High-resolution Land Research Articles

Influences of leaf area index and albedo on estimating energy fluxes with HOLAPS framework

The High resOlution Land Atmosphere surface Parameters from Space (HOLAPS) programme provides a modeling system to maximize the use of satellite-based products and ensure internally consistent estimation of surface water and energy fluxes. Leaf area index (LAI) and land surface albedo are two key parameters for estimation of latent and sensible heat fluxes with HOLAPS. Thus, to facilitate the generation of long-term high accuracy latent and sensible heat fluxes, high quality global long-term LAI and land surface albedo datasets are essential. The Quality Assurance for Essential Climate Variables (QA4ECV) project released quality-assured long-term LAI and albedo datasets with traceable and reliable uncertainty information provided in the dataset. Taking MODIS-BNU-LAI and MODIS albedo as reference, different global long-term LAI and albedo datasets including GlobAlbedo, QA4ECV and GLOBMAP were investigated for estimation of latent/sensible heat fluxes with HOLAPS in this study. The results show that all albedo datasets show similar accuracy for estimation of latent and sensible heat fluxes when validated against FLUXNET observations. The QA4ECV LAI leads to worse latent heat flux estimation due to its use of effective LAI rather than green leaf LAI. Sensitivity analysis also shows that the HOLAPS estimated latent heat flux (LE) is more sensitive to uncertainty in LAI than land surface albedo. Overall, the combined use of QA4ECV albedo and GLOBMAP LAI is suggested for estimation of latent/sensible heat fluxes with HOLAPS. The root mean square differences (RMSD) between estimations and FLUXNET measurements are 54 (30) W/m2 for hourly (monthly) latent heat flux, and 80.5 (24.5) W/m2 for sensible heat flux, which are comparable to estimates with MODIS and other reported studies.

Methods for integrating high-resolution land, climate, and infrastructure scenarios in a hydrologic simulation model

Global alterations of the hydrologic cycle by humans have led to alarming rates of water shortages and irreversible ecosystem change. Our ability to manage water resources lies in accurately modeling water availability at scales meaningful to management. Although hydrologic models have been used to understand the implications of future climate and land cover change on regional water availability, many modeling approaches fail to integrate human infrastructures (HI) with bio-geophysical drivers to facilitate sustainable regional water resource management. This paper presents an integrated framework, inclusive of modeling and data needs, to quantify the effects of both bio-geophysical and HI influence on regional surface water hydrology. The framework enables the integration of high spatial and temporal anthropogenic alterations of water availability for identifying hot-spots and hot-moments of hydrological stresses within individual river-segments using a hydrologic simulation model, Soil and Water Analysis Tool (SWAT).•A high-resolution river network for the study region with a greater spatial granularity compared to contemporary SWAT applications attempted to account for HI.•The anthropogenic influence on water balance for each river segment was estimated using data on human infrastructures, such as water intakes, power production facilities, discharges, dams, and land transformation.

Greenspace Pattern and the Surface Urban Heat Island: A Biophysically-Based Approach to Investigating the Effects of Urban Landscape Configuration

Surface urban heat islands (SUHIs) are influenced by the spatial distribution of green space, which in turn can be influenced by urban planning. When studying the relationship between structure and function it is critical that the scale of observation reflects the scale of the phenomenon being measured. To investigate the relationship between green space pattern and the SUHI in the Kansas City metropolitan area, we conducted a multi-resolution wavelet analysis of land surface temperature (LST) to determine the dominant length scales of LST production. We used these scales as extents for calculating landscape metrics on a high-resolution land cover map. We built regression models to investigate whether–controlling for the percent vegetated area–patch size, fragmentation, shape, complexity, and/or proximity can mitigate SUHIs. We found that while some of the relationships between landscape metrics and LST are significant, their explanatory power would be of little use in planning for green infrastructure. We also found that the relationships often reported between landscape metrics and LST are artifacts of the relationship between the percent of vegetation and LST. By using the dominant length scales of LST we provide a methodology for robust biophysically-based analysis of urban landscape pattern and demonstrate that the contributions of green space configuration to the SUHI are negligible. The simple result that increasing green space can lower LST regardless of configuration allows the prioritization of resources towards benefiting neighborhoods most vulnerable to the negative impacts of urban heat.

