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All Journal MANAJEMEN HUTAN TROPIKA Journal of Tropical Forest Management Jurnal Silvikultur Tropika (JST) MEDIA KONSERVASI Forum Pasca Sarjana Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) Jurnal Biologi Tropis Jurnal Penelitian Pengelolaan Daerah Aliran Sungai Journal of Environmental Engineering & Waste Management Jurnal Penelitian Kehutanan Bonita Jurnal Sylva Lestari Aceh International Journal of Science and Technology Jurnal Penelitian Kehutanan Wallacea
Nining Puspaningsih
Fakultas Kehutanan Institut Pertanian Bogor Jl. Lingkar Akademik, Kampus IPB Dramaga, PO Box 168, Bogor 16680, Jawa Barat, Indonesia.
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Energy Engineering Environmental Science Immunology & microbiology Materials Science & Nanotechnology Social Sciences

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Developing a Spatial Mathematical Model for Assessing the Rate of Natural Forest Changes Dahlan, Dahlan; Jaya, I Nengah Surati; Saleh, Muhammad Buce; Puspaningsih, Nining; Affan, Muzailin
Aceh International Journal of Science and Technology Vol 12, No 1 (2023): April 2023
Publisher : Graduate School of Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13170/aijst.12.1.31703

Abstract

Establishing a spatial mathematical model that uses diverse data types such as ratio data, interval data, and ordinal and nominal data is a challenge. This paper describes how the mathematical model of the rate of natural forest cover change was developed by considering the causes and/or driving forces that come from the society's biophysical and/or socioeconomic aspects. The main objective of this research is to establish a spatial mathematical model using the environmental and socioeconomic variables that play a significant role in determining the rate of natural forest cover change. From a number of variables considered in the analysis, coupled with any other reason, the rate of natural forest cover change (y), in units of ha per year), this study found that there are 10 potential variables, namely the proximity of the road (x4), the proximity of the river (x5), the proximity of the settlement (x6), proximity from the regency capital (x8), the proximity of the capital city of the district (x9), proximity of the edge of the forest in 2015 (x11), the proximity of the plantation area in 2009 (x12), the proximity of the plantation in 2015 (x13), slope class (x16), and elevation class (x17). The standardization process successfully transformed the non-ratio data type into a ratio data type. Using the standardized data, the study obtained spatially mathematical models that are reliable in estimating the rate of forest cover change, namely y = 0.017 + 0.00040x9 with SR of 17.3% and R2 is 88.0%. The study concludes that the most significant factor affecting the natural forest cover change in the study site is the proximity from sub-district capital (x9). Therefore, a spatial mathematical model can facilitate the government in monitoring forest cover.
Model Development of the Forest Quality Assessment using Second-Order Confirmatory Factor Analysis Zulkarnain; Saleh, Muhammad Buce; Kuncahyo, Budi; Tiryana, Tatang; Puspaningsih, Nining
Jurnal Sylva Lestari Vol. 13 No. 2 (2025): May
Publisher : Department of Forestry, Faculty of Agriculture, University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jsl.v13i2.1064

Abstract

Forest quality plays a crucial role in sustaining the functions of forest ecosystems. This study aims to develop a valid and reliable model for assessing forest quality through six dimensions: forest productivity, forest structure, soil factors, climatic conditions, topography, and anthropogenic factors. Vegetation data were collected from 138 sample plots using a stratified purposive sampling method. Soil, topography, and climate data were obtained from the SoilGrids, DEMNAS, CHIRPS, and NASA POWER websites, respectively. Anthropogenic data were derived from Sentinel-2 imagery. The forest quality assessment model was developed using confirmatory factor analysis (CFA). Results showed that forest structure, forest productivity, soil, and anthropogenic factors are valid and reliable in assessing forest quality, with forest productivity as the primary determinant. However, topographic and climatic factors were not valid for assessing forest quality due to the low variation in topographic and climatic data within the study area. The goodness-of-fit model evaluation indicated a good fit based on criteria including the chi-square, RMSEA, GFI, SRMR, AGFI, TLI, CFI, NFI, and CMIN/DF. Based on the relative weights of each dimension and indicator and using linear additive equations, a mathematical equation for the forest quality index is derived, providing a practical framework for assessing forest quality at the landscape scale, particularly in heterogeneous tropical ecosystems. Keywords: confirmatory factor analysis, forest quality assessment, Rawa Aopa Watumohai National Park, sustainable forest management
Optimal Land Use for Rainfall-Runoff Transformation in Wae Ruhu Watershed Laturua, Aly; Hendrayanto, .; Puspaningsih, Nining
Media Konservasi Vol. 23 No. 1 (2018): Media Konservasi Vol. 23 No. 1 April 2018
Publisher : Department of Forest Resources Conservation and Ecotourism - IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (805.349 KB) | DOI: 10.29244/medkon.23.1.52-64

