Performance Analysis of FOREST HEALTH MONITORING AND MAPPING
Implementation plan:
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Step 1: Initially we load and pre-process the forest environment dataset.
Step 2: Next we Implement the inverse watershed segmentation (IWS) algorithm for the pre segmentation process.
Step 3: Next we Implement the DSM to evaluate forest structure, estimate biomass, and assess environmental health.
Step 4: Next we Implement theSimple Linear iterative Clustering and pointnet architecture (SLC-PA) in order to achieve precise segmentation subsequent to mapping.
step 5: Next we Implement the BEST-C-GRU algorithm for detecting and categorizing forestation, degradation, and deforestation.
Step 6: Next we predict the land disturbance using the NRT-MONITOR algorithm.
Step 7: Finally, Generate the graph for,
7.1 : Number of epochs vs Accuracy
7.2 : Number of epochs vs. Precision
7.3 : Number of epochs vs. recall
7.4 : Number of epochs vs. F-measure
7.5 : Number of epochs vs detection rate
Software Requirement:
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1. Development Tool: Python – 3.11.4
2. Operating System: Windows 10 (64-bit)
Dataset link:
Note: –
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1. If the above plan does not satisfy your requirement, please provide the processing details, like the above step-by-step.
2. Please note that this implementation plan does not include any further steps after it is put into implementation.
3. If the above plan satisfies your requirement please confirm with us.
Existing:
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Title: Timeliness in forest change monitoring: A new assessment framework demonstrated using Sentinel-1 and a continuous change detection algorithm
