Relationship between land degradation, biophysical and social factors in Lekso Watershed, East Java, Indonesia

Lestariningsih, Iva Dewi; Widianto, W; Agustina, Christanti; Sudarto, S; Kurniawan, Syahrul

doi:10.15243/jdmlm.2018.053.1283

Cited by 4 publications

(1 citation statement)

References 18 publications

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“…Meanwhile, the mTPI and Landforms data obtained from Conservation Sciences Partners have the spatial resolution of 270 m and 90 m, respectively(Theobald et al 2015). (b) Biophysical variablesBiophysical is one of the important and relevant variables that have a contribution related to the phenomenon of land degradation in an environment(Lestariningsih et al 2018;Jiménez-Rodríguez et al 2022). In addition, monitoring of biophysical variables over a long period of time is very important to overcome challenges regarding productivity, climate change impacts and food security in a region(Kandasamy 2014…”

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Prediction and mapping of land degradation in the Batanghari watershed, Sumatra, Indonesia: utilizing multi-source geospatial data and machine learning modeling techniques

Yulianto¹,

Raharjo²,

Pramono³

et al. 2023

Model. Earth Syst. Environ.

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The study area is a tropical environment located in the Batanghari watershed, Sumatra, Indonesia. The existence of the environmental problems and damages in the study area can be identi ed based on land degradation. It can be interpreted as a complex process and is in uenced by human activities, climate change, and natural events. This study proposes the latest Geospatial Arti cial Intelligence (Geo-AI) model using multi-sources geospatial data that is speci cally used to address challenges and phenomena related to the identi cation of land degradation in the study area. The novelty of this study is that it is the rst time to integrate the 6 (six) main variables of multi-source geospatial data -Topographical, Biophysical, Bioclimatic, Geoenvironmental, Global human modi cation, and Accessibility -in predicting potential land degradation in the tropical environment, such as Indonesia.Machine learning-based prediction Support Vector Machine (SVM), Minimum Distance (MD), Classi cation and Regression Trees (CART), Gradient Tree Boost (GTB), Naïve Bayes (NB), Random Forest (RF) algorithms were used to predict and to map land degradation in the study area. The overall accuracy of the results of comparison and evaluation of machine learning-based predictions on the RF, CART, GTB, SVM, NB, and MD in the study area are 86.2%, 85.8%, 81.2%, 52.8%, 36.3%, and 34.5%, respectively. Therefore, the study concluded that the RF, CART, and GTB algorithms are proposed to be applied to produce land degradation map in the study area.

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confidence: 99%

Prediction and mapping of land degradation in the Batanghari watershed, Sumatra, Indonesia: utilizing multi-source geospatial data and machine learning modeling techniques

Yulianto¹,

Raharjo²,

Pramono³

et al. 2023

Model. Earth Syst. Environ.

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Evaluation on biophysical carrying capacity to support land rehabilitation planning in the upstream watershed

Supangat¹,

Wahyuningrum²

2021

IOP Conf. Ser.: Earth Environ. Sci.

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A comprehensive Soil and Water Conservation (SWC) plan is needed, supported by information on the condition of the Biophysical Carrying Capacity (BCC) of the watershed so that land rehabilitation activities will be right on target. The research aims to evaluate the BCC to support the SWC planning for the upstream watershed. The research area was in the Naruan Micro Watershed (NMW), the upstream of Keduang Sub Watershed, the Bengawan Solo Watershed. The analysis of the BCC used evaluation criteria based on the Ministry of Forestry Regulation (PerMenhut) No. P.61/Menhut-II/2014 concerning monitoring and evaluation of watershed management. The evaluation showed that the BCC in the research area was in the “Bad” category with a value of 128.0. It means that the watershed needs to be restored, particularly in terms of biophysical features. Several parameters indicated a poor category, i.g. the land aspect such as Percentage of Degraded Land (PDL) and Erosion Index (EI) parameters, while in the water system aspect such as the Flow Regime Coefficient (FRC), Annual Flow Coefficient (AFC), and Sediment Load (SL) parameters. These five parameters must become a concern and an important starting point for land rehabilitation planning in the form of SWC measures.

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Extent and drivers of land productivity degradation at the territorial level in Eastern DR Congo

Nteranya,

Kiplagat,

Ucakuwun

et al. 2024

J. Degrade. Min. Land Manage.

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Land degradation is a global environmental problem that affects the livelihoods of communities. Understanding the extent and causative factors of this phenomenon is important to design the cause-targeted strategies to cope with this problem. This study aimed to assess the effects of biophysical and socio-economic factors on the occurrence of land productivity degradation at the territorial level in Eastern DR Congo, using the Kalehe territory as a case study. The land productivity dynamics were determined based on the trend analysis of Landsat-based NDVI annual composites over the 1987-2020 period. Additionally, a binary logistic regression model was developed to identify the determinant factors and to map the susceptibility of this degradation at the territorial level. The results revealed that 31.25% (1303.175 km2) of the territory had experienced a decrease in land productivity, with 10.8% (453.83 km2) characterized by a significant decrease at the confidence level of 90%, representing the hotspots area of land productivity degradation where conservation actions should be prioritized. This decrease in land productivity is location-specific as it depends on the land cover types and health zones. It is likely to occur at high altitudes, in areas close to artisanal mining sites, rivers, and administrative centers. These results imply that policy interventions to cope with the problem of land productivity degradation should be developed following the heterogeneity of the landscape, and sustainable land management practices that take into account both the biophysical and socio-economic drivers of land degradation should be implemented in this region to achieve the land degradation neutrality by 2030.

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Relationship between land degradation, biophysical and social factors in Lekso Watershed, East Java, Indonesia

Cited by 4 publications

References 18 publications

Prediction and mapping of land degradation in the Batanghari watershed, Sumatra, Indonesia: utilizing multi-source geospatial data and machine learning modeling techniques

Prediction and mapping of land degradation in the Batanghari watershed, Sumatra, Indonesia: utilizing multi-source geospatial data and machine learning modeling techniques

Evaluation on biophysical carrying capacity to support land rehabilitation planning in the upstream watershed

Extent and drivers of land productivity degradation at the territorial level in Eastern DR Congo

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