International Remote Sensing Applied Journal

MAPPING OF AREAS OF FOREST AND LAND FIRE VULNERABILITY IN THE SANIANG BAKAR AREA, X KOTO DISTRICT, SOLOK DISTRICT

sri rezki, Helfia Edial, Iswandi Iswandi, Triyatno Triyatno — Tue, 21 Jan 2025 00:00:00 +0000

This research uses quantitative descriptive analysis which has the title "Mapping Areas of Forest and Land Fire Vulnerability in the Saniang Bakar Area, X Koto District, Solok Regency." This research aims to determine the distribution of forest and land fire vulnerability based on the influence of each parameter: land cover, Rain intensity, soil type, height in the Saniang Baka area, research results based on each parameter of land cover which is quite large, fires are dominated by forests covering an area of 4946.5 ha and shrubs covering an area of 3810.2 ha, the rain intensity parameter is dominated by the very low category. around 200 mm/year, the majority of soil type parameters are Andisols, the height parameters are generally dominated by the sloping category. Understanding the distribution of land surface temperatures using the Land Surface Temperature (LST) algorithm in the Saniang Baka area shows a minimum temperature value of 14.8OC, a maximum temperature of 45.6OC and an average temperature of 30.6OC. The results of the analysis used in the Saniang Bakar area have a general level of vulnerability to forest and land fires in the high category with an area of around 2358.64 Ha.

UTILIZATION OF SENTINEL-2 IMAGES FOR MAPPING THE CORAL REEF AREA IN THE CONSERVATION AREA OF PIEH ISLAND WATERS 2022

Ulfi Rahmi Amatullah, Febriandi Febriandi, Ernawati Ernawati, Dian Adhetya Arif — Tue, 21 Jan 2025 04:22:42 +0000

Coral reefs are known as complex and productive shallow tropical marine ecosystems. They serve as a habitat for various species of marine plants, marine animals, and marine microorganisms. The deterioration of coral reefs threatens the survival of this shallow marine ecosystem. Mapping the distribution of coral reefs using remote sensing technology is a crucial instrument in the effort to monitor and protect coral reefs while preserving the marine environment. This research aims to map and measure the extent of coral reefs in the Pieh Islands Marine Conservation Area in 2022. In this study, we used Sentinel-2 imagery from 2022 and applied the Object-Based Image Analysis (OBIA) method to detect the extent of coral reefs. Sentinel-2 imagery was processed using ArcGIS and eCognition software, involving atmospheric correction, image clipping, image compositing, segmentation, image classification, and accuracy testing. The data processing results indicate that coral reefs are distributed around the waters of Pieh Islands, with the highest density located to the south of Pieh Islands's waters. The total extent of detected coral reefs in this study is 15.76 hectares. The use of Sentinel-2 imagery with the OBIA method has proven to be effective in detecting the extent of coral reefs in the Pieh Islands Marine Conservation Area.

MAPPING OF LAND USE CHANGES AND ALIGNMENT OF SPATIAL PATTERN PLANS IN PADANG CITY

Yusran Rizky Ananda Delta, Muhammad Ismail, Fitriana Syahar, Dedy Fitriawan — Tue, 21 Jan 2025 00:00:00 +0000

Changes in land use in accordance with spatial pattern plans are a challenge for the government as the population in an area increases, resulting in increased land requirements. This greatly influences the spatial pattern plans that have been planned previously. These land use changes can be obtained from Remote Sensing data which has the advantage and ease of obtaining land use information. This research uses Sentinel-2A satellite image data for 2017 and 2023. The objectives of this research are (1) To determine the ability of Sentinel-2A imagery to interpret land use (2) To determine changes in land use in 2017 and 2023 (3) To find out the alignment of land use identification with the Padang City spatial pattern plan. The method used in this research is a quantitative method with an approach spatial (spatial approach). The method used for land use classification is the manual digitization method (on screen) and land use area calculation using the Geometry Calculator tool in ArcGIS 10.6.1 software. The research results show the ability of Sentinel-2A imagery to produce 15 land uses, namely highland forest, residential/mixed buildings, rivers, cultivated open land, rice fields, dry land seasonal crops, bushes and thickets, mining, runways, ports, buildings industry and trade, grass, mangrove forests, savannas and stretches of coastal sand. In a period of 5 years there were 13 land uses that experienced changes, namely highland forests, residential/mixed buildings, open cultivated land, bushes and shrubs, mining, industrial/commerce buildings, dry land seasonal crops, rice fields, mangrove forests, stretches of beach sand and grass. The harmony between the land use of Padang City and the Padang City Spatial Pattern Plan is dominated by harmony, but there are also those which are not in harmony, namely highland forests, residential/mixed buildings, rice fields, dry land annual crops and mining.

