scholarly journals Accuracy Assessment of the FROM-GLC30 Land Cover Dataset Based on Watershed Sampling Units: A Continental-Scale Study

2020 ◽  
Vol 12 (20) ◽  
pp. 8435
Author(s):  
Zitian Guo ◽  
Chunmei Wang ◽  
Xin Liu ◽  
Guowei Pang ◽  
Mengyang Zhu ◽  
...  
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Large Scale ◽  
Reference Data ◽  
Accuracy Assessment ◽  
Visual Interpretation ◽  
Land Cover Types ◽  
Practical Applications ◽  
Land Type ◽  
Continental Scale

Land cover information plays an essential role in the study of global surface change. Multiple land cover datasets have been produced to meet various application needs. The FROM-GLC30 (Finer Resolution Observation and Monitoring of Global Land Cover) dataset is one of the latest land cover products with a resolution of 30 m, which is a relatively high resolution among global public datasets, and the accuracy of this dataset is of great concern in many related researches. The objective of this study was to calculate the accuracy of the FROM-GLC30 2017 dataset at the continental scale and to explore the spatial variation differences of each land type accuracy in different regions. In this study, the visual interpretation land cover results at 20,936 small watershed sampling units based on high-resolution remote sensing images were used as the reference data covering 65 countries in Asia, Europe, and Africa. The reference data were verified by field survey in typical watersheds. Based on that, the accuracy assessment of the FROM-GLC30 2017 dataset was carried out. The results showed (1) the area proportion of different land cover types in the FROM-GLC30 2017 dataset was generally consistent with that of the reference data. (2) The overall accuracy of the FROM-GLC30 2017 dataset was 72.78%, and was highest in West Asia–Northeast Africa, and lowest in South Asia. (3) Among all the seven land cover types, the accuracy of bareland and forest was relatively higher than that of others, and the accuracy of shrubland was the lowest. The accuracy for each land cover type differed among regions. The results of this work can provide useful information for land cover accuracy assessment researches at a large scale and promote the further practical applications of the open-source land cover datasets.

scholarly journals Automated Production of a Land Cover/Use Map of Europe Based on Sentinel-2 Imagery

2020 ◽  
Vol 12 (21) ◽  
pp. 3523
Author(s):  
Radek Malinowski ◽  
Stanisław Lewiński ◽  
Marcin Rybicki ◽  
Ewa Gromny ◽  
Małgorzata Jenerowicz ◽  
...  
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Data Processing ◽  
Spatial Resolution ◽  
Large Scale ◽  
Observation Data ◽  
European Continent ◽  
Continental Scale ◽  
Land Cover Maps ◽  
Sentinel 2

Up-to-date information about the Earth’s surface provided by land cover maps is essential for numerous environmental and land management applications. There is, therefore, a clear need for the continuous and reliable monitoring of land cover and land cover changes. The growing availability of high resolution, regularly collected remote sensing data can support the increasing number of applications that require high spatial resolution products that are frequently updated (e.g., annually). However, large-scale operational mapping requires a highly-automated data processing workflow, which is currently lacking. To address this issue, we developed a methodology for the automated classification of multi-temporal Sentinel-2 imagery. The method uses a random forest classifier and existing land cover/use databases as the source of training samples. In order to demonstrate its operability, the method was implemented on a large part of the European continent, with CORINE Land Cover and High-Resolution Layers as training datasets. A land cover/use map for the year 2017 was produced, composed of 13 classes. An accuracy assessment, based on nearly 52,000 samples, revealed high thematic overall accuracy (86.1%) on a continental scale, and average overall accuracy of 86.5% at country level. Only low-frequency classes obtained lower accuracies and we recommend that their mapping should be improved in the future. Additional modifications to the classification legend, notably the fusion of thematically and spectrally similar vegetation classes, increased overall accuracy to 89.0%, and resulted in ten, general classes. A crucial aspect of the presented approach is that it embraces all of the most important elements of Earth observation data processing, enabling accurate and detailed (10 m spatial resolution) mapping with no manual user involvement. The presented methodology demonstrates possibility for frequent and repetitive operational production of large-scale land cover maps.

