The Machine Learning to Detect Drought Risk in Central Java Using Landsat 8 OLI Remote Sensing Images

Prasetyo, Sri Yulianto Joko; Hartomo, Kristoko Dwi; Paseleng, Mila Chrismawati; Candra, Dian Widiyanto; Simanjuntak, Bistok Hasiholan

doi:10.1109/icst47872.2019.9166197

Cited by 8 publications

(7 citation statements)

References 26 publications

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“…Perubahan iklim yang sangat berdampak pada kawasan daratan dan kawasan laut dengan tingkat dan sifat adaptasi yang berbeda. Penelitian tentang Climate Change (CC) yang dikaitkan dengan adaptasi atau vulnerability dari kawasan daratan telah banyak dilakukan seperti penelitian tentang dampak pemanasan global terhadap bencana alam dan kekeringan(Yulianto Joko Prasetyo et al 2018). Penelitian tentang dampak CC terhadap ketahanan vegetasi hutan (Webb et al 2005), (Luo et al 2020), (Eigentler and Sherratt 2020), (Nazir et al 2018), (Trebicki 2020), (Mendes et al 2020).…”

Section: Pendahuluan (Arial 11pt Bold)unclassified

Pola Keterkaitan Perubahan Distribusi Kepadatan Vegetasi dengan Penggunaan Lahan DAS Pesisir, Case Study : DAS Garang

Sudarmanto¹,

Susilo²,

Rofi’i³

2023

ijsp

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<p><em>The diversity and density of vegetation is key in managing sedimentation throughout the watershed, especially in the central and downstream regions. Measures of vegetation diversity and density that can be used are the Normalized Difference Vegetation Index (NDVI) which is a measure of vegetation greenness (chlorophyll levels), Normalized Difference Water Index (NDVI) to measure vegetation wetness levels, and soil adjusted vegetation index (SAVI) for low canopy vegetation cover. For watersheds that stretch long to downstream or coastal areas, the measure of land diversity needs to be supplemented by using the Normalized Difference Built-Up Index (NDBI) indicator that is useful for land use planning. Information on this vegetation index can be obtained by processing a satellite imagery map. The benefits of processing vegetation indexes in watershed management are becoming increasingly important in the era of climate change, especially with regard to efforts to harmonization of relationships between environmental elements that include the diversity of the interests of the resident population. That's why this research was done in order to find the link between natural factors including the impacts of climate change and humans. The test began by looking for changes in the NDVI, NDWI, SAVI, and NDBI indexes from the last 5 years with the Global Indicator Spatial Association (GISA) analysis using Moran's I Global Index, followedby looking for the association with the pattern of population change and the percentage of the area of the settlement. The results showed a link between moran's I Global index change pattern of population change and the spread of residential developments. The tendency of the distribution of this settlement area is an important point in analyzing the influence of its dispersal patterns in a watershed so that the continued impact of potential erosion and sedimentation triggered will be the main consideration in watershed management.</em></p>

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Section: Pendahuluan (Arial 11pt Bold)unclassified

Pola Keterkaitan Perubahan Distribusi Kepadatan Vegetasi dengan Penggunaan Lahan DAS Pesisir, Case Study : DAS Garang

Sudarmanto¹,

Susilo²,

Rofi’i³

2023

ijsp

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“…SAVI and NDVI using the steps described in the materials and methods chapter, are presented in Figures 9,10,11,and 12. Finally, the data presented, including the distribution of residentials, inhabitants, and vegetation indices, were calculated by GMI using R Studio. The R Studio program script used is as follows: Script R for Global Moran's I #SPATIAL AUTOCORRELATION# library(maptools) library(spdep) library(sf) location <-read_sf("F:/Project/subdistrict_garang_watershed.shp") plot(location) lokasi.q.nb <-poly2nb(location,queen=TRUE) lokasi.b <-nb2listw(location.q.nb,style="B") var <-location$r2 #morans I Global moran.test(var, location.b, alternative="two.sided") mI.norm <-moran.test(var, location.b, randomisation=TRUE) mI.norm$estimate [1] c(as.matrix(mI.norm$estimate [1])) mI_perm99 <-moran.mc(var, location.b, nsim=99)…”

Section: The Changes Of Residentials Inhabitants and Vegetation Indic...mentioning

confidence: 99%

“…The role of vegetation in climate change adaptation is crucial, especially in maintaining the sustainability of watershed ecosystems in the face of rising temperatures, land droughts, landslides, and erosion. Many studies on climate changes in association with the adaptation or vulnerability of mainland areas have been widely conducted, such as the impact of global warming on natural disasters and droughts [1], the impact of climate changes on forest vegetation resilience [2][3][4][5][6], the impact of climate changes on human behaviours [7] [8] [9] [10], the relationship of climate changes with the mainland vulnerability by combining other causing factors with unfriendly activities from human to environment [11] [12], as well as research on climate changes associated with vegetation biodiversity disasters [13].…”

