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Accurate and reliable mangrove datasets are essential for the protection and management of mangrove ecosystems. Therefore, the evaluation of the current mangrove datasets and understanding the differences among them are critical. This study takes the Guangxi Beibu Gulf (GBG) and Guangdong-Hong Kong-Macao Greater Bay Area (GBA) as the study areas and analyzes the agreement and accuracy of eight mangrove datasets from 2000 to 2020 using area comparison, spatial agreement analysis, and absolute accuracy evaluation. The results show that; 1) significant differences exist in mangrove area and spatial distribution among the different mangrove datasets, with the percentage of high agreement areas ranging from 10% to 42%. 2) The overall accuracy of the evaluated mangrove datasets ranges from 56.2% to 95.6%, and the classification accuracy of mangrove datasets in inland areas is lower than the overall level. 3) There are regional differences in the quality of mangrove datasets, with the agreement and accuracy of mangrove datasets in the GBG being greater than those in the GBA. 4) Fine-scale mangrove mapping based on high-resolution remote sensing images, such as Sentinel, and global mangrove mapping based on the Google Earth Engine (GEE) cloud platform should be emphasized in the future. The findings of this study can provide guidance for data users to select appropriate mangrove datasets and a reference for future mangrove mapping research.

The recurring flash floods in northeastern Bangladesh have been causing significant damage to the lives and livelihoods of local people every year, underscoring the necessity of effective flood management and mitigation efforts. Identifying flood hazard areas is the very first step in achieving such ends. While machine learning algorithms have been widely used in flood susceptibility studies, there is a significant dearth of research addressing their application for local-scale assessment of flood susceptibility in Bangladesh. Besides, in most of the flood susceptibility studies, only statistical metrics are used to evaluate the prediction capability of machine learning algorithms, overlooking their ability to account for spatial consistency. Therefore, this study attempts to evaluate the performance of Random Forest (RF) and Support Vector Machine (SVM) algorithms in flash flood susceptibility assessment at local scale using statistical and spatial metrics. RF model performed better than SVM with area under the curve (0.964), accuracy (92.1 %) and kappa (0.894). Five susceptibility zones were identified using a natural breaks method: very high, high, moderate, low and very low. The overall spatial agreement between the susceptibility maps of RF and SVM was 73.3 %. For very low susceptible classes, the spatial agreement was highest for RF-SVM (97.15 %) and SVM-RF (81.54 %). The findings of this study can be useful for a more accurate assessment of flash flood susceptibility in similar local settings. Moreover, the prepared susceptibility map can be helpful for taking sustainable measures to mitigate the devastating effects of flood hazard in northeastern Bangladesh.

The provision of precise and dependable information regarding the extent and distribution of cropland is imperative for the evaluation of food security, agricultural planning, and resource management. Cropland is an important component of land cover type and is offered in multiple existing global/regional land cover products. However, global-scale accuracy evaluation may not be representative of class-specific or local-area accuracy, such as in Northeast China, which is an important grain-producing region of China and has various types of cultivated land (e.g., wheat, rice) and diverse terrains. It poses a great challenge in generating precise cropland classification by automated mapping. Thus, it is indispensable to evaluate the accuracy and reliability of these various land cover datasets before using them. In this study, we collected thirteen sets of global or national-scale land cover datasets. Through the visual interpretation of high-resolution images, ground "truth" samples were collected to evaluate the data accuracy across Northeast China. The overall accuracy (OA) evaluation results in Phase-2020 show that CLCD has the highest value with 0.914, followed by GlobeLand30 (0.906), GLC_FCS30 (0.902), and Esri (0.896) for cropland classification in Northeast China. CGLS-LC100 has the lowest OA (0.710). For the commission and omission errors of six datasets in Phase-2020, CGLS-LC100 has an obvious overestimation (larger commission error), while the two national-scale datasets (CLCD and CLUDs) perform relatively better. In terms of spatial consistency, high spatial agreement among the nine Phase-2015 datasets or in the six Phase-2020 datasets could be discovered in traditional agricultural regions like the Sanjiang–Songnen–Liaohe Plain, and low agreement is found in the transition areas of mountains (hills) and plains with the mixed landscape of forest (grassland) and farmland. In the aspect of comparison pairwise data, CLCD is in good agreement with GLC_FCS30, GlobeLand30, and Esri, while CGLS-LC100 is in the poorest agreement with any other dataset. The comparison and evaluation results are expected to provide a reference on which aspects and to what extent these land cover products may be consistent and guide the cropland data product selection for Northeast China. [ABSTRACT FROM AUTHOR]

