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Land-Cover Change Detection in Batur Catchment Area Using Remote Sensing Febrianti, Ni Kadek Oki; Danoedoro, Projo; Widayani, Prima
JURNAL GEOGRAFI Vol. 15 No. 1 (2023): JURNAL GEOGRAFI
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/jg.v15i1.32670

Abstract

Land cover information is an essential aspect in the planning and management of earth modeling and understanding. Land cover changes impact the physical and social environment, such as hydrological conditions and ecological systems. This study aimed to identify spatial differences in the land cover of the Batur catchment area from 2015-2021 by using a remote sensing approach to describe the existing land-cover site and to detect its changes. The methods used in this study are a combination of the vegetation index and a supervised classification maximum likelihood algorithm with Landsat 8 OLI/TIRS in 2015 and 2021. Furthermore, the Change Detection Feature, identified from two image periods in 2015-2021 and processed, is used to detect changes in land cover. The accuracy assessment utilized QuickBird imagery recorded in 2015; field survey data were taken in 2021. The results showed that between 2015 to 2021, built-up area, bare land, shrubs, and lake have increased by 102,66% (306,01 ha), 27,95% (452,25 ha), 15,20% (215,72 ha) and 4,05 % (62,73 ha) while dryland forest and dry-dry-field have decreased by -25,84% (-606,29 ha) and -14.59% (-430,42 ha), respectively. The overall accuracy of the multispectral classification results in 2015 and 2021 was 82,63% and 89,57%.Keywords: Land-Cover Change; Batur; Catchment Area; Remote Sensing 
MODIS Satellite Imagery for Monitoring Carbon Sequestration Potential and Its Drivers in Jambi Province, Indonesia Widayani, Prima; Arrafi, Muhammad
JURNAL GEOGRAFI Vol. 17 No. 1 (2025): JURNAL GEOGRAFI
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/jg.v17i1.62343

Abstract

Jambi Province is a province in Indonesia whose land use is dominated by forests and plantations. Threats to land conversion and forest fires in the region have reduced vegetation and will threaten carbon absorption in the future. This study aims to map and assess the potential for carbon absorption and triggering factors by evaluating the spatiotemporal Net Primary Productivity (NPP) pattern to estimate Jambi Province's carbon absorption. This study uses remote sensing data to obtain NPP values ​​and several variables that will be assessed for their influence on NPP. MODIS satellite imagery is used to obtain NPP data, forest cover, Normalized Data Vegetation Index (NDVI) and Land Surface Temperature (LST). Shuttle Radar Topography Map (SRTM) imagery obtains topography and slope data. Population data in the form of the Human Development Index, total population and population in urban areas were obtained from the Central Statistics Agency of Jambi Province. The average NPP value 2003 in Jambi Province was 0.911 kgC/m/year, then the average NPP decreased to 0.754 kgC/m/year in 2023. Based on statistical analysis, there is a correlation between NPP and NDVI, slope, and topography.
RESIDENTIAL CLASSIFICATION USING GEOBIA IN PART OF JAKARTA SUBURBAN AREA Akmal Hafiudzan; Prima Widayani; Nurwita Mustika Sari
International Journal of Remote Sensing and Earth Sciences Vol. 20 No. 2 (2023)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2023.v20.a3862

Abstract

The increasing of urban population followed by socioeconomic problems leads to emerging various number of researchs in urban area, especially in Jakarta Metropolitan Area. One of them are escalated tension-conflict due to rise of newly Gated Communities residential that sprawl across local residents (Kampung Kota). There is urgency to map all 3 types of residential (Kampung Kota, Perumnas, Cluster) through satellite imagery on a wide-scale. This study uses WorldView-2 imagery data recorded for 2020. The method used is an object-based method, namely GEOBIA using the eCognition Developer 64 software. The GEOBIA process is carried out through three stages, firstly the segmentation to separate residential blocks from surrounding land cover objects (bodies of water, vegetation, open land, non-residential built-up land) as well as exploring the variable values of each object, then sample-based classification using the SVM algorithm on Google Earth Engine application, and accuracy test to evaluate semantic and geometric accuracy levels. The results of the mapping are 3 classes of residential types followed by 4 classes of land cover. The overall accuracy of the three types of residential is 80% which means that the GEOBIA approach is able to show good performance.
MODELING FOREST AND LAND FIRE VULNERABILITY IN BANJAR DISTRICT USING RANDOM FOREST CLASSIFICATION METHOD IN 2023 Wicaksana, Muhammad Akbar; Widayani, Prima; Windartono, Barandi Sapta
JURNAL SOCIUS Vol 14, No 2 (2025): JURNAL SOCIUS
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/js.v14i2.22935

