Deforestation Detection, Forest Health, and Biodiversity Monitoring

Module: Sustainable Tech | Difficulty: Premium

Vegetation Indices

Forest Carbon Stock

Comparison

| Metric | Healthy | Stressed | Degraded | |--------|---------|----------|----------| | NDVI | > 0.7 | 0.4-0.7 | < 0.4 | | Canopy cover | > 80% | 50-80% | < 50% | | Tree density | > 500/ha | 200-500/ha | < 200/ha | | Biodiversity index | > 3.0 | 2.0-3.0 | < 2.0 |

Python Implementation

import numpy as np
from sklearn.cluster import KMeans

class ForestMonitor:
    def __init__(self):
        self.baseline = None

    def calculate_indices(self, nir, red, blue, swir):
        ndvi = (nir - red) / (nir + red + 1e-10)
        evi = 2.5 * (nir - red) / (nir + 6 * red - 7.5 * blue + 1)
        ndmi = (nir - swir) / (nir + swir + 1e-10)
        return ndvi, evi, ndmi

    def detect_deforestation(self, current, threshold=0.3):
        if self.baseline is None:
            self.baseline = current
            return np.zeros_like(current, dtype=bool)
        return (self.baseline - current) > threshold

    def estimate_biomass(self, canopy_height, canopy_cover, wood_density=0.5):
        return canopy_height * canopy_cover * 100 * wood_density * 1.5

Research Insight: Transfer learning enables deforestation detection with 95% accuracy using only 100 labeled examples per region.