The provision of precise and dependable information regarding the extent and distribution of cropland is imperative for the evaluation of food security, agricultural planning, and resource management. Cropland is an important component of land cover type and is offered in multiple existing global/regional land cover products. However, global-scale accuracy evaluation may not be representative of class-specific or local-area accuracy, such as in Northeast China, which is an important grain-producing region of China and has various types of cultivated land (e.g., wheat, rice) and diverse terrains. It poses a great challenge in generating precise cropland classification by automated mapping. Thus, it is indispensable to evaluate the accuracy and reliability of these various land cover datasets before using them. In this study, we collected thirteen sets of global or national-scale land cover datasets. Through the visual interpretation of high-resolution images, ground “truth” samples were collected to evaluate the data accuracy across Northeast China. The overall accuracy (OA) evaluation results in Phase-2020 show that CLCD has the highest value with 0.914, followed by GlobeLand30 (0.906), GLC_FCS30 (0.902), and Esri (0.896) for cropland classification in Northeast China. CGLS-LC100 has the lowest OA (0.710). For the commission and omission errors of six datasets in Phase-2020, CGLS-LC100 has an obvious overestimation (larger commission error), while the two national-scale datasets (CLCD and CLUDs) perform relatively better. In terms of spatial consistency, high spatial agreement among the nine Phase-2015 datasets or in the six Phase-2020 datasets could be discovered in traditional agricultural regions like the Sanjiang–Songnen–Liaohe Plain, and low agreement is found in the transition areas of mountains (hills) and plains with the mixed landscape of forest (grassland) and farmland. In the aspect of comparison pairwise data, CLCD is in good agreement with GLC_FCS30, GlobeLand30, and Esri, while CGLS-LC100 is in the poorest agreement with any other dataset. The comparison and evaluation results are expected to provide a reference on which aspects and to what extent these land cover products may be consistent and guide the cropland data product selection for Northeast China.

Accurate and reliable land cover information is vital for ecosystem management and regional sustainable development, especially for ecologically vulnerable areas. The South China Karst, one of the largest and most concentrated karst distribution areas globally, has been undergoing large-scale afforestation projects to combat accelerating land degradation since the turn of the new millennium. Here, we assess five recent and widely used global land cover datasets (i.e., CCI-LC, MCD12Q1, GlobeLand30, GlobCover, and CGLS-LC) for their comparative performances in land dynamics monitoring in the South China Karst during 2000–2020 based on the reference China Land Use/Cover Database. The assessment proceeded from three aspects: areal comparison, spatial agreement, and accuracy metrics. Moreover, divergent responses of overall accuracy with regard to varying terrain and geomorphic conditions have also been quantified. The results reveal that obvious discrepancies exist amongst land cover maps in both area and spatial patterns. The spatial agreement remains low in the Yunnan–Guizhou Plateau and heterogeneous mountainous karst areas. Furthermore, the overall accuracy of the five datasets ranges from 40.3% to 52.0%. The CGLS-LC dataset, with the highest accuracy, is the most accurate dataset for mountainous southern China, followed by GlobeLand30 (51.4%), CCI-LC (50.0%), MCD12Q1 (41.4%), and GlobCover (40.3%). Despite the low overall accuracy, MCD12Q1 has the best accuracy in areas with an elevation above 1200 m or a slope greater than 25°. With regard to geomorphic types, accuracy in non-karst areas is evidently higher than in karst areas. Additionally, dataset accuracy declines significantly (p < 0.05) with an increase in landscape heterogeneity in the region. These findings provide useful guidelines for future land cover mapping and dataset fusion. [ABSTRACT FROM AUTHOR]

This paper addresses the issue of social sustainability in the daily spatial practices in dwellings. In particular, it discusses the establishment of sacred spaces in the everyday activities of Moslems in their dwellings, as manifested through the boundaries between clean and dirty zones related to religious rituals. The study employed a qualitative approach and the use of a case study as the method. The case study consists of six Moslem dwellings in urban areas, using in-depth interviews and observations to obtain data about the activities and behaviors of the dwellers. The boundaries between clean and dirty zones were found to be established based on the journey of footwear in the house. The formation of these boundaries could be considered a mechanism for maintaining the place’s sustainability—a way to maintain the cultural identity of Moslems in their dwellings. The sustainability of sacred places was established through social agreement on the use of spaces and the boundaries defined for temporary spaces.

