Bambang H. Trisasongko

Bio: Bambang H. Trisasongko is an academic researcher from Bogor Agricultural University. The author has contributed to research in topics: Synthetic aperture radar & Normalized Difference Vegetation Index. The author has an hindex of 8, co-authored 59 publications receiving 351 citations. Previous affiliations of Bambang H. Trisasongko include University of New South Wales & Australian Defence Force Academy.

Identification of Agricultural Drought Extent Based on Vegetation Health Indices of Landsat Data: Case of Subang and Karawang, Indonesia☆

Abstract: Agricultural drought monitoring is paramount in order to maintain food security in Indonesia, particularly in Subang and Karawang as the national rice production centers. In this paper, satellite-borne remote sensing data are tested for monitoring drought extent in about 184.486 ha of both regencies. Vegetation Health Index (VHI), a vegetative drought indices based on remote sensing data, is studied in this case using long term sequence of 2000, 2005, 2010, and 2015 dry season Landsat data. VHI collates overall vegetation health, which in turn suitable to indicate agricultural drought extent. It measures either moisture vegetation/vegetation condition (VCI) or thermal condition of vegetation (TCI). Both indices were derived from Normalized Difference Vegetation Index (NDVI) and land surface temperature (LST) data respectively. The results revealed that VHI decreased more than 50 percent, from 30.86 in 2000 to 14.66 in 2015. This figure indicated drought extent intensified in research area, from mild drought to severe drought. The severity was mainly triggered by the rising LST from 27°C in 2000 to 40°C in 2015. In addition, there was a decreasing tendency of NDVI values in recent years, leading agricultural fields more susceptible to drought.

Comparing six pixel-wise classifiers for tropical rural land cover mapping using four forms of fully polarimetric SAR data

Abstract: This study evaluates four commonly used forms of synthetic aperture radar (SAR) data for land-cover classification in tropical rural areas. The backscatter coefficient of linearly polarized L-band ...

The Use of Polarimetric SAR Data for Forest Disturbance Monitoring

Abstract: Tropical forest degradation has been a major area of interest for the remote sensing community. Various sensors have been dedicated to monitor its changes; however, due to widespread cloud cover, limited information could be retrieved through optical datasets. Synthetic Aperture Radar (SAR) sensors provide an alternative for such purpose. This paper discusses an application of SAR polarimetry data coupled with the Cloude-Pottier decomposition theorem as a non-invasive method for the assessment of degraded forests in Indonesia. It was found that Cloude-Pottier feature space provides a convenient way to describe degradation levels, especially using P-band datasets. Both L- and P-band data provided appreciable classification accuracy through Support Vector Machine methods. Results suggest that fully polarimetric SAR data, combined with polarimetric parameters, can be useful for operational monitoring.

Remote Sensing And Image Interpretation

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New Theory of the Earth

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