Global Land Cover Research Articles

ASSESSING FOREST AND AGRICULTRAL LAND UNDER LAND USE CHANGE USING REMOTE SENSING: A CASE STUDY OF BAHAWALPUR CITY (PAKISTAN)


 
 
 Rapid urbanization and development build a great pressure on the agricultural land in urban and semi-urban areas. As a result, there is reduction in agricultural and forest areas in different cities of the developing countries. The present study was conducted in Bahawalpur district of Pakistan. Six remote sensing satellite images of Landsat Enhanced Thermal Mapper and ETM+ were obtained from Global Land Cover Facility of United States Geological Survey’s Earth Explorer and GLOVISE with an interval of three years from 2003 to 2018 (16 years) were processed for the study area (Bahawalpur City). Major land use classes identified for this study included forest, agriculture, barren and urban. Temporal changes of the four land use classes were detected and its impact on covered area by each land use class over the study period was computed. Results indicated that acreage of forest and barren land was decreased by 89.3% and 57.6%, respectively, while that of agriculture and urban area was increased by 34.5% and 45.5%, respectively, over the period of sixteen years from 2003 to 2018. These changes in land use cover might be owing to urbanization, deforestation and land conversion in the study area.
 
 

Landscape Pattern Consistency Assessment of 10 m Land Cover Products in Different Ecological Zoning Contexts of Sichuan Province, China

The research on land cover and its changing value to the ecology environment and ecosystem service is of great importance. Understanding the landscape patterns and accuracy in remote sensing land cover data from multiple sources under eco-regionalization is important for relevant research under eco-regionalization. Land cover and land use in different ecological regionalization contexts influence massive ecosystem processes on a global scale, and many ecosystem models are dependent on accurate land cover information. It is, therefore, important to assess the available global land cover products based on different ecological zoning contexts and to understand the differences between them so that different researchers can apply them in a rational way. This study took Sichuan Province as an experimental case. Based on the three methods of spatial superposition, landscape index, and validation sample, we discussed and then analyzed the consistency of landscape patterns for the three 10 m global land cover data under different ecogeographic zones. The results showed that the spatial consistency of FROM-GLC, ESA, and ESRI land cover data were high under the ecological zoning of Palearctic0518 (PA0518) and PA1017, while the spatial pattern was less consistent for the three land cover data under the ecological zoning of PA0509 and PA0437. The fully consistent areas for the three data were 44,420.9 km2 and 53,368.9 km2, respectively. The results of the quantitative analysis of the landscape index showed significant differences in the degree of landscape fragmentation, landscape shape complexity, and the connectivity among landscape patches of several land cover data were significantly different under different ecological zones. Based on the three kinds of independent validation samples to achieve the absolute accuracy of evaluation, the overall accuracy of the FROM-GLC, ESA, and ESRI land cover data was less than 60%, and future drawing still needs to further improve the regional land cover data mapping accuracy under different ecological zones in order to carry out ecological environment monitoring, land ecological security evaluation, and related research to provide a reference.

How climate, landscape, and economic changes increase the exposure of Echinococcus Spp.

Echinococcosis is a global enzootic disease influenced by different biological and environmental factors and causes a heavy financial burden on sick families and governments. Currently, government subsidies for the treatment of patients with echinococcosis are only a fixed number despite patients' finical income or cost of treatment, and health authorities are demanded to supply an annual summary of only endemic data. The risk to people in urban areas or non-endemic is increasing with climate, landscape, and lifestyle changes. We conducted retrospective descriptive research on inpatients with human echinococcosis (HE) in Lanzhou hospitals and analyzed the healthcare expenditure on inpatient treatment and examined the financial inequalities relating to different levels of gross domestic product. The livestock losses were also estimated by infection ratio. The occurrence records of Echinococcus spp. composed of hospitalized patients and dogs infected in the Gansu province were collected for Ecological niche modeling (ENM) to estimate the current suitable spatial distribution for the parasite in Gansu province. Then, we imported the resulting current niche model into future global Shared Socioeconomic Pathways scenarios for estimation of future suitable habitat areas. Between 2000 to 2020, 625 hospitalized HE patients (51% men and 49% women) were identified, and 48.32 ± 15.62 years old. The average cost of hospitalization expenses per case of HE in Gansu Province was ￥24,370.2 with an increasing trend during the study period and was negative with different counties' corresponding gross domestic product (GDP). The trend of livestock losses was similar to the average cost of hospitalization expenses from 2015 to 2017. The three factors with the strongest correlation to echinococcosis infection probability were (1) global land cover (GLC, 56.6%), (2) annual precipitation (Bio12, 21.2%), and (3) mean temperature of the Wettest Quarter (Bio12, 8.5% of variations). We obtained a robust model that provides detail on the distribution of suitable areas for Echinococcus spp. including areas that have not been reported for the parasite. An increasing tendency was observed in the highly suitable areas of Echinococcus spp. indicating that environmental changes would affect the distributions. This study may help in the development of policies for at-risk populations in geographically defined areas and monitor improvements in HE control strategies by allowing targeted allocation of resources, including spatial analyses of expenditure and the identification of non-endemic areas or risk for these parasites, and a better comprehension of the role of the environment in clarifying the transmission dynamics of Echinococcus spp. Raising healthcare workers' and travelers' disease awareness and preventive health habits is an urgent agenda. Due to unpredictable future land cover types, prediction of the future with only climatic variables involved needs to be treated cautiously.

