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Water Provision Benefits from Karst Ecosystems Threatened by Limestone Mining: An Example for Watuputih Groundwater Basin, North Kendeng Mountain, Indonesia

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SSRN 2024年

作者： Sumarga, Elham Willemen, Louise Rosleine, Dian Fitria, Faradhitya Syahida Agatha, Karla Sinaga, Novayeni School of Life Sciences and Technology Institut Tekonologi Bandung Jalan Ganesa 10 Bandung40132 Indonesia University of Twente PO Box 217 Enschede7500 AE Netherlands Forestry Engineering Study Program School of Life Sciences and Technology Institut Teknologi Bandung Sumedang45363 Indonesia

Karst ecosystems hold immense value as providers of diverse ecosystem services. Protecting these ecosystems is a challenge, as they face significant threats from degradation and land-use conversion, including limestone mining. This study investigates the water provision service derived from karst ecosystems threatened by limestone mining in the Watuputih groundwater basin, Central Java, Indonesia. Water supply was quantified by measuring water discharge rates from major springs around the basin. Water usage was quantified for household consumption, agriculture, and recreation. This study also measured water infiltration rates in mining and non-mining areas to indicate the effects of limestone mining to hydrological processes. This study identified three principal springs around the Watuputih groundwater basin: Sumber Seribu spring, Brubulan spring, and Kalutan spring, with discharge rates of 1,080 liters per second, 85 liters per second, and 0.76 liters per second, respectively. A local water company extracts water from Sumber Seribu spring at a rate of 80 liters per second, catering to the water demands of approximately 57,600 individuals. The combined water supply from the three springs can irrigate approximately 1,594 hectares of rice fields. Water from Sumber Seribu spring also supports recreational activities in Sumber Semen recreation park, attracting an average of 19,173 visitors per year. This study revealed a complete impairment of soil's water infiltration capacity in limestone mining areas. These findings underscore the pressing need to safeguard the Watuputih groundwater basin and to mitigate the detrimental effects of limestone mining on hydrological processes. © 2024, The Authors. All rights reserved.

关键词： Limestone

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Dispersion of Surface Floating Plastic Marine Debris from Indonesian Waters Using Hydrodynamic and Trajectory Models

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SSRN 2023年

作者： Kisnarti, Engki A. Ningsih, Nining Sari Putri, Mutiara R. Hendiarti, Nani Mayer, Bernhard Study Program of Oceanography Faculty of Engineering and Marine Science University of Hang Tuah Indonesia Research Group of Oceanography Institute Technology of Bandung Indonesia Deputy for Coordination of Environment and Forestry Management Coordinating Ministry for Maritime and Investment Affairs Indonesia Institute of Oceanography University of Hamburg Hamburg Germany

Plastic waste has become the most significant component of marine debris, while research on traces of marine plastic waste related to the condition of Indonesian waters is still limited. Therefore, this study examines the movement patterns of plastic marine debris in Indonesian waters. Simulations were carried out for one year (2013) using the HAMSOM hydrodynamic and Lagrange trajectory. The approach treated the simulated particles as conservative particles floating at sea. Even though the percentage is small (±16%), Indonesia contributes to transboundary marine debris in the Pacific Ocean, Indian Ocean, and the South China Sea due to the influence of the south equatorial currents moving from north and south of Indonesia. Most of the plastic marine debris remains in Indonesian waters (±80-84%) and moves back and forth due to the influence of the monsoon currents. Therefore, the Java Sea becomes a crossing point and accumulates plastic marine debris. © 2023, The Authors. All rights reserved.

关键词： Hydrodynamics

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Hypocenter dimension of 7.5 mw Palu earthquake using fractal approach 2

Hypocenter dimension of 7.5 mw Palu earthquake using fractal...

