Wettelijke Audit Basisregistratie Topografie 2020 : resultaten van de vierde externe audit op de kwaliteit van de BRT

Storm, M.H.; Knotters, M.; Schuiling, Rini; Smidt, R.A.; Dorland, Gert van; Wegman, R.M.A.; Soil, water Alterra; Biodiversity, Alterra

doi:10.18174/554475

Cited by 1 publication

(4 citation statements)

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“…To the authors' knowledge there was no concurrent validation data in East Africa. Therefore, we validated both the high resolution surface water map and the global JRC water map in the Netherlands using the TOP25Raster (25 cm resolution) (Kadaster, 2021) as validation data. Most water in the Netherlands has little seasonal variation (Lintsen, 2002).…”

Section: Methodsmentioning

confidence: 99%

“…To validate the surface water maps, we determined the Kappa Coefficient (KC) and Error of Ommission and Commission with the TOP25raster data set in the Netherlands (Section 2.4.2), as detailed concurrent surface water validation data was unavailable in East Africa. TOP25raster is a digital cartographic representation at 25 cm resolution of the TOP10NL topographical base map of the Netherlands (Kadaster, 2021). TOP10NL is made through aerial photos, panorama photos, fieldwork, and information from external sources (Kadaster, 2021).…”

Section: Methods and Datamentioning

confidence: 99%

“…TOP25raster is a digital cartographic representation at 25 cm resolution of the TOP10NL topographical base map of the Netherlands (Kadaster, 2021). TOP10NL is made through aerial photos, panorama photos, fieldwork, and information from external sources (Kadaster, 2021). The version used in this study was updated using aerial photos collected in flights during the spring and summer of 2020.…”

Section: Methods and Datamentioning

confidence: 99%

“…Planet basemaps (Norway's International Climate and Forest Initiative, 2021) and Sentinel‐2 (Drusch et al., 2012) satellite scenes were used for the “2021” map. The JRC surface water map (Pekel et al., 2016), and the TOP10NL (Kadaster, 2021) were used to validate and compare the “2021” map. The ERA5‐Land (Muñoz Sabater, 2019), NDVI and NDWI were obtained from Google Earth Engine (Gorelick et al., 2017), and the WorldClim 2 data set from Fick and Hijmans (2017).…”

Section: Data Availability Statementmentioning

confidence: 99%

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Predicting Continental Scale Malaria With Land Surface Water Predictors Based on Malaria Dispersal Mechanisms and High‐Resolution Earth Observation Data

Kalthof,

Gravey,

Wijnands

et al. 2023

GeoHealth

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Despite malaria prevalence being linked to surface water through vector breeding, spatial malaria predictors representing surface water often predict malaria poorly. Furthermore, precipitation, which precursors surface water, often performs better. Our goal is to determine whether novel surface water exposure indices that take malaria dispersal mechanisms into account, derived from new high‐resolution surface water data, can be stronger predictors of malaria prevalence compared to precipitation. One hundred eighty candidate predictors were created by combining three surface water malaria exposures from high‐accuracy and resolution (5 m resolution, overall accuracy 96%, Kappa Coefficient 0.89, Commission and Omission error 3% and 13%, respectively) water maps of East Africa. Through variable contribution analysis a subset of strong predictors was selected and used as input for Boosted Regression Tree models. We benchmarked the performance and Relative Contribution of this set of novel predictors to models using precipitation instead of surface water predictors, alternative lower resolution predictors, and simpler surface water predictors used in previous studies. The predictive performance of the novel indices rivaled or surpassed that of precipitation predictors. The novel indices substantially improved performance over the identical set of predictors derived from the lower resolution Joint Research Center surface water data set (+10% R2, +17% Relative Contribution) and over the set of simpler predictors (+18% R2, +30% Relative Contribution). Surface water derived indices can be strong predictors of malaria, if the spatial resolution is sufficiently high to detect small waterbodies and dispersal mechanisms of malaria related to surface water in human and vector water exposure assessment are incorporated.

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Section: Methodsmentioning

confidence: 99%

Section: Methods and Datamentioning

confidence: 99%