Landslide susceptibility modeling is complex as it involves different types of landslides and diverse interests of the end-user. Developing mitigation strategies for the landslides depends on their typology. Therefore, a landslide susceptibility based on different types should be more appealing than a susceptibility model based on a single inventory set. In this research, susceptibility models are generated considering the different types of landslides. Prior to the development of the model, we analyzed landslide inventory for understanding the complexity and scope of alternative landslide susceptibility mapping. We conducted this work by examining a case study of Kalimpong region (Himalayas), characterized by different types of landslides. The landslide inventory was analyzed based on its differential attributes, such as movement types, state of activity, material type, distribution, style, and failure mechanism. From the landslide inventory, debris slides, rockslides, and rockfalls were identified to generate two landslide susceptibility models using deep learning algorithms. The findings showed high accuracy for both models (above 0.90), although the spatial agreement is highly varied among the models. [ABSTRACT FROM AUTHOR]

Accurate and reliable land cover information is vital for ecosystem management and regional sustainable development, especially for ecologically vulnerable areas. The South China Karst, one of the largest and most concentrated karst distribution areas globally, has been undergoing large-scale afforestation projects to combat accelerating land degradation since the turn of the new millennium. Here, we assess five recent and widely used global land cover datasets (i.e., CCI-LC, MCD12Q1, GlobeLand30, GlobCover, and CGLS-LC) for their comparative performances in land dynamics monitoring in the South China Karst during 2000–2020 based on the reference China Land Use/Cover Database. The assessment proceeded from three aspects: areal comparison, spatial agreement, and accuracy metrics. Moreover, divergent responses of overall accuracy with regard to varying terrain and geomorphic conditions have also been quantified. The results reveal that obvious discrepancies exist amongst land cover maps in both area and spatial patterns. The spatial agreement remains low in the Yunnan–Guizhou Plateau and heterogeneous mountainous karst areas. Furthermore, the overall accuracy of the five datasets ranges from 40.3% to 52.0%. The CGLS-LC dataset, with the highest accuracy, is the most accurate dataset for mountainous southern China, followed by GlobeLand30 (51.4%), CCI-LC (50.0%), MCD12Q1 (41.4%), and GlobCover (40.3%). Despite the low overall accuracy, MCD12Q1 has the best accuracy in areas with an elevation above 1200 m or a slope greater than 25°. With regard to geomorphic types, accuracy in non-karst areas is evidently higher than in karst areas. Additionally, dataset accuracy declines significantly (p < 0.05) with an increase in landscape heterogeneity in the region. These findings provide useful guidelines for future land cover mapping and dataset fusion.

Accurate geo-information of cropland is critical for food security strategy development and grain production management, especially in Africa continent where most countries are food-insecure. Over the past decades, a series of African cropland maps have been derived from remotely-sensed data, existing comparison studies have shown that inconsistencies with statistics and discrepancies among these products are considerable. Yet, there is a knowledge gap about the factors that influence their consistency. The aim of this study is thus to estimate the consistency of five widely-used cropland datasets (MODIS Collection 5, GlobCover 2009, GlobeLand30, CCI-LC 2010, and Unified Cropland Layer) in Africa, and to explore the effects of several limiting factors (landscape fragmentation, climate and agricultural management) on spatial consistency. The results show that total cropland area for Africa derived from GlobeLand30 has the best fitness with FAO statistics, followed by MODIS Collection 5. GlobCover 2009, CCI-LC 2010, and Unified Cropland Layer have poor performances as indicated by larger deviations from statistics. In terms of spatial consistency, disagreement is about 37.9 % at continental scale, and the disparate proportion even exceeds 50 % in approximately 1/3 of the countries at national scale. We further found that there is a strong and significant correlation between spatial agreement and cropland fragmentation, suggesting that regions with higher landscape fragmentation generally have larger disparities. It is also noticed that places with better consistency are mainly distributed in regions with favorable natural environments and sufficient agricultural management such as well-developed irrigated technology. Proportions of complete agreement are thus located in favorable climate zones including Hot-summer Mediterranean climate (Csa), Subtropical highland climate (Cwb), and Temperate Mediterranean climate (Csb). The level of complete agreement keeps rising as the proportion of irrigated cropland increases. Spatial agreement among these datasets has the most significant relationship with cropland fragmentation, and a relatively small association with irrigation area, followed by climate conditions. These results can provide some insights into understanding how different factors influence the consistency of cropland datasets, and making an appropriate selection when using these datasets in different regions. We suggest that future cropland mapping activities should put more effort in those regions with significant disagreement in Sub-Saharan Africa.

