Land Cover Dataset Research Articles

Vegetation Cover Change Analysis of Phytogeographic Regions of Turkey Based on CORINE Land Cover Datasets from 1990 to 2018

Abstract
 Aim of study: The current state of the land cover and its temporal changes are one of the most popular topics nowadays, and it is a very useful subject in the protection of ecosystems and combating global climate change. The main objective of this study was to reveal vegetation cover change on Turkey and its phytogeographic region within the last 28 years.
 Area of study: The study area borders of the Republic of Turkey, covers an area of 779.646 square kilometers. In addition, Turkey is home to three phytogeographic regions (Mediterranean, Euro-Siberia, and Irano-Turanian) with different floristic features. Both phytogeographic regions and Turkey were examined individually in this study.
 Material and methods: 1990, 2000, 2006, 2012, and 2018 CORINE land cover datasets were used in order to examine vegetation cover of the Republic of Turkey and its phytogeographic regions in this study.
 Main results: According to the results, it was determined that forests and sparsely vegetated areas decreased in the period from 1990 to 2018. It has been observed that the change in forests mainly caused by the reduction in mixed forest stands. In general, all phytogeographic regions have shown their characteristic features in the last 28 years. 
 Highlights: The quantitative and statistical results obtained within this study are considered to be decisive for the actions to be implemented in the upcoming years.
 Keywords:

DGFNet: Dual Gate Fusion Network for Land Cover Classification in Very High-Resolution Images

Deep convolutional neural networks (DCNNs) have been used to achieve state-of-the-art performance on land cover classification thanks to their outstanding nonlinear feature extraction ability. DCNNs are usually designed as an encoder–decoder architecture for the land cover classification in very high-resolution (VHR) remote sensing images. The encoder captures semantic representation by stacking convolution layers and shrinking image spatial resolution, while the decoder restores the spatial information by an upsampling operation and combines it with different level features through a summation or skip connection. However, there is still a semantic gap between different-level features; a simple summation or skip connection will reduce the performance of land-cover classification. To overcome this problem, we propose a novel end-to-end network named Dual Gate Fusion Network (DGFNet) to restrain the impact of the semantic gap. In detail, the key of DGFNet consists of two main components: Feature Enhancement Module (FEM) and Dual Gate Fusion Module (DGFM). Firstly, the FEM combines local information with global contents and strengthens the feature representation in the encoder. Secondly, the DGFM is proposed to reduce the semantic gap between different level features, effectively fusing low-level spatial information and high-level semantic information in the decoder. Extensive experiments conducted on the LandCover dataset and the ISPRS Potsdam dataset proved the effectiveness of the proposed network. The DGFNet achieves state-of-art performance 88.87% MIoU on the LandCover dataset and 72.25% MIoU on the ISPRS Potsdam dataset.

How is the Asian economy recovering from COVID-19? Evidence from the emissions of air pollutants

This study examines how economic and social activities in Asia were affected by the COVID-19 pandemic, using the emissions of various air pollutants as representative measures of those activities. Our review of emissions data suggests that the amount of air pollutants emitted decreased in most subnational regions from 2019 to 2020. We also determined that economic and social activities have restarted in some regions in many countries. Moreover, we conduct regression analyses to identify the types of regions that restarted earlier. Regional characteristics are distinguished by employing a remotely sensed land cover dataset and OpenStreetMap. Results reveal that in the case of the Association of Southeast Asian Nations (ASEAN) forerunners, economic and social activities in cropland, industrial estates, accommodations, restaurants, education, and public services have not yet returned to previous levels.