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2nd International Conference of Interdisciplinary Research on Green Environmental Approach for Sustainable Development, ICROEST 2020

作者： Rasimeng, Syamsurijal Alimuddin Rustadi Sinambela, Rudy Z. Mandang, Idris Bakri, Samsul Zaenudin, Ahmad Department of Geophysical Engineering Faculty of Engineering University of Lampung Indonesia Disaster and Mitigation Center Faculty of Engineering University of Lampung Indonesia Study Program of Geophysics Faculty of Mathematics and Natural Science Mulawarman University Indonesia Department of Forestry Graduate Program of Environmental Sciences University of Lampung Indonesia

Research on the Palu earthquake in the period of August to October 2018 has been carried out in the Palu Koro Fault (PKF) zone with a hypocenter of 5-20 km. This study aims to determine the fractal dimensions of these earthquakes based on the relationship between logarithmic frequency and earthquake magnitude correlation introduced by Gutenberg-Richter. Statistical correlation between logarithmic frequency and earthquake magnitude was calculated using the inversion approach. Based on the calculation results obtained fractal dimension value of 1.0378, which describes the seismicity pattern at a depth of 5-20 km. It indicates that the earthquakes triggered by the movement in rupture segments which seen as points along the PKF zone. © Published under licence by IOP Publishing Ltd.

关键词： Earthquakes

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Advanced Blb Disease Assessment in Paddy Fields Using Multispectral Uav Data and Patch Fragmentation Metrics

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SSRN 2024年

作者： Wijayanto, Arif Kurnia Prasetyo, Lilik Budi Hudjimartsu, Sahid Agustian Sigit, Gunardi Hongo, Chiharu Department of Forest Resource Conservation and Ecotourism Faculty of Forestry and Environment IPB University Bogor16680 Indonesia Ronpaku Fellow Tokyo102-0083 Japan Environmental Research Center IPB University Bogor16680 Indonesia Study Program of Information Technology Faculty of Science and Engineering Ibn Khaldun University Bogor16129 Indonesia Department Agriculture Forestry and Bioresources Seoul National University Seoul08826 Korea Republic of Regional Office of Food Crops Service West Java Province Cianjur43283 Indonesia Chiba University Chiba263-8522 Japan

This study presents a new method for assessing bacterial leaf blight (BLB) in paddy fields using multispectral UAV (Unmanned Aerial Vehicle) data through patch fragmentation analysis. Unlike traditional pixel-based methods, which often fail to capture the spatial context, our approach treats pixels as objects and analyzes their spatial relationships to evaluate BLB severity. We used six patch fragmentation metrics derived from binarized vegetation indices to classify BLB damage scores using a random forest algorithm. This method achieves high accuracy (AUC of 0.938) with a 0.5-meter sampling window, offering a more detailed assessment than conventional pixel-based analysis. This study highlights the benefits of incorporating the spatial context and improving BLB monitoring and management. This innovative approach allows agricultural stakeholders to gain valuable insights into the extent and severity of BLB disease infestation, enabling targeted intervention strategies for disease management and mitigation. © 2024, The Authors. All rights reserved.

关键词： Pixels

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Textural Features for Blb Disease Damage Assessment in Paddy Fields Using Drone Data and Machine Learning: Enhancing Disease Detection Accuracy

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SSRN 2024年

作者： Wijayanto, Arif Kurnia Prasetyo, Lilik Budi Hudjimartsu, Sahid Agustian Sigit, Gunardi Hongo, Chiharu Department of Forest Resource Conservation and Ecotourism Faculty of Forestry and Environment IPB University Bogor16680 Indonesia Ronpaku Fellow Tokyo102-0083 Japan Environmental Research Center IPB University Bogor16680 Indonesia Study Program of Information Technology Faculty of Science and Engineering Ibn Khaldun University Bogor16129 Indonesia Regional Office of Food Crops Service West Java Province Cianjur43283 Indonesia Chiba University Chiba263-8522 Japan

Detecting Bacterial Leaf Blight (BLB) in paddy fields is one of the essential issues in Indonesia to prevent losses in rice production. This study reports the effectiveness of textural analysis using drone-acquired data over paddy fields in West Java, evaluating the potential of integrating textural and thermal characteristics. We used drone data combined with machine learning techniques to distinguish different levels of BLB impact. The normalized difference texture index derived from Haralick textural features was used as a predictor. The findings revealed that incorporating textural features enhanced the disease detection accuracy significantly compared with the traditional approach based on spectral indices. The random forest algorithm, which merges texture and vegetation indices, achieved a classification accuracy of 0.984. The proposed method offers a robust, noninvasive solution for BLB disease detection, contributing substantially to safeguarding crop yields and mitigating global food security issues. © 2024, The Authors. All rights reserved.