Many African countries are facing increasing risks of food insecurity due to rising populations. Accurate and timely information on the spatial distribution of cropland is critical for the effective management of crop production and yield forecast. Most recent cropland products (2015 and 2016) derived from multi-source remote sensing data are available for public use. However, discrepancies exist among these cropland products, and the level of discrepancy is particularly high in several Africa regions. The overall goal of this study was to identify and assess the driving factors contributing to the spatial discrepancies among four cropland products derived from remotely sensed data. A novel approach was proposed to evaluate the spatial agreement of these cropland products and assess the impact of environmental factors such as elevation dispersion, field size, land-cover richness and frequency of cloud cover on these spatial differences. Results from this study show that the overall accuracies of the four cropland products are below 65%. In particular, large disagreements are seen on datasets covering Sahel zone and along the West African coasts. This study has identified land-cover richness as the driving factor with the largest contribution to the spatial disagreement among cropland products over Africa, followed by the high frequency of cloud cover, small and fragmented field size, and elevation complexity. To improve the accuracy of future cropland products for African regions, the data producers are encouraged to take a multi-classification approach and incorporate multi-sensors into their cropland mapping processes.

This paper addresses the issue of social sustainability in the daily spatial practices in dwellings. In particular, it discusses the establishment of sacred spaces in the everyday activities of Moslems in their dwellings, as manifested through the boundaries between clean and dirty zones related to religious rituals. The study employed a qualitative approach and the use of a case study as the method. The case study consists of six Moslem dwellings in urban areas, using in-depth interviews and observations to obtain data about the activities and behaviors of the dwellers. The boundaries between clean and dirty zones were found to be established based on the journey of footwear in the house. The formation of these boundaries could be considered a mechanism for maintaining the place's sustainability--a way to maintain the cultural identity of Moslems in their dwellings. The sustainability of sacred places was established through social agreement on the use of spaces and the boundaries defined for temporary spaces. [ABSTRACT FROM AUTHOR]

Statistically based landslide susceptibility mapping has become an important research area in the last decades, and several bivariate and multivariate statistical approaches to landslide susceptibility assessments have been applied and compared in all regions of the world. The aim of this study was to compare different statistical approaches and to analyse the degree of spatial agreement between the landslide susceptibility maps produced. To this end, we selected seven statistical methods for comparison, namely, landslide density, likelihood ratio, information value, Bayesian model, weights of evidence, logistic regression and discriminant analysis, and then applied these to an inventory comprising 940 translational landslides, in the southeast region of Minas Gerais state in Brazil, at the western edge of the Quadrilátero Ferrífero (642.13 km2). In some statistical approaches, modifications were made to the input dependent variables. The landslides registered in the inventory map have been used in punctual and polygonal form. Six factors were considered as input landslide predisposing factors: slope angle, geomorphological units, slope curvature, lithological units, slope aspect and inverse wetness index. The combination order of the landslide predisposing factors was established based on a sensitivity analysis, which gave rise to five different cartographic combinations. In total, 58 statistical models of landslide susceptibility were produced, and the results were validated using success and prediction rate curves. The spatial agreement evaluation between the model results was carried out with kappa statistics. There were 214 comparisons of spatial agreement involving classified models at three relative degrees of susceptibility (high, medium and low landslide susceptibility classes). The results showed that all of the models so produced had satisfactory validation rates. The best landslide susceptibility models obtained areas under the curve of > 0.80 in the success and prediction rate curves, with emphasis on the weights of evidence, the information value and the likelihood ratio statistical methods. These statistical approaches were performed with the landslides mapped in the form of points. The landslide susceptibility classes of these models visually demonstrated a slightly more irregular spatial distribution when compared to the models performed with landslide polygons. The likelihood ratio model performed with landslide points presented one of the smallest areas for the high susceptibility class and the largest area for the low susceptibility class. The analysis of the spatial agreement showed that the models produced with a polygonal dependent variable tend to be more concordant, regardless of the statistical technique used. Moreover, we verified that spatial agreement tends to increase with increasing accuracy of the models. Despite the discrepancies found, most of the models compared showed a substantial or almost perfect degree of agreement. [ABSTRACT FROM AUTHOR]