Unveiling the Factors Responsible for Australia’s Black Summer Fires of 2019/2020

The summer season of 2019–2020 has been named Australia’s Black Summer because of the large forest fires that burnt for months in southeast Australia, affecting millions of Australia’s citizens and hundreds of millions of animals and capturing global media attention. This extensive fire season has been attributed to the global climate crisis, a long drought season and extreme fire weather conditions. Our aim in this study was to examine the factors that have led some of the wildfires to burn over larger areas for a longer duration and to cause more damage to vegetation. To this end, we studied all large forest and non-forest fires (>100 km2) that burnt in Australia between September 2019 and mid-February 2020 (Australia’s Black Summer fires), focusing on the forest fires in southeast Australia. We used a segmentation algorithm to define individual polygons of large fires based on the burn date from NASA’s Visible Infrared Imaging Radiometer Suite (VIIRS) active fires product and the Moderate Resolution Imaging Spectroradiometer (MODIS) burnt area product (MCD64A1). For each of the wildfires, we calculated the following 10 response variables, which served as proxies for the fires’ extent in space and time, spread and intensity: fire area, fire duration (days), the average spread of fire (area/days), fire radiative power (FRP; as detected by NASA’s MODIS Collection 6 active fires product (MCD14ML)), two burn severity products, and changes in vegetation as a result of the fire (as calculated using the vegetation health index (VHI) derived from AVHRR and VIIRS as well as live fuel moisture content (LFMC), photosynthetic vegetation (PV) and combined photosynthetic and non-photosynthetic vegetation (PV+NPV) derived from MODIS). We also computed more than 30 climatic, vegetation and anthropogenic variables based on remotely sensed derived variables, climatic time series and land cover datasets, which served as the explanatory variables. Altogether, 391 large fires were identified for Australia’s Black Summer. These included 205 forest fires with an average area of 584 km2 and 186 non-forest fires with an average area of 445 km2; 63 of the forest fires took place in southeast (SE) Australia (the area between Fraser Island, Queensland, and Kangaroo Island, South Australia), with an average area of 1097 km2. Australia’s Black Summer forest fires burnt for more days compared with non-forest fires. Overall, the stepwise regression models were most successful at explaining the response variables for the forest fires in SE Australia (n = 63; median-adjusted R2 of 64.3%), followed by all forest fires (n = 205; median-adjusted R2 of 55.8%) and all non-forest fires (n = 186; median-adjusted R2 of 48.2%). The two response variables that were best explained by the explanatory variables used as proxies for fires’ extent, spread and intensity across all models for the Black Summer forest and non-forest fires were the change in PV due to fire (median-adjusted R2 of 69.1%) and the change in VHI due to fire (median-adjusted R2 of 66.3%). Amongst the variables we examined, vegetation and fuel-related variables (such as previous frequency of fires and the conditions of the vegetation before the fire) were found to be more prevalent in the multivariate models for explaining the response variables in comparison with climatic and anthropogenic variables. This result suggests that better management of wildland–urban interfaces and natural vegetation using cultural and prescribed burning as well as planning landscapes with less flammable and more fire-tolerant ground cover plants may reduce fire risk to communities living near forests, but this is challenging given the sheer size and diversity of ecosystems in Australia.

A hybrid deep convolutional neural network for accurate land cover classification

Land cover classification provides updated information regarding the Earth's resources, which is vital for agricultural investigation, urban management, and disaster monitoring. Current advances in sensor technology on satellite and aerial remote sensing (RS) devices have improved the spatial-spectral, radiometric, and temporal resolutions of images over time. These improvements offer invaluable chances of understanding land cover information. However, land cover classification from RS images is an intricate task because of the high intra-class disparities, low inter-class similarities, and image variation types. We propose a cascaded residual dilated network (CRD-Net) for land cover classification using very high spatial resolution (VHSR) images to address these challenges. The proposed hybrid network follows the encoder-decoder concept with a spatial attention block to guide the network on learnable discriminate features coupled with an intermediary loss to enhance the training process. Moreover, a cascaded residual dilated module increases the network's receptive field to enrich multi-contextual features further, thus boosting the resultant feature descriptor. Extensive experimental results demonstrate that the proposed CRD-Net outperformed state-of-the-art methods, achieving an overall accuracy (OA) of 90.73% and 90.51% on the ISPRS Potsdam land cover dataset and ISPRS Vaihingen dataset, respectively.