Section: Introductionmentioning

confidence: 99%

Identifying the relationship between changes in vegetation indices and the anthropogenic activities in the tropical Semarang City of Indonesia using space imagery

Sudarmanto

Suranto

Suntoro

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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The existence of vegetation in the watershed area as a function of maintaining the continuity of the process of soil infiltration, erosion resistance, and landslide resistance is essential. Studies that measure the existence of vegetation through vegetation index parameters by utilizing optical-based satellite imagery maps, as well as radiometric waves or Radio Detection and Ranging (RADAR), have been widely carried out. However, attempts to test it to formulate it into vegetation resilience indices on the scale of functional areas such as watersheds are still a challenge in itself. In this study, the vegetation resilience index was measured by looking for spatial change patterns from Normalized Difference Vegetation Index (NDVI) and the Soil-Adjusted Vegetation Index (SAVI) in 2015-2021. This spatial pattern change is associated with anthropogenic factors represented by the dynamics of changes in vegetation to housing and population distribution. The use of residential land is obtained from the interpretation of optical-based satellite imagery maps from Google Earth sources mitigated by the administrative boundaries of villages. The population change was obtained from statistical data on the village’s population in the same year. The change in vegetation index, residential, and inhabitants were examined by the Spatial Autocorrelation (SA) using the Global Moran’s Index (GMI). Finally, the changes in the spatial patterns in vegetation indices, residentials index, and inhabitant index were conducted with correlation analysis to determine the relationship between these parameters. The results of GMI indicate a tendency towards dispersion in NDVI and SAVI, which showed a linear index decline in the dry season but weak linear indices incline in the wet season. The trend of dispersion patterns also occurs in residential and inhabitants, showing a linear index decline with a high confidence level (R2=0.9171 in residentials and R2=0.5872 in inhabitants). The relationship between pattern changes in NDVI and SAVI and Residential and Inhabitant found by the following formula; NDVI = 0.214173 -3.12952 * Residential + 6.457788 * Inhabitant; SAVI = 0.101544 + 0.120294 * Residential + 1.963223 * Inhabitant.

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Section: Pendahuluanunclassified

Pendekatan Eko-Sosio Sistem Dalam Pengelolaan Potensi Banjir Kota Semarang

Sudarmanto

Rofii

2021

RIPTEK

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The diversity and density of vegetation is key in managing flood throughout the watershed, especially in the central and downstream regions. Measures of vegetation diversity and density that can be used are the Normalized Difference Vegetation Index (NDVI) which is a measure of vegetation greenness (chlorophyll levels), Normalized Difference Water Index (NDVI) to measure vegetation wetness levels, and soil adjusted vegetation index (SAVI) for low canopy vegetation cover. For watersheds that stretch long to downstream or coastal areas, the measure of land diversity needs to be supplemented by using the Normalized Difference Built-Up Index (NDBI) indicator that is useful for land use planning. Information on this vegetation index can be obtained by processing a satellite imagery map. The benefits of processing vegetation indexes in watershed management are becoming increasingly important in the era of climate change, especially with regard to efforts to harmonization of relationships between environmental elements that include the diversity of the interests of the resident population. That's why this research was done in order to find the link between natural factors including the impacts of climate change and humans. The test began by looking for changes in the ND VI, ND WI, SA VI, and NDBI indexes from the last 5 years with the Global Indicator Spatial Association (GISA) analysis using Moran's I Global Index followed by looking for the association with the pattern of population change and the percentage of the area of the settlement. The results showed a link between Moran's I Global Index change pattern of population change and the spread of residential developments. The tendency of the distribution of this settlement area is an important point in analyzing the influence of its dispersal patterns in a watershed so that the continued impact of potential erosion, sedimentation, and flooding triggered that will be the main consideration in watershed management.

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The Machine Learning to Detect Drought Risk in Central Java Using Landsat 8 OLI Remote Sensing Images

Cited by 8 publications

References 26 publications

Pola Keterkaitan Perubahan Distribusi Kepadatan Vegetasi dengan Penggunaan Lahan DAS Pesisir, Case Study : DAS Garang

Pola Keterkaitan Perubahan Distribusi Kepadatan Vegetasi dengan Penggunaan Lahan DAS Pesisir, Case Study : DAS Garang

Identifying the relationship between changes in vegetation indices and the anthropogenic activities in the tropical Semarang City of Indonesia using space imagery

Pendekatan Eko-Sosio Sistem Dalam Pengelolaan Potensi Banjir Kota Semarang

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