Forests are a vital ecosystem in the global world, serving as an important carbon store playing a vital role within the carbon cycle. The protection of these areas is becoming increasingly necessary, as these regions can help to serve in the essential process of climate change adaptation and mitigation. In recent times, the need for accurate estimations of forest conditions is increasingly essential. The estimation of forest properties is vital for the improvement of ecosystem services. In recent times, the traditional methods used to analyze forest properties do not give adequate results. However, this has been successfully changed through the introduction of GIS, remote sensing, and numerical methods.
The monitoring of forest land, through satellites and drones, is useful for the deep analysis of forest properties. Daily, monthly and yearly data aids the prediction of hazards and gives an accurate representation of where new plantations of forest are required, in degraded areas. In order to provide the optimal predictability and forecasting of the forest environment, an improved analytical and numerical understanding of this ecosystem is essential. Successful implementation of advanced GIS and remote sensing techniques provides a variety of statistical characteristics including; kriging, semi-kriging, interpolation, inverse distance weighted, triangle network, buffer, cluster, zonal statistics, unclassified and classified classification, etc., which aid ecosystem analysis.
To understand the structural characteristics and dynamics of the forest biome, spatial analysis is required. Spatial properties can be used to demonstrate key health indicators of the forest state, this includes the topography, slope, inclinations, azimuths, types of forest land, types of forest, the density of forest vegetation, amount of precipitation in the forest, forest temperatures and the climate of the forest. Using deeper numerical analysis, the current characteristics of the forest can be used to model its past state, as well as predict what the future environment may be like. With the use of advanced GIS and numerical analysis, it is possible for scientists to work together to return the forest to its optimal state.
In addition to this, these methods and techniques can be very useful in providing a global assessment of the forest environment. This can be achieved using precise GIS and remote sensing analysis of the forest environment in various regions and countries to conclude a global view of forest state. Vectorized and digitized data of forests from different regions may be collected in one database to produce a mapped grid point statistical interpolation. This can be used to highlight which forests globally require more attention and where environmental management is a priority.
The main goal of this Research Topic is to provide a better understanding of the forest environment using Spatio-temporal techniques, for improved ecosystem management. This collection aims to highlight advances in analytical methods using advanced GIS and remote sensing compared to traditional techniques. Within this Research Topic, articles addressing the following aspects are desired:
• Techniques that identify and define detailed forest properties and characteristics (e.g. forest type, land cover type, plant species, percentage covered, ecosystem type, etc.).
• Techniques that assess forest risks and means of preservation (e.g. fires, floods, strong winds, extremely high temperatures, unplanned cutting of trees, landslides, acid rain, forest disease, pest attacks, etc.).
• Improved planning of forest planting by the use of advanced remote sensing and GIS.
• Tools for sustainable and smart forest management.
Forests are a vital ecosystem in the global world, serving as an important carbon store playing a vital role within the carbon cycle. The protection of these areas is becoming increasingly necessary, as these regions can help to serve in the essential process of climate change adaptation and mitigation. In recent times, the need for accurate estimations of forest conditions is increasingly essential. The estimation of forest properties is vital for the improvement of ecosystem services. In recent times, the traditional methods used to analyze forest properties do not give adequate results. However, this has been successfully changed through the introduction of GIS, remote sensing, and numerical methods.
The monitoring of forest land, through satellites and drones, is useful for the deep analysis of forest properties. Daily, monthly and yearly data aids the prediction of hazards and gives an accurate representation of where new plantations of forest are required, in degraded areas. In order to provide the optimal predictability and forecasting of the forest environment, an improved analytical and numerical understanding of this ecosystem is essential. Successful implementation of advanced GIS and remote sensing techniques provides a variety of statistical characteristics including; kriging, semi-kriging, interpolation, inverse distance weighted, triangle network, buffer, cluster, zonal statistics, unclassified and classified classification, etc., which aid ecosystem analysis.
To understand the structural characteristics and dynamics of the forest biome, spatial analysis is required. Spatial properties can be used to demonstrate key health indicators of the forest state, this includes the topography, slope, inclinations, azimuths, types of forest land, types of forest, the density of forest vegetation, amount of precipitation in the forest, forest temperatures and the climate of the forest. Using deeper numerical analysis, the current characteristics of the forest can be used to model its past state, as well as predict what the future environment may be like. With the use of advanced GIS and numerical analysis, it is possible for scientists to work together to return the forest to its optimal state.
In addition to this, these methods and techniques can be very useful in providing a global assessment of the forest environment. This can be achieved using precise GIS and remote sensing analysis of the forest environment in various regions and countries to conclude a global view of forest state. Vectorized and digitized data of forests from different regions may be collected in one database to produce a mapped grid point statistical interpolation. This can be used to highlight which forests globally require more attention and where environmental management is a priority.
The main goal of this Research Topic is to provide a better understanding of the forest environment using Spatio-temporal techniques, for improved ecosystem management. This collection aims to highlight advances in analytical methods using advanced GIS and remote sensing compared to traditional techniques. Within this Research Topic, articles addressing the following aspects are desired:
• Techniques that identify and define detailed forest properties and characteristics (e.g. forest type, land cover type, plant species, percentage covered, ecosystem type, etc.).
• Techniques that assess forest risks and means of preservation (e.g. fires, floods, strong winds, extremely high temperatures, unplanned cutting of trees, landslides, acid rain, forest disease, pest attacks, etc.).
• Improved planning of forest planting by the use of advanced remote sensing and GIS.
• Tools for sustainable and smart forest management.