Spatial Differentiation and Driving Mechanisms in Ecosystem Service Value of Arid Region：A case study in the middle and lower reaches of Shule River Basin, NW China

The determination of spatiotemporal variation in ecosystem service value and its drivers is fundamental to ecosystem service management and decision-making. This paper selects a typical oasis irrigation district in the arid regions of northwest China as the research object. Using the benefit transfer method to evaluate the ecosystem service value variation caused by land use and land cover change and characteristics of its spatial distribution based on multi-temporal land use and land cover data sets (1977, 1987, 1997, 2007, 2017). Meanwhile, the contributions of factors driving ecosystem service value and their interactions were explored using geographical detector. The results showed the following: 1) The land use and land cover structure was stable from 1977 to 2017, and the overall ecosystem service value increased slightly. The services provided by the oasis ecosystem dominated the fluctuations in ecosystem service value throughout the study region. 2) Ecosystem service value exhibited a strong positive spatial autocorrelation. The high values were concentrated in the oasis area in the north of the study area, while the low values mainly appeared in the desert ecosystem. 3) The land use degree and human activity intensity index of human factors are the main factors leading to the differentiation of ecosystem service value. Synergized interactions among human activities, changes in landscape patterns, and natural factors produced the spatial differentiation in ecosystem service value of the study region. The results suggest that in future decision-making for ecosystem management, the direction of human activities within the ecological environment should be controlled. Improve the diversity of patches, reduce the degree of landscape fragmentation, improve the ecosystem service function of LULC, optimize the allocation of ecological landscape resources.

The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019

Abstract. Land cover (LC) determines the energy exchange, water and carbon cycle between Earth's spheres. Accurate LC information is a fundamental parameter for the environment and climate studies. Considering that the LC in China has been altered dramatically with the economic development in the past few decades, sequential and fine-scale LC monitoring is in urgent need. However, currently, fine-resolution annual LC dataset produced by the observational images is generally unavailable for China due to the lack of sufficient training samples and computational capabilities. To deal with this issue, we produced the first Landsat-derived annual China land cover dataset (CLCD) on the Google Earth Engine (GEE) platform, which contains 30 m annual LC and its dynamics in China from 1990 to 2019. We first collected the training samples by combining stable samples extracted from China's land-use/cover datasets (CLUDs) and visually interpreted samples from satellite time-series data, Google Earth and Google Maps. Using 335 709 Landsat images on the GEE, several temporal metrics were constructed and fed to the random forest classifier to obtain classification results. We then proposed a post-processing method incorporating spatial–temporal filtering and logical reasoning to further improve the spatial–temporal consistency of CLCD. Finally, the overall accuracy of CLCD reached 79.31 % based on 5463 visually interpreted samples. A further assessment based on 5131 third-party test samples showed that the overall accuracy of CLCD outperforms that of MCD12Q1, ESACCI_LC, FROM_GLC and GlobeLand30. Besides, we intercompared the CLCD with several Landsat-derived thematic products, which exhibited good consistencies with the Global Forest Change, the Global Surface Water, and three impervious surface products. Based on the CLCD, the trends and patterns of China's LC changes during 1985 and 2019 were revealed, such as expansion of impervious surface (+148.71 %) and water (+18.39 %), decrease in cropland (−4.85 %) and grassland (−3.29 %), and increase in forest (+4.34 %). In general, CLCD reflected the rapid urbanization and a series of ecological projects (e.g. Gain for Green) in China and revealed the anthropogenic implications on LC under the condition of climate change, signifying its potential application in the global change research. The CLCD dataset introduced in this article is freely available at https://doi.org/10.5281/zenodo.4417810 (Yang and Huang, 2021).

Towards an open and synergistic framework for mapping global land cover.

Global land-cover datasets are key sources of information for understanding the complex inter-actions between human activities and global change. They are also among the most critical variables for climate change studies. Over time, the spatial resolution of land cover maps has increased from the kilometer scale to 10-m scale. Single-type historical land cover datasets, including for forests, water, and impervious surfaces, have also been developed in recent years. In this study, we present an open and synergy framework to produce a global land cover dataset that combines supervised land cover classification and aggregation of existing multiple thematic land cover maps with the Google Earth Engine (GEE) cloud computing platform. On the basis of this method of classification and mosaicking, we derived a global land cover dataset for 6 years over a time span of 25 years. The overall accuracies of the six maps were around 75% and the accuracy for change area detection was over 70%. Our product also showed good similarity with the FAO and existing land cover maps.

