Eucalyptus forest plantation assessment of vegetation health using satellite remote sensing techniques. Issue 1 (November 2021)

• Record Type: Journal Article
• Authors: Razali, S M; Lion, M
• Is Part Of: IOP conference series. Volume 918:Issue 1(2021)
• Publication Date: 2021-11
• Subjects: Earth sciences -- Periodicals; Environmental sciences -- Congresses; Environmental sciences -- Periodicals; 550.5
• DOI: 10.1088/1755-1315/918/1/012041
• Languages: English
• ISSNs: 1755-1307

Abstract

Abstract: Early examination of the water condition of the plants utilizing remote sensing technology can be used to assess the health of the vegetation in the Eucalyptus forest plantation. To preserve a sustainable wood supply and wooded region that is necessary to human life and vital wood supplies, the forested region should be protected from disease and environmental damage. Disease and environmental impacts are two of the most critical challenges in Eucalyptus forest management. To calculate the vegetation index and identify land cover in the research region, remote sensing with Catalyst Professional software based on Object Analyst (OBIA) tools was utilized. The NDVI (Normalized Difference Vegetation Index) is a valuable index for assessing early vegetation health. For atmospheric correction and haze removal, the image was first pre-processed with ATCOR tools. Second, the image was converted to NDVI using algorithm library tools. In addition, for land cover classification in the area, an OBIA based on Support Vector Machine (SVM) was utilized, followed by an accuracy assessment. Using ArcGIS software, zonal statistics were used to calculate the NDVI value for each land cover category. According to the method, the map produced roads, plantations, buildings, low-density vegetation, oil palm, and open area classifications. Based on accuracy assessment in OBIA, plantation, oil palm, and open area were all 100% accurate, whereas low-density vegetation and oil palm were 100% accurate according to the user. Producer accuracy was lowest on roads, whereas user accuracy was lowest in open areas. Non-vegetated land is difficult to classify at this site, according to the accuracy assessment results. The map improved accuracy since the study used a lower segmentation scale factor of 50, which produced fine vectors ascribed for classification. The average NDVI for oil palm area was 0.71, and 0.69 for plantation. Because it was difficult to classify open areas and roads, the NDVI for the class was low, at 0.37 and 0.22, respectively. From land use classification, the plantation was classified (37%), low-density vegetation area (28%), and oil palm (21%). Others make up only 2 to 7% of the site's overall area. According to the study, NDVI is a useful indicator for assessing the health of vegetation in areas where NDVI values are larger than 0.70 and presents pf mixed landscape and non-vegetated features. A higher NDVI value implies that the plant is in good enough shape to conduct photosynthetic activities thus producing biomass for sustaining vegetation health. This type of inquiry can forecast more indices to produce higher accuracy of land use maps for the Eucalyptus plantation. At the same time, this type of research can assist forest managers in detecting large areas in their plantation for vegetation health assessment such as for early disease detection.