Identifikasi Wilayah Resiko Kerusakan Lahan Terbangun Sebagai Dampak Tsunami Berdasarkan Analisis Building Indices

Permatasari, Aurilia Dinda; Prasetyo, Sri Yulianto Joko

doi:10.26623/transformatika.v20i1.5209

Cited by 4 publications

(5 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The dryness index is one of the important indicators in ecological-environmental monitoring and evaluation. In general, the dryness index is constructed by the bare soil index (SI) [60] and building index (NDBI) [61]. The surface temperature represents the heat index, and the atmospheric correction method in the single-channel algorithm [62] is used to do the heat index inversion.…”

Section: Methodsmentioning

confidence: 99%

Temporal and Spatial Changes of Ecological Environment Quality Based on RSEI: A Case Study in Ulan Mulun River Basin, China

et al. 2022

View full text Add to dashboard Cite

The Ulan Mulun River Basin is an essential ecological protective screen of the Mu Us Desert and a necessary energy base in Ordos City. With the acceleration of industrialization and urbanization, human activities have caused enormous challenges to the local ecological environment. To achieve the region’s economic sustainability and make local development plans more objective, it is necessary to evaluate the basin’s ecological environment quality over a period of time. First, in the Landsat historical images, we selected 5 years of data to investigate the changes in this time-period (2000–2020). Second, based on the opened remote sensing database on Google Earth Engine, we calculated the remote-sensing ecological index (RSEI) distribution map. RSEI includes greenness, temperature, humidity, and dryness. Thirdly, we assessed the ecological-environmental distribution and change characteristics in the Ulan Mulun River Basin. Finally, we analyzed the RSEI spatial auto-correlation distribution characteristics in the study area. The mean values of RSEI in 2000, 2005, 2010, 2015, and 2020 were 0.418, 0.421, 0.443, 0.456, and 0.507, respectively, which indicated that the ecological environment quality had gradually improved. The ecological environment quality from 2000 to 2005 had the biggest change, as the area with drastically changed water levels accounted for 78.98% of the total basin. It showed a downward trend in the central and western regions. It showed an upward trend in the eastern region. For 20 years, the area of deterioration decreased by 24.37%, and the slight change area increased by 45.84%. The Global Moran’s I value ranged from 0.324 to 0.568. The results demonstrated that the Ulan Mulun River Basin ecological environment quality spatial distribution was positively correlated, and the clustering degree decreased gradually. Local spatial auto-correlation of RSEI showed that high-high(H-H) was mainly distributed in the basin’s eastern and southern regions, where the population density was low and the vegetation was in good condition. Low-low(L-L) was mainly distributed in the basin’s central regions and western regions, where the population density was high, and the industrial and mining enterprises were concentrated. This study provided a theoretical basis for the sustainable development of the Ulan Mulun River Basin, which is crucial for the local ecological environment and economic development.

show abstract

Section: Methodsmentioning

confidence: 99%

Temporal and Spatial Changes of Ecological Environment Quality Based on RSEI: A Case Study in Ulan Mulun River Basin, China

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The RSEI is quantified by coupling the four ecological indicators, namely, greenness (NDVI), wetness (WET), heat (LST) and dryness (NDBSI) using principal component analysis, where the greenness indicator is represented by the normalized difference vegetation index (NDVI), which represents the growth of vegetation in the study area [40]. The wetness index is represented by the converted moisture component (WET) based on the tasseled cap transformation, which reflects the wetness level of the soil and vegetation [41]; the heat index (i.e., land surface temperature, LST) is an important indicator that reflects ecological processes and climate change [42]; and the dryness index is expressed as the average of two indicators, the bare soil index (SI) [43] and the index-based built-up index (IBI) [44], which reflects the dryness level of the land surface. The formulae and parameters of the four indicators are listed below (Table 1).…”

Section: The Rsei Quantificationmentioning

confidence: 99%

The Assessment of the Spatiotemporal Characteristics of the Eco-Environmental Quality in the Chishui River Basin from 2000 to 2020