SateLoc: A Virtual Fingerprinting Approach to Outdoor LoRa Localization Using Satellite Images

2021 ◽  
Vol 17 (4) ◽  
pp. 1-28
Author(s):  
Yuxiang Lin ◽  
Wei Dong ◽  
Yi Gao ◽  
Tao Gu
Keyword(s):  
Land Cover ◽  
Large Scale ◽  
Satellite Images ◽  
Low Cost ◽  
Tracking Error ◽  
Location Based Services ◽  
Distance Constraint ◽  
Combination Strategy ◽  
Land Cover Types ◽  
High Resolution Satellite Images

With the increasing relevance of the Internet of Things and large-scale location-based services, LoRa localization has been attractive due to its low-cost, low-power, and long-range properties. However, existing localization approaches based on received signal strength indicators are either easily affected by signal fading of different land-cover types or labor intensive. In this work, we propose SateLoc, a LoRa localization system that utilizes satellite images to generate virtual fingerprints. Specifically, SateLoc first uses high-resolution satellite images to identify land-cover types. With the path loss parameters of each land-cover type, SateLoc can automatically generate a virtual fingerprinting map for each gateway. We then propose a novel multi-gateway combination strategy, which is weighted by the environmental interference of each gateway, to produce a joint likelihood distribution for localization and tracking. We implement SateLoc with commercial LoRa devices without any hardware modification, and evaluate its performance in a 227,500-m urban area. Experimental results show that SateLoc achieves a median localization error of 43.5 m, improving more than 50% compared to state-of-the-art model-based approaches. Moreover, SateLoc can achieve a median tracking error of 37.9 m with the distance constraint of adjacent estimated locations. More importantly, compared to fingerprinting-based approaches, SateLoc does not require the labor-intensive fingerprint acquisition process.

scholarly journals Regional environmental controllers influence continental scale soil carbon stocks and future carbon dynamics

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Daniel Ruiz Potma Gonçalves ◽  
Umakant Mishra ◽  
Skye Wills ◽  
Sagar Gautam
Keyword(s):  
Environmental Factors ◽  
Land Cover ◽  
Environmental Conditions ◽  
Climate Feedback ◽  
Soil Types ◽  
Climatic Data ◽  
Earth System ◽  
Land Cover Types ◽  
Continental Scale ◽  
Soc Stocks

AbstractUnderstanding the influence of environmental factors on soil organic carbon (SOC) is critical for quantifying and reducing the uncertainty in carbon climate feedback projections under changing environmental conditions. We explored the effect of climatic variables, land cover types, topographic attributes, soil types and bedrock geology on SOC stocks of top 1 m depth across conterminous United States (US) ecoregions. Using 4559 soil profile observations and high-resolution data of environmental factors, we identified dominant environmental controllers of SOC stocks in 21 US ecoregions using geographically weighted regression. We used projected climatic data of SSP126 and SSP585 scenarios from GFDL-ESM 4 Earth System Model of Coupled Model Intercomparison Project phase 6 to predict SOC stock changes across continental US between 2030 and 2100. Both baseline and predicted changes in SOC stocks were compared with SOC stocks represented in GFDL-ESM4 projections. Among 56 environmental predictors, we found 12 as dominant controllers across all ecoregions. The adjusted geospatial model with the 12 environmental controllers showed an R2 of 0.48 in testing dataset. Higher precipitation and lower temperatures were associated with higher levels of SOC stocks in majority of ecoregions. Changes in land cover types (vegetation properties) was important in drier ecosystem as North American deserts, whereas soil types and topography were more important in American prairies. Wetlands of the Everglades was highly sensitive to projected temperature changes. The SOC stocks did not change under SSP126 until 2100, however SOC stocks decreased up to 21% under SSP585. Our results, based on environmental controllers of SOC stocks, help to predict impacts of changing environmental conditions on SOC stocks more reliably and may reduce uncertainties found in both, geospatial and Earth System Models. In addition, the description of different environmental controllers for US ecoregions can help to describe the scope and importance of global and local models.