USE OF MEDIUM RESOLUTION IMAGERY FOR PREDICTION MAPPING OF LAND COVER CHANGES IN SOLOK DISTRICT

Yolanda Indah Permata Sari, Dedy Fitriawan, Yudi Antomi, Dian Adhetya Arif — Tue, 21 Jan 2025 04:30:44 +0000

This research aims to determine changes in land cover from 2017-2022 in Solok Regency, to find out predictions of changes in land cover until 2032 in Solok Regency, to find out the results of land cover accuracy tests in Solok Regency. This research uses the Supervised (Maximum Likelihood) method to identify changes in land cover in Solok Regency in 2017 and 2022. This research was carried out in several stages, namely the preprocessing stage including radiometric and atmospheric correction, image cropping according to the research area. The processing stage uses the Supervised (Maximum Likelihood) method to determine the classification, then creating a land cover change identification matrix, creating sample points in the field, accuracy testing, and finally making predictions using the Cellular Automata model to predict land cover in 2032. Identification results in areas there was a change in land cover from 2012 to 2017 to 2022, land cover that changed, namely primary forest in 2012 to 2017 experienced a change in 2022 to 206,362.04ha, built-up land also experienced an area change of 3,162.37ha, followed by open land experiencing changes 283.98ha, mixed plantation land experienced a change of 78,176.71ha, wetland farming experienced a change of 12,751.07ha and dry land farming experienced a change of 20,707.08ha in 2022. Then the results of land cover predictions in 2032 are forest land area primary area in Solok Regency changed to 207,382.99ha, while the area of water bodies changed to 6,889.05ha, then built-up land experienced a change of 3,288.13ha, then open land cover changed to 77,912.95ha, then mixed plantation cover changed to 13,248.51 , in wetland agriculture it changed to 13,248.51ha and dryland agriculture to 19,164.11ha.

COMPARISON OF LAND COVER CLASSIFICATION ACCURACY TEST USING PIXEL BASED AND OBJECT BASED IMAGE ANALYSIST (OBIA) METHODS ON SENTINEL-2A IMAGE IN 2023 IN PADANG CITY

Nurfadilah Nurfadilah, Dedy Fitriawan, Triyatno Triyatno, Azhari Syarief — Tue, 21 Jan 2025 00:00:00 +0000

Land cover is the physical and biological cover of the earth's surface, whether formed naturally such as swamps, rivers, hills or man-made such as rice fields, gardens, forests and buildings. One alternative to obtain information about land cover is by utilizing remote sensing technology. These include pixel-based classification and object-based classification. The fundamental difference between object-based classification and pixel-based classification lies in the object separation process, object-based classification divides based on segmentation results, not just based on single pixel values in pixel-based classification. This research aims to compare the level of classification accuracy of pixel-based methods with object-based methods in identifying land cover in 2023 Sentinel-2A image data in Padang City. The sampling method used was random sampling with a total of 7 classes. To get accuracy results, the same ground truth data is given to both methods.The results of the comparison of object-based classification and pixel-based classification on the two images were tested for accuracy using a confusion matrix which resulted in land cover classification accuracy on the 2023 Sentinel-2A image. The overall kappa accuracy for the pixel-based classification method was 89.80%, while for the pixel-based classification method the object obtained a value of 94.88%. The overall accuracy results show that object-based classification is better than pixel-based classification in classifying land cover.