Studies integrating functional near-infrared spectroscopy (fNIRS) with functional MRI (fMRI) employ heterogeneous methods in defining common regions of interest in which similarities are assessed. Therefore, spatial agreement and temporal correlation may not be reproducible across studies. In the present work, we address this issue by proposing a novel method for integration and analysis of fNIRS and fMRI over the cortical surface. Eighteen healthy volunteers (age mean±SD 30.55 ± 4.7, 7 males) performed a motor task during non-simultaneous fMRI and fNIRS acquisitions. First, fNIRS and fMRI data were integrated by projecting subject- and group-level source maps over the cortical surface mesh to define anatomically constrained functional ROIs (acfROI). Next, spatial agreement and temporal correlation were quantified as Dice Coefficient (DC) and Pearson's correlation coefficient between fNIRS-fMRI in the acfROIs. Subject-level results revealed moderate to substantial spatial agreement (DC range 0.43 – 0.64), confirmed at the group-level only for blood oxygenation level-dependent (BOLD) signal vs. Hb O 2 (0.44 – 0.69), while lack of agreement was found for BOLD vs. HbR in some instances (0.05 – 0.49). Subject-level temporal correlation was moderate to strong (0.79 – 0.85 for BOLD vs. Hb O 2 and −0.62 to −0.72 for BOLD vs. HbR), while an overall strong correlation was found for group-level results (0.95 – 0.98 for BOLD vs. Hb O 2 and −0.91 to −0.94 for BOLD vs. HbR). The proposed method directly compares fNIRS and fMRI by projecting individual source maps to the cortical surface. Our results indicate spatial and temporal correspondence between fNIRS and fMRI, and promotes the use of fNIRS when more ecological acquision settings are required, such as longitudinal monitoring of brain activity before and after rehabilitation. • Ex-post multimodal data analysis and integration in the same processing environment. • Development of a surface-based approach for integrating fMRI and fNIRS data. • Reliability assessment of fNIRS compared to fMRI for quantifying spatial agreement and temporal correlation. [ABSTRACT FROM AUTHOR]

Wide-coverage spatial information on irrigated croplands is a vital foundation for foodsecurity and water resources studies at the regional level. Several global irrigated-cropland mapshave been released to the public over the past decade due to the efforts of the remote sensingcommunity. However, the consistency and discrepancy between these maps is largely unknownbecause of a lack of comparative studies, limiting their use and improvement. To close this knowledgegap, we compared the latest four irrigated-cropland datasets (GMIA, GRIPC, GlobCover, andGFSAD) in mainland China. First, the four maps were compared quantitatively and neutral regional-and provincial-level statistics of the relative proportions of irrigated land were obtained throughregression analysis. Second, we compared the similarities and discrepancies of the datasets on spatialgrids. Furthermore, the contributions of mosaic cropland pixels in GlobCover and GFSAD were alsoanalyzed because of their extensive distribution and ambiguous content. Results showed that GMIAhas the lowest dispersion and best statistical correlation followed by GRIPC, while the correspondingfeatures of GlobCover and GFSAD are approximately equal. Spatial agreement of the four mapsis higher in eastern than western China, and disagreement is contributed mostly by GlobCoverand GFSAD. However, divergence exists in the ratios of the different agreement levels, as well astheir sources, on a regional scale. Mosaic pixels provide more than half of the irrigated areas forGlobCover and GFSAD, and they include both correct and incorrect information. Our results indicatea need for a uniform quantitative classification system and for greater focus on heterogeneous regions.Furthermore, the results demonstrate the advantage of numerical restriction in the calculations.Therefore, special attention should be paid to integrating databases and to exploring remote sensingfeatures and methods for spatial reconstruction and identification of untypical irrigation areas. [ABSTRACT FROM AUTHOR]

