Land Cover Dataset Research Articles

Index for the Consistent Measurement of Spatial Heterogeneity for Large-Scale Land Cover Datasets

Recognizing land cover heterogeneity is essential for the assessment of spatial patterns to guide conservation planning. One of the top research priorities is the quantification of land cover heterogeneity using effective landscape metrics. However, due to the diversity of land cover types and their varied distribution, a consistent, larger-scale, and standardized framework for heterogeneity information extraction from this complex perspective is still lacking. Consequently, we developed a new Land Cover Complexity Index (LCCI), which is based on information-theory. The LCCI contains two foundational aspects of heterogeneity, composition and configuration, thereby capturing more comprehensive information on land cover patterns than any single metric approach. In this study, we compare the performance of the LCCI with that of other landscape metrics at two different scales, and the results show that our newly developed indicator more accurately characterizes and distinguishes different land cover patterns. LCCI provides an alternative way to measure the spatial variation of land cover distribution. Classification maps of land cover heterogeneity generated using the LCCI provide valuable insights and implications for regional conservation planning. Thus, the LCCI is shown to be a consistent indicator for the quantification of land cover heterogeneity that functions in an adaptive way by simultaneously considering both composition and configuration.

White‐faced darter distribution is associated with coniferous forests in Great Britain

Abstract Understanding of dragonfly distributions is often geographically comprehensive but less so in ecological terms. White‐faced darter (Leucorhinnia dubia) is a lowland peatbog specialist dragonfly which has experienced population declines in Great Britain. White‐faced darter is thought to rely on peat‐rich pool complexes within woodland, but this has not yet been empirically tested. We used dragonfly recording data collected by volunteers of the British Dragonfly Society from 2005 to 2018 to model habitat preference for white‐faced darter using species distribution models across Great Britain and, with a more detailed landcover data set, specifically in the North of Scotland. Across the whole of Great Britain, our models used the proportion of coniferous forest within 1 km as the most important predictor of habitat suitability but were not able to predict all current populations in England. In the North of Scotland, our models were more successful and suggest that habitats characterised by native coniferous forest and areas with high potential evapotranspiration represent the most suitable habitat for white‐faced darter. We recommend that future white‐faced darter monitoring should be expanded to include areas currently poorly surveyed but with high suitability in the North of Scotland. Our results also suggest that white‐faced darter management should concentrate on maintaining Sphagnum rich‐pool complexes and the maintenance and restoration of native forests in which these pool complexes occur.

Assessing UN indicators of land degradation neutrality and proportion of degraded land for Botswana using remote sensing based national level metrics

AbstractAchieving land degradation neutrality (LDN) has been proposed as a way to stem the loss of land resources globally. To date, LDN operationalization at the country level has remained a challenge both from a policy and science perspective. Using an approach incorporating cloud‐based geospatial computing with machine learning, national level datasets of land cover, land productivity dynamics, and soil organic carbon stocks were developed. Using the example of Botswana, LDN and proportion of degraded land were assessed. Between 2000 and 2015, grassland lost approximately 17% of its original extent, the highest level of loss for any land category; land productivity decline was highest in artificial surface areas (11%), whereas 36% of croplands show early signs of decline. With the use of national metrics (NM), degraded areas were found to be 32.6% compared to 51.4% of the total land area when global default datasets (DD) were used. Estimates of degraded land computed with NM and DD were validated in Palapye, an agro‐pastoral region in eastern Botswana, where Composite Land Degradation Index (CLDI) field‐based data exists. Comparing land degradation (LD) in the three datasets (NM, DD, and CLDI), NM estimates were closest to the field data. The extra efforts put into developing national level data for LD assessment in this study is, thus, well‐justified. Beyond demonstrating remote sensing viability for LD assessment, the study developed procedures for generating and validating national level datasets. Using these procedures, LD monitoring will be enhanced in Botswana and elsewhere since these remote sensing datasets can be updated using freely available satellite datasets.

