Urban Heat IslandAir Quality DegradationFlooding (Yamuna)

Satellite Analysis for Delhi, India

Name: Satalyse
Author: Satalyse

Delhi NCR is one of the world's fastest-expanding urban agglomerations, converting agricultural land to concrete at a rate detectable on a quarterly satellite revisit. Urban heat island, groundwater depletion, and loss of tree cover are all measurable from orbit, making the region a priority for environmental compliance monitoring and urban planning analytics.

28.67°, 77.22°

Semi-arid with extreme summer heat (Apr–Jun), monsoonal rains (Jul–Sep), and dense winter fog, the dry season produces clear satellite imagery windows ideal for change detection.

What you can analyze here

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

Change Detection

POST /api/v1/satellite/detect-changes

Feature Extraction

POST /api/v1/satellite/extract-features

Anomaly Detection

POST /api/v1/satellite/detect-anomalies

Code example

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

delhi_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            [77.19, 28.64],
11            [77.25, 28.64],
12            [77.25, 28.7],
13            [77.19, 28.7],
14            [77.19, 28.64]
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 Delhi, India

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