FRF-Net: Land Cover Classification From Large-Scale VHR Optical Remote Sensing Images

Deep learning (DL) technique is widely applied in remote sensing (RS) applications because of its outstanding nonlinear feature extraction ability. However, with regard to the issues of large-scale and very high-resolution (VHR) land cover classification, multi-object distributions and clear appearance with large intraclass difference become challenges for refined pixelwise land cover mapping. Focusing on these problems, the letter proposed a novel encoding-to-decoding method called the full receptive field (RF) network (FRF-Net) based on two types of attention mechanism. In the FRF-Net, ResNet-101 is used as the basic backbone. Then, the ensemble feature is generated by encoding the high-level features based on the self-attention mechanism which could achieve full RF to capture long-range semantic. Next, the encoding result is decoded by the fusion attention mechanism combined with the low-level feature to produce a fusion feature which contains a refined semantic description for accurate land cover mapping. Extensive experiments based on the GID and ISPRS data sets proved that the proposed network outperforms the state-of-the-art methods. The FRF-Net achieved 66.71% and 64.17% of the mean of classwise Intersection over Union (mIOU) with smaller computation cost on ISPRS and GID, respectively.

Social media-based analysis of cultural ecosystem services and heritage tourism in a coastal region of Mexico

Understanding spatial patterns of visitation and benefits accrued to different types of natural and cultural heritage tourists may have important implications for the sustainable management of their destinations. We investigate cultural services accrued to local, domestic and international visitors to the Usumacinta floodplain, a coastal region with one of the highest biological and cultural diversities in Mexico. We combine analysis of social media photographs and high-resolution land cover mapping to identify different cultural services and their association with specific ecosystem and land cover types. Hotspots for international tourists are spatially restricted to well-known and accessible sites. Locals are 2.2–2.5 times more likely than international visitors to be associated with aesthetic appreciation and birdwatching. Locals upload more photographs of coastal lagoons, mangroves, beach and sea. Results are analyzed in light of land cover changes in the region and provide valuable information to decision makers for improved tourism management and conservation strategies.

A New Approach to High-Resolution Urban Land Use Classification Using Open Access Software and True Color Satellite Images

Urbanization nowadays results in the most dynamic and drastic changes in land use/land cover, with a significant impact on the environment. A detailed analysis and assessment of this process is necessary to take informed actions to reduce its impact on the environment and human well-being. In most parts of the world, detailed information on the composition, structure, extent, and temporal changes of urban areas is lacking. The purpose of this study is to present a methodology to produce high-resolution land use/land cover maps by the use of free software and satellite imagery. These maps can help to understand dynamic urbanizations processes to plan, design, and coordinate sustainable urban development plans, especially in areas with limited resources and advancing environmental degradation. A series of high-resolution true color images provided by Google Earth Pro were used to do initial classifications with the Semi-Automatic Classification Plug-in in QGIS. Afterwards, a new methodology to improve the classification by the elimination of shadows and clouds, and a reduction of misclassifications through superimposition was applied. The classification was carried out for three urban areas in León, Nicaragua, with different degrees of urbanization for the years 2009, 2015, and 2018. Finally, the accuracy of the classification was analyzed using randomly defined validation polygons. The results are three sets of high-resolution land use/land cover maps of the initial and the improved classification, showing the detailed structures and temporal dynamics of urbanization. The average accuracy of classification reaches 74%, but up to 85% for the best classification. The results clearly identify advancing urbanization, the loss of vegetation and riparian zones, and threats to urban ecosystems. In general, the level of detail and simplicity of our methodology is a valuable tool to support sustainable urban management, although its application is not limited to these areas and can also be employed to track changes over time, providing therefore, relevant information to a wide range of decision-makers.