Methods of Rapid Quality Assessment for National-Scale Land Surface Change Monitoring

Providing rapid access to land surface change data and information is a goal of the U.S. Geological Survey. Through the Land Change Monitoring, Assessment, and Projection (LCMAP) initiative, we have initiated a monitoring capability that involves generating a suite of 10 annual land cover and land surface change datasets across the United States at a 30-m spatial resolution. During the LCMAP automated production, on a tile-by-tile basis, erroneous data can occasionally be generated due to hardware or software failure. While crucial to assure the quality of the data, rapid evaluation of results at the pixel level during production is a substantial challenge because of the massive data volumes. Traditionally, product quality relies on the validation after production, which is inefficient to reproduce the whole product when an error occurs. This paper presents a method for automatically evaluating LCMAP results during the production phase based on 14 indices to quickly find and flag erroneous tiles in the LCMAP products. The methods involved two types of comparisons: comparing LCMAP values across the temporal record to measure internal consistency and calculating the agreement with multiple intervals of the National Land Cover Database (NLCD) data to measure the consistency with existing products. We developed indices on a tile-by-tile basis in order to quickly find and flag potential erroneous tiles by comparing with surrounding tiles using local outlier factor analysis. The analysis integrates all indices into a local outlier score (LOS) to detect erroneous tiles that are distinct from neighboring tiles. Our analysis showed that the methods were sensitive to partially erroneous tiles in the simulated data with a LOS higher than 2. The rapid quality assessment methods also successfully identified erroneous tiles during the LCMAP production, in which land surface change results were not properly saved to the products. The LOS map and indices for rapid quality assessment also point to directions for further investigations. A map of all LOS values by tile for the published LCMAP shows all LOS values are below 2. We also investigated tiles with high LOS to ensure the distinction with neighboring tiles was reasonable. An index in this study shows the overall agreement between LCMAP and NLCD on a tile basis is above 71.5% and has an average at 89.1% across the 422 tiles in the conterminous United States. The workflow is suitable for other studies with a large volume of image products.

SPATIAL DATA QUALITY EVALUATION FOR LAND COVER CLASSIFICATION APPROACHES

Abstract. Data gaps and poor data quality may lead to flawed conclusions and data-driven policies and decisions, such as the measurement of Sustainable Development Goals progress. This is particularly important for land cover data, as an essential source of data for a wide range of applications and real-world challenges including climate change mitigation, food security planning, resource allocation and mobilization. While global land cover datasets are available, their usability is limited by their coarse spatial and temporal resolutions. Furthermore, having a good understanding of the fitness for the purpose is imperative. This paper compares two datasets from a spatial data quality perspective: (1) a global land cover map, and (2) a fit-for-purpose training dataset that is generated using visual inspection of very high-resolution satellite data. The latter dataset is created using Google Earth Engine (GEE), a cloud-based computing platform and data repository. We systematically evaluate the two datasets from spatial data quality (SDQ) perspective using the Analytic Hierarchy Process (AHP) to prioritise the criteria, i.e. SDQ. To validate the results, land cover classifications are conducted using both datasets, also within GEE. Based on the results of the SDQ evaluation and land cover classification, we find that the second training dataset significantly outperformed the global land cover maps. Our study also shows that cloud-based computing platforms and publicly available data repositories can provide an effective approach to filling land cover data gaps in data-scarce regions.

Application of Empirical Land-Cover Changes to Construct Climate Change Scenarios in Federally Managed Lands

Sagebrush-dominant ecosystems in the western United States are highly vulnerable to climatic variability. To understand how these ecosystems will respond under potential future conditions, we correlated changes in National Land Cover Dataset “Back-in-Time” fractional cover maps from 1985-2018 with Daymet climate data in three federally managed preserves in the sagebrush steppe ecosystem: Beaty Butte Herd Management Area, Hart Mountain National Antelope Refuge, and Sheldon National Wildlife Refuge. Future (2018 to 2050) abundance and distribution of vegetation cover were modeled at a 300-m resolution under a business-as-usual climate (BAU) scenario and a Representative Concentration Pathway (RCP) 8.5 climate change scenario. Spatially explicit map projections suggest that climate influences may make the landscape more homogeneous in the near future. Specifically, projections indicate that pixels with high bare ground cover become less bare ground dominant, pixels with moderate herbaceous cover contain less herbaceous cover, and pixels with low shrub cover contain more shrub cover. General vegetation patterns and composition do not differ dramatically between scenarios despite RCP 8.5 projections of +1.2 °C mean annual minimum temperatures and +7.6 mm total annual precipitation. Hart Mountain National Antelope Refuge is forecast to undergo the most change, with both models projecting larger declines in bare ground and larger increases in average herbaceous and shrub cover compared to Beaty Butte Herd Management Area and Sheldon National Wildlife Refuge. These scenarios present plausible future outcomes intended to guide federal land managers to identify vegetation cover changes that may affect habitat condition and availability for species of interest.