Land cover is a fundamental component of crucial importance in the earth sciences. To date, many excellent international teams have created a variety of land cover products covering the entire globe. To provide a reference for researchers studying the Arctic, this paper evaluates four commonly used land cover products. First, we compare and analyze the four land cover products from the perspectives of land cover type, distribution and spatial heterogeneity. Second, we evaluate the accuracy of such products by using two sets of sample points collected from the Arctic region. Finally, we obtain the spatial consistency distribution of the products by means of superposition analysis. The results show the following: (a) among the four land cover products, Climate Change Initiative Land Cover (CCI-LC) has the highest overall accuracy (63.5%) in the Arctic region, GlobeLand30 has an overall accuracy of 62.2% and the overall accuracy of the Global Land Cover by the National Mapping Organization (GLCNMO) is only 48.8%. When applied in the Arctic region, the overall accuracy of the Moderate Resolution Imaging Spectroradiometer (MODIS) is only 29.5% due to significant variances. Therefore, MODIS and GLCNMO are not recommended in Arctic-related research as their use may lead to major errors. (b) An evaluation of the consistency of the four products indicates that the classification of the large-scale homogeneous regions in the Arctic yields satisfactory results, whereas the classification results in the forest−tundra ecotone are unsatisfactory. The results serve as a reference for future research. (c) Among the four products, GlobeLand30 is the best choice for analyzing finely divided and unevenly distributed surface features such as waters, urban areas and cropland. Climate Change Initiative Land Cover (CCI-LC) has the highest overall accuracy, and its classification accuracy is relatively higher for forests, shrubs, sparse vegetation, snow/ice and water. GlobeLand30 and CCI-LC do not vary much from each other in terms of overall accuracy. They differ the most in the classification accuracy of shrub-covered land; CCI-LC performed better than GlobeLand30 in the classification of shrub-covered land, whereas the latter obtained higher accuracy than that of the former in the classification of urban areas and cropland.

Land cover (LC) is the vital foundation to Earth science. Up to now, several global LC datasets have arisen with efforts of many scientific communities. To provide guidelines for data usage over China, nine LC maps from seven global LC datasets (IGBP DISCover, UMD, GLC, MCD12Q1, GLCNMO, CCI-LC, and GlobeLand30) were evaluated in this study. First, we compared their similarities and discrepancies in both area and spatial patterns, and analysed their inherent relations to data sources and classification schemes and methods. Next, five sets of validation sample units (VSUs) were collected to calculate their accuracy quantitatively. Further, we built a spatial analysis model and depicted their spatial variation in accuracy based on the five sets of VSUs. The results show that, there are evident discrepancies among these LC maps in both area and spatial patterns. For LC maps produced by different institutes, GLC 2000 and CCI-LC 2000 have the highest overall spatial agreement (53.8%). For LC maps produced by same institutes, overall spatial agreement of CCI-LC 2000 and 2010, and MCD12Q1 2001 and 2010 reach up to 99.8% and 73.2%, respectively; while more efforts are still needed if we hope to use these LC maps as time series data for model inputting, since both CCI-LC and MCD12Q1 fail to represent the rapid changing trend of several key LC classes in the early 21st century, in particular urban and built-up, snow and ice, water bodies, and permanent wetlands. With the highest spatial resolution, the overall accuracy of GlobeLand30 2010 is 82.39%. For the other six LC datasets with coarse resolution, CCI-LC 2010/2000 has the highest overall accuracy, and following are MCD12Q1 2010/2001, GLC 2000, GLCNMO 2008, IGBP DISCover, and UMD in turn. Beside that all maps exhibit high accuracy in homogeneous regions; local accuracies in other regions are quite different, particularly in Farming-Pastoral Zone of North China, mountains in Northeast China, and Southeast Hills. Special attention should be paid for data users who are interested in these regions. [ABSTRACT FROM AUTHOR]