Land Use/Cover Change and Its Driving Mechanism in Thailand from 2000 to 2020

Land use/cover change (LUCC) research is important for regional ecological conservation and sustainable development. There is a lack of exploration of long-time-series dynamics and driving mechanisms at the national scale in the study of land use/cover change in Thailand. Based on the Global Land Cover product with Fine Classification System in 30m (GLC_FCS30) dataset and socioeconomic statistics, we studied the temporal and spatial evolution characteristics and driving mechanisms of LUCC in Thailand from 2000 to 2020 through indicators and methods such as land use dynamic attitude, mapping of a Sankey diagram, principal component analysis, and multiple linear stepwise regression analysis. The results showed that: (1) Thailand has developed in terms of agriculture and forestry. In 2020, the cropland and forest areas accounted for 53.77% and 32.15% of the land area, respectively. (2) From 2000 to 2020, the area of rainfed cropland, irrigated cropland, and forest continued to shrink; the area of impervious surfaces expanded rapidly, and the area of shrubland, other cropland, and wetlands increased. (3) The LUCC process mainly occurred in the two-way conversion between forest and shrubland, rainfed cropland and irrigated farmland, forest and rainfed cropland, and forest and other farmland. The LUC with the largest area transformed into other land types was forest (2.25 × 104 km2), and the LUC with the largest area transformed from other land types transferring into the area was shrubland (1.40 × 104 km2). (4) From 2000 to 2020, the LUCC process in Thailand was mainly influenced by socio-economics and tourism. Gross population, main grain output, industrial value added, passenger income, and urban population were the key factors driving the LUCC in Thailand. Our research can provide the basis and decision support for the future planning and management of land in Thailand.

Generating annual high resolution land cover products for 28 metropolises in China based on a deep super-resolution mapping network using Landsat imagery

ABSTRACT High resolution of global land cover dynamic is indicative for understanding the influence of anthropogenic activity on environmental change. However, most of the land cover products are based on Landsat image that only has 30 m resolution, which is insufficient to distinguish the heterogenous urban structure; while very high spatial resolution image usually has low temporal resolution, which is difficult to monitor the urban dynamic. Deep-learning-driven super-resolution mapping is a prevailing way of achieving very-high-resolution land cover dynamic products in aspect of alleviating the mixed pixel problem of Landsat image. However, two limitations are obvious: 1) the fixed grid of kernel during the upsampling process favors spatial homogeneity and suppresses the learning of spatial heterogeneity of urban composition and 2) geometric or radiation variation over large spatial and long temporal extent in remote sensing images makes the super-resolution mapping approach difficult to transfer for application. Here, we attempt to solve these two limitations: 1) a progressive edge-guided super-resolution architecture is designed to allow nonuniformed kernel specific at the low-confidence edge region and intensify the learning of heterogenous compositions’ patterns and 2) an alternating optimization strategy is designed to minimize the resultant entropy and modulate the classification hyperplane to accommodate to the manifold of the discrepant region. Validation experiments are investigated based on a fine-grained and large-extent super-resolution (FLAS) dataset constructed in this study, and it is found that our approach remarkably enhances rich detailed patterns of heterogenous region and outperforms other state-of-the-art algorithms. Besides, we applied DETNet to the large spatial extent of 28 metropolises in China (>40,000 km2) and the large temporal extent of continuous 21-year (2000–2020) in Wuhan city to examine transferability. From the land cover areas variation, we find that the expansion rate of cropland is faster than the urban expansion over the past 10 years, which are gradually becoming the principal source for the encroachment of forest and lakes. From detailed urban dynamic reflected by the 21-year products, we find that urban-villages between the old city zone and the outer high-tech development zone are gradually disappeared. The captured dynamic is consistence with the urban-village renovation policy during this period, which is meant to redistribute the spatial configuration of the city for a more sustainable urban structure. We believe that the proposed method can facilitate a seamless and fine-grained observation system that can fill the weakness of the existing land cover activities and provide a brand-new insight into the urban dynamic and its underlying mechanism.