Building Volume Per Capita (BVPC): A Spatially Explicit Measure of Inequality Relevant to the SDGs

Building Volume Per Capita (BVPC - cubic meters of building per person) is presented as a proxy measure of economic inequality and a direct measure of housing inequality. The Sustainable Development Goal 10 (SDG 10: Reduced Inequalities) is synergic for achieving SDG 11 on Sustainable Cities and Communities. Access to safe and affordable housing, transport systems, and public spaces are some of the targets of SDG 11 that mostly link with reducing inequalities. The Habitat III New Urban Agenda sets equal access to urban spaces, infrastructures and basic services as crucial for developing sustainable cities. Earth Observation (EO) data including remotely sensed satellite data, airborne data, and model outputs, in combination with demographic, and other statistical data, have been gaining importance for monitoring progress of the SDGs. High spatial resolution building footprint data derived from aerial photographs, stereo imagery, and LIDAR data, obtained for the cities of California, between 2010-2015, were used in this study. These measures of building volume were rasterized and juxtaposed with (divided by) a variety of demographic data including vector-based census data of 2015 and LandScan raster data of population counts of 2015. The National Landcover dataset of 2011 was used to characterize the land cover variability of the cities. Using these datasets, the spatial pattern and distribution of BVPC for nine cities in California were studied. The results showed that BVPC was inversely related with intensity of development, and positively related with median household income within cities. A BV-GINI was also developed to characterize the variability of the BVPC at the census tract level and the pixel level. The results were inconclusive and require further investigation.

Remote sensing modeling of urban density dynamics across 36 major cities in China: Fresh insights from hierarchical urbanized space

Rapid urbanization is considered to be of high relevance for global/local environment change, but its multi-dimensional change features over time have rarely been reported, especially for the process, states and consequences of urban density growth at hierarchical scales of urbanized area. To fill this gap, here, we firstly integrated 27-years’ worth of consistent nighttime light (NTL) time series with land cover dataset, to reconstruct 4 basic, 13 archetypes of temporal trajectories of urban density dynamics with an objected-oriented analysis, and measured their spatio-temporal patterns over hierarchical urbanized space that spanned from landscape patches to 36 major cities in China. Our results showed that, from the micro patch dynamics, the logistic pattern is striking among 4 models over most of time periods, accounting for ~45.63% of the total patch amounts and ~43.73% of the total patch area, respectively, while the exponential pattern contributes the least. Moreover, the structural transition of thematic features revealed their spatial gradient delay from established patches to newly developed patches, from infilling patches to outlying patches. In terms of the space of cities, seven typical clusters of urbanization evolution were identified, however, under different policies, reform practice and developmental history. Additional three alternative states of urban density were also organized for estimated future pathways. Therefore, according to our new insights into inter- and intra-city analysis, both the differentiated strategies for horizontal cities and trade-offs of spatial configuration for urban interior areas were informed to formulate effective science-policy interface.

Comparative Assessment of the Built-Up Area Expansion Based on Corine Land Cover and Landsat Datasets: A Case Study of a Post-Socialist City

Monitoring uncontained built-up area expansion remains a complex challenge for the development and implementation of a sustainable planning system. In this regard, proper planning requires accurate monitoring tools and up-to-date information on rapid territorial transformations. The purpose of the study was to assess built-up area expansion, comparing two freely available and widely used datasets, respectively, Corine Land Cover and Landsat, to each other, as well as the ground truth, with the goal of identifying the most cost-effective and reliable tool. The analysis was based on the largest post-socialist city in the European Union, the capital of Romania, Bucharest, and its neighboring Ilfov County, from 1990 to 2018. This study generally represents a new approach to measuring the process of urban expansion, offering insights about the strengths and limitations of the two datasets through a multi-level territorial perspective. The results point out discrepancies between the datasets, both at the macro-scale level and at the administrative unit’s level. On the macro-scale level, despite the noticeable differences, the two datasets revealed the spatiotemporal magnitude of the expansion of the built-up area and can be a useful tool for supporting the decision-making process. On the smaller territorial scale, detailed comparative analyses through five case-studies were conducted, indicating that, if used alone, limitations on the information that can be derived from the datasets would lead to inaccuracies, thus significantly limiting their potential to be used in the development of enforceable regulation in urban planning.