Upgrading Land-Cover and Vegetation Seasonality in the ECMWF Coupled System: Verification With FLUXNET Sites, METEOSAT Satellite Land Surface Temperatures, and ERA5 Atmospheric Reanalysis.

In this study, we show that limitations in the representation of land cover and vegetation seasonality in the European Centre for Medium‐Range Weather Forecasting (ECMWF) model are partially responsible for large biases (up to ∼10°C, either positive or negative depending on the region) on the simulated daily maximum land surface temperature (LST) with respect to satellite Earth Observations (EOs) products from the Land Surface Analysis Satellite Application Facility. The error patterns were coherent in offline land‐surface and coupled land‐atmosphere simulations, and in ECMWF's latest generation reanalysis (ERA5). Subsequently, we updated the ECMWF model's land cover characterization leveraging on state‐of‐the‐art EOs—the European Space Agency Climate Change Initiative land cover data set and the Copernicus Global Land Services leaf area index. Additionally, we tested a clumping parameterization, introducing seasonality to the effective low vegetation coverage. The updates reduced the overall daily maximum LST bias and unbiased root‐mean‐squared errors. In contrast, the implemented updates had a neutral impact on daily minimum LST. Our results also highlighted the complex regional heterogeneities in the atmospheric sensitivity to land cover and vegetation changes, particularly with issues emerging over eastern Brazil and northeastern Asia. These issues called for a re‐calibration of model parameters (e.g., minimum stomatal resistance, roughness length, rooting depth), along with a revision of several model assumptions (e.g., snow shading by high vegetation).

Old-growth forest loss and secondary forest recovery across Amazonian countries

There is growing recognition of the potential of large-scale forest restoration in the Amazon as a ‘nature-based solution’ to climate change. However, our knowledge of forest loss and recovery beyond Brazil is limited, and carbon emissions and accumulation have not been estimated for the whole biome. Combining a 33 year land cover dataset with estimates of above-ground biomass and carbon sequestration rates, we evaluate forest loss and recovery across nine Amazonian countries and at a local scale. We also estimate the role of secondary forests in offsetting old-growth deforestation emissions and explore the temporal trends in forest loss and recovery. We find secondary forests across the biome to have offset just 9.7% of carbon emissions from old-growth deforestation, despite occupying 28.8% of deforested land. However, these numbers varied between countries ranging from 9.0% in Brazil to 23.8% in Guyana for carbon offsetting, and 24.8% in Brazil to 56.9% in Ecuador for forest area recovery. We reveal a strong, negative spatial relationship between old-growth forest loss and recovery by secondary forests, showing that regions with the greatest potential for large-scale restoration are also those that currently have the lowest recovery (e.g. Brazil dominates deforestation and emissions but has the lowest recovery). In addition, a temporal analysis of the regions that were >80% deforested in 1997 shows a continued decline in overall forest cover. Our findings identify three important challenges: (a) incentivising large-scale restoration in highly deforested regions, (b) protecting secondary forests without disadvantaging landowners who depend on farm-fallow systems, and (c) preventing further deforestation. Combatting all these successfully is essential to ensuring that the Amazon biome achieves its potential in mitigating anthropogenic climate change.

Overview of recent land cover changes, forest harvest areas, and soil erosion trends in Nordic countries

