Multimodal change monitoring using multitemporal satellite images.

Datta, Urmila

doi:10.1117/12.2600099

Cited by 1 publication

(1 citation statement)

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“…Another approach involves classifying satellite images, resulting in thematic maps based on input data. (Akkartal et al, 2004;Akkartal et al, 2005;Sunar et al, 2017). Numerous spectral indices and transformations described in the literature can be utilized as input data for the classification process subsequent to their application.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Impact of Beet Webworm Moths on Sunflower Fields Using Multitemporal Sentinel-2 Satellite Imagery and Vegetation Indices

Kara,

Maden,

Ercan

et al. 2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Remote sensing technology plays a crucial role in detecting and monitoring environmental issues, offering the ability to monitor large areas, diagnose problems early, and facilitate accurate interventions. By integrating in-situ data with qualitative measurements obtained from satellite images, comprehensive insights can be obtained, and statistical inferences can be established. This study focuses on analyzing the damages caused by beet webworm moths (Loxostege sticticalis) in sunflower fields located in the Ortaca neighborhood of Tekirdağ province in Thrace region, utilizing Sentinel-2 satellite images and in-situ data collected from the sunflower fields in Ortaca. The relationship between different spectral indices, such as the Enhanced Vegetation Index, Chlorophyll Index Green, and spectral transformation techniques like Tasseled Cap Greenness, derived from Sentinel-2 satellite images, and the observed damage rates in various sunflower fields' in-situ data was investigated. The results revealed a negative correlation between the variables, highlighting EVI as the most effective indicator of damage among the plant indices. Leveraging these findings, a damage map was generated using EVI, enabling visual interpretation of the damage status in other sunflower fields within the study area. These findings offer valuable insights into the impact of pests on sunflower crops, despite the accuracy evaluation results falling below the desired level, with an overall accuracy of 75% and a Kappa accuracy of 65%, attributed to the limited availability of in-situ data.

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Section: Introductionmentioning

confidence: 99%