Comparison of Different Cropland Classification Methods under Diversified Agroecological Conditions in the Zambezi River Basin

Having updated knowledge of cropland extent is essential for crop monitoring and food security early warning. Previous research has proposed different methods and adopted various datasets for mapping cropland areas at regional to global scales. However, most approaches did not consider the characteristics of farming systems and apply the same classification method in different agroecological zones (AEZs). Furthermore, the acquisition of in situ samples for classification training remains challenging. To address these knowledge gaps and challenges, this study applied a zone-specific classification by comparing four classifiers (random forest, the support vector machine (SVM), the classification and regression tree (CART) and minimum distance) for cropland mapping over four different AEZs in the Zambezi River basin (ZRB). Landsat-8 and Sentinel-2 data and derived indices were used and synthesized to generate thirty-five layers for classification on the Google Earth Engine platform. Training samples were derived from three existing landcover datasets to minimize the cost of sample acquisitions over the large area. The final cropland map was generated at a 10 m resolution. The performance of the four classifiers and the viability of training samples were analysed. All classifiers presented higher accuracy in cool AEZs than in warm AEZs, which may be attributed to field size and lower confusion between cropland and grassland classes. This indicates that agricultural landscape may impact classification results regardless of the classifiers. Random forest was found to be the most stable and accurate classifier across different agricultural systems, with an overall accuracy of 84% and a kappa coefficient of 0.67. Samples extracted over the full agreement areas among existing datasets reduced uncertainty and provided reliable calibration sets as a replacement of costly in situ measurements. The methodology proposed by this study can be used to generate periodical high-resolution cropland maps in ZRB, which is helpful for the analysis of cropland extension and abandonment as well as intensity changes in response to the escalating population and food insecurity.

Romanian Natura 2000 Network: Evaluation of the Threats and Pressures through the Corine Land Cover Dataset

The present paper aims to evaluate if the Natura 2000 sites in Romania are placed over dynamic areas from a land cover changes perspective, or if they are placed in areas with low human interest and what the impact of these changes are. The effectiveness of conservation measures was addressed by analyzing the number of land cover changes and their areas in Natura 2000 sites, before and after declaring them as protected areas. Corine Land Cover (CLC) data were used as a tool to identify threats and pressures from each Natura 2000 site, and also assess whether land cover changes are more frequent in sites with a high biodiversity index, compared to those with low diversity, in order to estimate the conservation status. Changes in the land cover during 1990–2018 are characterized by three types of events, from 1990 to 2000 with most changes recorded, followed by a relative period of stability from 2000 to 2012; the most dynamic period is from 2012 to 2018. The main changes are due to deforestation. Only 29.7% ROSCI (Romanian Sites of Community Importance) and 36.5% ROSPA (Romanian Special Protected Areas) sites are characterized by a good degree of conservation without or with low modifications regarding the land cover. The most frequent threats and pressures that were found through CLC changes in the ROSCIs in Romania are related to forestry, grazing, the extent of the urbanized environment and those related to agriculture. The correspondence between Corine Land Cover and Natura 2000 specific threats and pressures emphasizes new guidelines for the Corine Land Cover program; therefore, this correspondence can be a potential tool to get more information for Natura 2000 sites.

Remotely Sensed Urban Surface Ecological Index (RSUSEI): An Analytical Framework for Assessing the Surface Ecological Status in Urban Environments

