SSRS Journal A: Agro-Environmental Research

Urban Forest Distribution in Metropolitan City at Java Island: A Study of the Application of Google Earth Engine Geospatial Technology

Lenny Eka Nurhawaillah — 2024-10-06

Google Earth Engine (GEE) is a geospatial technology based on cloud computing that has various advantages compared to manual software. Data usage on this platform is integrated with free-access satellite imagery data sources such as ESA's Sentinel-2 Multispectral Instrument (MSI) imagery. In addition, the ability to run a combination of index algorithms (vegetation, water, and built land indices) and Random Forest (RF) classification algorithms can help detect the distribution of urban forests. This research took place from April to May 2022 by taking case studies in several metropolitan cities on Java Island. The combination of indices involving the EVI, SAVI, NDWI, and IBI indices can distinguish forest vegetation from other land covers. This index algorithm successfully detected urban forests spread over five metropolitan cities: Jakarta, Bandung, Semarang, Yogyakarta, and Surabaya. Sentinel-2 MSI imagery with a medium resolution of 10 x 10 meters is considered capable of quickly detecting urban forests in the study area with good classification results.

Data Collection of Macrofungi Diversity at Arboretum Forest in IPB University Dramaga Campus

Nurah Anggraeni — 2024-10-06

Characteristics of forests can affect the species richness of macrofungi. Macrofungi data was collected based on three forest areas in the IPB University Campus Forest: conservation parks, inspiration lakes, and bamboo arboretums. Data was collected by opportunistic methods and by recording macroscopic morphological characters. The results showed that 16 species were identified (1 species of the order Xylariales, 11 species of the order Agaricales, and four species of the order Polyporales). The species with the highest diversity distribution were at station 1 (dominating 80%). This is because station 1 has a higher shade density and more diverse vegetation types. Macrofungi found growing on various substrates ranging from litter (types include Marasmius sp3.), soil (Leucocoprinus sp., Agaricus sp., Collybiopsis sp., Termitomyces sp., and Cystoagaricus sp.), soil mixed with litter (Enrtoloma sp.), african seeds (Xylaria sp.), twigs (Marasmius sp2.), weathered wood (Lentinus sp., Lepiota sp., Marasmius sp1., Coprinellus sp., Ganoderma sp., Amauroderma sp. and Deconia sp.). It is noteworthy that the discovery of Entoloma sp. in this urban forest is a rare occurrence.

Data Indo InaPeat: An Environmental Monitoring Platform on Peatlands Area and PHU (Peat Hydrological Units) using Earth Engine Apps

Hanum Resti Saputri — 2024-10-06

Peat is one of the wetland ecosystems in Indonesia which is often damaged due to unsustainable human intervention. This causes fires in peatlands, thereby eliminating ecological functions which are characterized by changes in land cover. The protection of peat ecosystems in Indonesia relies on hydrological aspects, namely the Peat Hydrological Unit (PHU) to maintain the Ground Water Levels (GWL) to minimize the potential for forest and peat land fires. This research aims to develop the DATAINDO InaPeat platform for monitoring PHU ecosystems in Jambi Province based on changes in land cover and hotspot distribution from 2002 to 2022. Data analysis was carried out by identifying the distribution of hotspots for each type of land cover. Hotspot and land cover data were obtained through the Moderate Resolution Imaging Spectroradiometer (MODIS) Fire Information for Resource Management System (FIRMS) and Landsat 9 OLI 2-TIRS2 imagery imported from the Google Earth Engine (GEE) dataset. The land cover classification was carried out using guided classification in 2021-2023. There are two types of classification used, namely classification to assess the condition of peatland damage (degraded and natural areas) and land cover (swamps, swamp forests, open land, plantations, mixed plantations, settlements, and water bodies). Based on the data analysis, the distribution of hotspots that dominated natural areas was 940 hotspots in 2002, 731 hotspots in 2007, and 651 hotspots in 2012, meanwhile, in degraded areas, 279 and 152 points in 2017 and 2022, respectively. This shows that hotspots caused damage to peatlands, thereby increasing the degraded area to 63.73% of the total PHU area of 874,951.7 ha. These were caused by the opening of oil palm plantations which can reduce GWL, thus potentially triggering forest and peatland fires. Data on hotspots and degraded areas that have been recorded are displayed on the Earth Engine Apps platform in the form of DATAINDO InaPeat. It is hoped that the use of this monitoring platform can become a tool for monitoring the physical condition of peat ecosystems and supporting the sustainable use of peatlands in Indonesia.

Spatio-temporal analysis of Mangroves in Subang Regency using Sentinel-2 TimeSeries Data

Ali Dzulfigar — 2024-10-06

The mangrove forest ecosystem is one of the types of ecosystems that grow in the tidal areas of the ocean and play an essential role in addressing global issues such as climate change. Remote sensing technology can be used to monitor mangrove areas accurately and efficiently. This study aims to detect the spatiotemporal distribution of mangroves from 2017 to 2022 using remote sensing techniques, identify the spectral characteristics and threshold values of the indices involved in mangrove area detection, and determine the accuracy of mangrove area detection in Subang Regency, West Java. Mangrove area detection is carried out using Land Use Land Cover (LULC) classification involving several vegetation, water, and built-up indices to obtain the mangrove area from 2017 to 2022. The research results showed that the mangrove area was 1972.98 ha and distributed in areas A, B, C, and D. Area D showed an increase in mangrove area due to natural succession or planting.The spectral vegetation index tends to increase while the water index and built-up index tend to decrease. MNDWI has the ability to distinguish between mangrove and non-mangrove vegetation. The research results show that there are examples of mangrove succession points (area D) which can be used as a consideration for policy-making to optimize the role and function of mangroves as a natural barrier.

Agricultural and Fishery Activities on Mangrove Ecosystem Area, Bekasi Regency: Exploration Studies of Land Use Conditions in The Coastline Buffer Area

Azelia Dwi Rahmawati — 2024-10-06

As a country with the widest mangrove forest area in the world, Indonesia has potential and challenges in preserving the mangrove ecosystem. Bekasi Regency is an area with a mangrove ecosystem that has experienced degradation, so research needs to be conducted to monitor the mangrove area and uncover land use by coastal communities. This research uses field data (including agricultural and fishing activities) in mangrove forest area in Bekasi Coastline Buffer Area, and remote sensing data from Sentinel-2 MultiSpectral Instrument (MSI) satellite data. In process analysis, using machine learning (ML) algorithm is the Support Vector Machine (SVM) and through in the advanced GEE computing platform. The analysis involves 11 indices consisting of vegetation indices, namely NDI, MNDVI, SAVI, SLAVI, and ARVI; water indices, namely NDWI, LSWI, and ANDWI; and building indices, namely IBI. The results of mangrove mapping obtained an area of 836.91 ha or equivalent to 6.57% of the area of Bekasi Regency and the Overall Accuracy (OA) results reached 90%, and Kappa Statistics (KS) were 0.8. The mapped mangrove areas have great potential in various aspects, especially playing a role in controlling land erosion, protecting against atmospheric disasters, and also contributing to controlling climate change. The silvofishery area which is a balance between mangroves and ponds, has great potential in sustainable land use and coastal ecosystem restoration.This research is expected to encourage policymakers to become one of the strategic policy supports that can quickly restore the mangrove ecosystem, thus controlling the threat of sea-level inundation that adds to the vulnerability area every year.