Abstract

Flooding hit the island of Ambon in 2012 and 2013. Many analyzes has been developed to estimate the cause of the flooding. The study aims topredict optimal land management for reducing run-off. The method is simulation of CN value based on spatial analysis on watershed characteristics.The rainfall can’t be managed by watershed. The level of run-off can be determined by CN value that depends on the type of land cover. The resultshows that the land cover has changed about 90 ha, with the higher rainfall intensity is 2.118 in 2013. The result of simulation indicated that tochange of shrub and bare land, mix dryland forest, and secondary dryland forest with agroforestry. Agroforestry can decrease run-off amount 0,86%.The change of land cover and high rainfall are the main factors that caused the flooding in 2012 and 2013. It is necessary to add a rainfallobservation station so that the observation of surface flow can be done well.Keywords: curve number, land cover change, watershed 
Monitoring Land Cover Change Using Change Vector Analysis (CVA) in Central Bengkulu Regency, Indonesia Tikaputra, Firman; Puspaningsih, Nining; Tiryana, Tatang
Jurnal Biologi Tropis Vol. 25 No. 3 (2025): Juli-September
Publisher : Biology Education Study Program, Faculty of Teacher Training and Education, University of Mataram, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jbt.v25i3.9724

Abstract

Monitoring land cover change is essential for sustainable spatial planning in regions undergoing rapid development. This study aimed to evaluate Land cover changes in Central Bengkulu Regency during the 2008–2024 period were analyzed using the Maximum Likelihood Classification (MLC) method, which identified nine land cover classes with moderately good accuracy (Overall Accuracy of 66.7% and Kappa coefficient of 61.3% for Landsat 5 imagery in 2008, and 58.0% OA and 50.1% Kappa in 2024). Significant land cover dynamics were observed, with notable increases in shrub and plantation areas, and substantial decreases in primary dryland forest, rice fields, and open land. Change Vector Analysis (CVA), combining NDVI and NDBI indices, proved effective in detecting both the magnitude and direction of land cover change. The largest change category was "Stable/Not Significant" (±65,000 ha), followed by "Rehabilitation/Recovery" (±24,700 ha), and "Urban Development" (±10,800 ha). These changes reflect the strong influence of socio-economic drivers such as population growth, land conversion for oil palm plantations, and settlement expansion, as well as ecological factors such as degradation and natural succession. The results indicate that the integrative approach of CVA and spectral indices can serve as a reliable spatio-temporal analysis tool to support spatial planning and sustainable land management policies, particularly in newly established regions vulnerable to land conversion.
Developing a Decision Tree Algorithm for Detecting Agroforestry and Monoculture Coffee Plantations Using Landsat 8 Imagery: A Case Study inBandung Regency, Indonesia Adhiguna, Agasta; Surati Jaya, I Nengah; Puspaningsih, Nining
Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) Vol 15 No 6 (2025): Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (JPSL)
Publisher : Pusat Penelitian Lingkungan Hidup, IPB (PPLH-IPB) dan Program Studi Pengelolaan Sumberdaya Alam dan Lingkungan, IPB (PS. PSL, SPs. IPB)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jpsl.15.6.1009

Abstract

Kopi arabika merupakan komoditas unggulan di Kabupaten Bandung, Provinsi Jawa Barat, Indonesia, yang memiliki potensi pengembangan yang besar dengan menggunakan sistem penanaman agroforestri. Oleh karena itu, penelitian ini bertujuan untuk mendeskripsikan pengembangan algoritma pohon keputusan dengan mengkombinasikan variabel spektral yang berasal dari citra Landsat 8 dan variabel sosio-geo-biofisik. Variabel yang dikaji meliputi citra sintetis dan faktor sosio-geo-fisik, seperti elevasi, kemiringan lereng, jarak dari jalan dan sungai, jarak dari permukiman, kepadatan penduduk, jarak dari desa, dan peta tutupan lahan yang ada. Algoritma decision tree machine learning (DTML) dikembangkan untuk mendeteksi distribusi spasial penanamn kopi agroforestri dan kopi monokultur di Kabupaten Bandung. Parameter pohon keputusan yang diuji untuk mengidentifikasi bobot masing-masing variabel adalah gain ratio, information gain, dan gini indeks. Sementara itu, metode brute force diterapkan untuk memilih variabel yang paling signifikan dalam model. Hasil penelitian menunjukkan bahwa variabel yang paling signifikan untuk mengidentifikasi agroforestry dan monokultur kopi adalah kombinasi dari variabel spektral, biogeofisik, dan tutupan lahan, dengan kriteria terbaik adalah information gain. Penggunaan peta penggunaan dan tutupan lahan yang ada merupakan variabel yang paling berpengaruh dalam model. Dalam konteks ini, akurasi keseluruhan (OA) yang diperoleh adalah 84,65%, dengan akurasi kappa (KA) sebesar 82,60%.
Co-Authors Adhiguna, Agasta Agus Rudi Darmawan Andini Tribuana Tunggadewi, Andini Tribuana Atfi Indriany Putri Budi Kuncahyo Budi Kuncahyo Dahlan Dahlan Dahlan Dahlan Donny Satria Gatot Setiawan Gatot Setiawana Hariaji Setiawan Hendrayanto . Hengki Simanjuntak I Nengah Surati Jaya Kukuh Murtilaksono Kusnadi Lailan Syaufina Laturua, Aly Lukman Hakim Makin Basuki Mohammad Malik Muhammad Buce Saleh Muzailin Affan Muzailin Affan Naik Sinukaban Omo Rusdiana Rahimahyuni Fatmi Noor'an Ricca Rohani Hutauruk Rosaprana, Wanella Tatang Tiryana Tikaputra, Firman Witno Witno Yadi Setiadi Yudi Armanda Syahputra Zulkarnain