Urban Surface Ecological Status (USES) reflects the structure and function of an urban ecosystem. USES is influenced by the surface biophysical, biochemical, and biological properties. The assessment and modeling of USES is crucial for sustainability assessment in support of achieving sustainable development goals such as sustainable cities and communities. The objective of this study is to present a new analytical framework for assessing the USES. This analytical framework is centered on a new index, Remotely Sensed Urban Surface Ecological index (RSUSEI). In this study, RSUSEI is used to assess the USES of six selected cities in the U.S.A. To this end, Landsat 8 images, water vapor products, and the National Land Cover Database (NLCD) land cover and imperviousness datasets are downloaded for use. Firstly, Land Surface Temperature (LST), Wetness, Normalized Difference Vegetation Index (NDVI), and Normalized Difference Soil Index (NDSI) are derived by remote sensing methods. Then, RSUSEI is developed by the combination of NDVI, NDSI, Wetness, LST, and Impervious Surface Cover (ISC) with Principal Components Analysis (PCA). Next, the spatial variations of USES across the cities are evaluated and compared. Finally, the association degree of each parameter in the USES modeling is investigated. Results show that the spatial variability of LST, ISC, NDVI, NDSI, and Wetness is heterogeneous within and between cities. The mean (standard deviation) value of RSUSEI for Minneapolis, Dallas, Phoenix, Los Angeles, Chicago and Seattle yielded 0.58 (0.16), 0.54 (0.17), 0.47 (0.19), 0.63 (0.21), 0.50 (0.17), and 0.44 (0.19), respectively. For all the cities, PC1 included more than 93% of the surface information, which is contributed by greenness, moisture, dryness, heat, and imperviousness. The highest and lowest mean values of RSUSEI are found in “Developed, High intensity” (0.76) and “Developed, Open Space” (0.35) lands, respectively. The mean correlation coefficient between RSUSEI and LST, ISC, NDVI, NDSI, and Wetness, is 0.47, 0.97, −0.31, 0.17, and −0.27, respectively. The statistical significance of these correlations is confirmed at 95% confidence level. These results suggest that the association degree of ISC in USES modeling is the highest, despite the differences in land cover and biophysical characteristics in the cities. RSUSEI could be very useful in modeling and comparing USES across cities with different geographical, climatic, environmental, and biophysical conditions and can also be used for assessing urban sustainability over space and time.

US EPA EnviroAtlas Meter-Scale Urban Land Cover (MULC): 1-m Pixel Land Cover Class Definitions and Guidance.

This article defines the land cover classes used in Meter-scale Urban Land Cover (MULC), a unique, high resolution (one meter2 per pixel) land cover dataset developed for 30 US communities for the United States Environmental Protection Agency (US EPA) EnviroAtlas. MULC data categorize the landscape into these land cover classes: impervious surface, tree, grass-herbaceous, shrub, soil-barren, water, wetland and agriculture. MULC data are used to calculate approximately 100 EnviroAtlas metrics that serve as indicators of nature's benefits (ecosystem goods and services). MULC, a dataset for which development is ongoing, is produced by multiple classification methods using aerial photo and LiDAR datasets. The mean overall fuzzy accuracy across the EnviroAtlas communities is 88% and mean Kappa coefficient is 0.84. MULC is available in EnviroAtlas via web browser, web map service (WMS) in the user's geographic information system (GIS), and as downloadable data at EPA Environmental Data Gateway. Fact Sheets and metadata for each MULC Community are available through EnviroAtlas. Some MULC applications include mapping green and grey infrastructure, connecting land cover with socioeconomic/demographic variables, street tree planting, urban heat island analysis, mosquito habitat risk mapping and bikeway planning. This article provides practical guidance for using MULC effectively and developing similar high resolution (HR) land cover data.

Connections-between Daily Greenness Exposure and Health Outcomes.

A compelling body of research demonstrates that exposure to nature, especially trees, is beneficial to human health. We know little, however, about the extent to which understory vegetation that does not reach the height of trees, impacts human health. An additional gap in our knowledge concerns the extent to which daily variations in exposure to various forms of vegetation are related to human health outcomes. Many previous findings describing such connections were achieved in laboratory settings or through semi-controlled experiments, which do not reflect the dynamic variations of people’s daily exposure to nature. Thus, we conducted an online survey to address these questions. We used the National Land Cover Dataset 2011 and Google Street View images to estimate participants’ daily exposure to nature, and two standard questionnaires (General Health SF-12 and the Perceived Stress Scale) to assess health. Results show that greater exposure to trees in daily life is associated with better health outcomes. Specifically, higher neighborhood concentrations of tree canopy are related to better physical health, overall health and an increased capacity to control stress. In contrast, the results exploring the health associations of understory vegetation were inconsistent. In most cases, understory vegetation had a negative relationship with stress and mental health measures.

Content-based remote sensing image retrieval using multi-scale local ternary pattern

This paper discusses a content based remote sensing image retrieval technique using multi-scale, patch-based local ternary pattern and fisher vector encoding. The technique downsamples an image in three scales, each downsampled image utilizes local ternary pattern to obtain upper and lower texture images, and divides them into dense patches to build a final histogram representation. This representation is then encoded into normalized fisher vectors. To this end, we focus on two standard remote sensing datasets namely 20-class satellite remote sensing dataset and 21-class land-cover dataset. Visual and quantitative results signify high precision for the proposed technique with an improvement of approximately 5.71% and 6.57% for 21-class land-cover and 20-class satellite remote sensing datasets respectively.

