Remote sensing is a powerful tool for forestry management, but it also poses some challenges. How can you optimize your data collection, processing, and analysis to get the most out of your imagery? In this article, you will learn some tips and best practices for using geographic information systems (GIS) to improve your remote sensing workflow and outcomes.
Depending on your objectives, budget, and availability, you may have different options for acquiring remote sensing data. You can use satellite, aerial, or drone imagery, or a combination of them, to capture different spatial and temporal scales. You can also choose from various sensors, such as optical, radar, lidar, or thermal, to measure different spectral and physical properties of the forest. Each sensor and platform has its advantages and limitations, so you need to consider factors such as resolution, coverage, frequency, cost, accuracy, and accessibility when selecting your data sources.
Optimizing remote sensing data for forestry management involves utilizing satellite or aerial imagery to monitor, assess, and manage forested areas efficiently. Selecting the Right Sensors, Data Pre-processing, Correct sensor-specific issues like atmospheric interference, radiometric calibration, and geometric correction to ensure data accuracy. Image Fusion, Feature Extraction, Change Detection, Forest Health Assessment, Identify stress factors, such as pest infestations or disease outbreaks. Biomass Estimation, Wildfire Detection and Monitoring, Utilize thermal infrared and multispectral imagery to detect and monitor wildfires in real-time, Monitor fire perimeters and assess post-fire impacts, Deforestation Detection.
Before you can analyze your remote sensing data, you need to pre-process it to correct for errors, distortions, and inconsistencies. Pre-processing steps may include radiometric, geometric, and atmospheric correction, image registration, mosaicking, cropping, masking, and resampling. These steps will ensure that your data is comparable, compatible, and ready for further processing and analysis. You can use various GIS software and tools to perform pre-processing tasks, such as ArcGIS, QGIS, ENVI, or Google Earth Engine.
One of the most common applications of remote sensing for forestry management is to classify and segment the forest into different types, classes, or regions. Classification and segmentation techniques can help you identify and map forest cover, species, density, health, structure, change, and disturbance. You can use different methods, such as supervised, unsupervised, or object-based, to assign labels or categories to your data based on spectral, spatial, or contextual features. You can also use machine learning or deep learning algorithms to improve your classification and segmentation accuracy and efficiency.
You can integrate the processed remote sensing data into a Geographic Information System (GIS) for spatial analysis and mapping. Utilize time-series remote sensing data to monitor changes in forest cover, including deforestation, reforestation, and disturbances. Identify areas of interest for further analysis. Overlay forest data with other geospatial layers, like land ownership or topography, for comprehensive forestry management.
Another important application of remote sensing for forestry management is to extract and calculate indicators and metrics that reflect the status and dynamics of the forest. Indicators and metrics can help you monitor and evaluate forest resources, functions, services, and impacts. You can use various GIS functions and operations to derive indicators and metrics from your data, such as area, perimeter, shape, density, volume, biomass, carbon stock, growth rate, fragmentation, diversity, and vulnerability.
The final step of optimizing your remote sensing data for forestry management is to visualize and communicate your results in a clear and effective way. Visualization and communication can help you present and share your findings, insights, and recommendations with different audiences and stakeholders. You can use various GIS techniques and tools to create maps, charts, graphs, animations, dashboards, or reports that showcase your data and analysis. You can also use GIS to integrate your data and results with other sources of information, such as field surveys, inventories, models, or scenarios.
Boots on the ground verification of the remote sensing data collection, how much and what frequency? Is there anything published for planted forest restoration?
I’m running lidar heights backwards to correspond with VBAR on the ground, so height translates to ba/tree… and then get volume per given height, density through lidar points, spp from color and ground plots -fde