scholarly journals Domain-Adversarial Training of Self-Attention-Based Networks for Land Cover Classification Using Multi-Temporal Sentinel-2 Satellite Imagery

2021 ◽  
Vol 13 (13) ◽  
pp. 2564
Author(s):  
Mauro Martini ◽  
Vittorio Mazzia ◽  
Aleem Khaliq ◽  
Marcello Chiaberge
Keyword(s):  
Land Cover ◽  
Large Scale ◽  
Domain Adaptation ◽  
Learning Approaches ◽  
Atmospheric Conditions ◽  
Practical Applications ◽  
Area Of Interest ◽  
Multi Temporal ◽  
Significant Performance ◽  
Adversarial Training

The increasing availability of large-scale remote sensing labeled data has prompted researchers to develop increasingly precise and accurate data-driven models for land cover and crop classification (LC&CC). Moreover, with the introduction of self-attention and introspection mechanisms, deep learning approaches have shown promising results in processing long temporal sequences in the multi-spectral domain with a contained computational request. Nevertheless, most practical applications cannot rely on labeled data, and in the field, surveys are a time-consuming solution that pose strict limitations to the number of collected samples. Moreover, atmospheric conditions and specific geographical region characteristics constitute a relevant domain gap that does not allow direct applicability of a trained model on the available dataset to the area of interest. In this paper, we investigate adversarial training of deep neural networks to bridge the domain discrepancy between distinct geographical zones. In particular, we perform a thorough analysis of domain adaptation applied to challenging multi-spectral, multi-temporal data, accurately highlighting the advantages of adapting state-of-the-art self-attention-based models for LC&CC to different target zones where labeled data are not available. Extensive experimentation demonstrated significant performance and generalization gain in applying domain-adversarial training to source and target regions with marked dissimilarities between the distribution of extracted features.

scholarly journals Characterizing the Spatial and Temporal Availability of Very High Resolution Satellite Imagery in Google Earth and Microsoft Bing Maps as a Source of Reference Data

Land ◽  
2018 ◽  
Vol 7 (4) ◽  
pp. 118 ◽  
Author(s):  
Myroslava Lesiv ◽  
Linda See ◽  
Juan Laso Bayas ◽  
Tobias Sturn ◽  
Dmitry Schepaschenko ◽  
...  
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Urban Areas ◽  
Reference Data ◽  
Temporal Distribution ◽  
Google Earth ◽  
Training Data ◽  
Visual Interpretation ◽  
The Usa ◽  
Very High

Very high resolution (VHR) satellite imagery from Google Earth and Microsoft Bing Maps is increasingly being used in a variety of applications from computer sciences to arts and humanities. In the field of remote sensing, one use of this imagery is to create reference data sets through visual interpretation, e.g., to complement existing training data or to aid in the validation of land-cover products. Through new applications such as Collect Earth, this imagery is also being used for monitoring purposes in the form of statistical surveys obtained through visual interpretation. However, little is known about where VHR satellite imagery exists globally or the dates of the imagery. Here we present a global overview of the spatial and temporal distribution of VHR satellite imagery in Google Earth and Microsoft Bing Maps. The results show an uneven availability globally, with biases in certain areas such as the USA, Europe and India, and with clear discontinuities at political borders. We also show that the availability of VHR imagery is currently not adequate for monitoring protected areas and deforestation, but is better suited for monitoring changes in cropland or urban areas using visual interpretation.

scholarly journals Consistency Analysis and Accuracy Assessment of Three Global 30-m Land-Cover Products over the European Union using the LUCAS Dataset

