SSRS Journal B: Spatial Research

Spatial Distribution of Urban Tree Canopy in Private Residential Property in Jakarta Bay Reclamation using Google Earth Engine Cloud Computing

2024-01-08T17:32:15+07:00

Jakarta Bay reclamation is a solution to Jakarta flood control and development of business units including property business. Reclamation was carried out with the construction of 18 small islands in front of the coastline and giant embankments. Pantai Indah Kapuk (one of the developers) has invested in the Jakarta Bay reclamation program with plans to build islands A, B, C, D, and E as residential and entertainment areas. However, until 2022 only Islands C and D were almost ready. Jakarta Bay Reclamation presents various threats, especially in terms of ecology. Pantai Indah Kapuk should have a green open space as an urban area. The purpose of this study was to look at the distribution of trees in the private residential property area of Pantai Indah Kapuk in the reclamation of Jakarta Bay C and D Islands. The research uses the Google Earth Engine Cloud Computing application with sentinel-2A image data source and classification by using several indexes to obtain the area of vegetation. The results showed that the classification area of water bodies, vegetation, land area, and buildings were 622.14 ha, 63.27 ha, 220.23 ha, and 139.71 ha, respectively. The accuracy test obtained an overall accuracy of 87.5% and a Kappa accuracy of 83.3%. The conclusion of this study is vegetation area in the private residential property area of Pantai Indah Kapuk covers 63.27 ha. The results of this study can be used as a basis for making reclamation management policies that prioritize ecological roles and functions to minimize negative impacts on the environment.

Mining Reclamation Monitoring using Sentinel-2 Temporal Data: Case Study in PT Adaro Energy Indonesia Mining Area

2024-10-02T03:40:00+07:00

Indonesia is one of the countries that has a wealth of energy and mineral resources. However, mining activities carried out to exploit these mineral resources have a negative impact on ecosystems, especially forest ecosystems, so there is a need for ecosystem rehabilitation which is realized in the form of post-mining land reclamation. Along with the development of science and technology, remote sensing is a technology that can be used in monitoring revegetated land so that it has the potential to be developed in monitoring post-mining land reclamation land. This research aims to study the spectral characteristics of post-mining land in PT Adaro Energi Indonesia, Calculate the revegetation area from 2016 to 2023 and Map the results of PT Adaro Energi Indonesia's post-mining land reclamation spatiotemporally from 2016 to 2023. This research involves several vegetation indices in analyzing the spectral characteristics of land and monitoring the results of revegetation spasiotemporally from 2016 to 2023. The results of the spectral characteristics analysis show that RVI and SLAVI have the ability to distinguish vegetation density. Meanwhile, the results of spatiotemporal analysis show that ARVI has a fairly fluctuating pattern of increase while NDVI shows the opposite pattern in response to the increase in vegetation. During the period 2016 to 2023, PT Adaro's reclamation area experienced high revegetation from an initial 342.53 ha revegetated area, in 2023 the vegetated area increased to 1,234.41 ha. The results of this research show that PT Adaro Energi has successfully revegetated the post-mining land area. In addition, the use of remote sensing technology has the potential to be used in monitoring reclamation areas using vegetation indices and certain algorithms.

Investigating the Impact of the Tsunami at Handeleum Island Resort, Ujung Kulon National Park Using Geospatial Technology

2024-10-02T03:42:59+07:00

Natural disasters are events that often occur anywhere and anytime. The tsunami natural disaster is a tidal wave disaster generated by tectonic earthquakes, volcanic eruptions in the ocean, or landslides that can cause damage, loss and even take lives. Ujung Kulon National Park (UNKT) is one of 12 existing national parks on the island of Java and is home to key species of the Java plain. Biodiversity in the national park is threatened by the volcanic activity of Anak Krakatau. The most recent activity generated a tsunami wave in 2018 that damaged most of the coastal forest ecosystems on the northern Ujung Kulon peninsula. The geographical condition of UNKT which varies from flat, sloping, wavy, hilly, to mountainous, causes the high flora and fauna in the area. The tsunami event will cause considerable damage, especially to the flora and fauna in the area. Affected areas are identified and evaluated using a comparison of the vegetation index Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Vegetation Index (MNDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), Atmospherically Resistant Vegetation Index (ARVI), Specific Leaf Area Vegetation Index (SLAVI), and Green Normalized Difference Vegetation Index (GNDVI), the water index is Augmented Normalized difference water index (ANDWI), Modified Normalized Difference Water Index (MNDWI), and Land Surface Water Index (LSWI), and the Normalized Difference Built-up Index (NDBI) and Index-Based Built-up Index (IBI). The classification shows that there are 1.268,53 ha (1,06% of the total conservation area). The analysis shows that the tsunami had a negative impact on the coastal forest vegetation on the Ujung Kulon peninsula as well as the surrounding settlements. Therefore, these problems need special attention, especially in the UKNT ecosystem. This study is expected to be a consideration for the management of the UKNT area in order to preserve the existing ecosystem to protect the endangered Javan Rhino.

Monitoring Spatiotemporal Changes of Limboto’s Lake Condition Using Sentinel-II MSI Images Based on Google Earth Engine (GEE).

2024-10-02T03:43:40+07:00

Lake Limboto is the largest lake in Gorontalo Province and is a critical lake prioritized for recovery (National Priority Lake). The criticality of Lake Limboto is influenced by various surrounding land use activities that affect the lake's water quality through runoff and possible pollution from domestic waste from surrounding settlements. Changes in the water quality of Lake Limboto are essential for spatial assessment using remote sensing data for efficient periodic monitoring. Spatiotemporal monitoring of Lake Limboto's condition was conducted using Sentinel-2 MSI (MultiSpectral Instrument) satellite imagery and involving the Random Forest (RF) machine learning classification method. RF classification was carried out by mapping water and non-water cover in the 2017 - 2023 timeframe and obtained a classification accuracy of 0.93 (kappa). Based on the monitoring of lake water conditions, the distribution of turbidity in 2017, 2019, 2021, and 2023. Lake water quality in 2017 with dirty water condition class had an area of 0.45 ha and decreased in 2019 (0.04 ha), 2021 (0.03 ha) and 2023 (0.03 ha). The increase in lake water quality and the expansion of water cover in Lake Limboto during the study period indicate the success of lake recovery. It is hoped that this research can be used as a basis for decision-making for protecting the Limboto Lake area.

Hotspot Distribution Assessment on The Peat Hydrological Unit (PHU) in Riau Province

2024-10-05T16:29:03+07:00

Indonesia is the third country with the largest tropical forest area in the world after Brazil and Congo. However, as time goes by the area of forest in Indonesia is decreasing. One of the dominant causes of forest destruction in Indonesia is forest fires. The type of forest that is frequently and susceptible to forest fires is forest on peatlands. Monitoring the distribution of hotspots is an important strategy in preventing forest fires on peatlands. Therefore, this research was carried out to design a platform for monitoring forest and land fires in forests on peatlands using a case study location in Riau Province. This monitoring platform utilizes remote sensing technology which uses NASA FIRMS MODIS hotspot data, BMKG rainfall data, and Sentinel-2 hydrology data. During the period 2005-2023, the most hotspots were found in 2005-2015, while the fewest hotspots were found in 2016-2023. The accumulated hotspot data is found in peat soil (PHU) as many as 138,523 hotspot (77.8%), while in mineral soil (non PHU) there are 39,583 points (22.2%). Riau Province, especially Rokan Hilir, Bengkalis and Pelalawan Regencies, are districts that require special attention in efforts to prevent forest and peatland fires because they have been detected to have the largest distribution of hotspots.