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