Document Type : Original Article
Authors
1
Environmental Studies Department, National Authority for Remote Sensing and Space Sciences (NARSS), Cairo, Egypt
2
Department of Microbiology, Faculty of Science, Ain Shams University, Cairo, Egypt
3
Land Use Department, National Authority for Remote Sensing and Space Sciences (NARSS), Cairo, Egypt
Abstract
Actinobacteria are widely distributed across various habitats, including diverse soils and they play an important role in maintaining ecological balance and for producing bioactive metabolites. Therefore, the present study adopts an innovative approach to assess actinobacterial suitability growth along the eastern and southern part of Nile delta of Egypt using remote sensing and cartographic modeling. Multispectral Landsat imagery were utilized to retrieve land use/cover and environmental variables associated with actinobacterial growth. Multiple spectral indices as NDVI, SAVI, NDMI, MNDWI, NDBI, NDSI, ferrous minerals and LST were utilized as environmental input criteria for the model. Based on the optimal conditions of the environmental variables for actinobacterial growth, a novel cartographic model was developed to assess actinobacterial growth suitability along the eastern and southern part of Nile delta region. An observed fluctuation was reported in multiple spectral indices; ferrous minerals (0.342 to 1.113), NDBI (-0.491 to 0.054), NDSI (-0.639 to -0.142), NDMI (-0.054 to 0.491), SAVI (0.072 to 0.537), MNDWI (-0.398 to 0.089), NDVI (0.142 to 0.639) and temperature (33.06 to 39.94°C). The actinobacterial suitability model resulted in six levels. The highest two suitability levels; very high (0%) and high (27%), which represent the greatest potential for actinobacterial growth, were predominantly situated in the northern part of the study area, encompassing the governorates of Sharkiah, Qalyubia, Monofia, and the northern part of Ismalia. On the other hand, the lowest suitability level, (level six, 0.3%) was detected in the western and southern regions of the study area. The accuracy of the developed model was 87.2%, indicating a high level of reliability. Based on this percentage, the model can be confidently used to predict the potential presence of actinobacteria. This novel model showed a promising result and can be widely applied in mapping the potential areas for actinobacterial growth using environmental variables retrieved from Landsat imagery.
Keywords
Main Subjects
)
View on SCiNiTO