关键词： Drones

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The NDVI algorithm utilization on the google earth engine platform to monitor changes in forest density in mining area

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IOP Conference Series: Earth and Environmental Science 2021年第1期886卷

作者： Munajat Nursaputra Siti Halimah Larekeng Nasri Andi Siady Hamzah Forestry Study Program Faculty of Forestry Hasanuddin University Makassar Indonesia Forest Engineering Study Program Faculty of Forestry Hasanuddin University Makassar Indonesia Forest Conservation Study Program Faculty of Forestry Hasanuddin University Makassar Indonesia

Periodic forest monitoring needs to be done to avoid forest degradation. In general, forest monitoring can be conducted manually (field surveys) or using technological innovations such as remote sensing data derived from aerial images (drone results) or cloud computing-based image processing. Currently, remote sensing technology provides large-scale forest monitoring using multispectral sensors and various vegetation index processing algorithms. This study aimed to evaluate the use of the Google Earth Engine (GEE) platform, a geospatial dataset platform, in the Vale Indonesia mining concession area to improve accountable forest monitoring. This platform integrates a set of programming methods with a publicly accessible time-series database of satellite imaging services. The method used is NDVI processing on Landsat multispectral images in time series format, which allows for the description of changes in forest density levels over time. The results of this NDVI study conducted on the GEE platform have the potential to be used as a tool and additional supporting data for monitoring forest conditions and improvement in mining regions.

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Canopy cover estimation based on support vector regression derived from Lidar & Landsat 8 OLI 40

Canopy cover estimation based on support vector regression d...

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40th Asian Conference on Remote Sensing: Progress of Remote Sensing Technology for Smart Future, ACRS 2019

作者： Hudjimartsu, Sahid Tampinongkol, Felliks Rudianto, Yoga Setiawan, Yudi Prasetyo, Lilik Budi Forests2020 Programme Environmental Analysis and Spatial Modelling Laboratory Department of Forest Resource Conservation Faculty of Forestry Bogor Agricultural University Bogor West Java Indonesia Geoinformatic of Informatics Engineering Study Program Faculty of Engineering and Science Ibn Khaldun University Jalan KH Soleh Iskandar KM 2 Tanah Sereal Bogor West Jawa Indonesia

Light detection and ranging (LiDAR) provides geospatial information with massive amounts of data to use in a variety of applications. Canopy cover percentage of tree crown is one application that is estimated through LiDAR data. This study aims to estimate a canopy cover derived from LiDAR used as reference data with multispectral Landsat 8 OLI. Statistical or machine learning approaches can be used to estimate a canopy cover. In statistical inference, the form of distribution chosen by the analyst and parameter is estimated from the data. This will be a problem in the construction function for the National scale, due to many ecosystems and then will have many functions for each ecosystem. Machine learning (ML) methods, in contrast, are various kinds of algorithms that are used to learn the mapping function or classification rules inductively, directly from the training data. ML can solve problems related to big data and global method and it will be used as a methodical approach to estimate canopy cover on the National scale. In this study, we use a support vector regression (SVR) as ML method to estimate the canopy cover. The estimated percentage of canopy cover will be used as one of the parameters to classify a forest. © 2020 40th Asian Conference on Remote Sensing, ACRS 2019: "Progress of Remote Sensing Technology for Smart Future". All rights reserved.

关键词： Ecosystems

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Effect of heat moisture treatment on psychochemical modification of sago starch

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AIP Conference Proceedings 2023年第1期2596卷

作者： Diyah Yumeina Kasmira Makkarennu Agricultural Engineering Study Program Faculty of Agriculture Hasanuddin University Jl. Perintis Kemerdekaan km. 10 Makassar Indonesia Faculty of Forestry Hasanuddin University Jl. Perintis Kemerdekaan km. 10 Makassar Indonesia

Sago starch produced in South Sulawesi has limited applications, including the loss of viscosity under low pH conditions, high temperature, or mechanical treatment. Physical modification to change the physicochemical characteristic of sago starch without destroying its granular structure is Heat-Moisture Treatment (HMT). HMT method was heating temperature variation treatment. During HMT, sago starch granular at low moisture levels is heated. This research of engineering technology means to physically modify sago starch by using different Heat Moisture Treatment (HMT), which in due course obtained sago starch with physiochemistry appropriate for specific application and consequently could be manufactured as raw materials for popular foods such as bread, various kinds of noodles, and biscuits.