Mapping spatiotemporal land cover changes offers opportunities to better understand trends and drivers of environmental change and helps to identify more sustainable land management strategies. This study investigates the spatiotemporal patterns of changes in land covers, forest harvest areas and soil erosion rates in Nordic countries, namely Norway, Sweden, Finland, and Denmark. This region is highly sensitive to environmental changes, as it is experiencing high levels of human pressure and among the highest rates of global warming. An analysis that uses consistent land cover dataset to quantify and compares the recent spatiotemporal changes in land cover in the Nordic countries is missing. The recent products issued by the European Space Agency and the Copernicus Climate Change Service framework provide the possibility to investigate the historical land cover changes from 1992 to 2018 at 300 m resolution. These maps are then integrated with time series of forest harvest areas between 2004 and 2018 to study if and how forest management is represented in land cover products, and with soil erosion data to explore status and recent trends in agricultural land. Land cover changes typically involved from 4% to 9% of the total area in each country. Wetland showed the strongest reduction (11,003 km2, −11% of the wetland area in 1992), followed by forest (8,607 km2, −1%) and sparse vegetation (5,695 km2, −7%), while agriculture (15,884 km2, 16%) and settlement (3,582 km2, 84%) showed net increases. Wetland shrinkage dominated land cover changes in Norway (5,870 km2, −18%), followed by forest and grassland with a net gain of 3,441 km2 (3%) and 3, 435 km2 (10%), respectively. In Sweden, forest areas decreased 13,008 km2 (−4%), mainly due to agriculture expansion (9,211 km2, 29%). In Finland, agricultural areas increased by 5,982 km2 (24%), and wetland decreased by 6,698 km2 (−22%). Settlement had the largest net growth in Denmark (717 km2, 70%), mainly from conversion of agriculture land. Soil erosion rates in Nordic countries are lower than the global average, but they are exacerbating in several locations (especially western Norway). The integration of the land cover datasets with maps of forest harvest areas shows that the majority of the losses in forest cover due to forestry operations are largely undetected, but a non-negligible share of the forest-to-agriculture (up to 19%) or forest-to-grassland (up to 51%) transitions overlap with the harvested sites. Forestry activity in the study region primarily involves small-scale harvest events that are difficult to be detected at the 300 m resolution of the land cover dataset. An accurate representation of forest management remains a challenge for global datasets of land cover time series, and more interdisciplinary international efforts are needed to address this gap. Overall, this analysis provides a detailed overview of recent changes in land cover and forest management in Nordic countries as represented by state-of-the-art global datasets, and offers insights to future studies aiming to improve these data or apply them in land surface models, climate models, landscape ecology, or other applications.

INTEGRATING REMOTE-SENSED AND HISTORICAL GEODATA TO ASSESS INTERACTIONS BETWEEN RURAL BUILDINGS AND AGROFORESTRY LAND

The rural built heritage constitutes a unique example, due to architectural and technical issues, which plays a central role in the formation of rural landscape. In this research, interactions between rural buildings and the surrounding land have been examined. Two case studies exemplifying some of typical dynamics of some internal mountain areas of Mediterranean region have been considered. These areas are located in Basilicata Region (Southern Italy) and suffer from land and rural buildings heritage abandonment, as well as from the concurrent disappearance of agroforestry systems with high ecological value. A multi-chronological geo-database incorporating different land cover datasets over a period of 62 years (1955–1988–2008–2017) has been implemented into a GIS to assess relationships between some rural buildings and the surrounding land have been then assessed. This has been achieved by integrating different types of remote-sensed geodata: historical aerial photos, digital orthophotos and satellite images. The analyses carried out have shown that the intensity of land abandonment can also be related to the type of rural building and prevalent agricultural activity. Moreover, thanks to this methodology, it has been possible to produce several spatial information useful to support public decisionmakers at different level.

QUALITY ASSESSMENT OF LAND COVER CLASSIFICATION PRODUCTS BASED ON THE FUZZY–AHP SYNTHESIS MODEL

Abstract. As one of the critical products National Geographic Conditions Monitoring Achievements (NGCMA), land cover dataset has great value for the practical needs of China natural resources investigation and monitoring. For providing This paper proposed a fuzzy-AHP synthesis model, for assessing the quality of land cover classification products, based on the current qualifications of inspection and acceptance as well the characteristics of the inspection process of this type of results. The model is practiced on a data set that is the inspection results from 27 surveying areas in northern China. By comparing the results to the results from minimum method (MinM) and weighted average methods (WAM), this study pay attention on giving the analysing of the reasonability and feasibility of this model on practical uses and to provide a technical reference for quality evaluation of the new-type of fundamental surveying and mapping products.