Accuracy assessment of NLCD 2011 percent impervious cover for selected USA metropolitan areas

The emergence of high-resolution land cover data has created the opportunity to assess the accuracy of impervious cover (IC) provided by the National Land Cover Database (NLCD). We assessed the accuracy of the 900 m2 NLCD2011 %IC for 18 metropolitan areas throughout the conterminous United States using reference data from 1 m2 land cover data developed as part of the United States Environmental Protection Agency’s EnviroAtlas project. Agreement was assessed from two perspectives: 1) sensitivity to the size of the assessment unit used for the comparison, and 2) utility of NLCD %IC to serve as a proxy for high-resolution IC. The former perspective was considered because statistical relationships can be sensitive to assessment unit size and shape, and the latter perspective was considered because high resolution (reference) %IC data are not available nationwide. The utility of NLCD %IC as a proxy for the high resolution data was assessed for seven lattice (square) cell sizes ranging from 1 ha to 200 ha using four EnviroAtlas IC indicators: 1) %IC per 100 ha (1 km2); 2) %IC by Census block group; 3) %IC within a 15 m (radius) of the riparian zone, and; 4) %IC within a 50 m (radius) of the riparian zone. Agreement was quantified as per assessment unit deviation (NLCD %IC – reference %IC) and summarized as Mean Absolute Deviation (MAD) and Mean Deviation (MD) both within and across the 18 metropolitan areas. Ordinary least squares (OLS) regression (y = reference %IC and x = NLCD %IC) was also used to evaluate the quality of the NLCD %IC data. MAD was ≤ 5% for six of the seven lattice cell sizes. MAD was also ≤ 5% for Census block groups > 100 ha and for both riparian units. These results suggest that uncertainty attributable to the measurement of %IC was no greater than the uncertainty related to the effect of IC on aquatic resources that have been derived from studies of aquatic condition (e.g., benthic fauna) over a range of %IC. Overall, agreement was variable from one metropolitan area to the next. Agreement improved as assessment unit size increased and declined as the level of urbanization (NLCD %IC) increased. NLCD %IC tended to underestimate reference %IC overall, but NLCD %IC was sometimes greater than reference %IC in urbanized settings.

Effect of 1-km Subgrid Land-Surface Heterogeneity on the Multi-year Simulation of RCM-Modelled Surface Climate Over the Region of Complex Topography

Effects of parameterization of subgrid-scale topography and land cover scheme (SubBATS) at 1-km resolution were investigated over the Alpine region using a regional climate model. Two multi-year simulations were carried out with the Regional Climate Model of International Centre for Theoretical Physics. The control simulation was carried out at 10-km horizontal resolution using standard land-surface model; while for the SubBATS simulation, the land-surface model was employed at much higher resolution (1 km) to investigate the effect of land-surface heterogeneity on the Alpine climate. In SubBATS, near-surface atmospheric state variables from coarse (10-km) atmospheric model were disaggregated to 1 km before passing to high-resolution land surface scheme. Comparison of these two multi-year simulation was done for the Great Alpine Region. The analysis shows the added value imparted by very high-resolution SubBATS in simulating hydrology processes in the complex terrain. The direct effects of the scheme are evident on height-dependent variables; temperature and snow pack. The better representation of topographic height in sub-scale scheme leads to more refined temperature field which subsequently results in more realistic representation of snow cover and snow melt. At 1-km resolution, the influence of resolved mountain peaks and valleys results in decrease of snow-covered area. The subgrid scheme not only improves the overall simulation by feedback process but also provides high-resolution meteorological fields that can be used for adaptation and impact studies. Therefore, more accurate representation of land-surface heterogeneity in sub-grid approach improves the temperature and snow fields over the complex terrain and can be useful for coupling with impact models, although further improvements are desirable.

