Continental-scale land cover mapping at 10 m resolution over Europe (ELC10)

Widely used European land cover maps such as CORINE are produced at medium spatial resolutions (100 m) and rely on diverse data with complex workflows requiring significant institutional capacity. We present a high resolution (10 m) land cover map (ELC10) of Europe based on a satellite-driven machine learning workflow that is annually updatable. A Random Forest classification model was trained on 70K ground-truth points from the LUCAS (Land Use/Cover Area frame Survey) dataset. Within the Google Earth Engine cloud computing environment, the ELC10 map can be generated from approx. 700 TB of Sentinel imagery within approx. 4 days from a single research user account. The map achieved an overall accuracy of 90% across 8 land cover classes and could account for statistical unit land cover proportions within 3.9% (R2 = 0.83) of the actual value. These accuracies are higher than that of CORINE (100 m) and other 10-m land cover maps including S2GLC and FROM-GLC10. We found that atmospheric correction of Sentinel-2 and speckle filtering of Sentinel-1 imagery had minimal effect on enhancing classification accuracy (< 1%). However, combining optical and radar imagery increased accuracy by 3% compared to Sentinel-2 alone and by 10% compared to Sentinel-1 alone. The conversion of LUCAS points into homogenous polygons under the Copernicus module increased accuracy by <1%, revealing that Random Forests are robust against contaminated training data. Furthermore, the model requires very little training data to achieve moderate accuracies - the difference between 5K and 50K LUCAS points is only 3% (86 vs 89%). At 10-m resolution, the ELC10 map can distinguish detailed landscape features like hedgerows and gardens, and therefore holds potential for aerial statistics at the city borough level and monitoring property-level environmental interventions (e.g. tree planting)

Testing different remote sensing products for observing urban tree canopy and their implications for valuation of regulating ecosystem services in monetary ecosystem accounts

Barton, D.N. & Venter, Z.S. 2023. Testing different remote sensing of urban tree canopy and implications for valuation of regulating ecosystem services in monetary ecosystem accounts. NINA Report 2261. Norwegian Institute for Nature Research This report assesses regulating ecosystem services (ES) from urban trees in an accounting pilot area in a subset of Oslo’s built zone for which different remote sensing data were available (Ter-raSAR-X, Sentinel-1, Sentinel-2, LiDAR; LiDAR-canopy segmentation. We tested ecosystem service estimates derived from these sources of remote sensing data. We used i-Tree Eco to model ecosystem services from individual trees. We used a non-parametric Bayesian network to generalize the regulating services calculated by i-Tree Eco for municipal trees managed by Oslo’s Urban Environment Agency, to all trees in the accounting study area. For 108 000 tree canopy objects within the study area, we find that monetary estimates for carbon storage vary between 77 – 176 million NOK, and annual flows of carbon sequestration, air pollution mitigation and run-off regulation vary between 6 – 11 million NOK / year. The variation in these estimates is explained by two factors: (i) the difference in the remote sensing data sources that are used to identify tree canopy heights, and (ii) the increasing overestimation of canopy area with a de-creasing spatial resolution of remote sensing data. For future urban ecosystem accounts, we recommend building an emulation model for value generalization purposes using a parametric regression model, to avoid the loss of precision due to the discretization of the data required by the non-parametric approach of Bayesian Networks used in this study. I-Tree Eco is not open source model code. Testing the open-source INCA-Tool (Buchhorn et al., 2022) on bespoke LiDAR data of vegetation structure collected by municipalities seems a promising way forward. Given the periodicity of LiDAR data updating in Oslo of approximately 4 years, and the corre-spondence with the municipal planning cycle, we would recommend that any urban ecosystem physical accounts for Oslo are updated every 4 years as well. In future research, optimal report-ing periods for change detection could also be evaluated.Barton, D.N. & Venter, Z.S. 2023. Testing different remote sensing of urban tree canopy and implications for valuation of regulating ecosystem services in monetary ecosystem accounts. NINA Report 2261. Norwegian Institute for Nature Research Rapporten beregner regulerende økosystemtjenester fra bytrær i et pilotområde innenfor Oslo’s byggesone der vi hadde tilgang til fjernmålingsdata for det samme området (TerraSAR-X, Senti-nel-1, Sentinel-2, LiDAR; LiDAR-trekrone-segmentering). Vi beregnet økosystemtjenester fra enkelt-trær basert på ulike typer fjernmålingsdata ved hjelp av i-Tree Eco modellen. Vi brukte en ikke-parametrisk Bayesiansk nettverksmodell for å generalisere de fysiske økosystemtjenes-tene beregnet på bytrær forvaltet av Oslo Kommunes Bymiljøetat til alle trær i studie-området. For de 108 000 trekrone-objektene vi identifiserte i studieområdet, beregnet vi kroneverdien av karbonlagring til mellom 77 – 176 million NOK totalt, og samlet årlig verdi av karbonopptak, luftrensing og overvannsregulering til mellom 6- 11 millioner NOK/år. Usikkerheten i disse estimatene skyldes to forhold: (i) forskjellen i fjernmålingsdata i identifisering av trekronehøyder, og (ii) økende over-estimering av trekrone-areale ved reduksjon i romlig oppløsning av ulike fjernmålingsdata. I fremtidige beregninger for bynaturregnskap, anbefaler vi å bruke en parametrisk simuleringsmodell for å generalisere økosystemtjenester fra et utvalg til hele regnskapsområder. Dette vil unngå tap av nøyaktighet som i denne studien skyldes diskretisering av data som gjøres ved å bruke en ikke-parametrisk Bayesiansk modell. I-Tree Eco modellen er ikke åpen-kilde-kode. Uttesting av INCA-Tool modellene (Buchhorn et al., 2022) – en EU-standardisert modellpakke med åpen kildekode for å beregne økosystemtjenester - sammen med kommunale LiDAR data for vegetasjonsstruktur virker lovende. Gitt et omløp hittil på omtrent 4 år på LiDAR data i Oslo, som også samsvarer med periode for kommunevalg, anbefaler vi bytre-regnskap som også oppdateres hvert 4 år. I fremtidig utviklingsarbeid bør man også teste hva som er optimale regnskapsperiode i forhold til deteksjon av endring i trekrone-dekket

