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 Department of Environment,  Tonga

Environmental conditions and anthropogenic impacts are key influences on ecological processes and associated ecosystem services. Effective management of Tonga’s marine ecosystems therefore depends on accurate and up-to-date knowledge of environmental and anthropogenic variables. Although many types of environmental and anthropogenic data are now available in global layers, they are often inaccessible to end users, particularly in developing countries with limited accessibility and analytical training.

 Pacific Data Hub

Communities in Fiji rely on provisioning services from landscape resources such as agricultural and forestry-related production, and climate regulation determined by the mix of landscape resources across space. Accurate mapping and monitoring of patterns of land use and land cover (LULC) over time at scales relevant to livelihood processes is important for informing landscape management, land use policies, and climate-smart sustainable development.

 Pacific Data Hub

## Overview

A geospatial dataset of point geometries with a land use / land cover label and several remote-sensing derived predictor variables that can be used to train and test a land use / land cover classifier.

This dataset was generated with support from a Climate Change AI Innovation Grant and the Australian Centre for International Agricultural Research.

Each of the point geometries was assigned one of the following class labels:

 Pacific Data Hub

To evaluate land use and land cover (LULC) maps an independent and representative test dataset is required. Here, a test dataset was generated via stratified random sampling approach across all areas in Fiji not used to generate [training data](https://github.com/livelihoods-and-landscapes/ccai-data/tree/main/fiji-…) (i.e. all Tikinas which did not contain a training data point were valid for sampling to generate the test dataset). Following equation 13 in [Olofsson et al.