2020 ◽  
Vol 12 (21) ◽  
pp. 3479
Author(s):  
Yuan Gao ◽  
Liangyun Liu ◽  
Xiao Zhang ◽  
Xidong Chen ◽  
Jun Mi ◽  
...  
Keyword(s):  
European Union ◽  
Land Cover ◽  
Hydrological Cycle ◽  
Accuracy Assessment ◽  
Confusion Matrix ◽  
The European Union ◽  
Mountain Areas ◽  
Land Cover Types ◽  
Spatial Consistency ◽  
The Eu

Land-cover plays an important role in the Earth’s energy balance, the hydrological cycle, and the carbon cycle. Therefore, it is important to evaluate the current global land-cover (GLC) products and to understand the differences between these products so that they can be used effectively in different applications. In this study, three 30-m GLC products, namely GlobeLand30-2010, GLC_FCS30-2015, and FROM_GLC30-2015, were evaluated in terms of areal consistency and spatial consistency using the Land Use/Cover Area frame statistical Survey (LUCAS) reference dataset over the European Union (EU). Given the limitations of the traditional confusion matrix used in accuracy assessment, we adjusted the confusion matrices from sample counts by accounting for the class proportions of the map and reported the standard errors of the descriptive accuracy measures in the accuracy assessment. The results revealed the following. (1) The overall accuracy of the GlobeLand30-2010 product was the highest at 88.90 ± 0.68%; this was followed by GLC_FCS30-2015 (84.33 ± 0.80%) and FROM_GLC2015 (65.31 ± 1.0%). (2) The consistency between the GLC_FCS30-2015 and GlobeLand30-2010 is higher than the consistency between other products, with an area correlation coefficient of 0.930 and a proportion of consistent pixels of 52.41%, respectively. (3) Across the area of the EU, the dominant land-cover types such as forest and cropland are the most consistent across the three products, whereas the spatial consistency for bare land, grassland, shrubland, and wetland is relatively low. (4) The proportion of pixels for which the consistency is low accounts for less than 16.17% of pixels, whereas the proportion of pixels for which the consistency is high accounts for about 39.12%. The disagreement between these products primarily occurs in transitional zones with mixed land cover types or in mountain areas. Overall, the GlobeLand30 and GLC-FCS30 products were found to be the most consistent and to have good classification accuracy in the EU, with the disagreement between the three 30-m GLC products mainly occurring in heterogeneous regions.

scholarly journals Evaluating seasonal hydrological extremes in mesoscale (pre-)Alpine basins at coarse 0.5° and fine hyperresolution

2019 ◽  
Vol 23 (3) ◽  
pp. 1593-1609 ◽  
Author(s):  
Joost Buitink ◽  
Remko Uijlenhoet ◽  
Adriaan J. Teuling
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Swiss Alps ◽  
Hydrological Response ◽  
Low Resolution ◽  
Continental Scale ◽  
Wide Range ◽  
Resolution Model ◽  
High Resolution Model ◽  
Basin Response

Abstract. Hydrological models are being applied for impact assessment across a wide range of resolutions. In this study, we quantify the effect of model resolution on the simulated hydrological response in five mesoscale basins in the Swiss Alps using the distributed hydrological model Spatial Processes in Hydrology (SPHY). We introduce a new metric to compare a range of values resulting from a distributed model with a single value: the density-weighted distance (DWD). Model simulations are performed at two different spatial resolutions, matching common practices in hydrology: 500 m × 500 m matching regional-scale models, and 40 km × 40 km matching global-scale modeling. We investigate both the intra-basin response in seasonal streamflow and evapotranspiration from the high-resolution model and the difference induced by the two different spatial resolutions, with a focus on four seasonal extremes, selected based on temperature and precipitation. Results from the high-resolution model show that the intra-basin response covers a surprisingly large range of anomalies and show that it is not uncommon to have both extreme positive and negative flux anomalies occurring simultaneously within a catchment. The intra-basin response was grouped by land cover, where different dominant runoff-generating processes are driving the differences between these groups. The low-resolution model failed to capture the diverse and contrasting response from the high-resolution model, since neither the complex topography nor land cover classes were properly represented. DWD values show that, locally, the hydrological response simulated with a high-resolution model can be a lot more extreme than a low-resolution model might indicate, which has important implications for global or continental scale assessments carried out at coarse grids of 0.5∘×0.5∘ or 0.25∘×0.25∘ resolution.

