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A consortium of organizations has developed a Landscape Monitor for the Netherlands that consists of six indicators that together are suitable for describing changes in the landscape. It concerns buildings, land use, openness, green landscape elements, relief and historical line elements. In this study we describe the quality of the indicators, which are based on national source data., Een consortium van organisaties heeft een Monitor Landschap voor Nederland ontwikkeld die bestaat uit zes indicatoren die samen geschikt zijn om veranderingen in het landschap te beschrijven. Het gaat om bebouwing, landgebruik, openheid, opgaand groen, reliëf en historische lijnelementen. In deze studie beschrijven we de kwaliteit van de indicatoren, die gebaseerd zijn op landelijk brondata.

Land use and its change impact food security, carbon cycling, biodiversity, and, hence, the condition of ecosystems to mitigate and adapt to climate change, support economic prosperity, and human well-being. To support and guide policy actions between the economy and the environment, harmonized time series datasets, transparent methodologies, and easily interpretable statistics are needed. Therefore, monitoring of the function and condition of lands and their change, along with properly agreed methodologies and freely accessible data, are essential. The Copernicus Land Monitoring Service has produced over 20 years of Corine Land Cover datasets for 39 countries in Europe, which allows continental-wide harmonized and comparable monitoring and accounting of land cover and land use change at a high thematic resolution and in a long time series (2000–2018). With the upcoming 2024 update, the time series will reach a unique product worldwide in terms of time series length, spatial resolution, extent, and thematic detail, enabling policymakers and the scientific community to address the main anthropogenic drivers of land and ecosystem degradation. This paper describes a unified approach for producing continental-wide land accounts that aligns with internationally agreed-upon standards for measuring the environment and its relationship with the economy. Furthermore, the study provides a harmonized time series of geospatial data for deriving land accounts and provides statistics of land cover and land use status and changes for a twenty-year period. All geospatial data and statistics presented in this paper are freely accessible and downloadable to serve other studies.

LGN2022 is a grid file that shows Dutch land use in 2022 with a spatial resolution of 5m. The legendconsists of 51 land use classes and is comarable with LGN2021. However, some LGN classes changed theirdefinition slightly due to adaptations in the production methodology. LGN2022 is distinguishing the main agricultural crops, forest, water, nature and urban classes. LGN2022 is acontinuation of the LGN2021. Some small modifications were made. The most important ones are:- Use of OSM and Top10NL for mapping solar parks- Introduction of small linear water elements- Subdivision of BNL class 200 'Overig grondgebruik' into 200, 220 'Basaltblokken en steenglooiingen and 230'bebouwd gebied'- Introduction of MRT rules for new nature areas- New dataset 'Agrarisch Natuur- en Landschapsbeheer (ANLb 030 verantwoordingsaanvraag (versie februari 2023)'to define 'Natuurlijk beheerde agrarische graslanden'.- New way to produce the LGN classes 251, 252 and 252. See also 'Factsheet productie LGN2022' for more details. The following files were used to create LGN2022: Top10NL (version February 2023), Basis Registratie Percelen2022 (BRP2022), Basiskaart Natuur en Landschap 2022 (BNL2022), Basisregistratie Adressen en Gebouwen (BAG May2023), Open Streetmap (OSM January 2023), Bestand BodemGebruik 2017 (BBG2017), Beheertypen IMNA (February2023), Agrarisch Natuur- en Landschapsbeheer (ANLb 030 verantwoordingsaanvraag (versie februari 2023)) andLGN2021 for the nature areas. The dataset is public since September 2023 which is made possible by WOT Natuur & Milieu, Het Waterschapshuis,Interprovinciaal Overleg, RIVM and Wageningen Environmental Research., LGN2022 is a grid file that shows Dutch land use in 2022 with a spatial resolution of 5m. The legendconsists of 51 land use classes and is comarable with LGN2021. However, some LGN classes changed theirdefinition slightly due to adaptations in the production methodology. LGN2022 is distinguishing the main agricultural crops, forest, water, nature and urban classes. LGN2022 is acontinuation of the LGN2021. Some small modifications were made. The most important ones are:- Use of OSM and Top10NL for mapping solar parks- Introduction of small linear water elements- Subdivision of BNL class 200 'Overig grondgebruik' into 200, 220 'Basaltblokken en steenglooiingen and 230'bebouwd gebied'- Introduction of MRT rules for new nature areas- New dataset 'Agrarisch Natuur- en Landschapsbeheer (ANLb 030 verantwoordingsaanvraag (versie februari 2023)'to define 'Natuurlijk beheerde agrarische graslanden'.- New way to produce the LGN classes 251, 252 and 252. See also 'Factsheet productie LGN2022' for more details. The following files were used to create LGN2022: Top10NL (version February 2023), Basis Registratie Percelen2022 (BRP2022), Basiskaart Natuur en Landschap 2022 (BNL2022), Basisregistratie Adressen en Gebouwen (BAG May2023), Open Streetmap (OSM January 2023), Bestand BodemGebruik 2017 (BBG2017), Beheertypen IMNA (February2023), Agrarisch Natuur- en Landschapsbeheer (ANLb 030 verantwoordingsaanvraag (versie februari 2023)) andLGN2021 for the nature areas. The dataset is public since September 2023 which is made possible by WOT Natuur & Milieu, Het Waterschapshuis,Interprovinciaal Overleg, RIVM and Wageningen Environmental Research.

