Towards Zero Hunger – Hands-on: Land Cover Classification with Optical & Radar Data

Land Cover Classification
with Optical & Radar Data

with Optical Data
using Random Forest Classifier
in Google Earth Engine


This hands-on tutorial is part of a series of online learning materials.

Towards Zero Hunger will give you insights into the potential of remote sensing technologies to support the United Nations’ Sustainable Development Goal 2: Zero Hunger.


This tutorial will guide you through the process of creating a land cover map using both SAR and optical data in Google Earth Engine. The tutorial involves working with optical data from Landsat 8 and SAR data from Sentinel-1. Start by loading the dataset and preparing it for classification. Then use a supervised classification algorithm, Random Forest, to generate the land cover map. Finally, assess the accuracy of the classification using error (confusion) matrices.

Benefits of using both Radar and optical data (Podest et all. 2020)]:

  • Improved land cover classification
  • Ability to provide a more detailed characterization of land changes
    • Broad classes of land cover and change (optical)
    • Land surface roughness and soil moisture (radar)
  • Ability to monitor vegetation health more accurately for agricultural purposes, forest disturbances, and land degradation
    • NDVI and/or EVI (optical)
    • Plant structure and volume (radar)

SDG 2: Zero Hunger Relevance

Land-use and land-cover maps are essential tools for decision-makers in formulating policies for sustainable development. Owing to continuous data acquisition at repetitive intervals, EO data enables the evaluation of the static (types, area, and arrangement) and dynamic attributes of land cover (rates of change).

Land cover maps can be utilized for Zero Hunger targets 2-3, 2-4, and 2-C by providing information on the distribution of land cover types, their extent, and their change in time. Moreover, they can reliably and consistently contribute to the monitoring, and reporting of the SDG indicator: 2.4.1 Proportion of agricultural area under productive and sustainable agriculture.

For more detail check the documentation ‘Specifications of land cover datasets for SDG indicator monitoring from (Carter & Herold, 2019) on:

Explore below the SDG 2: Zero Hunger relevance of applications related to

  • Crop & Grazing Land Monitoring Agriculture & Livestock
  • Forest & Water Monitoring Forestry & Agroforestry – Fishery & Aquaculture
  • Environmental Monitoring Agriculture & Livestock – Forestry & Agroforestry – Fishery & Aquaculture

Source Tutorial

This tutorial is part of the NASA Applied Remote Sensing Training Program (ARSET). Original creators: Erika Podest, Amber McCullum, Juan Luis Torres Perez & Sean McCartney

The original content can be found at:

NASA-ARSET Training: Forest Mapping and Monitoring with SAR Data
Part 2: Land Cover Classification with Radar and Optical Data

YouTube

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On the ARSET training site you will find a video with step-by-step instructions and explanations for the tutorial. The first part of the video provides relevant background information. It gives brief information on attributes of optical and SAR data relevant for forest monitoring and explains image classification with Random Forest. You will also find the presentation slides and Q&A transcript from the course.

Note that there are some updates in the version provided here. For instance, USGS Landsat 8 Surface Reflectance Tier 1 data is deprecated and replaced by USGS Landsat 8 Level 2, Collection 2, Tier 1. The cloud masking function is adapted to the new dataset (Stack Exchange, n.d.).


Credit

This topic was created with the help of learning materials that were kindly provided by:

Sources


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