Zhou

Zhang

et al. 2023

Sustainability

View full text Add to dashboard Cite

The Chishui River Basin is located in the bordering area of Yunnan, Guizhou and Sichuan provinces, which serves as an important ecological barrier in the upper reaches of the Yangtze River, and plays a leading role in preserving natural environments, protecting water resources, and maintaining soil functions. However, the eco-environmental quality in the basin has encountered serious challenges in recent years, and the conflict between eco-environmental protection and economic development becomes increasingly prominent. Therefore, it is particularly important to quantitatively assess the extent of the eco-environmental changes in this basin. The present study acquired Landsat series remote sensing images based on the Google Earth Engine (GEE) platform, constructed a remote sensing ecological index (RSEI) as the assessment index that reflects the eco-environmental quality using principal component analysis, studied the changing trend in the eco-environmental quality using the Sen–Mann–Kendall trend test, analyzed the spatial clustering distribution patterns of the eco-environmental quality, based on spatial autocorrelation analysis, and applied the geographical detector model to determine the impacts of natural and anthropogenic factors on the eco-environmental quality. We further applied the CA–Markov model to simulate and predict the eco-environmental quality of the basin in 2025. The results showed the following: (1) between 2000 and 2020, the eco-environmental quality of the Chishui River Basin had been greatly improved. The average RSEI value increased from 0.526 in 2000 to 0.668 in 2020, and the percentage of areas belonging to the good or excellent quality category increased from 42.65% to 68.48%. (2) The main drivers of the eco-environmental quality included population density, mean annual temperature, land use type and elevation. The interactive effect between these drivers was significantly higher than that of individual drivers, and thus possessed stronger explanatory power for quality differences. (3) It is predicted that in 2025, the eco-environmental quality of the basin will continue to improve, and the proportion of land areas with good or excellent quality will continuously increase. The present study can provide reference value for local environmental protection and regional planning.

show abstract

“…The sample closest to these hyperplanes is called the support vector, and the difference is expressed as the sum of the weights of the sample subsets, which limits the complexity of the problem [24]. To separate the two classifications through the optimal hyperplane, use the following equation (3).…”

Section: Machine Learningmentioning

confidence: 99%

“…From 1600 to 2007, Indonesia experienced several natural disasters, including tsunamis [2]. Indonesia experienced approximately 172 tsunami natural disasters [3]. Physically, a tsunami is described as a chain of ocean waves due to entrapping huge quantities of seawater.…”

Section: Introductionmentioning

confidence: 99%

Tsunami Vulnerability and Risk Assessment in Banyuwangi District using machine learning and Landsat 8 image data

Wibowo,

Prasetyo,

Sembiring

2023

matrik

View full text Add to dashboard Cite

The tsunami is a disaster that often occurs in Indonesia, there are no valid indicators to assess and monitor coastal areas based on functional land use and based on land cover which refers to the biophysical characteristics of the earth's surface. One of the recommended methods is the vegetation index. Vegetation index is a method from LULC that can be used to provide information on how severe the impact of the tsunami was on the area.In this study, an increase in the vegetation index was carried out using machine learning. The purpose of this study was to develop a tsunami vulnerability assessment model using the Vegetation Index extracted from Landsat 8 satellite imagery optimized with KNN, Random Forest and SVM. The stages of study, are: 1)extraction Landsat 8 images using algorithms NDVI, NDBI, NDWI, MSAVI, and MNDWI; 2) prediction of vegetation indices using KNN, Random Forest, and SVM algorithms. 3) accuracy testing using the MSE, RMSE, and MAE,4) spatial prediction using the Kriging function and 5) tsunami modelling vulnerability indicators. The results of this study indicate that the NDVI interpolation value is 0 - 0.1 which is defined as vegetation density, biomass growth, and moderate to low vegetation health. the NDWI value is 0.02 - 0.08 and the MNDWI value is 0.02 - 0.09 which is interpreted as the presence of surface water along the coast. MSAVI is a value of 0.1 – 0 which is defined as the absence of vegetation. The NDBI interpolation value is -0.05 - (-0.08) which is interpreted as the existence of built-up land with social and economic activities. From the results of research on the 10 areas studied, there are 3 areas with conditions that have a high level of tsunami vulnerability. 2 areas with medium vulnerability and 5 areas with low vulnerability to tsunami.

show abstract

Identifikasi Wilayah Resiko Kerusakan Lahan Terbangun Sebagai Dampak Tsunami Berdasarkan Analisis Building Indices

Cited by 4 publications

References 10 publications

Temporal and Spatial Changes of Ecological Environment Quality Based on RSEI: A Case Study in Ulan Mulun River Basin, China

Temporal and Spatial Changes of Ecological Environment Quality Based on RSEI: A Case Study in Ulan Mulun River Basin, China

The Assessment of the Spatiotemporal Characteristics of the Eco-Environmental Quality in the Chishui River Basin from 2000 to 2020

Tsunami Vulnerability and Risk Assessment in Banyuwangi District using machine learning and Landsat 8 image data

Contact Info

Product

Resources

About