LGN2021 is a grid database presenting the Dutch land use in 20218 at a spatial resolution of 5 m. The database has 51 land use classes, distinguishing the main agricultural crops, forest, water, nature and urban classes.LGN2021 is a continuation of the LGN 2018-2020 versions. However, next to the increase in land use classes from 48 to 51, the production method has also been modified. The production method of LGN2021 is as close as possible to Basiskaart Natuur en Landschap (BNL) (resolution 2.5 m, the same versions of the base files, the same derived files, class definitions are taken over as much as possible and unambiguous recording of LGN classes based on Multi Reclass Tool (MRT)). The following files were used to create LGN2021: Top10NL (version November 2021), Basis Registration Parcels 2021 (BRP2021), Land use database 2015 (BBG2015), naturemanagement types from Information Model Nature 2021 (IMNa2021), terrain elevation database ofNetherlands (AHN3/4), satellite images (particularly Sentinel-2) and aerial photos from 2021. Every year a newLGN version is published that shows the land use of the previous reference year. The development of LGN2021took place on a commercial basis within the Applied Spatial Research team of Wageningen Environmental Research (WENR). The file is currently used by, among others, the WOT, the RIVM, the provinces and the waterboards. From 2023, the LGN file will be made available as Open Data., LGN2021 is een gridbestand dat het Nederlands landgebruik in 2021 met een ruimtelijke resolutie van 5 m weergeeft. Het bestand kent 51 landgebruiksklassen waarbij de belangrijkste landbouwgewassen, bos, water, natuur en stedelijke klassen worden onderscheiden. LGN2021 is een voortzetting van de LGN 2018-2020 versies. Echter, naast een toename van het aantal klassen van 48 naar 51 is ook de productiemethodiek aangepast. De productiemethode van LGN2021 sluit zoveel mogelijk aan bij Basiskaart Natuur en Landschap (BNL) (resolutie 2.5 m, dezelfde versies van de basisbestanden, zelfde afgeleide bestanden, overgenomen klasse definities en eenduidige vastlegging van LGN-klassen op basis van MultiReclass Tool (MRT)). Voor het maken van LGN2021 zijn onder andere de volgende bestanden gebruikt: Top10NL (versie november 2021), Basis Registratie Percelen 2021 (BRP2021), Bestand Bodem Gebruik 2015 (BBG2015), beheertypen uit Informatie Model Natuur 2021 (IMNa2021), Algemeen Hoogte bestand Nederland (AHN3/4), satellietbeelden (m.n. Sentinel-2) en luchtfoto’s uit 2021. Elk jaar verschijnt er een nieuwe LGN-versie die het landgebruik van het voorgaande referentiejaar weergeeft. De ontwikkeling van LGN2021 heeft nog op commerciële basis plaatsgevonden binnen het team Applied Spatial Research van Wageningen Environmental Research (WENR). Het bestand wordt momenteel gebruikt door o.a. de WOT, het RIVM, de provincies en de waterschappen. Vanaf 2023 wordt het LGN-bestand als Open Data beschikbaar gesteld.

Questions were asked in the House of Representatives about the explosive increase in solar parks and the effects on agriculture, soil, biodiversity and landscape. Both negative (for example on the soil) and positive effects are predicted when opportunities for the development of biodiversity are exploited. In order to gain an impression of whether the concern about the soil is justified and whether there is already a benefit for biodiversity, an exploration was carried out in 25 solar parks in 2019 and 2020. The goals of this project were:1.Determining the vegetation on solar parks and the effect of layout and management on the diversity of species.2.Drawing up guidelines for the design and management of solar parks for an optimal balance between economic power production and biodiversity and landscape.3.Obtaining an up-to-date overview of solar parks in the Netherlands based on satellite images.4.Updating a literature study into the effects of solar parks on soil quality. Trefwoorden: zonneparken, zonne-energie, natuur, biodiversiteit, beheer, bodem, In de Tweede Kamer zijn vragen gesteld over de explosieve toename van zonneparken en de effecten op landbouw, bodem, biodiversiteit en landschap. Er worden zowel negatieve (bijvoorbeeld op de bodem) als positieve effecten voorspeld, wanneer kansen voor ontwikkeling van biodiversiteit worden benut. Om een indruk te krijgen of de bezorgdheid over de bodem terecht is en of er al winst voor biodiversiteit valt vast te stellen, is in 2019 en 2020 een verkenning uitgevoerd in 25 zonneparken. De doelen van dit project waren: 1.Het beschrijven van de vegetaties op zonneparken en het effect van inrichting en beheer op de soortenrijkdom.2.Het opstellen van richtlijnen voor inrichting en beheer van zonneparken voor een optimale balans tussen economische stroomproductie en biodiversiteit en landschap.3.Het verkrijgen van een actueel overzicht van zonneparken in Nederland op basis van satellietbeelden. 4.Het updaten van een literatuuronderzoek naar de effecten van zonneparken op de bodemkwaliteit.