Accurate information of cropland area and spatial location is critical for studies of national food security, global environmental change, terrestrial ecosystem geophysics and the geochemical cycle. In this paper, we compared five global cropland datasets in circa 2010 of China from in terms of cropland area and spatial location, including GlobalLand30, FROM-GLC, GlobCover, MODIS Collection 5, and MODIS Cropland. The results showed that the accuracies of cropland area and spatial location of GlobeLand30 were higher than the other four products. The cropland areas of the five products varied in most of the provinces. Compared with the statistical data, the best goodness of fit was obtained using GlobeLand30, followed by MODIS Collection 5 and FROM-GLC, with MODIS Cropland and GlobCover having the poorer accuracies. Regarding the spatial location of cropland, GlobeLand30 achieved the best accuracy, followed by FROM-GLC and MODIS Collection 5, with GlobCover and MODIS Cropland having the poorer accuracies. In addition, the spatial agreement of the five datasets was reduced from agricultural production area to pastoral area and significantly affected by elevation and slope factors. Although the spatial resolution of MODIS Collection 5 was the lowest, accuracies of the cropland area and spatial location were better than those of GlobCover and MODIS Cropland. Therefore, high spatial resolution remote sensing images can help to improve the accuracy of the dataset during land cover mapping, while it is also important to select a suitable classification method. Furthermore, in northwestern and southeastern China, spectral mixing pixels are universal because of the complicated landscape and fragmentized topography and result in uncertainty and poor consistency when using the five products. Therefore, these regions require additional attention in future cropland mapping studies. [ABSTRACT FROM AUTHOR]

Participatory mapping is increasingly used to map spatial variation in people's perceptions about ecosystem services. It has growing use in the identification of locations where places perceived to be important converge. Few recommendations have been published to navigate decisions about sampling effort in participatory mapping research when polygon data is collected, although one recommendation is for ≥ 25 participants assuming each participant maps c. 4-5 polygons per ecosystem service. Underlying data informing this recommendation reflects a particular context: collected using postal questionnaires to map a vast spatial area in southern Australia. Although not intended as definitive or suited to all contexts, the 25 participant (or 100-125 polygon) minimum sometimes informs participatory mapping research. Our empirical work, undertaken using face-to-face questionnaires in a small Vietnamese coastal study area, suggests the recommendation may not be appropriate in all contexts. We propose a modified stepwise approach which:•Prioritises spatial agreement (polygon overlap) rather than polygon count and participant numbers to assess data sufficiency•Uses narratives to triangulate outputs generated from participatory mapping data to reduce uncertainty related to low polygon counts., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Authors. Published by Elsevier B.V.)

Detailed and harmonized information on spatial forest distribution is an essential input for forest-related environmental assessments, in particular, for biomass and growing stock modeling. In the last years, several mapping approaches have been developed in order to provide such information for Europe in a harmonized way. Each of these maps exhibits particular properties and varies in accuracy. Yet, they are often used in parallel for different modeling purposes. A detailed spatial comparison seemed necessary in order to provide information on the advantages and limitations of each of these forest cover maps in order to facilitate their selection for modeling purposes. This article confronts the high-resolution forest cover map recently developed by the Joint Research Centre for the year 2000 (FMAP2000) with previously existing maps for the same time period: the CORINE Land Cover 2000 (CLC2000) and the Calibrated European Forest Map 1996 (CEFM1996). The spatial comparison of these three maps was carried out based on forest proportion maps of 1 km derived from the original maps. To characterize differences according to biogeographic regions, two criteria were used: detail of thematic content within each map and local spatial agreement. Concerning thematic content, CLC2000 displayed a surfeit of non-forested areas at the cost of low forest proportions, while FMAP2000 showed a more balanced distribution likely to preserve more detail in forest spatial pattern. Good spatial agreement was found for CLC2000 and FMAP2000 within about 70% of the study area, while only 50% agreement was found when compared with CEFM1996. The largest spatial differences between all maps were found in the Alpine and Mediterranean regions. Reasons for these might be different input data and classification techniques and, in particular, the calibration of CEFM1996 to reported national statistics. [ABSTRACT FROM AUTHOR]