Land Cover Maps Production with High Resolution Satellite Image Time Series and Convolutional Neural Networks: Adaptations and Limits for Operational Systems

The Sentinel-2 satellite mission offers high resolution multispectral time-series image data, enabling the production of detailed land cover maps globally. When mapping large territories, the trade-off between processing time and result quality is a central design decision. Currently, this machine learning task is usually performed using pixel-wise classification methods. However, the radical shift of the computer vision field away from hand-engineered image features and towards more automation by representation learning comes with many promises, including higher quality results and less engineering effort. In particular, convolutional neural networks learn features which take into account the context of the pixels and, therefore, a better representation of the data can be obtained. In this paper, we assess fully convolutional neural network architectures as replacements for a Random Forest classifier in an operational context for the production of high resolution land cover maps with Sentinel-2 time-series at the country scale. Our contributions include a framework for working with Sentinel-2 L2A time-series image data, an adaptation of the U-Net model (a fully convolutional neural network) for dealing with sparse annotation data while maintaining high resolution output, and an analysis of those results in the context of operational production of land cover maps. We conclude that fully convolutional neural networks can yield improved results with respect to pixel-wise Random Forest classifiers for classes where texture and context are pertinent. However, this new approach shows higher variability in quality across different landscapes and comes with a computational cost which could be to high for operational systems.

INTER-COMPARISON OF THE GLOBAL LAND COVER MAPS IN AFRICA SUPPLEMENTED BY SPATIAL ASSOCIATION OF ERRORS

Abstract. Recent advances in Earth Observations supported development of high-resolution land cover (LC) maps on a large-scale. This is an important step forward, especially for developing countries, which experienced problems in the past due to absence of reliable LC information. Nevertheless, increasing number of LC products is imposing additional validation workload to confirm their quality. In this paper inter-comparison of two recent LC products (GlobeLand30 and S2 prototype LC 20m map of Africa) for country of Rwanda in Africa was done. It is a way to facilitate validation by identifying the areas with higher probability of error. Specific approach of comparison of single pixel of one map with multiple pixels of another map provided confusion matrix and sub-pixel agreement table. In this work, accuracy indexes based on the confusion matrix were computed as a measure of similarity between the two maps. Furthermore, Moran’s I index was computed for estimation of spatial association of the pixels in disagreement. Also, total disagreement, as well as disagreement of particularly confused classes was visualised to analyse their spatial distribution. The results are showing that similarity of the two maps is about 66%. Disagreements are spatially associated and the most evident in the eastern and north-western part of the area of interest. This coincides also with the distribution of the two most confused classes Wetland and Shrubland. The results delineate areas of inconsistency between the two maps, and therefore areas where careful accuracy analysis are needed.

Mapping Aedes aegypti (Diptera: Culicidae) and Aedes albopictus Vector Mosquito Distribution in Brownsville, TX.

Aedes mosquitoes are vectors of several emerging diseases and are spreading worldwide. We investigated the spatiotemporal dynamics of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) mosquito trap captures in Brownsville, TX, using high-resolution land cover, socioeconomic, and meteorological data. We modeled mosquito trap counts using a Bayesian hierarchical mixed-effects model with spatially correlated residuals. The models indicated an inverse relationship between temperature and mosquito trap counts for both species, which may be due to the hot and arid climate of southern Texas. The temporal trend in mosquito populations indicated Ae. aegypti populations peaking in the late spring and Ae. albopictus reaching a maximum in winter. Our results indicated that seasonal weather variation, vegetation height, human population, and land cover determine which of the two Aedes species will predominate.

A Community EcoHealth Index from EnviroAtlas Ecosystem Services Metrics.