The Impact of Land Cover on Selected Water Quality Parameters in Polish Lowland Streams during the Non-Vegetative Period

The search for the best landscape predictors explaining the spatial variability of stream water chemistry is one of the most important and recent research issues. Thus, in the current study, relationships between land cover indices and selected water quality parameters were evaluated regarding the example of 54 lowland temperate streams located in central Poland. From November 2021 to March 2022, water samples were collected in the monthly timescale, and the concentrations of NH4+, NO3−, and NO2−, as well as electrical conductivity, were correlated with the percentage of land cover types calculated for total catchment area, buffer zones, cut buffer zones, and radius. For such computing, Corine Land Cover 2018 and Sentinel 2 Global Land Cover datasets were used. In the case of both datasets, results indicate significant dependence of NO3−, and NO2− concentrations, as well as EC values on cover metrics. Overall, agricultural lands favored higher concentrations of NO3− and NO2−, whereas mainly coniferous forests reduced nitrogen pollution. Significant correlations were not documented in the case of NH4+ ions, the concentrations of which could be linked to point sources from municipal activity. Correlation performance was slightly better in the case of the S2GLC dataset, while the best spatial scales were generally seen for wider buffer zones (250 and 500 m) and total catchment area. The study provided spatially extensive insight into the impact of land cover predictors at different scales on nitrogen compounds in a lowland landscape.

Impact of land use/land cover on land surface temperature and its relationship with spectral indices in Dakahlia Governorate, Egypt

Land surface temperature (LST) is a direct impact of urbanization and a crucial factor in global climate and land cover changes. In this research, we aim to identify the impact of land use/land cover (LULC) on LST as well as analyze the relationship between LST and three spectral indices using linear, polynomial and multiple regression models. The LST was first retrieved from Landsat imagery using single-channel algorithm. Afterwards, LULC maps were developed using maximum likelihood (ML) classifier and three spectral indices, namely Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI) and Normalized Difference Water Index (NDWI). Finally, regression analysis was conducted to model the relationship between LST and the three spectral indices. Landsat 8 OLI/TIRS imagery of year 2019 of Dakahlia Governorate in Egypt was processed for LST retrieval as well as LULC classification. The ML classifier achieved an overall accuracy and kappa coefficient of 95.14% and 0.857, respectively, while of those based on spectral indices were 94.86% and 0.777, respectively. The results demonstrated an average temperature of 35.8°C, 31.2°C and 27.6°C for urban, vegetation and water, respectively. The LST statistics difference between classification methods of the three land covers was less 2°C. Based on the regression analysis, the NDVI and NDWI indicated a negative correlation with LST, while the NDBI indicated a positive correlation with LST. The polynomial regression analysis of LST against NDVI and NDWI demonstrated a better coefficient of determination (R2) than linear regression analysis of 0.341 and 0.305, respectively. For NDBI, linear and polynomial regression analysis demonstrated very close R2 of 0.624 and 0.628, respectively. The multiple regression analysis of LST against NDVI, NDBI and NDWI revealed R2 of 0.699. Consequently, the three spectral indices can be used as effective indicators for separating terrain into different classes, and hence relate their LST.