Global 10 m Land Use Land Cover Datasets: A Comparison of Dynamic World, World Cover and Esri Land Cover

The European Space Agency&rsquo;s Sentinel satellites have laid the foundation for global land use land cover (LULC) mapping with unprecedented detail at 10 m resolution. We present a cross-comparison and accuracy assessment of Google&rsquo;s Dynamic World (DW), ESA&rsquo;s World Cover (WC) and Esri&rsquo;s Land Cover (Esri) products for the first time in order to inform the adoption and application of these maps going forward. For the year 2020, the three global LULC maps show strong spatial correspondence (i.e., near-equal area estimates) for water, built area, trees and crop LULC classes. However, relative to one another, WC is biased towards over-estimating grass cover, Esri towards shrub and scrub cover and DW towards snow and ice. Using global ground truth data with a minimum mapping unit of 250 m2, we found that Esri had the highest overall accuracy (75%) compared to DW (72%) and WC (65%). Across all global maps, water was the most accurately mapped class (92%), followed by built area (83%), tree cover (81%) and crops (78%), particularly in biomes characterized by temperate and boreal forests. The classes with the lowest accuracies, particularly in the tundra biome, included shrub and scrub (47%), grass (34%), bare ground (57%) and flooded vegetation (53%). When using European ground truth data from LUCAS (Land Use/Cover Area Frame Survey) with a minimum mapping unit of &lt;100 m2, we found that WC had the highest accuracy (71%) compared to DW (66%) and Esri (63%), highlighting the ability of WC to resolve landscape elements with more detail compared to DW and Esri. Although not analyzed in our study, we discuss the relative advantages of DW due to its frequent and near real-time data delivery of both categorical predictions and class probability scores. We recommend that the use of global LULC products should involve critical evaluation of their suitability with respect to the application purpose, such as aggregate changes in ecosystem accounting versus site-specific change detection in monitoring, considering trade-offs between thematic resolution, global versus. local accuracy, class-specific biases and whether change analysis is necessary. We also emphasize the importance of not estimating areas from pixel-counting alone but adopting best practices in design-based inference and area estimation that quantify uncertainty for a given study area

Green Apartheid: Urban green infrastructure remains unequally distributed across income and race geographies in South Africa

Urban green infrastructure provides ecosystem services that are essential to human wellbeing. A dearth of national-scale assessments in the Global South has precluded the ability to explore how political regimes, such as the forced racial segregation in South Africa during and after Apartheid, have influenced the extent of and access to green infrastructure over time. We investigate whether there are disparities in green infrastructure distributions across race and income geographies in urban South Africa. Using open-source satellite imagery and geographic information, along with national census statistics, we find that public and private green infrastructure is more abundant, accessible, greener and more treed in high-income relative to low-income areas, and in areas where previously advantaged racial groups (i.e. White citizens) reside

Comparing Global Sentinel-2 Land Cover Maps for Regional Species Distribution Modeling

