Land cover classification using maximum likelihood method (2000 and 2019) at Khandgait valley in Mongolia

Norovsuren, Bayanmunkh; Tseveen, Batchuluun; Batomunkuev, V. S.; Renchin, Tsolmon; Natsagdorj, Enkhjargal; Yangiv, A.; Mart, Zaya

doi:10.1088/1755-1315/381/1/012054

Cited by 18 publications

(8 citation statements)

References 3 publications

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“…Considering the effect of land cover changes on fire risk mapping, it is important that fire risk management plans incorporate those changes and reallocate resources accordingly on a local scale. In this way the SVM algorithm, along with other classification algorithms [33][34][35]51], can offer a powerful tool for updating fire risk maps year by year. Finally, it is notable to point out that both October 2020 and July 2021 fires started near areas classified as 'extreme high' fire risk.…”

Section: Discussionmentioning

confidence: 99%

“…In view of this fact, GIS can be used to classify land cover and vegetation from satellite imagery with the implementation of machine learning algorithms [31,32]. Various algorithms have been used by different studies for land classification, such as k-means clustering [33], maximum likelihood classification [34], and support-vector machines (SVM) [35]. In fact, support-vector machine models have been used for land classification with promising results [31,35].…”

Section: Introductionmentioning

confidence: 99%

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Fire Risk Probability Mapping Using Machine Learning Tools and Multi-Criteria Decision Analysis in the GIS Environment: A Case Study in the National Park Forest Dadia-Lefkimi-Soufli, Greece

Maniatis

Doganis²,

Chatzigeorgiadis

2022

Applied Sciences

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Fire risk will increase in the upcoming years due to climate change. In this context, GIS analysis for fire risk mapping is an important tool to identify high risk areas and allocate resources. In the present study, we aimed to create a fire risk estimation model that incorporates recent land cover changes, along with other important risk factors. As a study area, we selected Dadia-Lefkimi-Soufli National Forest Park and the surrounding area since it is one of the most important protected areas in Greece. The area selected for the case study is a typical Mediterranean landscape. As a result, the outcome model is generic and can be applied to other areas. In order to incorporate land cover changes in our model, we used a support vector machine (SVM) algorithm to classify a satellite image captured in September 2021 and an image of the same period two years ago to obtain comparable results. Next, two fire risk maps were created with a combination of land cover and six other factors, using the analytic hierarchy process (AHP) on a GIS platform. The results of our model revealed noticeable clusters of extreme high risk areas, while the overall fire risk in the National Park Forest of Dadia-Lefkimi-Soufli was classified as high. The wildfires of 1st October 2020 and 9th July 2021 confirmed our model and contributed to quantification of their impact on fire risk due to land cover change.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fire Risk Probability Mapping Using Machine Learning Tools and Multi-Criteria Decision Analysis in the GIS Environment: A Case Study in the National Park Forest Dadia-Lefkimi-Soufli, Greece

Maniatis

Doganis²,

Chatzigeorgiadis

2022

Applied Sciences

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“…Despite the fact that RS techniques have significant drawbacks, they have a number of advantages over conventional methods in terms of repeating coverage, inexpensive data capture, and extensive coverage (Xie et al, 2008;Kadhim et al, 2016). MLC algorithm was used for the LULC classification and determine their changes over the time and space (Norovsuren et al, 2019;Tripathi et al, 2020;Rakhmonov et al, 2021;Regasa et al, 2021). Initially, land use classification map for 2010 showed that agriculture land is distributed between the dense forest area and the banks of rivers (Figure 2a).…”

Section: Discussionmentioning

confidence: 99%

Assessment of land use land cover dynamics and its impact on springs water in Ritung Khola Sub-Watershed, Myagdi district, Nepal