Human health is inextricably tied to ecosystem services (ES), including those associated with greenspace in urban communities. EnviroAtlas provides close to 100 maps of ES metrics based on high-resolution land cover data in featured communities across the contiguous United States. Using selected EnviroAtlas ES metrics, a Community EcoHealth Index (CEHI) was created based on an ecohealth framework including health promotion and hazard buffering domains. Aggregation of eight selected ES metrics in these domains entailed a weighted distance measure, where objective, data-driven weights were generated. CEHI was calculated by Census Block Group (CBG) at both the local level and the national level for 22 EnviroAtlas communities. Results were mapped to show one- to five-star CBGs or neighborhoods within and across all 22 featured communities. At the national level, CEHI favors communities in forested ecoregions. The local version of CEHI is more appropriate to inform social, economic, and environmental decision-making for improving community ES associated with human health.

Upscaling Gross Primary Production in Corn-Soybean Rotation Systems in the Midwest

The Midwestern US is dominated by corn (Zea mays L.) and soybean (Glycine max [L.] Merr.) production, and the carbon dynamics of this region are dominated by these production systems. An accurate regional estimate of gross primary production (GPP) is imperative and requires upscaling approaches. The aim of this study was to upscale corn and soybean GPP (referred to as GPPcalc) in four counties in Central Iowa in the 2016 growing season (DOY 145–269). Eight eddy-covariance (EC) stations recorded carbon dioxide fluxes of corn (n = 4) and soybean (n = 4), and net ecosystem production (NEP) was partitioned into GPP and ecosystem respiration (RE). Additional field-measured NDVI was used to calculate radiation use efficiency (RUEmax). GPPcalc was calculated using 16 MODIS satellite images, ground-based RUEmax and meteorological data, and improved land use maps. Seasonal NEP, GPP, and RE ( x ¯ ± SE) were 678 ± 63, 1483 ± 100, and −805 ± 40 g C m−2 for corn, and 263 ± 40, 811 ± 53, and −548 ± 14 g C m−2 for soybean, respectively. Field-measured NDVI aligned well with MODIS fPAR (R2 = 0.99), and the calculated RUEmax was 3.24 and 1.90 g C MJ−1 for corn and soybean, respectively. The GPPcalc vs. EC-derived GPP had a RMSE of 2.24 and 2.81 g C m−2 d−1, for corn and soybean, respectively, which is an improvement to the GPPMODIS product (2.44 and 3.30 g C m−2 d−1, respectively). Corn yield, calculated from GPPcalc (12.82 ± 0.65 Mg ha−1), corresponded well to official yield data (13.09 ± 0.09 Mg ha−1), while soybean yield was overestimated (6.73 ± 0.27 vs. 4.03 ± 0.04 Mg ha−1). The approach presented has the potential to increase the accuracy of regional corn and soybean GPP and grain yield estimates by integrating field-based flux estimates with remote sensing reflectance observations and high-resolution land use maps.

Relationships between landslides and land use changes in the Sanbagawa and Mikabu Belts, Shikoku, Japan

Abstract. Changing land use is an increasingly important issue as human habits, behaviors, and needs change. There has been an increase in land and agricultural abandonment in some places of the world. In Japan, movement of the population from rural to urban areas have resulted in much land and agricultural abandonment. In 2016, a land ministry survey showed that 4.1 million hectares of land in Japan had unclear ownership, with farmland making up 16.9% of the total. As vegetation cover changes after land abandonment, this temporal and spatial effect may have important effects on geomorphic processes such as landslide susceptibility and landslide kinematics.Here we track long-term land use changes over vegetated landslide areas of the Sanbagawa and Mikabu Belts of Shikoku Island, Japan. The Sanbagawa and Mikabu Belts are metamorphic belts that run across Southwest Japan, and are home to numerous large crystalline schist landslides, including the widely-studied slow but continuously moving Zentoku landslide. Villages and communities have been built on these landslide areas due to historical and cultural factors, as well as the fertility of the soil. Consequently, given the changing land uses including land abandonment in these landslide areas over time, we use long-term high-resolution land cover vegetation datasets to examine first the long-term land use changes, and then use statistical methods to explore their relationships with landslide susceptibility and kinematics. Mapping of spatial data and their analysis using GIS constitute a core part of the research. The results suggest interconnections between land use changes and land movement.

