Inter-Sensor Comparison between THEOS and Landsat 5 TM Data in a Study of Two Crops Related to Biofuel in Thailand

Abstract

Knowledge of the spatial distribution of biofuel crops is an important criterion to determine the sustainability of biofuel energy production. Remotely sensed image analysis is a proven and effective tool for describing the spatial distribution of crops using vegetation characteristics. Increases in the number of options and availability of satellite sensors have expanded the horizon of choices of imagery sources for appropriate image acquisitions. The Thailand Earth Observation System (THEOS) satellite is one of the newest satellite sensors. The growing number of satellite sensors warrants their comparative evaluation and the standardization of data obtained from various sensors. This study conducted an inter-sensor comparison of the visible/near-infrared surface reflectance and Normalized Difference Vegetation Index (NDVI) data collected from the Landsat 5 Thematic Mapper (TM) and THEOS. The surface reflectance and the derived NDVI of the sensors were randomly obtained for two biofuel crops, namely, cassava and sugarcane. These crops had low values of visible surface reflectance, which were not significantly (p < 0.05) different. In contrast, the crops had high values of near-infrared surface reflectance that differed significantly (p > 0.05) between the crops. Strong linear relationships between the remote sensing products for the examined sensors were obtained for both cassava and sugarcane. The regression models that were developed can be used to compute the NDVI for THEOS using those determined from Landsat 5 TM and vice versa for the given

Experimental Evaluation and Consistency Comparison of UAV Multispectral Minisensors

“…The results showed that the reflectance and NDVI of Landsat-8 were both greater than those of Landsat-7. In order to accurately distinguish cassava and sugarcane in images, Phongaksorn et al [33] compared the reflectance and NDVI of Landsat-5 and THEOS. The results showed that THEOS can better distinguish the two crops.…”

Mapping of sugarcane crop area in the Paraná State using Landsat/TM/OLI and IRS/LISS-3 images

“…Phongaksorn et al (2012), using the Landsat-/TM satellite in a sugarcane area in Thailand, obtained a KI of 0.92. Silva Junior et al (2013) made an automatic detection of fires in sugarcane plantations and obtained the best result (KI = 0.90) with the Maxver classification with the treatment of radiometric correction.…”

“…The supervised classification method of Maximum Likelihood (Maxver) was used to map sugarcane areas, following the example of other studies (Phongaksorn et al, 2012;Silva Junior et al, 2013;Mulianga et al, 2015). This algorithm makes the 'pixel-to-pixel' classification of the images, using the multispectral information of each pixel obtained in previously acquired training samples to find homogeneous regions (classes).…”