Timilsina

SHRESTHA

Tripathi

et al. 2023

EJSS

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Land Use Land Cover (LULC) Change has emerged as a significant environmental issue and a worry for the sustainable use of natural resources. This study was performed to determine the rate in change of land cover and its significant impact on springs water in the Ritung Khola sub-watershed of Myagdi district, Nepal, between 2010 and 2020. This study analyzes LULC dynamics and it’s impact on springs water using satellite imageries (Landsat 5 TM and Landsat 8 OLI/TIRS) and focus group discussions with the inhabitants. We used Supervised Maximum Likelihood Classification algorithm to classify attributes of the LULC changes. The results demonstrated a significant change in LULC during those ten years (2010-2020). The area covered by agricultural land and human settlements significantly increased by 313.54% and 367.14%, respectively. On the contrary, barren land, water bodies and forest cover have been reduced by 37.52%, 13.16% and 5.26%, respectively. The number of active springs followed decreasing trend as many of them were completely displaced or dried due to erosions and frequent landslides. The findings from this study are expected to facilitate the planning process adopted to prevent springs under the threat of extension and mitigate the water scarcity problem.

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“…Where, ( | ) -testing most probability; ( | )conditional probability; ( ) -prior probability the probability that is observed; ( ) -probability of pixel for any class; -that class; -pixel [11].…”

Section: Study Areamentioning

confidence: 99%

Impact of the Spatial Resolution of Satellite’s Data for Mapping Land use Land Cover in Karmala Tehsil, Solapur, India

Kolambe

Raj

Loahkare³

et al. 2021

IJECC

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Land use and land cover (LULC) classification mapping is important for evaluating, monitoring, protecting and planning for land resources. A key factor in extracting desired information from satellite images is choosing the right the spatial resolution. The scale of a pixel on the ground is known as spatial resolution. A pixel is the smallest ‘dot' that makes up an optical satellite image which defines the level of detail as in image. In this paper estimation of the areal extent of water, built up, barren land, vegetation land and fallow land classes with its classification accuracy were reviewed particularly for January 2013 and November 2016 in Karmala tehsil of Solapur district, India. LULC is implied by different spatial resolution images of Advanced Wide Field Sensor (AWiFS), Linear Imaging Self Scanning Sensor (LISS-III), Landsat-8 Operational Land Imager (OLI) and Sentinel-2A imageries in QGIS environment while the classification was carried out using the maximum likelihood algorithm (MLA). The classified maps obtained from AWiFS and LISS-III sensors, as well as Sentinel-2A and Landsat-8 OLI data sets, were compared separately. Spatial analysis depicts that the Kappa coefficient of Sentinal-2A, Landsat-8, LISS III and AWiFS was found 96.96%, 91.64%, 87.30% and 89.36%. Furthermore, overall accuracy of was found to be 99.07%, 94.49%, 89.84% and 94.08% respectively. The accuracy of the classified image with higher spatial resolution (Sentinal-2A) proved more informative than that of lower resolution (AWiFS) sensor. On the response, the finer spatial resolution of Sentinal-2A (10 m) delivered more precise details and enhanced LULC classification accuracy most reliably than the coarser spatial resolution of Landsat-8 (30m), LISS III (23m) and AWiFS (56m) image. A perusal of data revealed that the overall accuracy and Kappa coefficient was found proportionate to spatial resolution of satellite imageries. The higher resolution spatial data also greatly reduces the mixed-pixel problem. The study revealed that the spatial resolution plays an important role and affects classification details and accuracy of LULC level.

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Land cover classification using maximum likelihood method (2000 and 2019) at Khandgait valley in Mongolia

Cited by 18 publications

References 3 publications

Fire Risk Probability Mapping Using Machine Learning Tools and Multi-Criteria Decision Analysis in the GIS Environment: A Case Study in the National Park Forest Dadia-Lefkimi-Soufli, Greece

Fire Risk Probability Mapping Using Machine Learning Tools and Multi-Criteria Decision Analysis in the GIS Environment: A Case Study in the National Park Forest Dadia-Lefkimi-Soufli, Greece

Assessment of land use land cover dynamics and its impact on springs water in Ritung Khola Sub-Watershed, Myagdi district, Nepal

Impact of the Spatial Resolution of Satellite’s Data for Mapping Land use Land Cover in Karmala Tehsil, Solapur, India

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