Mapping the spatial and temporal dynamics of species distributions is necessary for biodiversity conservation land-use planning decisions. Recent advances in remote sensing and machine learning have allowed for high-resolution species distribution modeling that can inform landscape-level decision-making. Here we compare the performance of three popular Sentinel-2 (10-m) land cover maps, including dynamic world (DW), European land cover (ELC10), and world cover (WC), in predicting wild bee species richness over southern Norway. The proportion of grassland habitat within 250 m (derived from the land cover maps), along with temperature and distance to sandy soils, were used as predictors in both Bayesian regularized neural network and random forest models. Models using grassland habitat from DW performed best (RMSE = 2.8 ± 0.03; average ± standard deviation across models), followed by ELC10 (RMSE = 2.85 ± 0.03) and WC (RMSE = 2.87 ± 0.02). All satellite-derived maps outperformed a manually mapped Norwegian land cover dataset called AR5 (RMSE = 3.02 ± 0.02). When validating the model predictions of bee species richness against citizen science data on solitary bee occurrences using generalized linear models, we found that ELC10 performed best (AIC = 2278 ± 4), followed by WC (AIC = 2367 ± 3), and DW (AIC = 2376 ± 3). While the differences in RMSE we observed between models were small, they may be significant when such models are used to prioritize grassland patches within a landscape for conservation subsidies or management policies. Partial dependencies in our models showed that increasing the proportion of grassland habitat is positively associated with wild bee species richness, thereby justifying bee conservation schemes that aim to enhance semi-natural grassland habitat. Our results confirm the utility of satellite-derived land cover maps in supporting high-resolution species distribution modeling and suggest there is scope to monitor changes in species distributions over time given the dense time series provided by products such as DW

Kart over norske hovedøkosystemer – en mulighetsstudie

Framstad, E., Bjørkelo, K., Bakkestuen, V., Mathiesen, H.F., Nowell, M.S., Strand, G.-H. & Venter, Z. 2021. Kart over norske hovedøkosystemer – en mulighetsstudie. NINA Rapport 2055. Norsk institutt for naturforskning. Et viktig grunnlag for økosystembasert forvaltning av norsk natur er heldekkende og omforente kart over hovedøkosystemene. Miljødirektoratet ønsker derfor å utforske mulighetene for å utvikle et slikt kart. NINA og NIBIO har i den forbindelse fått i oppdrag å utrede mulige tilnærminger. Dette omfatter å vurdere aktuelle inndelinger av økosystemtyper, eksisterende kartdata og nye teknikker. Miljødirektoratet har identifisert en rekke nasjonale og internasjonale bruksområder for et kart over hovedøkosystemer. Disse omfatter kartgrunnlag for fagsystemet for økologisk tilstand, økosystembasert forvaltning, kommunal forvaltning, grunnlag for arealanalyser og nasjonal statistikk, samt internasjonal rapportering. Det er et krav at et hovedøkosystemkart skal tilfredsstille nasjonale kartstandarder og være omforent på tvers av sektorer. Flere bruksområder krever en så detaljert tematisk inndeling og romlig presisjon at disse neppe kan dekkes ved et hovedøkosystemkart. Noen bruksområder vil kunne dekkes ved å knytte annen stedfestet miljøinformasjon, f.eks. artsforekomster, verdifulle naturområder eller områder med store karbonlagre, til hovedøkosystemkartet. Enkelte behov, f.eks. arealstatistikk, kan trolig løses mer kostnadseffektivt ved utvalgskartlegging enn ved et heldekkende hovedøkosystemkart. Det finnes ingen etablert forståelse av begrepet hovedøkosystem, men det oppfattes vanligvis som forholdsvis grove økosystemenheter med viktige fellestrekk, slik disse f.eks. er omtalt i Norges handlingsplan for naturmangfoldet (Natur for livet). Det er flere utfordringer knyttet til entydig definisjon og avgrensing av hovedøkosystemer. Disse knytter seg i hovedsak til grensedragning på tvers av økologiske gradienter, som mellom fastmark og våtmark, til grad av dekning av trær, og grader av menneskelig påvirkning. Det er særlige utfordringer knyttet til definisjoner av ulike typer av åpen fastmark og våtmark. Fjell og kyst som hovedøkosystemer kan avgrenses til fastmark eller omfatte fastmark, våtmark, snø/is og ferskvann. Det er mange ulike nasjonale og internasjonale inndelinger som kan vurderes som grunnlag for en inndeling av hovedøkosystemer. De nasjonale inndelingene er mer eller mindre sammenfallende med inndelingen brukt i Natur for livet, men kriteriene for avgrensing mellom enhetene varierer. Blant ulike internasjonale inndelinger er trolig inndelinger brukt i EU (EUNIS, CLC, ny inndeling for økosystemregnskap) og IUCNs nye Global Ecosystem Typology mest aktuelle å vurdere for inndeling av norske hovedøkosystemer. For å dekke de vanligste inndelingene av grove hovedøkosystemer kan vi definere ni klasser av hovedøkosystemer, som igjen kan deles inn på basis av plassering under/over skoggrensa eller i/utenfor en definert kystregion. Rapporten drøfter ulike karttekniske aspekter som må avklares for et hovedøkosystemkart. Formålet med hovedøkosystemkartet avgjør hvilke tilnærminger som er mest hensiktsmessige. Det er i alle tilfeller viktig å avklare den tematiske og geografiske nøyaktigheten opp mot formålene med kartet. Datagrunnlag for et hovedøkosystemkart kan dels hentes fra ulike eksisterende kartprodukter og dels ved utvikling av nye kart fra ulike typer fjernmålingsdata. Kjente norske kartprodukter som N50, FKB felles kartdatabase med arealdekke gitt ved AR5, og mer generaliserte produkter som AR50 og AR-fjell kan være aktuelle. Det finnes også europeiske eller globale fjernmålingsbaserte kartprodukter som EUs Corine Land Cover, med videreutviklingen CLC+ Backbone, Det er også mulig å utvikle egne fjernmålingsbaserte kartprodukter spesifikt tilpasset formålet for et hovedøkosystemkart, gjerne ved kombinasjon av data fra eksisterende kart, lasermåling og satellittdata. Målsettingene for hovedøkosystemkartet er avgjørende for valg av tilnærming og datagrunnlag, og hvor mye ressurser man kan bruke på å utvikle kartet. Uavhengig av tilnærming er det viktig å sørge for god kvalitetssikring og systematisk ajourhold av underliggende kartdata. Ingen etablerte kartprodukter kan brukes eller videreutvikles til et hovedøkosystemkart som tilfredsstiller alle brukerbehov. Ved supplering med en utvalgsundersøkelse for å få fram forventningsrett arealstatistikk kan imidlertid alle skisserte løsninger dekke brukerbehovene. Et hovedøkosystemkart basert på data fra fjernmåling, vil kreve grundig uttesting for å avklare muligheter for detaljert og presis tematisk og geografisk kartlegging