Surface representation impacts on turbulent heat fluxes in the Weather Research and Forecasting (WRF) model (v.4.1.3)

Abstract. The water and energy transfers at the interface between the Earth's surface and the atmosphere should be correctly simulated in numerical weather and climate models. This implies the need for a realistic and accurate representation of land cover (LC), including appropriate parameters for each vegetation type. In some cases, the lack of information and crude representation of the surface lead to errors in the simulation of soil and atmospheric variables. This work investigates the ability of the Weather Research and Forecasting (WRF) model to simulate surface heat fluxes in a heterogeneous area of southern France using several possibilities for the surface representation. In the control experiments, we used the default LC database in WRF, which differed significantly from the actual LC. In addition, sub-grid variability was not taken into account since the model uses, by default, only the surface information from the dominant LC category in each pixel (dominant approach). To improve this surface simplification, we designed three new interconnected numerical experiments with three widely used land surface models (LSMs) in WRF. The first one consisted of using a more realistic and higher-resolution LC dataset over the area of analysis. The second experiment aimed at investigating the effect of using a mosaic approach; 30 m sub-grid surface information was used to calculate the final grid fluxes based on weighted averages from values obtained for each LC category. Finally, in the third experiment, we increased the model stomatal conductance for conifer forests due to the large flux errors associated with this vegetation type in some LSMs. The simulations were evaluated with gridded area-averaged fluxes calculated from five tower measurements obtained during the Boundary-Layer Late Afternoon and Sunset Turbulence (BLLAST) field campaign. The results from the experiments differed depending on the LSM and displayed a high dependency of the simulated fluxes on the specific LC definition within the grid cell, an effect that was enhanced with the dominant approach. The simulation of the fluxes improved using the more realistic LC dataset except for the LSMs that included extreme surface parameters for coniferous forest. The mosaic approach produced fluxes more similar to reality and served to particularly improve the latent heat flux simulation of each grid cell. Therefore, our findings stress the need to include an accurate surface representation in the model, including soil and vegetation sub-grid information with updated surface parameters for some vegetation types, as well as seasonal and man-made changes. This will improve the modelled heat fluxes and ultimately yield more realistic atmospheric processes in the model.

Dataset of 1 km cropland cover from 1690 to 1999 in Scandinavia

Abstract. Spatially explicit historical land cover datasets are essential not only for simulations of climate and environmental dynamics but also for projections of future land use, food security, climate, and biodiversity. However, widely used global datasets are developed for continental- to global-scale analysis and simulations. Their accuracy depends on the verification of more regional reconstruction results. This study collects cropland area data of each administrative unit (parish/municipality/county) in Scandinavia from multiple sources. The cropland area data are validated, calibrated, interpolated, and allocated into 1 km×1 km grid cells. Then, we develop a dataset with spatially explicit cropland area from 1690 to 1999. Results indicate that the cropland area increased from 1.82×106 ha to 6.71×106 ha from 1690 to 1950 and then decreased to 5.90×106 ha in 1999. Before 1810, cropland cover expanded in southern Scandinavia and remained stable in northern Scandinavia. From 1810 to 1910, northern Scandinavia experienced slight cropland expansion. The cropland area increased rapidly in the southern part of the study area before changing slightly. After 1950, the cropland areas began to decrease in most regions, especially in eastern Scandinavia. When comparing global datasets with this study, although the total Scandinavia cropland area is in agreement among SAGE (Center for Sustainability and the Global Environment), HYDE (History Database of the Global Environment ) 3.2, PJ (Pongratz Julia), and this study, the spatial patterns show considerable differences, except for in Denmark between HYDE 3.2 and this study. The dataset can be downloaded from https://doi.org/10.1594/PANGAEA.926591 (Wei et al., 2021).

