Land change modeler for predicting land cover change in Banjarmasin City, South Borneo (2014 - 2022)

Aryaguna, Prama Ardha; Saputra, Aswin Nur

doi:10.1088/1755-1315/500/1/012002

Cited by 5 publications

(5 citation statements)

References 10 publications

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“…The images used are Landsat 8 on 13 August 2015 and 5 May 2020. The classification process uses supervised classification with the maximum likelihood classification method (Aryaguna & Saputra, 2020a;Khatami et al, 2016). Determination of classification using land cover classification from Anderson with four land cover classes that can be found in this study according to Table 1.…”

Section: Methodsmentioning

confidence: 99%

“…Determination of classification using land cover classification from Anderson with four land cover classes that can be found in this study according to Table 1. (Aryaguna & Saputra, 2020b) In the early stages of this research, radiometric and geometric corrections were made to Landsat 8 OLI/TIRS images before being used for the classification process. Radiometric correction is done to correct image errors due to atmospheric phenomena when recording and converting digital number values into reflectance values (Arisanty et al, 2019;López-Serrano et al, 2016;Pons et al, 2014).…”

Section: Methodsmentioning

confidence: 99%

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Modeling of Land Cover Changes in Banjarbaru City South Kalimantan Province

Saputra,

Fatchurohman

et al. 2024

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Urban areas often experience land cover changes. Banjarbaru is one of several cities in Indonesia that has experienced land changes. The relocation of the administrative center of Banjarmasin City to Banjarbaru City led to the development of settlements. One spatial analysis carried out to examine the phenomenon of land change is remote sensing techniques. The method that can be used is the Land Change Modeler from MOLUSCE in QGIS. This model uses the CAM (Cellular Automata Markov) method to identify land cover change and predict land cover distribution. CAM can understand and predict land change patterns by considering land use, vegetation, and cell spatial interactions. This modeling is based on land cover data for 2015 and 2020 and several supporting parameters such as DEM data and distance to roads. Based on the modeling results from 2015 and 2020, Banjarbaru City experienced a change in built-up land, with most of it occurring in the center of Banjarbaru City. Based on the Markov Chain method by looking at land changes in the previous year, the development of built-up land increased by about 8% of the Banjarbaru City area of 32917.41 hectares. Based on the prediction results, the development of built-up land is centered in the middle of Banjarbaru City, such as North and South Banjarbaru Districts, due to the development of residential development.Keywords: Land Cover, Land Change Modeller, Cellular Automata, Markov Chain

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Modeling of Land Cover Changes in Banjarbaru City South Kalimantan Province

Saputra,

Fatchurohman

et al. 2024

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“…Evaluasi lahan diperlukan sebagai dasar ilmiah dalam penataan tata ruang wilayah. Pemanfaatan ruang yang memperhitungkan daya dukung lingkungan akan menghasilkan kelestarian bagi lingkungan (Aryaguna & Saputra, 2020). Perencanaan penggunaan lahan lahan diperlukan untuk menentukan jenis penggunaan lahan di suatu daerah agar dapat digunakan secara optimal.…”

Section: Pendahuluanunclassified

Evaluasi Kesesuaian Lahan Untuk Tanaman Kelapa Sawit Di Kecamatan Padang Batung Kabupaten Hulu Sungai Selatan

Rifki

Arisanty

Muhaimin

et al. 2023

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Padang Batung Sub-district is a sub-district in Hulu Sungai Selatan Regency, an Agropolitan Development area with the leading commodity of Oil Palm which is experiencing a decline in production. The decline in palm oil production continues to occur every year. The purpose of this study was to analyze the level of land suitability for oil palm plantations in Padang Batung District, Hulu Sungai Selatan regency. This research was conducted on 16 land units in Padang Batung District with 23 samples. The results of the study stated that the actual land suitability in Padang Batung sub-district consisted of 3 classes, namely Moderately Suitable (S2) Marginally Suitable (S3), and Currently not suitable Class (N1) with rooting media inhibiting factors (r), water availability (w), the level of erosion hazard (e), available nutrients (n), flood hazard (b), and nutrient retention (f). The results of the study stated that there were 2 potential land suitability classes for oil palm plantations in Padang Batung District, namely Moderately Suitable (S2) and Marginally Suitable (S3) with inhibiting factors for rooting media (r), erosion hazard level (e), available nutrients (n), and nutrient resistance (f). The potential land suitability class is achieved if improvements are made to the land.

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“…To manage natural resources (biodiversity) influences, and to analyze and forecast spatial LULC changes, the Land Change Modeler (LCM) in TerrSet (formerly known as IDRISI) software was developed [54,55]. LCM is an ArcGIS-integrated suite of tools for the assessment of future LULC changes, detecting gains and losses, net change, persistence, and identification of transitions between LULC classes [56].…”

Section: Prediction Of Future Lulc and Associated Driving Forcesmentioning

confidence: 99%

Past and Future Land Use/Land Cover Changes in the Ethiopian Fincha Sub-Basin

Regasa

Nones

2022

Land

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The increasing human pressure on African regions is recognizable when looking at Land Use Land Cover (LULC) change maps, generally derived from satellite imagery. Using the Ethiopian Fincha watershed as a case study, the present work focuses on (i) identifying historical LULC change in the period 1989–2019; (ii) estimating LULC in the next thirty years, combining Geographical Information Systems (GIS) with Land Change Modelling (LCM). Landsat5/8 images were combined with field evidence to map LULC in three reference years (1989, 2004, 2019), while the Multi-Layer Markov Chain (MPL-MC) model of LCM was applied to forecast LULC in 2030, 2040, and 2050. The watershed was classified into six classes: waterbody, grass/swamp, built-up, agriculture; forest; and shrub. The results have shown that, in the past 30 years, the Fincha watershed experienced a reduction in forest and shrubs of about −40% and −13%, respectively, mainly due to ever-increasing agricultural activities, and such a trend is also expected in the future. In fact, for the period 2019–2050, LCM simulated a significant decrease in both forest and shrubs (around −70% and −20%, respectively), in favor of more areas covered by grass (19%) and built-up (20%). It is worth noting that a decrease in natural forests can drive an increase in soil erosion, fostering siltation in the water reservoirs located in the sub-basin. The study pointed out the urgency of taking actions in the sub-basin to counteract such changes, which can eventually lead to a less sustainable environment.

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Land change modeler for predicting land cover change in Banjarmasin City, South Borneo (2014 - 2022)

Cited by 5 publications

References 10 publications

Modeling of Land Cover Changes in Banjarbaru City South Kalimantan Province

Modeling of Land Cover Changes in Banjarbaru City South Kalimantan Province

Evaluasi Kesesuaian Lahan Untuk Tanaman Kelapa Sawit Di Kecamatan Padang Batung Kabupaten Hulu Sungai Selatan

Past and Future Land Use/Land Cover Changes in the Ethiopian Fincha Sub-Basin

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