Geospatial Assessment and Mapping of Suitable Sites for a Utility-scale Solar PV Farm

Geospatial and multi-criteria decision-making techniques are applied to process and analyse datasets for determining suitable areas for multiple utilityscale solar photovoltaic farms in the city of Akure, Ondo State, southwestern Nigeria. Data processed include local electric power distribution system data, Shuttle Radar Topographic Mission elevation data, Landsat 8 and solar global horizontal irradiance. Multi-criteria decision-making techniques adopted are the analytical hierarchy process, reclassification, and overlay. These techniques were carried out considering criteria for siting solar photovoltaic farms. Criteria considered in this study are climate, topography, economic, environmental impact operational and technical while sub-criteria are solar global horizontal irradiance, slope, proximity and land cover. The outcome of the study shows that the study area covering a total extent of ~33,200 ha, 15%, 8%, 13% and 64% are highly suitable, suitable, moderately suitable, and unsuitable respectively for siting utility-scale solar photovoltaic farms within the study area. The study reveals the potential of multiple utility-scale solar photovoltaic farms in the study area. However, the proportions of areas suitable for solar photovoltaic farms are quite lower compared to findings from similar studies conducted in northwestern Nigeria. The study recommends solar photovoltaic sources as an alternative energy source in and around the study area.

Exploring spatio-temporal change in global land cover using categorical intensity analysis

The rates of land degradation and urbanization has increased worldwide during the past century. Herein, we evaluate the spatio-temporal changes in global land cover via categorical intensity analysis of the European Space Agency’s climate change initiative (ESA-CCI) data for the period 1992 to 2018. Specifically, we evaluated intensity analysis at the category level for five time intervals, namely 1992–1997, 1997–2002, 2002–2007 and 2007–2012, 2012–2018. We also, evaluate the decrease and increase in the land cover at continental and climate zone. The study evaluates the following land cover categories: Cropland, Forest, SGO (Shrubland, Grassland, and Other), Urban, Bare areas, and WIS (Water, ice, and snow). After accuracy assessment, the global land-cover map for 2009 from the GlobCover data is selected, and a reclassified version of this map is used as a verification tool for comparison with the reclassified study data. The analysis of changes over the last 26 years shows that the loss for Cropland are dormant during the first and second time intervals, but active during the third, fourth, and fifth time intervals. By contrast, Forest experienced loss during all time intervals, and SGO experienced active loss only during the second time interval. Urban is the only category that experienced active gain during all time intervals. The present study also indicates that urbanization has and converted land in temperate regions during the past 26 years. Additionally, in South America and the tropical regions, the expansion of Cropland is the largest contributor to the decline in Forests and SGO.

A Field-Data-Aided Comparison of Three 10 m Land Cover Products in Southeast Asia

To study global and regional environment protection and sustainable development and also to optimize mapping methods, it is of great significance to compare three existing 10 m resolution global land cover products in terms of accuracy: FROM-GLC10, the ESRI 2020 land cover product (ESRI2020), and the European Space Agency world cover 2020 product (ESA2020). However, most previous validations lack field collection points in large regions, especially in Southeast Asia, which has a cloudy and rainy climate, creating many difficulties in land cover mapping. In 2018 and 2019, we conducted a 56-day field investigation in Southeast Asia and collected 3326 points from different places. By combining these points and 14,808 other manual densification points in a stratified random sampling, we assessed the accuracy of the three land cover products in Southeast Asia. We also compared the impacts of the different classification standards, the different sample methods, and the different spatial distributions of the sample points. The results show that in Southeast Asia, (1) the mean overall accuracies of the FROM-GLC10, ESRI2020, and ESA2020 products are 75.43%, 79.99%, and 81.11%, respectively; (2) all three products perform well in croplands, forests, and built-up areas; ESRI2020 and ESA2020 perform well in water, but only ESA2020 performs well in grasslands; and (3) all three products perform badly in shrublands, wetlands, or bare land, as both the PA and the UA are lower than 50%. We recommend ESA2020 as the first choice for Southeast Asia’s land cover because of its high overall accuracy. FROM-GLC10 also has an advantage over the other two in some classes, such as croplands and water in the UA aspect and the built-up area in the PA aspect. Extracting the individual classes from the three products according to the research goals would be the best practice.