Simulation of location-specific severe thunderstorm events using high resolution land data assimilation

In this study, the impact of different land initial conditions on the simulation of thunderstorms and monsoon depressions is investigated using the Weather Research and Forecasting (WRF) model. A control run (CNTL) and a simulation with an improved land state (soil moisture and temperature) using the High Resolution Land Data Assimilation System (HRLDAS, experiment name: EHRLDAS) are compared for three different rainfall cases in order to examine the robustness of the assimilation system. The study comprises two thunderstorm cases (one in the pre-monsoon and one during the monsoon) and one monsoon depression case that occurred during the Interaction of Convective Organisation, Atmosphere, Surface and Sea (INCOMPASS) field campaign of the 2016 Indian monsoon. EHRLDAS is shown to yield improvements in the representation of location-specific rainfall, particularly over land. Further, it is found that surface fluxes as well as convective indices are better captured for the pre-monsoon thunderstorm case in EHRLDAS. By analysing components of the vorticity tendency equation, it is found that the vertical advection term is the major contributor towards the positive vorticity tendency in EHRLDAS compared to CNTL, hence improving localised convection and consequently facilitating rainfall. Significant improvements in the simulation of the pre-monsoon thunderstorm are noted, as seen using Automatic Weather Station (AWS) validation, whereas improvements in the monsoon depression are minimal. Further, it is found that vertical advection (moisture flux convergence) is the major driver modulating the convective circulation in localised thunderstorm (monsoon depression) cases and these dynamics are better represented by EHRLDAS compared to CNTL. These findings underline the importance of accurate and high resolution land-state conditions in model initial conditions for forecasting severe weather systems, particularly the simulation of localised thunderstorms over India.

Mapping Urban Areas Using a Combination of Remote Sensing and Geolocation Data

Urban areas are essential to daily human life; however, the urbanization process also brings about problems, especially in China. Urban mapping at large scales relies heavily on remote sensing (RS) data, which cannot capture socioeconomic features well. Geolocation datasets contain patterns of human movement, which are closely related to the extent of urbanization. However, the integration of RS and geolocation data for urban mapping is performed mostly at the city level or finer scales due to the limitations of geolocation datasets. Tencent provides a large-scale location request density (LRD) dataset with a finer temporal resolution, and makes large-scale urban mapping possible. The objective of this study is to combine multi-source features from RS and geolocation datasets to extract information on urban areas at large scales, including night-time lights, vegetation cover, land surface temperature, population density, LRD, accessibility, and road networks. The random forest (RF) classifier is introduced to deal with these high-dimension features on a 0.01 degree grid. High spatial resolution land cover (LC) products and the normalized difference built-up index from Landsat are used to label all of the samples. The RF prediction results are evaluated using validation samples and compared with LC products for four typical cities. The results show that night-time lights and LRD features contributed the most to the urban prediction results. A total of 176,266 km2 of urban areas in China were extracted using the RF classifier, with an overall accuracy of 90.79% and a kappa coefficient of 0.790. Compared with existing LC products, our results are more consistent with the manually interpreted urban boundaries in the four selected cities. Our results reveal the potential of Tencent LRD data for the extraction of large-scale urban areas, and the reliability of the RF classifier based on a combination of RS and geolocation data.