The importance of Indigenous Peoples’ lands for the conservation of terrestrial mammals

Indigenous Peoples? lands cover over one-quarter of Earth's surface, a significant proportion of which is still free from industrial-level human impacts. As a result, Indigenous Peoples and their lands are crucial for the long-term persistence of Earth's biodiversity and ecosystem services. Yet, information on species composition within Indigenous Peoples? lands globally remains largely unknown. Here, we provide the first comprehensive analysis of terrestrial mammal composition across mapped Indigenous lands by using area of habitat data for 4,460 IUCN-assessed mammal species. We estimated that 2,175 species (49%) have ≥ 10% of their ranges in Indigenous Peoples? lands, and 646 species (14%) have > half of their ranges within these lands. For the threatened species assessed, 413 (41%) occur in Indigenous Peoples? lands. We also found that 935 mammal species (of which 131 are threatened with extinction) have ≥ 10% of their range in Indigenous Peoples? lands that have low human pressure. This analysis shows how important Indigenous Peoples and their lands are to the successful implementation of international conservation and sustainable development agendas. Article impact statement: Indigenous peoples? lands are important for the successful implementation of international conservation and sustainable development agendas. This article is protected by copyright. All rights reservedIndigenous Peoples’ lands cover over one-quarter of Earth's surface, a significant proportion of which is still free from industrial-level human impacts. As a result, Indigenous Peoples and their lands are crucial for the long-term persistence of Earth's biodiversity and ecosystem services. Yet, information on species composition on these lands globally remains largely unknown. We conducted the first comprehensive analysis of terrestrial mammal composition across mapped Indigenous lands based on data on area of habitat (AOH) for 4460 mammal species assessed by the International Union for Conservation of Nature. We overlaid each species’ AOH on a current map of Indigenous lands and found that 2695 species (60% of assessed mammals) had ≥10% of their ranges on Indigenous Peoples’ lands and 1009 species (23%) had >50% of their ranges on these lands. For threatened species, 473 (47%) occurred on Indigenous lands with 26% having >50% of their habitat on these lands. We also found that 935 mammal species (131 categorized as threatened) had ≥ 10% of their range on Indigenous Peoples’ lands that had low human pressure. Our results show how important Indigenous Peoples’ lands are to the successful implementation of conservation and sustainable development agendas worldwide.Peer reviewe