Assessment of acetochlor use areas in the sahel region of Western Africa using geospatial methods

The goal of this study was to determine the co-occurrence between acetochlor use on crops and potentially vulnerable soils in the Permanent Interstate Committee for Drought Control in the Sahel region of Western Africa. Acetochlor, a pre-emergence herbicide, is used primarily on row crops and has the potential to reach groundwater or surface water following a rain event shortly after application. Off-field transport is often determined by soil properties; therefore, soils within potential use areas were assessed and mapped to establish areas with soils vulnerable to leaching and/or runoff. Corn and cotton production areas were used as surrogate crops for high potential use areas of acetochlor within areas identified using GlobCover land use data and the Spatial Production Allocation Model agricultural statistics data. The geospatial analysis identified approximately 462 million ha of potentially vulnerable soils in the Sahel region of which 65.7 million ha are within agricultural areas. An adjustment for corn and cotton production areas showed that 2.2 million ha or 3.3% of agricultural fields could have potential restrictions for acetochlor use. Approximately 0.159 million ha of soils or 0.24% of agricultural fields are in the presence of shallow groundwater, defined by depth < 9 m. In addition, 0.0128 million ha or 0.02% were determined to be adjacent to surface water bodies. To understand the uncertainty associated with the use of specific land cover datasets, an overlay assessment was conducted using alternative data sources. Overlap between selected land cover datasets in the Sahel region varies and ranges from 24.7% to 75.5% based on a merged 2009 GlobCover and CCI LC datasets. In comparison with the merged 2005 and 2009 GlobCover dataset, the cropland overlaps range from 38.9% to 85.0%. This demonstrates that the choice of land cover dataset can have a significant impact on a spatial assessment. Results from this assessment demonstrate that only a small fraction of vulnerable agricultural soils across the region may be a risk for contamination by acetochlor of groundwater or surface resources, based on product label recommendations. Given the availability of spatial data in a region, the methods contained herein may additionally be used in other localities to provide similar information that can be helpful for water quality management.

Assessment of Wetland Change on the Delmarva Peninsula from 1984 to 2010

Stubbs, Q.; Yeo, I.-Y.; Lang, M.; Townshend, J.; Sun, L.; Prestegaard, K., and Jantz, C., 2020. Assessment of wetland change on the Delmarva Peninsula from 1984 to 2010. Journal of Coastal Research, 36(3), 575–589. Coconut Creek (Florida), ISSN 0749-0208.The decline in wetland extent and condition emphasizes the need for sound wetland restoration and conservation policies, which require baseline information on wetland status, change, and change drivers. Multiple wetland maps are available, but they can be quite inconsistent bcause of different input and generation techniques, dates, and objectives. Moderate-resolution (30 m2) regional land-cover data sets (LCDs) were analyzed to (1) quantify historical wetland changes on the Delmarva Peninsula at multiple spatial scales between 1984 and 2010, (2) identify differences in the spatial area of wetland change and discuss the source of and implications for these differences, and (3) investigate the extent to which drivers of wetland change can be identified using existing LCDs. The following regional LCDs were considered: the National Oceanic and Atmospheric Administration Coastal Change Analysis Program (C-CAP), the U.S. Geological Survey (USGS) Chesapeake Bay Land Cover Data Series (CBLCD), and the USGS National Land Cover Database. The C-CAP and CBLCD had the highest spatial agreement at 97%, and an average of 76% spatial agreement with the U.S. Fish and Wildlife Service National Wetland Inventory. The highest percentages of net wetland loss occurred between 1992 and 2001, whereas net wetland gain occurred between 2001 and 2010. Wetlands were predominantly converted (e.g., lost) to croplands/grass/shrubs (67%) and water (11%), which could be linked to drivers such as agriculture and sea-level rise.