Global land cover trajectories and transitions

Global land cover (LC) changes threaten sustainability and yet we lack a comprehensive understanding of the gains and losses of LC types, including the magnitudes, locations and timings of transitions. We used a novel, fine-resolution and temporally consistent satellite-derived dataset covering the entire Earth annually from 1992 to 2018 to quantify LC changes across a range of scales. At global and continental scales, the observed trajectories of change for most LC types were fairly smooth and consistent in direction through time. We show these observed trajectories in the context of error margins produced by extrapolating previously published accuracy metrics associated with the LC dataset. For many LC classes the observed changes were found to be within the error margins. However, an important exception was the increase in urban land, which was consistently larger than the error margins, and for which the LC transition was unidirectional. An advantage of analysing the global, fine spatial resolution LC time-series dataset is the ability to identify where and when LC changes have taken place on the Earth. We present LC change maps and trajectories that identify locations with high dynamism, and which pose significant sustainability challenges. We focused on forest loss and urban growth at the national scale, identifying the top 10 countries with the largest percentages of forest loss and urban growth globally. Crucially, we found that most of these ‘worst-case’ countries have stabilized their forest losses, although urban expansion was monotonic in all cases. These findings provide crucial information to support progress towards the UN’s SDGs.

GLC_FCS30: global land-cover product with fine classification system at 30 m using time-series Landsat imagery

Abstract. Over past decades, a lot of global land-cover products have been released; however, these still lack a global land-cover map with a fine classification system and spatial resolution simultaneously. In this study, a novel global 30 m land-cover classification with a fine classification system for the year 2015 (GLC_FCS30-2015) was produced by combining time series of Landsat imagery and high-quality training data from the GSPECLib (Global Spatial Temporal Spectra Library) on the Google Earth Engine computing platform. First, the global training data from the GSPECLib were developed by applying a series of rigorous filters to the CCI_LC (Climate Change Initiative Global Land Cover) land-cover and MCD43A4 NBAR products (MODIS Nadir Bidirectional Reflectance Distribution Function-Adjusted Reflectance). Secondly, a local adaptive random forest model was built for each 5∘×5∘ geographical tile by using the multi-temporal Landsat spectral and texture features and the corresponding training data, and the GLC_FCS30-2015 land-cover product containing 30 land-cover types was generated for each tile. Lastly, the GLC_FCS30-2015 was validated using three different validation systems (containing different land-cover details) using 44 043 validation samples. The validation results indicated that the GLC_FCS30-2015 achieved an overall accuracy of 82.5 % and a kappa coefficient of 0.784 for the level-0 validation system (9 basic land-cover types), an overall accuracy of 71.4 % and kappa coefficient of 0.686 for the UN-LCCS (United Nations Land Cover Classification System) level-1 system (16 LCCS land-cover types), and an overall accuracy of 68.7 % and kappa coefficient of 0.662 for the UN-LCCS level-2 system (24 fine land-cover types). The comparisons against other land-cover products (CCI_LC, MCD12Q1, FROM_GLC, and GlobeLand30) indicated that GLC_FCS30-2015 provides more spatial details than CCI_LC-2015 and MCD12Q1-2015 and a greater diversity of land-cover types than FROM_GLC-2015 and GlobeLand30-2010. They also showed that GLC_FCS30-2015 achieved the best overall accuracy of 82.5 % against FROM_GLC-2015 of 59.1 % and GlobeLand30-2010 of 75.9 %. Therefore, it is concluded that the GLC_FCS30-2015 product is the first global land-cover dataset that provides a fine classification system (containing 16 global LCCS land-cover types as well as 14 detailed and regional land-cover types) with high classification accuracy at 30 m. The GLC_FCS30-2015 global land-cover products produced in this paper are free access at https://doi.org/10.5281/zenodo.3986872 (Liu et al., 2020).

Assessing natural resource change in Vhembe biosphere and surroundings.