With the unprecedented availability of satellite data and the rise of global binary maps, the collection of shared reference data sets should be fostered to allow systematic product benchmarking and validation. Authoritative global reference data are generally collected by experts with regional knowledge through photo-interpretation. During the last decade, crowdsourcing has emerged as an attractive alternative for rapid and relatively cheap data collection, beckoning the increasingly relevant question: can these two data sources be combined to validate thematic maps? In this article, we compared expert and crowd data and assessed their relative agreement for cropland identification, a land cover class often reported as difficult to map. Results indicate that observations from experts and volunteers could be partially conflated provided that several consistency checks are performed. We propose that conflation, i.e., replacement and augmentation of expert observations by crowdsourced observations, should be carried out both at the sampling and data analytics levels. The latter allows to evaluate the reliability of crowdsourced observations and to decide whether they should be conflated or discarded. We demonstrate that the standard deviation of crowdsourced contributions is a simple yet robust indicator of reliability which can effectively inform conflation. Following this criterion, we found that 70% of the expert observations could be crowdsourced with little to no effect on accuracy estimates, allowing a strategic reallocation of the spared expert effort to increase the reliability of the remaining 30% at no additional cost. Finally, we provide a collection of evidence-based recommendations for future hybrid reference data collection campaigns.

We developed a spatio-temporal model for the Netherlands to estimate environmental exposure to individual agricultural pesticides at the residential address for application in a national case-control study on Parkinson's disease (PD). Data on agricultural land use and pesticide use were combined to estimate environmental exposure to pesticides for the period 1961 onwards. Distance categories of 0-50 m, >50-100 m, >100-500 m and >500-1000 m around residences were considered. For illustration purposes, exposure was estimated for the control population (n=607) in the PD case-control study. In a small validation effort, model estimates were compared with pesticide measurements in air and precipitation collected at 17 stations in 2000-2001. Estimated exposure prevalence was higher for pesticides used on commonly cultivated (rotating) crops than for pesticides used on fruit and bulbs only. Prevalence increased with increasing distance considered. Moderate-to-high correlations were observed between model estimates (>100-500 m and >500-1000 m) and environmental pesticide concentrations measured in 2000-2001. Environmental exposure to individual pesticides can be estimated using relevant spatial and temporal data sets on agricultural land use and pesticide use. Our approach seems to result in accurate estimates of average environmental exposure, although it remains to be investigated to what extent this reflect personal exposure to agricultural pesticides.Journal of Exposure Science and Environmental Epidemiology advance online publication, 22 March 2017; doi:10.1038/jes.2017.3.

The GAES database (Version 01a) is a newly developed Global Agro-Environmental Stratification within the EU SIGMA (Stimulating Innovation for Global Monitoring of Agriculture) project. GAES will serve as a new agro-environmental stratification for better global monitoring of the agricultural production on the basis of Earth Observation data and crop growth models. It is anticipated that GAES will be exploited for a wider range of applications, some within SIGMA, towards data gap analysis that identifies agro-environmental strata with limited capacity and monitoring data on agricultural production. GAES was produced by applying segmentation techniques to newly available global agroenvironmental data with a high spatial resolution re-sampled to 1 km spatial resolution. The datasets were able to stratify the agricultural production zones according to the region’s agro-environmental characteristics, including climatic regimes, soil, terrain, elevation conditions, water availability and land cover proprieties. The GAES strata obtained by segmentation at four different spatial levels (with Level 4 as the most detailed) have been further characterised and described in terms of phenology (e.g. start and peak of the growing season), agricultural (water) management practices, field size, biotic constraints, national and sub-national crop production statistics, GDP, transport infrastructure conditions or market accessibility. The GAES database has four hierarchical layers, with 92 attributes. GAES Level 1 has 194 agro-environmental (AE) types (818 strata); GAES Level 2 has 300 AE types (1,688 strata); GAES Level 3 has 374 AE types (2,087 strata); GAES Level 4 has 516 AE types (3,208 strata). GAES typology is a combination of temperature, altitude, parent material and land cover characteristics. GAES Version 01 has become freely available.