Spatial Accuracy Assessment and Integration of Global Land Cover Datasets

Nandin-Erdene Tsendbazar,Martin Herold,Sytze De Bruin,Steffen Fritz

doi:10.3390/rs71215804

Nandin-Erdene Tsendbazar, Martin Herold + Show 2 more

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https://doi.org/10.3390/rs71215804

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Abstract

Along with the creation of new maps, current efforts for improving global land cover (GLC) maps focus on integrating maps by accounting for their relative merits, e.g., agreement amongst maps or map accuracy. Such integration efforts may benefit from the use of multiple GLC reference datasets. Using available reference datasets, this study assesses spatial accuracy of recent GLC maps and compares methods for creating an improved land cover (LC) map. Spatial correspondence with reference dataset was modeled for Globcover-2009, Land Cover-CCI-2010, MODIS-2010 and Globeland30 maps for Africa. Using different scenarios concerning the used input data, five integration methods for an improved LC map were tested and cross-validated. Comparison of the spatial correspondences showed that the preferences for GLC maps varied spatially. Integration methods using both the GLC maps and reference data at their locations resulted in 4.5%–13% higher correspondence with the reference LC than any of the input GLC maps. An integrated LC map and LC class probability maps were computed using regression kriging, which produced the highest correspondence (76%). Our results demonstrate the added value of using reference datasets and geostatistics for improving GLC maps. This approach is useful as more GLC reference datasets are becoming publicly available and their reuse is being encouraged.

Highlights

Multiple global land cover (GLC) maps have been produced over the past decades
The Globeland30 map had higher correspondence in the tropical forest regions of was obserwveesdternfoArfrtihcae, CGhalod,eUbgealnadna,dT3a0nzianniao, tMhaedragraescgairo, annsd. eaTshterensepardt iofffeSroeunthcAesfriacarerelaaltesdotohighlighted in Figure 7f, whicghrasislllaunsdtarraeatse.sAtgheenemralatepnsdewncyitohf ohviegr-hreepsretsecnotirnrgetshpe gornasdsleanndccelaasstwaasgailvsoeonbsleorvceadtfioornth.eThe strengths of the GLC maGploseboevlaenrd3o0nineoathnerortehgeiornsi.nThdesieffdeirffeenretncreesgaireonalssoshhigohwlighttehdeinpFoigteurnet7iaf,lwohfichcrileluasttirantegs an improved GLC map by inthteegmraapstiwnigth thhigehmest. correspondence at a given location
Our study provides an example of dealing with such diversities by harmonizing the thematic and spatial support differences of the reference datasets and using them for model-based geostatistical estimations

Summary

Introduction

Multiple global land cover (GLC) maps have been produced over the past decades. These maps are used for various applications such as climate modeling, food security, biodiversity, ecosystem services and environmental monitoring [1]. GLC map production is progressing towards higher resolution maps, namely the Land Cover-CCI (LC-CCI) maps at 300 m resolution and the Fine Resolution Observation and Monitoring (FROM-GLC) and Globeland maps at 30 m resolution [2,3]. These maps were developed using different input data and methods [4], and as a consequence, considerable disagreements amongst GLC maps have been found [4,5]. Such accuracies mostly do not meet the requirements of GLC map users [7] and there is a need to improve GLC maps

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