Multi-temporal SAR Interferometry (MTInSAR)-based study of surface subsidence and its impact on Krishna Godavari (KG) basin in India: a support vector approach

Tripathi, Akshar; Malik, Kapil; Reshi, Arjuman Rafiq; Moniruzzaman, Md; Tiwari, Reet Kamal

doi:10.1007/s10661-023-11896-1

Cited by 4 publications

(2 citation statements)

References 80 publications

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“…Urbanisation is an important factor influencing the decline of ecosystem services (Noviani, 2021). Similarly, urbanisation will reduce the value of ecosystem services providing clean water resulting in a relatively low level (Tripathi et al, 2023). García et al (2018) claim that trends generally indicate a decrease in the supply of ecosystem services due to changes in land cover in suburban areas.…”

Section: Discussionmentioning

confidence: 99%

Analysis of Urbanisation’s Relationship with Clean Water Supply Ecosystem Services in Sukoharjo Regency, Indonesia

Saputra,

Nugraha,

Noviani

2023

For. Geo.

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The phenomenon of urban population growth is a global concern which will result in a decrease in the value of ecosystem services in an area. Sukoharjo Regency is an area affected by the development of Surakarta City; therefore, rapid growth is taking place. The objective of this study is to investigate the interplay between urbanization, ecosystem services, and the provision of clean water in Sukoharjo Regency in 2022. The methods used in the study were calculating the percentage of the urban population to determine the level of urbanisation, AHP and overlay to ascertain the ecosystem service score, together with cross-tabulation to establish the relationship between these two variables. The result of this study is that the level of urbanisation produces a pattern that districts in the north tend to comprise a higher level. The clean water supply ecosystem services in Sukoharjo Regency obtained results dominated by the low to medium level. The situation regarding the level of urbanisation and ecosystem services in Sukoharjo Regency reveals a relationship where an increase in the level of urbanisation will reduce the value of ecosystem services.

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Section: Discussionmentioning

confidence: 99%

Analysis of Urbanisation’s Relationship with Clean Water Supply Ecosystem Services in Sukoharjo Regency, Indonesia

Saputra,

Nugraha,

Noviani

2023

For. Geo.

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“…Smoothing filtering is performed using the FocalMean tool in Google Earth Engine. There are several parameters used in the tool to perform smooth filtering on the Sentinel-1 image (Tripathi et al, 2023). These parameters include the radius, kernel type and units used.…”

Section: Data Acquisition and Pre-processingmentioning

confidence: 99%

Machine Learning-Based Rice Field Mapping in Kulon Progo using a Fusion of Multispectral and SAR Imageries

Khoirurrizqi,

Sasongko,

Utami

et al. 2023

For. Geo.

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The land-conversion of rice fields can reduce rice production and negatively impact food security. Consequently, monitoring is essential to prevent the loss of productive agricultural land. This study uses a combination of Sentinel-2 MSI, Sentinel-1 SAR, along with SRTM (elevation and slope data) to monitor rice fields land-conversion. NDVI, NDBI and NDWI indices are transformed from the annual median composite Sentinel-2 MSI images used to identify different rice fields with another object. A monthly median composite of SAR images from Sentinel-1 data are used to identify cropping patterns of rice fields in the inundation phase. The classification is performed by using the Random Forest machine learning algorithm in the Google Earth Engine (GEE) platform. Random Forest classification is run using 1000 trees, with a 70:30 ratio of training and testing data from sample features extracted by visual interpretation of high resolution Google Earth imagery. In this study, Random Forest classification is effective in computing a high amount of multi-temporal and multi-sensory data to map rice-field land conversion with an accuracy rate of 96.16% (2021) and 95.95% (2017) for mapping paddy fields. From the multitemporal rice field maps in 2017—2021, a conversion of 826.66 hectares of rice-fields to non-rice fields was identified. Based on the spatial distribution, the conversion from rice-field to non-rice field is higher at the area near the roads, built area and Yogyakarta International Airport. Therefore, it is important to assess and ensure that National Strategic Projects are managed with due regard to environmental impacts and food security.

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Spatial Variability of Ground Deformation of Coastal Regions of the Krishna Delta, East Coast of India Using SAR Interferometry

Reshma,

Murali,

Kumar

et al. 2024

J Indian Soc Remote Sens

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Multi-temporal SAR Interferometry (MTInSAR)-based study of surface subsidence and its impact on Krishna Godavari (KG) basin in India: a support vector approach

Cited by 4 publications

References 80 publications

Analysis of Urbanisation’s Relationship with Clean Water Supply Ecosystem Services in Sukoharjo Regency, Indonesia

Analysis of Urbanisation’s Relationship with Clean Water Supply Ecosystem Services in Sukoharjo Regency, Indonesia

Machine Learning-Based Rice Field Mapping in Kulon Progo using a Fusion of Multispectral and SAR Imageries

Spatial Variability of Ground Deformation of Coastal Regions of the Krishna Delta, East Coast of India Using SAR Interferometry

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