Abstract

Forest and land fires are a major environmental concern in Banjar Regency, South Kalimantan, with a burned area of 1,812.80 ha recorded in 2023. This study aims to model fire susceptibility levels using the Random Forest algorithm based on remote sensing data. The data utilized include Landsat 8 imagery from 2023 with extracted spectral indices such as NDVI, NBR, NDWI, MSI, and BAI, along with fire hotspot data from the Banjar Regency Disaster Management Agency. The model was trained using data from June to mid-September and validated with data from mid-September to November 2023. Results indi-cate that the northern and central areas of Banjar Regency exhibit the highest fire susceptibility. The susceptibility map was categorized into five zones based on fire probability. Accuracy assessment using a confusion matrix yielded an overall accuracy of 71.64% and a Kappa coefficient of 40.81%. These findings demonstrate that the Random Forest method is effective in iden-tifying fire-prone areas with high efficiency and minimal input data. This model provides a valuable tool for spatially targeted fire prevention and mitigation planning.
Hubungan Nilai Indeks Permukaan Terhadap Temperatur Permukaan di Kabupaten Bantul Rachmadhani, Dini; Zalsabilah, Putri; Kamal, Muhammad; Widayani, Prima
Jurnal Pembangunan Wilayah dan Kota Vol 21, No 4 (2025): JPWK Volume 21 No. 4 December 2025
Publisher : Universitas Diponegoro Publishing Group, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/pwk.v21i4.71617

Abstract

Perkembangan kota yang pesat menyebabkan perubahan penggunaan lahan di wilayah sekitarnya, termasuk di Kabupaten Bantul. Penelitian ini bertujuan menganalisis hubungan antara  Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Bare Soil Index (BSI), terhadap  Land Surface Temperature (LST) menggunakan data citra satelit Landsat serta teknik pemodelan spasial selama periode 2014, 2019, dan 2024. Hasil penelitian menunjukkan bahwa nilai NDVI, NDBI, dan BSI dan LST mengalami fluktuasi tahunan berdasarkan perekaman tahun 2014, 2019, dan 2024. Hubungan NDVI dan LST menunjukkan korelasi negatif, dengan nilai R² tertinggi sebesar 0,5819 pada tahun 2014, yang mengindikasikan semakin tinggi NDVI, maka suhu permukaan tanah cenderung lebih rendah. Sedangkan, NDBI menunjukkan korelasi positif terhadap LST, dengan nilai R² tertinggi sebesar 0,5312 pada tahun 2014. Hubungan BSI terhadap LST juga menunjukkan korelasi positif, di mana semakin tinggi nilai BSI, suhu permukaan tanah semakin meningkat, khususnya pada tahun 2014, 2019, dan 2024.
Co-Authors Achmad Fadhilah Achmad Fadilah Ade Febri Sandhini P Agatha Andriantari Agus Joko Pitoyo Akmal Hafiudzan Akmal Hafiudzan Andung Bayu Sekaranom Arief Wicaksono Arrafi, Muhammad Bagus Wiratmoko Bowo Susilo Dewi Miska Indrawati Dyah Kusuma, Dyah Edi Suharyadi Erika Yuliantari Fadilah, Achmad Fathilda, Intan Khaeruli Febrianti, Ni Kadek Oki Ghosh, Kapil Hamim Zaky Hadibasyir Hari Kusnanto Hidayatullah, Faqih Huwaida Nur Salsabila Indrawati, Dewi Miska Ira Nurmala Hani Irawan, Irfan Zaki Irfan Zaki Irawan Irfan Zaki Irawan Irsan, Laode Muhamad Iswari Nur Hidayati Kapil Ghosh Kusbaryanto Mahendra, Auzaie Ihza Mizan, Rahmat azul Muhammad Arrafi Muhammad Kamal Muhammad Kamal Muhammad Minan Chusni Muhammad Sufwandika Wijaya Muhammad Sufwandika Wijaya Muhammad Sufwandika Wijaya Murti Budi Santosa, Sigit Heru Ni Kadek Oki Febrianti Nur Mohammad Farda Nur Mohammad Farda Nurbandi, Wahyu Nurhadi, Muhammad Nurul Astuti, Nurul Nurweni, Susi Nurwita Mustika Sari Nurwita Mustika Sari Projo Danoedoro Projo Danoedoro Projo Danoedoro R. Suharyadi Rachmadhani, Dini Ramadhan Pasca Wijaya Rina Febriany Sandy Budi Wibowo Sanjiwana Arjasakusuma Sanjiwana Arjasakusuma, Sanjiwana Santosa, Sigit Herumurti Budi Seandrasto Abi Kharis Wardhani Shandra S Pertiwi Sigit Heru Murti Sitti Rahmah Umniyati Sudaryatno Sudaryatno Sugeng Juwono Mardihusodo Suherningtyas, Ika Afianita Totok Gunawan Totok Wahyu Wibowo Tri Wulandari Kesetyaningsih Ulfa Aulia Syamsuri Vandam Caesariadi Bramdito Wahyu Nurbandi Wicaksana, Muhammad Akbar Windartono, Barandi Sapta Wiratmoko, Bagus Wirayuda, I Kade Alfian Kusuma Zahrotunisa, Siti Zalsabilah, Putri