Geographic and cartographic inconsistency factors among different cropland classification datasets: A field validation case in Cambodia

Abstract Cropland cover datasets is of great significance for research on agricultural monitoring. The existing investigations on the inconsistency of different cropland datasets have mainly focused on first-class cropland and only analyzed the causes of this inconsistency in terms of cartography. To date, investigations have neglected the importance of fine cropland types in studies such as global food security assessment, and a comprehensive analysis of the causes of inconsistency from the perspectives of both cartography and geography is lacking. Moreover, the verification samples of existing studies have primarily been collected based on Google Earth. So, we examined the cropland resources of Cambodia using areal, spatial consistency, elevation classification, and field survey data assessment methods for the Global Food Security-support Analysis Data at 30 m for Southeast Asia, Global Land Cover Fine Surface Cover30-2015, Finer Resolution Observation and Monitoring of Global Land Cover2015, and SERVIR-Mekong datasets and comprehensively investigated the causes of inconsistency in terms of geography and cartography. The results revealed that the consistency of the extracted areas of first-class cropland among the four datasets was high. But, the cropland areas and statistical results from the Food and Agriculture Organization (FAO) of the United Nations are quite different. The overall accuracy (OA) for the first-class cropland of GFSAD30SEACE, GLC_FCS30-2015, and SERVIR-Mekong datasets were >82%. For fine cropland types, however, the OA of the SERVIR-Mekong dataset was relatively high, at 74.87%, while the accuracy levels of the global-scale GLC_FCS30-2015 and FROM_GLC2015 datasets were <50% due to the influence of scale size on mapping accuracy. In addition, in the eastern and northern portions of Cambodia with elevations of 50–200 m, the spatial consistency of the four datasets was low due to the serious confusion between cropland and forest, grassland, and shrub types. Therefore, land cover producers should adopt a zonal stratification strategy, focusing on remote sensing extraction techniques for confusing types in areas with high inconsistency to improve the accuracy of cropland.

Semantic segmentation of landcover for cropland mapping and area estimation using Machine Learning techniques

ABSTRACT The paper has focussed on the global landcover for the identification of cropland areas. Population growth and rapid industrialization are somehow disturbing the agricultural lands and eventually the food production needed for human survival. Appropriate agricultural land monitoring requires proper management of land resources. The paper has proposed a method for cropland mapping by semantic segmentation of landcover to identify the cropland boundaries and estimate the cropland areas using machine learning techniques. The process has initially applied various filters to identify the features responsible for detecting the land boundaries through the edge detection process. The images are masked or annotated to produce the ground truth for the label identification of croplands, rivers, buildings, and backgrounds. The selected features are transferred to a machine learning model for the semantic segmentation process. The methodology has applied Random Forest, which has compared to two other techniques, Support Vector Machine and Multilayer perceptron, for the semantic segmentation process. Our dataset is composed of satellite images collected from the QGIS application. The paper has derived the conclusion that Random forest has given the best result for segmenting the image into different regions with 99% training accuracy and 90% test accuracy. The results are cross-validated by computing the Mean IoU and kappa coefficient that shows 93% and 69% score value respectively for Random Forest, found maximum among all. The paper has also calculated the area covered under the different segmented regions. Overall, Random Forest has produced promising results for semantic segmentation of landcover for cropland mapping.

КАРТОГРАФИРОВАНИЕ ЭКОСИСТЕМНЫХ УСЛУГ НА ЛОКАЛЬНОМ УРОВНЕ: ОБЗОР СОВРЕМЕННОГО СОСТОЯНИЯ ИССЛЕДОВАНИЙ

The concept of ecosystem services (ES) has aroused interest among researchers around the world with the growing anthropogenic pressure on natural ecosystems. The meaning of the concept is taking into consideration the ecosystem functions during making decisions and ensuring sustainable development. Mapping of ES is crucial for territorial representation of ecosystem services, spatial analysis, dynamic changes detection, relationships between ecosystem elements, etc. The goal of the study is to review global scientific literature of mapping ecosystem services at the local level. The paper describes the aims, input datasets, types of mapping, and methods of 19 scientific publications. A summary contains a brief description of the initial data and the methods used. Cartographic material has been prepared to show the research geography on a world map. The most popular groups of ecosystem services for scholars have been identified. More than 39% of the mapped ES belong to the regulating services. Global land cover and land use maps are often used as input data for modeling and mapping. Regression models and software-based models (InVEST, KINEROS, etc) are more frequent among the methods for the ES assessment and mapping. Specially protected natural areas, as well as vulnerable mountain, river, and coastal ecosystems are primary objects of research. The study of ES in Russia is also confined to protected areas. Mapping of ecosystem functions and services of local objects is mostly developed in European countries.