Earth observation-based ecosystem services indicators for national and subnational reporting of the sustainable development goals

Decision-making that impacts sustainability occurs at national and subnational levels, highlighting the need for multi-scale Earth observations (EO) and geospatial data for assessing the United Nations' Sustainable Development Goals (SDGs). EnviroAtlas, developed by the United States Environmental Protection Agency and partners, provides a collection of web-based, interactive maps of environmental and socio-economic data relevant to the SDGs. EnviroAtlas maps ecosystem services indicators at national, regional, and local extents that can contribute to targets set forth in numerous goals, such as SDG 6 for clean water, SDG 11 for sustainable cities and communities, and SDG 15 for life on land. Examples of EnviroAtlas indicators that provide a way to view spatial inequalities, help fill gaps in environmental indicators, and integrate socio-economic and environmental data for the SDGs are explored herein. Remotely sensed EO data are essential for producing these indicators and informing planning and decision-making for the SDGs at subnational scales. The National Land Cover Dataset is the basis for many EnviroAtlas maps at the national extent, while National Agriculture Imagery Program and Light Detection and Ranging (LiDAR) data are used to classify Meter-scale Urban Land Cover in select US metro areas. These 30 m and 1 m land cover products are combined with demographic and other geospatial data (remotely sensed and otherwise) to produce integrated indicators that can aid in target setting of the SDGs. Though EnviroAtlas was created for the conterminous US, the methods for indicator creation are transferable, and the open-source code for the EnviroAtlas resource may serve as an example for other nations. Achieving the SDGs means assessing targets and decision-making outcomes at local, regional, and national levels using consistent and accurate data. Geospatial resources like EnviroAtlas that provide open access to indicators based on EO data and allow for assessment at multiple extents and resolutions are critical to broadly addressing national to subnational SDG goals and targets.

Structural Changes in the Romanian Economy Reflected through Corine Land Cover Datasets

During the last 30 years, the Romanian economy has faced different challenges due to structural readjustments, overcoming crisis and globalization. The share of primary and secondary sectors in the gross domestic product have strongly decreased, while the services have taken off. The main objective for this study is to observe how these economic readjustments can be assessed and measured using the Corine Land Cover datasets from 1990, 2000, 2006, 2012 and 2018 (with special observation on the range 2006 and 2018, after Romania was included in European Union). Despite some of the methodological limitations (like the minimum surface change), the Corine Land Cover turned out to be a powerful tool and it allowed us to detect an intense correlation between the socioeconomic and the structural trends in land use, in specific spatial contexts. The artificial surfaces are constantly increasing and this trend is rather visible as a distance function to the major Romanian cities. The most interesting changes occurred in the case of the agricultural polygons. The main trend emphasized by our analysis regards the redeployment of large farms in areas of agronomic and environmental territorial optimum. Such is the case for vineyards (after a decline during 2000–2006) and for annual cultures. All these changes in land-use patterns are too complex to be encompassed by a single methodology, which is why we used different tools, ranging from spatial analysis to geo-economic modeling, in order to detect how the Corine Land Cover datasets might be used for a better understanding of the Romanian economic readjustments.

Contrasting land use legacy effects on forest landscape dynamics in the Italian Alps and the Apennines

ContextLand use legacies of human activities and recent post-abandonment forest expansion have extensively modified numerous forest landscapes throughout the European mountain ranges. Drivers of forest expansion and the effects of changes on ecosystem services are currently debated.Objectives(i) To compare landscape transition patterns of the Alps and the Apennines (Italy), (ii) to quantify the dominant landscape transitions, and (iii) to measure the influence of climatic, topographic and anthropogenic driving factors.MethodsLand cover changes and landscape pattern modifications were investigated at the regional (over 28 years, Alps and Apennines, Corine Land Cover dataset) and landscape scale (over 58 years, 8 Alpine and 8 Apennine sites, aerial images). The main driving factors of post-abandonment forest landscape dynamics were assessed with a statistical modeling approach.ResultsForest expansion was the dominant landscape transition at both Italian mountain ranges, with an annual overall rate of 0.6%. Forest expansion was more extensive at lower elevations in the Apennines where climate is less limiting and extensive abandoned croplands and pastures were available throughout the study period. Distance from pre-existing forest edges in the Alps and elevation in the Apennines emerged as the most important predictors.ConclusionsForest expansion is most rapid where areas of recent agricultural abandonment coincide with favorable climatic conditions. Thus the prediction of forest landscape dynamics, in these mountain forests with a long history of cultural use, requires knowledge of how the magnitude and timing of land use changes intersect spatially and temporally with suitable conditions for tree establishment and growth.