Sea-Breeze Convergence Zones from AVHRR over the Iberian Mediterranean Area and the Isle of Mallorca, Spain

2009 ◽  
Vol 48 (10) ◽  
pp. 2069-2085 ◽  
Author(s):  
Cesar Azorin-Molina ◽  
Bernadette H. Connell ◽  
Rafael Baena-Calatrava
Keyword(s):  
Boundary Layer ◽  
High Resolution ◽  
Large Scale ◽  
Leading Edge ◽  
Sea Breeze ◽  
Heavy Rain ◽  
Detection Algorithm ◽  
Cloud Detection ◽  
Practical Applications ◽  
Convergence Zones

Abstract The aim of this study was to identify clear air boundaries and to obtain spatial distribution of convective areas associated with the sea breeze over the Iberian Mediterranean zone and the isle of Mallorca, both in Spain. Daytime Advanced Very High Resolution Radiometer (AVHRR) data from National Oceanic and Atmospheric Administration (NOAA) polar-orbiting satellites were collected for May–October 2004. A cloud detection algorithm was used to identify clouds to derive daytime sea-breeze cloud frequency composites over land. The high-resolution composites aided in identifying the location of five preferential sea-breeze convergence zones (SBCZ) in relation to the shape of coastline and orographic effects. Additionally, eight regimes were designated using mean boundary layer wind speed and direction to provide statistics about the effect of prevailing large-scale flows on sea-breeze convection over the five SBCZ. The offshore SW to W and the NW to N regimes were characterized by high cloud frequencies parallel to the coast. Small differences in mean cloud frequency values from morning to afternoon composites were detected with these regimes because sea-breeze fronts tended to form early and persist into the afternoon. Just the opposite occurred under the onshore NE to E and SE to S regimes. It was found that light to moderate (≤5.1 m s−1) winds aloft result in more clouds at the leading edge of sea breezes. In contrast, strong synoptic-scale (>5.1 m s−1) flows weaken boundary layer convergence. The results from this satellite meteorology study could have practical applications for many people including those that forecast the weather and those that use the forecast for making decisions related to energy use, fishing, recreation, or agriculture activities, as well as for estimating pollution or issuing warnings for heavy rain or flash flooding.

scholarly journals REVIEW OF HIGH-RESOLUTION GLOBAL LAND COVER

2021 ◽  
Vol XLIII-B4-2021 ◽  
pp. 175-182
Author(s):  
G. Bratic ◽  
A. Vavassori ◽  
M. A. Brovelli
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Spatial Resolution ◽  
Accuracy Assessment ◽  
General Information ◽  
Tree Canopy ◽  
Global Land Cover ◽  
Tree Canopy Cover ◽  
Global Land ◽  
Land Cover Maps

Abstract. The land cover detection on our planet at high spatial resolution has a key role in many scientific and operational applications, such as climate modeling, natural resources management, biodiversity studies, urbanization analyses and spatial demography. Thanks to the progresses in Remote Sensing, accurate and high-resolution land cover maps have been developed over the last years, aiming at detecting the spatial resolution of different types of surfaces. In this paper we propose a review of the high-resolution global land cover products developed through Earth Observation technologies. A series of general information regarding imagery and data used to produce the map, the procedures employed for the map development and for the map accuracy assessment have been provided for every dataset. The land cover maps described in this paper concern the global distribution of settlements (Global Urban Footprint, Global Human Settlement Built-Up, World Settlement Footprint), water (Global Surface Water), forests (Forest/Non-forest, Tree canopy cover), and a two land cover maps describing world in 10 generic classes (GlobeLand30 and Finer Resolution Observation and Monitoring of Global Land Cover). The advantages and shortcomings of these maps and of the methods employed to produce them are summarized and compared in the conclusions.

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