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An assessment of high carbon stock and high conservation value approaches in mining area

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IOP Conference Series: Earth and Environmental Science 2021年第2期807卷

作者： S H Larekeng M Nursaputra Nasri A S Hamzah A S Mustari A R Arif A P Ambodo Y Lawang A Ardiansyah Laboratory of Biotechnology Forestry Engineering Study Program Faculty of Forestry Universitas Hasanuddin Makassar 90245 Indonesia Laboratory of Forestry Planning and Information System Forestry Science Study Program Faculty of Forestry Universitas Hasanuddin Makassar 90245 Indonesia Laboratory of Conservation of Forest Resources and Ecotourism Forestry Science Study Program Faculty of Forestry Universitas Hasanuddin Makassar 90245 Indonesia Faculty of Engineering Universitas Hasanuddin Gowa 91711 Indonesia Faculty of Mathematics and Natural Sciences Universitas Hasanuddin Makassar 90245 Indonesia Vale Indonesia Sorowako East Luwu 92983 Indonesia

The impact of forest degradation due to open-pit mining activities causes sustainable ecological damage. Mining activities that are not organized due to the mineral exploration process have resulted in many forest areas experiencing degradation. This degradation certainly reduces the level of carbon sequestration in the area, impacting the sustainability of environmental functions around the mining area. The high carbon stock (HCS) approach is a way to help companies implement their 'no deforestation' commitments. The high carbon stock approach will identify forests that must be protected and land that can be developed. Forests with high carbon stocks are maintained because they function as carbon stores, habitats for biodiversity, and provide the necessities of life for local communities. Therefore, this study aims to obtain data on the potential for carbon stocks and create a spatial model for distributing carbon content in the concession area of PT. Vale Indonesia can later be determined as high conservation value (HCV) areas using the high carbon stock approach. Measurement of carbon content in the study used an allometric equation that has been developed, then the distribution of carbon was made using the spatial vegetation index model obtained from Sentinel 2 imagery. The results showed an area of 30,526.49 (42.97%), which had high carbon stock with an average carbon stock of 106.09 ton/ha, which needed to be maintained as an area of high conservation value in the mining area of PT. Vale Indonesia.

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Livelihood strategies of cocoa farmer households in Polewali Mandar district, West Sulawesi

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AIP Conference Proceedings 2024年第1期3098卷

作者： P. Astaman A. N. Hikmah M. Dassir A. R. Siregar H. Hariyadi St Aisyah Agriculture Science Program Graduate School Hasanuddin University Makassar Indonesia Faculty of Agriculture Muhammadiyah University of Sinjai Sinjai Indonesia Agribusiness Study Program Muhammadiyah Institute of Technology and Business Polewali Mandar Indonesia Faculty of Forestry Hasanuddin University Makassar Indonesia Faculty of Animal Husbandry Hasanuddin University Makassar Indonesia Engineering Faculty Muhammadiyah University of West Sumatera Medan Indonesia Faculty of Agriculture University of State Gorontalo Gorontalo Indonesia

Cocoa farming households carry out various livelihood strategies to maintain their survival. The decline in cocoa production is caused by pests and diseases, climate change, bad seeds, and the farmers’ cocoa plants are old. These constraints will affect the condition of cocoa farmer households. This study aims to identify the livelihood strategies and structures carried out by cocoa farming households. The method used in this study uses a quantitative approach using a questionnaire supported by a qualitative approach through in-depth interviews. The research respondents consisted of 30 cocoa farming households in Polewali Mandar Regency. The results of this study show that the livelihood strategies used by cocoa farmer households are engineering income sources and income diversification. The average livelihood structure of cocoa farmer households comes from the non-farm sector.

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