Climate Impacts on Sustainable Natural Resource Management. Группа авторов

aids in determining when and how much to irrigate by monitoring the water status of plants. This is done by measuring evapotranspiration rates and by estimating crop coefficients. Efficient use and monitoring of surface water using geospatial techniques have recently attracted the interest of irrigation water policymakers.

      2.2.5 Combating Desertification

      Desertification is an extreme type of condition faced worldwide. Remotely sensed data and geospatial techniques provide important information for assessing desertification and its mapping at a local and global extent. It is a change in land condition that was not desertic into desert type landscapes and is closely linked to factors like population growth, improper farming practices, and widespread crops in naturally fragile environments. It occurs due to a lack of water reserves, humus‐depleted soils, scarce vegetation, and repeated plowing. The consequences of desertification can be dreadful for societies. Geospatial technology is utilized to determine the soil types, vegetation classification, land use classification, and nutrient availability in a region. Integration and weighted overlay of various factors in a GIS system result in vegetation, climate, soil, and management indices. The final product created after superimposing other indices creates a desertification sensitivity index. This index can help assess the stage of desertification of the study area (Lamqadem et al. 2018; Bedoui 2020).

      2.2.6 Biodiversity Management

      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.

2.3 LiDAR Technology

      LiDAR is a remote sensing method in which a pulse of light is used to measure distances. The sensor emits a pulse of light to the earth's surface from an airborne or space‐borne laser for measurement. The technique provides a direct means to measure vegetation canopies' structure (Dubayah and Drake 2000). The pulse bounces off the tree canopy materials such as leaves and branches. The reflected energy is collected back at the instrument. Time taken for the pulse between emission, reflection, and recapture by the instrument is recorded. Various structure metrics are computed, analyzed, or modeled. Different LiDAR systems measure vegetation characteristics, mostly high pulse rate, small‐footprint, first‐ or last‐return‐only airborne systems which fly in the lower altitude region. Other systems are large footprint and full‐waveform digitizing that deliver superior vertical details about the vegetation canopy. Dubayah and Drake (2000) and Lefsky et al. (2002) provided a thorough overview of LiDAR application for land surface characterization and forest studies.

LiDAR systems have successfully recovered forest structure characteristics for different vegetation types quickly and directly. The technology has become an indispensable remote sensing tool for mapping forest inventory and structure. It has become popular for making informed decision‐making in forest management practices. LiDAR's ability to measure vertical as well as horizontal canopy structure can provide essential details for fuel estimation and fire behavior modeling. The flow chart in Figure 2.6 displays fusion of LiDAR and satellite data for improved image classification and feature extraction.

2.4 Artificial Intelligence and Remote Sensing

      Remote sensing coupled with artificial intelligence (AI) provides essential technical supports to natural resource monitoring using various applications, including target detection, quantitative extraction of information, change detection and analysis, as well as multi‐source remote sensing information processing (SuperMap 2019).

Land use control is the primary means of developing and protecting land space. It protects the land by ensuring that all‐natural resources are utilized strictly according to the established plan. On the basis of target detection tools and technologies, the targets and scenes of prime interest can be precisely detected in the raster image and their size‐position can be instantly confirmed to identify different natural resources accurately. By recognizing the location of natural resources and their mutual relationships, the technology can aid the land space utilization control along with geological disaster prevention, ecological restoration and provide valuable information to law enforcement inspectors. AI image segmentation and classification technology can be used for quick high‐precision image classification. The quantitative indicators and boundaries of different natural resources can be automatically acquired, thereby assisting in the monitoring and evaluating of these natural resources. The supervision of natural resources and protection of farmland require automatic comparison, self‐inspection, self‐reporting, and regular verification of large land‐use areas. Earlier, this supervision was mainly dependent on the analysis of remote sensing images conducted visually by monitoring experts. With the integration of AI technology, changes in different natural resource categories can be quickly detected for a particular area and time.

Figure 2.6 Satellite and LiDAR data fusion for natural resource management.

      The technology involving data processing and its optimization from different remote sensing sources can enrich the overall data quality. This technology can further improve the overall strength of the natural resources management process and may include:

      1 LiDAR 3D point cloud processing techniques based on AI can enhance the monitoring data like buildings and terrains and improve natural resource management accuracy.

      2 Optimization of remote sensing image quality using AI can improve the accuracy of image interpretation. Super‐resolution reconstruction and de‐clouding techniques can enhance the image quality and add more value to its use.

      3 Hyperspectral