Geospatial Approach for the Analysis of Forest Cover Change Detection using Machine Learning

Reddy, R. S.; Babu, G. Suresh; Reddy, A. Rama Mohan

doi:10.19184/geosi.v5i3.20157

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…This problem cannot seem to be eliminated, but efforts to minimize can be done by studying the causal factors and the driving force factors of deforestation that are influenced by various biophysical, socio-economic, cultural, and political factors. Several studies have studied the causes and the driving force of deforestation (Agaja et al, 2020;Fagariba et al, 2018;Prasetyo et al, 2009;Purwanto et al, 2015;Reddy et al, 2020;Sulistiyono et al, 2015;Wijaya et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Typology and Deforestation of Lowland Tropical Rain Forests

Dahlan¹,

Jaya²,

Saleh³

et al. 2023

IJASER

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Deforestation is a global issue that attracts international community attention. This paper describes the behavior of deforestation of lowland tropical rain forests within the period of 2009-2015. The main objective of this research is to develop a typology of deforestation rates and identify trends in deforestation according to land conversion groups. To develop a behavioural typology of the deforestation rates, this study investigates whether the unit analysis of the sub-districts can be classified or grouped into some typology classes. From the analysis by considering the sub-district level as the smallest unit analysis, the study found that the rate of deforestation of lowland tropical rain forests in West Kalimantan Province is similar over the province, where the sub-districts could not be grouped according to the biophysical and socio-economic aspects. This research also finds that the trend of deforestation in the lowland tropical rain forests of West Kalimantan by land conversion group is: (1) unauthorized land conversion of 1,256,862.80 ha (65.28%); (2) semi-authorized land conversion of 308,247.14 ha (16.01%); and (3) authorized land conversion of 360,231.36 ha (18.71%). Unauthorized deforestation reached 65.28%. Therefore, it is necessary to increase the capacity of the community towards the authorized use of forests and increase supervision from the government.

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Section: Introductionmentioning

confidence: 99%

Typology and Deforestation of Lowland Tropical Rain Forests

Dahlan¹,

Jaya²,

Saleh³

et al. 2023

IJASER

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“…Soil quality analysis based on remote sensing is needed to assess land variability (Reddy et al, 2020). Meanwhile, remote sensing technology is commonly used to classify land, based on several criteria with high accuracy (Hore et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Mapping of Soil Quality Index (SQI) for Paddy Fields Using Sentinel-2 Imagery, Laboratory Analysis, and Principal Component Analysis

et al. 2021

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The use of intensive chemical inputs causes lower availability of nutrients, organic matter, cation exchange capacity, and soil degradation.Therefore, this study aims to assess the soil quality index (SQI) for paddy fields in Jember, East Java, Indonesia. Input data for this study consist of land cover (interpreted from the Sentinel-2 image), soil type, and slope maps. Furthermore, the procedure to calculate soil quality index (SQI) include (1) spatial analysis to create the land unit, (2) preparation of soil sampling, (3) soil chemical analysis, (4) principal component analysis (PCA), and (5) reclassifying soil quality index (SQI). The PCA results showed that three variables i.e., % sand, total- P, and % silt were strongly correlated to SQI, while three classes namely very low, low, and medium of SQI were sufficiently used to describe the spatial variability of the paddy field. Furthermore, approximately 41.14% of the paddy field area were classed as very low while 52.23%, and 6.63% were categorized as low and medium SQI respectively. Based on the results, about 93.37% of paddy fields in Jember Regency still require improvement in soil quality via the addition of ameliorants such as organic fertilizers to increase quality and productivity. This application needs to focus on areas with very low-low quality hence, the quality increased to the medium category. Keywords : Mapping; Soil Quality Index (SQI); PCA; Paddy field Copyright (c) 2021 Geosfera Indonesia and Department of Geography Education, University of Jember This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License

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Ormancılıkta makine öğrenmesi kullanımı

Eker

Alkiş

Ucar

et al. 2023

Turkish Journal of Forestry | Türkiye Ormancılık Dergisi

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Gelişen teknolojiyle beraber diğer disiplinlerde olduğu gibi ormancılıkta da geleneksel uygulamaların daha ekonomik, etkin, hızlı ve kolay yapılabilmesi için yenilikçi yaklaşımların kullanımına talepler ve ihtiyaçlar artmaktadır. Özellikle son dönemde ortaya çıkan ormancılık bilişimi, hassas ormancılık, akıllı ormancılık, Ormancılık (Forestry) 4.0, iklim-akıllı ormancılık, sayısal ormancılık ve ormancılık büyük verisi gibi terimler ormancılık disiplinin gündeminde yer almaya başlamıştır. Bunların neticesinde de makine öğrenmesi ve son dönemde ortaya çıkan otomatik makine öğrenmesi (AutoML) gibi modern yaklaşımların ormancılıkta karar verme süreçlerine entegre edildiği akademik çalışmaların sayısında önemli artışlar gözlenmektedir. Bu çalışma, makine öğrenmesi algoritmalarının Türkçe dilinde anlaşılırlığını daha da artırmak, yaygınlaştırmak ve ilgilenen araştırmacılar için ormancılıkta kullanımına yönelik bir kaynak olarak değerlendirilmesi amacıyla ortaya konulmuştur. Böylece çeşitli ormancılık faaliyetlerinde makine öğrenmesinin hem geçmişten günümüze nasıl kullanıldığını hem de gelecekte kullanım potansiyelini ortaya koyan bir derleme makalesinin ulusal literatüre kazandırılması amaçlanmıştır.

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Geospatial Approach for the Analysis of Forest Cover Change Detection using Machine Learning

Cited by 3 publications

References 15 publications

Typology and Deforestation of Lowland Tropical Rain Forests

Typology and Deforestation of Lowland Tropical Rain Forests

Mapping of Soil Quality Index (SQI) for Paddy Fields Using Sentinel-2 Imagery, Laboratory Analysis, and Principal Component Analysis

Ormancılıkta makine öğrenmesi kullanımı

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