Large-Area, High Spatial Resolution Land Cover Mapping Using Random Forests, GEOBIA, and NAIP Orthophotography: Findings and Recommendations

Despite the need for quality land cover information, large-area, high spatial resolution land cover mapping has proven to be a difficult task for a variety of reasons including large data volumes, complexity of developing training and validation datasets, data availability, and heterogeneity in data and landscape conditions. We investigate the use of geographic object-based image analysis (GEOBIA), random forest (RF) machine learning, and National Agriculture Imagery Program (NAIP) orthophotography for mapping general land cover across the entire state of West Virginia, USA, an area of roughly 62,000 km2. We obtained an overall accuracy of 96.7% and a Kappa statistic of 0.886 using a combination of NAIP orthophotography and ancillary data. Despite the high overall classification accuracy, some classes were difficult to differentiate, as highlight by the low user’s and producer’s accuracies for the barren, impervious, and mixed developed classes. In contrast, forest, low vegetation, and water were generally mapped with accuracy. The inclusion of ancillary data and first- and second-order textural measures generally improved classification accuracy whereas band indices and object geometric measures were less valuable. Including super-object attributes improved the classification slightly; however, this increased the computational time and complexity. From the findings of this research and previous studies, recommendations are provided for mapping large spatial extents.

Spatially Consistent High-Resolution Land Surface Temperature Mosaics for Thermophysical Mapping of the Mojave Desert.

Daytime and nighttime thermal infrared observations acquired by the ASTER and MODIS instruments onboard the NASA Terra spacecraft have produced a dataset that can be used to map thermophysical properties across large regions, which have implications on surface processes, thermal environments and habitat suitability for desert species. ASTER scenes acquired between 2004 and 2012 are combined using new mosaicking and data-fusion techniques to produce a map of daytime and nighttime land surface temperature with coverage exclusive of the effects of clouds and weather. These data are combined with Landsat 7 visible imagery to generate a consistent map of apparent thermal inertia (ATI), which is related to the presence of exposed bedrock, rocks, fine-grained sediments and water on the surface. The resulting datasets are compared to known geomorphic units and surface types to generate an interpreted mechanical composition map of the entire Mojave Desert at 100 m per pixel that is most sensitive to large clast size distinctions in grain size distribution.

EXTENDING ACCURACY ASSESSMENT PROCEDURES OF GLOBAL COVERAGE LAND COVER MAPS THROUGH SPATIAL ASSOCIATION ANALYSIS

Abstract. High-resolution land cover maps are in high demand for many environmental applications. Yet, the information they provide is uncertain unless the accuracy of these maps is known. Therefore, accuracy assessment should be an integral part of land cover map production as a way of ensuring reliable products. The traditional accuracy metrics like Overall Accuracy and Producer’s and User’s accuracies – based on the confusion matrix – are useful to understand global accuracy of the map, but they do not provide insight into the possible nature or source of the errors. The idea behind this work is to complement traditional accuracy metrics with the analysis of error spatial patterns. The aim is to discover errors underlying features which can be later employed to improve the traditional accuracy assessment. The designed procedure is applied to the accuracy assessment of the GlobeLand30 global land cover map for the Lombardy Region (Northern Italy) by means of comparison with the DUSAF regional land cover map. Traditional accuracy assessment quantified the classification accuracies of the map. Indeed, critical errors were pointed out and further analyses on their spatial patterns were performed by means of the Moran’s I indicator. Additionally, visual exploration of the spatial patterns was performed. This allowed describing possible sources of errors. Both software and analysis strategies were described in detail to facilitate future improvement and replication of the procedure. The results of the exploratory experiments are critically discussed in relation to the benefits that they potentially introduce into the traditional accuracy assessment procedure.

Market-Driven Corn Monocropping in the U.S. Midwest

This study examines the market drivers of corn monocropping in the U.S. Midwest by empirically analyzing crop rotation responses to market fluctuation from 2005 to 2014 and the price shock induced by the recent biofuel mandate. We find that the expected market returns for crops have a significant impact on farmers' decisions about monocropping. We also find that corn monocropping is loosely associated with the presence of nearby ethanol plants. This study illustrates the emerging use of high-resolution land cover data to tackle critical agribusiness and agro-environmental policy questions that remain elusive with aggregate data.