Classification of Land Cover Remote-Sensing Images Based on Pattern Recognition

With the development of remote sensing technology, remote sensing image data plays an active role in the dynamic monitoring of global resource changes and land cover utilization. Remote sensing image land cover classification is an important application direction of remote sensing data; how to further improve the accuracy of remote sensing image land cover classification is very important for the effective application of remote sensing data. The traditional remote sensing image land cover classification is mainly to classify remote sensing data according to the spectral data of ground objects. However, due to the complex environment of remote sensing images and the dynamic changes of the environment, traditional classification methods based only on pixel spectral data are often unable to achieve. A satisfactory classification result is achieved. In addition, some researchers have also proposed to combine pixel neighborhood texture information to supplement spectral feature data. Although the traditional classification method based on spectral features solves the problem of time-consuming visual interpretation, to a certain extent, due to the limited semantic expression ability and poor generalization ability of the design features, the classification accuracy is still not very satisfactory. This paper mainly studies the classification method of land cover remote sensing image based on pattern recognition. This paper is based on the experimental results of remote sensing data in Nanjing Yuhuatai District in 2018 and 2019. The ground resolution of the data is 2.5 meters. Data is projected, corrected, and equalized. Half of the images covering 43.75 square kilometers are used as training samples, and the remaining 50 square kilometers are used for detection. In the classification results of this IndianPines data, OA only increased by nearly 10% to 86.2%, AA increased by 13%, r was 82.77%, and Kappa coefficient was 0.84. In the classification results of Salinas data, both OA and AA increase by about 5%, and the optimization effect is not obvious.

A high spatial resolution soil carbon and nitrogen dataset for the northern permafrost region based on circumpolar land cover upscaling

Abstract. Soils in the northern high latitudes are a key component in the global carbon cycle; the northern permafrost region covers 22 % of the Northern Hemisphere land surface area and holds almost twice as much carbon as the atmosphere. Permafrost soil organic matter stocks represent an enormous long-term carbon sink which is in risk of switching to a net source in the future. Detailed knowledge about the quantity and the mechanisms controlling organic carbon storage is of utmost importance for our understanding of potential impacts of and feedbacks on climate change. Here we present a geospatial dataset of physical and chemical soil properties calculated from 651 soil pedons encompassing more than 6500 samples from 16 different study areas across the northern permafrost region. The aim of our dataset is to provide a basis to describe spatial patterns in soil properties, including quantifying carbon and nitrogen stocks. There is a particular need for spatially distributed datasets of soil properties, including vertical and horizontal distribution patterns, for modeling at local, regional, or global scales. This paper presents this dataset, describes in detail soil sampling; laboratory analysis, and derived soil geochemical parameters; calculations; and data clustering. Moreover, we use this dataset to estimate soil organic carbon and total nitrogen storage estimates in soils in the northern circumpolar permafrost region (17.9×106 km2) using the European Space Agency's (ESA's) Climate Change Initiative (CCI) global land cover dataset at 300 m pixel resolution. We estimate organic carbon and total nitrogen stocks on a circumpolar scale (excluding Tibet) for the 0–100 and 0–300 cm soil depth to be 380 and 813 Pg for carbon, and 21 and 55 Pg for nitrogen, respectively. Our organic carbon estimates agree with previous studies, with most recent estimates of 1000 Pg (−170 to +186 Pg) to 300 cm depth. Two separate datasets are freely available on the Bolin Centre Database repository (https://doi.org/10.17043/palmtag-2022-pedon-1, Palmtag et al., 2022a; and https://doi.org/10.17043/palmtag-2022-spatial-1, Palmtag et al., 2002b).

Effects of Land Use and Land Cover Change on Temperature in Summer over the Yellow River Basin, China

As the main driving force of global climate change, land use and land cover change (LUCC) can affect the surface energy balance and the interaction between the surface and atmosphere. This effect will cause further surface temperature changes. The Yellow River Basin is an important ecological security barrier in China. Therefore, exploring the impact of its LUCC on temperature changes can provide certain help for future land-use planning in the Yellow River Basin. Here, we conducted two numerical simulation experiments (Case2015 and Case1995) by using the weather research and forecasting (WRF) model to quantify the effect of LUCC in the Yellow River Basin on the summer 2 m air temperature (T2 m). The results showed that LUCC led to an overall warming trend in T2 m in the Yellow River Basin. Urban expansion caused T2 m to rise by approximately 0.3 °C to 0.6 °C. A warming effect was also identified in the areas where farmland and bare areas were converted to grassland, with T2 m increasing by around 0.4 °C.

