Climate Impacts on Sustainable Natural Resource Management. Группа авторов
image processing using AI can highlight various natural resources' subtle features and improve the natural resource classification results.
2.5 Machine Learning Tools for Natural Resource Management
In addition to biodiversity management, environmental researchers are also interested in Species Distribution Models (SDMs), which are widely used for predicting suitable habitat in space‐time for the choice of species (Bolliger et al. 2000; Raxworthy et al. 2003; Phillips et al. 2006; Baldwin 2009; Robinson et al. 2011). SDMs are beneficial in generating maps and results that identify suitable habitat areas and determine key environmental factors for driving species occurrence. These tools also report the threshold for suitable habitat demarcation and accuracy assessment. They are a valuable asset for many ecological studies and for species with narrow ranges it may also direct direction for future field surveys (Phillips et al. 2006). Integration of machine learning and geospatial techniques can easily handle such complex modeling problems.
Maximum Entropy (Maxent) is a presence‐only species distribution modeling technique used in the identification of a species' niche in environmental space. The prediction is based on the relation between observed occurrences to a set of climatic and non‐climatic environmental variables (Phillips et al. 2006; Pearson et al. 2007). Maxent is a free to download machine learning tool that provides a platform to integrate the species occurrence data with the bioclimatic variables using remote sensing and Geographical Information System and provides species habitat suitability and predicting future occurrence scenarios (Phillips et al. 2004; Phillips et al. 2006). Maxent has several advantages:
1 It can characterize probability distributions from incomplete information.
2 Presence data alone is sufficient and no absence data is required.
3 It uses both continuous and categorical environmental variables.
4 It produces an output with continuous prediction ranging from zero to one, where a higher value pixel indicates greater suitability for a given species at that pixel.
2.6 Applications of Unmanned Aerial Systems in Natural Resource Management
Most environmental monitoring and data collection systems are currently based on ground‐based measurements, satellite observations, and manned airborne sensors. These processes have spatiotemporal constraints that limit the current monitoring platforms. UAS provides an excellent opportunity to bridge this gap (Figure 2.1) by providing great spatial detail and enhanced temporal retrieval cost‐effectively (Manfreda et al. 2018). UAS systems provide very high spatial and temporal resolution images which are unmatched by satellite alternatives at a fraction of the satellite acquisition charge. The UAS‐mounted sensors have multiple additional advantages for a wide array of applications. Moreover, they offer quick access to environmental data while offering the near real‐time capabilities necessary for a wide range of applications. UAS also has applications in accessing hazardous and other inaccessible sites efficiently without compromising on safety and accessibility issues. Another advantage of UAS is its ability to perform remote sensing data acquisition in cloudy conditions, which may not be the case for satellite data.
One of the most widely used environmental monitoring applications of UAS is its use for precision agriculture. The high spatial‐resolution data enables quick and economical detection and diagnosis so that preventive measures can be implemented for agricultural management issues. UAS can provide the necessary data for addressing farmers' need at the field scale, thus empowering them to make timely better decisions with the least possible environmental impact. UAS offers opportunities to collect high‐resolution data for describing ecological processes and surveying ecosystems in remote sites. Some habitats like peat bogs are not only inaccessible but can also be damaged through ground surveys. In such cases UAS can provide good‐quality and near‐comparable levels of information to those obtained through plot‐based measurements. UAS also has a beneficial application for undertaking quick damage assessment after extreme natural events and in the context of humanitarian relief.
2.7 Google Earth Engine as a Platform for Environmental Monitoring and NRM
For decades remote sensing has resulted in the collection of huge volumes of datasets. The management and analyzing of these voluminous datasets cannot practically be achieved using standard software packages and general computing systems. To address this challenge, Google has developed the first cloud computing platform of its kind, called Google Earth Engine (GEE), for effectively accessing and processing these datasets. GEE facilitates big geo‐data processing at country, continental, or world level and provides datasets for long periods (Amani et al. 2020). All the publicly available remote sensing data from multiple satellites, such as the Landsat series, Moderate Resolution Imaging Spectrometer (MODIS), Sentinel series, National Oceanographic and Atmospheric Administration Advanced very high‐resolution radiometer (NOAA AVHRR), Advanced Land Observing Satellite (ALOS), along with other gridded datasets is used. The complete list of datasets is available on the GEE webpage (https://earthengine.google.com/datasets) (Kumar and Mutanga 2018).
GEE connects with Google's computational infrastructure and provides users with access to the dataset in the backend using a web interface. Javascript and Python are the two scripting languages used to retrieve the data, process it, and analyze it. GEE has parallel processing and fast computation features to deal with a huge volume of data processing challenges effectively. In this cloud computing platform, the users do not download the dataset. Instead, all the processing is completed at the Google server‐side and the only final product can be downloaded. No additional software other than a web browser is required to perform analysis on GEE. Users have the flexibility to utilize software like various Python Integrated Development Environments (IDEs) or a simple browser interface. GEE also provides many built‐in algorithms for cloud correction, classification, and matrix operations as well. These algorithms are easy to use tools to analyze data at a planetary scale and can be used as a building block for developing user‐defined algorithms by scientists with less effort. Some of the wide range of GEE applications, including those for NRM, are shown in Figure 2.7. Its application for image classification is shown in Figure 2.6.
2.8 Conclusion
The ever‐increasing human population has put tremendous pressure on natural resources, and it has become critically important to manage these limited resources in an effective and cost‐efficient manner. Geospatial technology provides indispensable tools for sound decision‐making that ensures sustainable use of resources. Modern techniques and platforms of Remote Sensing, GIS, LiDAR, UAV, simulation models, AI, and machine learning tools provide an effective means of acquiring moderate as well as high‐resolution data, to analyze and utilize them for different applications that help in monitoring and management of natural resources. These techniques are cost‐effective and highly efficient in providing information at local to global scales on varying spatial and temporal resolutions. Geospatial tools provide timely and valuable information about land‐use, land‐cover, and natural resources, and allow detection of change and also capture critical indicators affecting these resources. With the emerging capabilities of remote sensing and GIS applications supported by improved computation, it is paramount that an upgraded methodology is developed that integrates geospatial techniques for natural resource management practices.
Figure 2.7 Various applications of Google Earth Engine for natural resource management.