Читать книгу Climate Impacts on Sustainable Natural Resource Management - Группа авторов - Страница 37

Biodiversity monitoring is essential for developing an adequate and timely management plan to safeguard the losses witnessed due to extreme human pressure or other natural causes. The LULC maps can be prepared using remote sensing observations and geospatial tools for understanding the rate of change of one land use category into another. Such assessment helps policymakers in developing plans that are effective in biodiversity conservation and management. This helps to ensure sustainable development and understanding of human activities' effect within and around protected areas. Geospatial data such as aerial and satellite photographs can be used to manage flora and fauna by determining the presence and distribution of vegetation and invasive species within a protected area (Kumar et al. 2019b). It helps in determining the extent of vegetation, water and food availability for animals in different seasons of the year. The animal census is usually assisted nowadays by aerial photographs or camera trap methods, which is again a useful application of geospatial technologies. Geospatial tools can also be used to show the intrusion of humans into protected areas and animal movements outside the protected areas. This is useful in resolving human‐wildlife conflicts. GPS technology can be utilized to monitor the activity of endangered species and protect them from poachers. GIS and remote sensing tools can also be used for conducting environmental impact assessment (EIA) of different projects, including building construction, road construction, pipe ways, dams, etc., within protected areas. Therefore, geospatial data has become essential in biodiversity management practices.

A study in Malaysia produced LULC for the Wildlife Reserves study area using the supervised classification of Landsat images and geospatial technologies. Different classification approaches such as support vector machine (SVM), spectral angle mapper (SAM), and artificial neural network (ANN) classifiers can be used for LULC mapping. To have a better understanding, the accuracy of classification can be improved by cloud patching and pan‐sharpening. The remotely sensed images can be used to classify area into the open, scrub and dense forest, delineation of water bodies, settlements and other important land use classes. The ANN‐based approach is reported to produce maps of high accuracy (Shaharum et al. 2018). In a study by Shaharum et al. (2018), it was observed that remote sensing tools can be used to assess socio‐economic activities that play a significant role in disturbing the natural environment of the study area.