DeforestationWildfireLand Conversion

Satellite Analysis for Rondônia, Brazil

Name: Satalyse
Author: Satalyse

Rondônia sits on the arc of deforestation, one of the most rapidly cleared forest frontiers on Earth. Satellite-derived NDVI change detection here has become critical for NGOs, government agencies, and ESG investors tracking Amazon loss through periodic satellite monitoring.

-10.90°, -61.90°

Tropical humid with a defined dry season (Jun–Sep) that correlates with peak fire activity and accelerated clearing.

What you can analyze here

The Satalyse API covers every major satellite analysis type for Rondônia, Brazil. These endpoints are most relevant for this location:

Change Detection

POST /api/v1/satellite/detect-changes

Anomaly Detection

POST /api/v1/satellite/detect-anomalies

Risk Scoring

POST /api/v1/satellite/score-risk

Code example

This snippet is pre-filled with Rondônia, Brazil's coordinates and calls the Change Detection endpoint.

rondonia_analysis.py

1"color:#c084fc">import requests
2
3response = requests.post(
4    "https://api.satalyse.dev/api/v1/satellite/detect-changes",
5    headers={"Authorization": "Bearer sat_sk_live_..."},
6    json={
7        "geometry": {
8            "type": "Polygon",
9            "coordinates": [[
10            [-61.93, -10.87],
11            [-61.87, -10.87],
12            [-61.87, -10.93],
13            [-61.93, -10.93],
14            [-61.93, -10.87]
15            ]]
16        },
17        "start_date": "2024-01-01",
18        "end_date":   "2025-01-01",
19        "sensitivity": "medium"
20    }
21)
22
23data = response.json()
24changes = data["changes_detected"]
25"color:#c084fc">print("color:#c084fc">f"Changed area: {changes['total_change_area_km2']} km²")
26"color:#c084fc">print("color:#c084fc">f"Change type:  {changes['change_type']}")
27"color:#c084fc">print("color:#c084fc">f"Confidence:   {changes['confidence_avg']:.0%}")

Data sources used for Rondônia, Brazil

Source	What it provides	Resolution
Sentinel-2	Multispectral optical imagery (NDVI, land cover)	10 m
Landsat 8	Long-term land surface reflectance and temperature	30 m
MODIS	Daily global coverage, fire, vegetation anomalies	250 m – 1 km
SRTM	Digital elevation model, terrain, slope, aspect	30 m
CHIRPS	Historical and near-real-time precipitation estimates	5 km
JRC Global Surface Water	Water presence history and seasonal flood extent	30 m