Land use and land cover change in East Java from 2015 to 2021: Use optical imagery and Google Earth engine

Mandala Marga, Indarto Indarto (), Rodhi Nova Nevila, Saputra Akhmad Andi and Hakim Farid Lukman
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Mandala Marga: University of Jember, Faculty of Agricultural, Department of Soil Science, Jl. Kalimantan No. 37, Kampus Tegalboto, Jember, 68121, East Java, Indonesia
Indarto Indarto: University of Jember, Faculty of Agricultural, Department of Soil Science, Jl. Kalimantan No. 37, Kampus Tegalboto, Jember, 68121, East Java, Indonesia
Rodhi Nova Nevila: University of Jember, Faculty of Agricultural Technology, Department of Agricultural Engineering, Jl. Kalimantan No. 37, Kampus Tegalboto, Jember, 68121, Indonesia; University of Bojonegoro, Faculty of Sains and Engineering, Department of Civil Engineering, Jl. Lettu Suyitno No 2 Kalirejo, Bojonegoro, 62119, East Java, Indonesia
Saputra Akhmad Andi: University of Gresik, Faculty of Engineering, Department of Civil Engineering, Jl. Arif Rahman Hakim Gresik No.2B, Kramatandap, Gapurosukolilo, Gresik, 61111, East Java, Indonesia
Hakim Farid Lukman: University of Jember, Faculty of Agricultural, Department of Soil Science, Jl. Kalimantan No. 37, Kampus Tegalboto, Jember, 68121, East Java, Indonesia

Environmental & Socio-economic Studies, 2024, vol. 12, issue 1, 69-80

Abstract: This study analysed the changes in land use and land cover (LULC) in East Java Province by comparing two LULC maps interpreted from optical imagery. The images captured from 2015 to 2017 were selected to represent the initial LULC maps. Then, the images collected from 2020 to 2021 were considered the recent LULC maps. The input imagery was prepared using the Google Earth engine (GEE). The Random Forest algorithm was used for classification. In this study, eight significant LULC classes were categorised, i.e., built-up area (BU), heterogeneous-agricultural land (HAL), bare soil (BS), paddy field (PF), open water (OW), vegetation (VG), shrubland (SH), and wetland (WL). Next, the training samples were interpreted from Google Earth Pro. Then, the GEE satellite base map and the ground control points (GCPs) were collected. The collected GCPs were split into 70% training and 30% validation data. The results showed that significant LULC Change was more marked in the most urbanised areas (in and around the big cities), followed by LULC change in and around medium towns and rural areas. Four classes experienced an area increase, i.e., BU (+30.23%), HAL (+30.77%), BS (+24.52%), and PF (+14.36%). As a consequence, the other four classes compensated for the increase, i.e., OW (−32.79%), VG (−25.72%), SH (−6.59%), and WL (−25.53%). Regional development from 2015 to 2021 has increased built-up areas. Conversely, the development has reduced OW, VG, SH, and WL. The LULC changes have significantly changed the natural landscape to a human-dominated one.

Keywords: LULC change; random forest algorithm; Sentinel; East Java (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:enviro:v:12:y:2024:i:1:p:69-80:n:7

DOI: 10.2478/environ-2024-0007

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