South Africa is a custodian of an immense wealth of natural and biodiversity resources in Africa. Natural resources are continually changing in different South African biospheres based on anthropogenic and non-anthropogenic causes. Land use activities like agriculture, cultivation, livestock rearing, commercial plantations, urbanisation and mining are among the major drivers of natural resource change and transformation. In this study, land cover change assessment was used to assess natural resource change in Vhembe biosphere and surroundings. To assess natural resource change in Vhembe biosphere, land use land cover change assessment was conducted using South African's national land-cover dataset, generated from multi-seasonal Landsat 5 and Sentinel-2 images. The 72× class land cover map was re-classified into 12× classes to fit the study objectives. Eight out of twelve classes quantified in hectares: indigenous forests, thicket/dense bush, natural woodland, shrubland, grassland, water bodies and wetlands were categorised as natural resources for which the natural resource change assessment for this study was based. Assessment findings established that land use and its related activities have contributed substantially to natural resource change where cultivated commercial, natural woodland and built-up residential contributed the most significant upward change in hectarage and percentage, from 132,246.9 to 365,644.92 (ha)-percentage change of 176%; from 94,665.42 to 257,889.68 (ha)-percentage changes of 172% and from 74,070.27 to 147,701.88(ha)-percentage change of 99% respectively. Shrubland, thicket/dense bush and indigenous forests registered the highest downward changes from 263,070.6 to 977.72 (ha); from 338,723.7 to 23,166.92 and from 13,211.91 to 7402.92 (ha) with percentage changes of -100%, -93% and -44% respectively in Vhembe biosphere and the surroundings from 1990 to 2018. The study showed how natural resources are changing and the use of remote sensing for environmental monitoring and assessment in the Vhembe district.

Assessment of organic matter temporal dynamics in the klyazma basin using remote sensing and qgis trends.earth

The article addresses the dynamics of biological processes in various landscapes within a holistic natural geosystem—a catchment area. The Klyazma river (the fourth order tributary to the Volga) was selected as the object of study. The natural complex of the Klyazma river basin is a combination of different landscapes, each marked by a diverse composition of geomorphological and soil-vegetation structures. The study is based on remote sensing data and the Trends.Earth Land Degradation Monitoring Project (Land Cover Dataset, European Space Agency 2015, 300 m spatial resolution) implemented using the open-source Quantum GIS 2.18. Four landscape provinces and eight site were identified in the studied catchment area according to the geomorphological structure and the soil and vegetation cover. The ecosystem parameters Gross Primary Productivity, Net Primary Productivity, and Ecosystem Respiration were measured in the identified sites. In different landscapes the biological processes, characterizing the organic matter dynamics in the form of plant production and organic matter accumulation, differ in both rate and intensity, and variously respond to the changes in climate parameters and land use. The river basin, as a holistic ecosystem, showed sufficient stability of the dynamic processes. This suggests that holistic natural ecosystems, such as catchment areas, have internal compensatory mechanisms that maintain the development stability over long period of time, while irrational land use remains the main damaging factor.

Impacts of Continuously Increasing Urbanization Ratios on Warming Rates and Temperature Extremes Observed Over the Beijing Area

AbstractEvaluations of the impacts of increasing urbanization ratios on air temperatures are less quantitative and accurate due to the lack of high‐resolution land cover data sets. Here, based on the latest released high‐resolution (30‐m) land cover data from 1985 to 2017, annual urbanization ratios around each station in Beijing were calculated. Warming rates and temperature extremes were compared between nonrural stations (17 stations with continuously increasing urbanization ratios) and purely rural stations (3 stations with continuous urbanization ratios of zero). Although urbanized areas only occupied 22.2% of the total area in Beijing, urbanization ratios at nonrural stations already reached an average of 62.4% (10 km2)−1in 2017, and their average rate reached 15.1% (10 km2)−1 decade−1. From 1985 to 2017, each 10% increase in the urbanization ratio with 10 km2had led to additional annual warming of 0.14 ± 0.11, 0.17 ± 0.08, and 0.30 ± 0.17°C at nonrural stations for daily maximum (Tmax), mean (Tmean), and minimum (Tmin) air temperatures, respectively. Meanwhile, each 10% increase in the urbanization ratio with 10 km2had led to additional changes of 11.6 ± 5.1, 12.7 ± 5.8, and −11.1 ± 5.2 days at nonrural stations for hot days, hot nights, and chilly nights, respectively. Additionally, the inner‐annual urbanization contributions to the warming increased significantly at rates of 10.7% decade−1and 14.1% decade−1forTmeanandTmin, and reached 49.0% and 52.6% in 2017, respectively. The inner‐annual urbanization contributions to the increasing hot nights and the decreasing chilly nights changed at rates of 16.7% decade−1and 15.6% decade−1, and reached 57.8% and 53.1% in 2017, respectively.