A Settlement Density Based Allocation Method for Historical Cropland Cover: A Case Study of Jilin Province, China

A key focus in research on changes in historical land cover has been to improve existing gridded cropland allocation methods based on land suitability for cultivation to generate credible historical cropland cover data. This study developed a settlement-density-based method for gridded cropland allocation using the locations of settlements to identify the cropland grid and the settlement density as the weight for allocating the cropland area to the grid. This method was applied to allocate the provincial cropland areas in Jilin Province, China, to a 5′ × 5′ cropland cover at six time points during the last 300 years. The credibility of the reconstruction was assessed using three methods. The following conclusions emerged. First, the settlement density method is funded on the fact of coexistence between rural settlements and cropland. Cropland is only distributed in the grid where the settlements exist, and the cropland area of a grid equals to the cropland area per settlement multiplying by the number of settlements within the grid, without considering differences of settlement size. Second, all three quantitative or qualitative assessments of Jilin Province confirmed the credibility and feasibility of the settlement density method. Therefore, the use of this method to reproduce the temporal and spatial changes in cropland cover in new reclamation regions, such as Jilin Province, is valid. This study provides valuable inputs for enhancing the credibility of historical global land cover data by incorporating human factors into the cropland allocation method.

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

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

Temporally-Consistent Annual Land Cover from Landsat Time Series in the Southern Cone of South America

The impact of land cover change across the planet continues to necessitate accurate methods to detect and monitor evolving processes from satellite imagery. In this context, regional and global land cover mapping over time has largely treated time as independent and addressed temporal map consistency as a post-classification endeavor. However, we argue that time can be better modeled as codependent during the model classification stage to produce more consistent land cover estimates over long time periods and gradual change events. To produce temporally-dependent land cover estimates—meaning land cover is predicted over time in connected sequences as opposed to predictions made for a given time period without consideration of past land cover—we use structured learning with conditional random fields (CRFs), coupled with a land cover augmentation method to produce time series training data and bi-weekly Landsat imagery over 20 years (1999–2018) across the Southern Cone region of South America. A CRF accounts for the natural dependencies of land change processes. As a result, it is able to produce land cover estimates over time that better reflect real change and stability by reducing pixel-level annual noise. Using CRF, we produced a twenty-year dataset of land cover over the region, depicting key change processes such as cropland expansion and tree cover loss at the Landsat scale. The augmentation and CRF approach introduced here provides a more temporally consistent land cover product over traditional mapping methods.

Assessment of the soil-erosion-sediment for sustainable development of South America

One of the greatest threats to maintaining sustainable agro-ecosystems is mitigating the episodic soil loss from farm operations, further exacerbated by meteorological extremes. The Revised Universal Soil Loss Equation (RUSLE) is a model that combines the effects of rain, soil erodibility, topography, land cover, and conservation practices for estimating the annual average soil losses. This study aims to quantify soil water erosion to continental South America (S.A.) through RUSLE using available datasets and characterizing the average sediment delivery rate (SDR) to the major S.A. basins. Soil erodibility was estimated from the Global Gridded Soil Information soil database. LS-factor's topographical parameter was derived from Digital Elevation Models using the “Shuttle Radar Topography Mission” dataset. The R-factor was estimated from a previous study developed for S.A. and the C-factor from the Global Land Cover (Copernicus Global Land Services) database. We used a modeling study for SDR that simulated the annual average sediment transport in 27 basins in S.A. RUSLE set up presented a satisfactory performance compared to other applications on a continental scale with an estimated averaged soil loss for S.A. of 3.8 t ha−1 year−1. Chile (>20.0 t ha−1 year−1) and Colombia (8.1 t ha−1 year−1) showed the highest soil loss. Regarding SDR, Suriname, French Guyana, and Guyana presented the lowest values (<1.0 t ha−1 year−1). The highest soil losses were found in the Andes Cordillera of Colombia and the Center-South Region of Chile. In the former, the combination of “high” K-factor, “very high” C-factor, and “very high” LS-factor were the leading causes. In the latter, agriculture, livestock, deforestation, and aggressive R-factor explained the high soil loss. Basins with the highest SDR were located in the North Argentina – South Atlantic basin (27.73%), Mar Chiquitita (2.66%), Amazon River basin (2.32%), Magdalena (2.14%) (in Andes Cordillera), and Orinoco (1.83%).