Statistical methods are widely used in environmental studies to evaluate natural hazards. Within groundwater vulnerability in particular, statistical methods are used to support decisions about environmental planning and management. The production of vulnerability maps obtained by statistical methods can greatly help decision making. One of the key points in all of these studies is the validation of the model outputs, which is performed through the application of various techniques to analyze the quality and reliability of the final results and to evaluate the model having the best performance. In this study, a groundwater vulnerability assessment to nitrate contamination was performed for the shallow aquifer located in the Province of Milan (Italy). The Weights of Evidence modeling technique was used to generate six model outputs, each one with a different number of input predictive factors. Considering that a vulnerability map is meaningful and useful only if it represents the study area through a limited number of classes with different degrees of vulnerability, the spatial agreement of different reclassified maps has been evaluated through the kappa statistics and a series of validation procedures has been proposed and applied to evaluate the reliability of the reclassified maps. Results show that performance is not directly related to the number of input predictor factors and that is possible to identify, among apparently similar maps, those best representing groundwater vulnerability in the study area. Thus, vulnerability maps generated using statistical modeling techniques have to be carefully handled before they are disseminated. Indeed, the results may appear to be excellent and final maps may perform quite well when, in fact, the depicted spatial distribution of vulnerability is greatly different from the actual one. For this reason, it is necessary to carefully evaluate the obtained results using multiple statistical techniques that are capable of providing quantitative insight into the analysis of the results. This evaluation should be done at least to reduce the questionability of the results and so to limit the number of potential choices. [Copyright &y& Elsevier]

Abstract: The aim of the study is to assess the degree of spatial agreement among different patterns of landslide susceptibility maps with almost similar success and prediction rate curves, obtained using different combinations of predictive factors. Our approach was tested in an alpine environment (Italian Alps) where debris flows represent one of the most frequent dangerous processes. A data-driven Bayesian method (the Weights of Evidence modelling technique) was successfully applied, and success and prediction rate curves were computed for supporting the modelling results and assessing the robustness of the models. The values of the area under curves were very similar for different models, ranging from 84.36% to 86.49% for the success rate curves and from 82.46% to 85.66% for the prediction rate curves. Then, the post-probability output maps were classified into rank-based maps, by using an equal-area criterion, to compare the predicted results. Afterwards, appropriate statistical techniques (kappa statistic, principal component analysis, and distance weighted entropy) were applied. Kappa statistic and principal component analysis outcomes called for significant differences within the output spatial patterns of the predicted maps as well as within the highest susceptibility classes. Moreover, the estimated distance weighted entropy values showed a very low overall entropy at the valley bottom, as all models predicted this area equally as low susceptible. In contrast, areas characterised by the highest values of entropy were more concentrated in the northern and southern slopes of the study site, lying in zones where landslide density was higher. Consequently, susceptibility maps with similar predictive power may not have the same meaning in terms of spatial pattern of predicted results. It is for this reason that landslide susceptibility maps should be distributed together with map documents aimed at defining the level of accuracy of the predicted results to provide the end-users with informative selection criteria. [Copyright &y& Elsevier]

• Spatial agreement among four remote-sensing-based landcover & cropland products was measured over Africa. • The Sahel zone and the West African coast are the two regions in Africa with high spatial disagreement and low cropland mapping accuracy. • The impact of four environmental factors (elevation dispersion, field size, land-cover richness and cloud frequency) was assessed. • Land-cover richness is the driving factor with the largest contribution to the spatial disagreement among cropland products over Africa. • The frequent clouds and fragmentation of agriculture fields limit the accurate mapping of cropland over The Sahel zone and the West African coast. Many African countries are facing increasing risks of food insecurity due to rising populations. Accurate and timely information on the spatial distribution of cropland is critical for the effective management of crop production and yield forecast. Most recent cropland products (2015 and 2016) derived from multi-source remote sensing data are available for public use. However, discrepancies exist among these cropland products, and the level of discrepancy is particularly high in several Africa regions. The overall goal of this study was to identify and assess the driving factors contributing to the spatial discrepancies among four cropland products derived from remotely sensed data. A novel approach was proposed to evaluate the spatial agreement of these cropland products and assess the impact of environmental factors such as elevation dispersion, field size, land-cover richness and frequency of cloud cover on these spatial differences. Results from this study show that the overall accuracies of the four cropland products are below 65%. In particular, large disagreements are seen on datasets covering Sahel zone and along the West African coasts. This study has identified land-cover richness as the driving factor with the largest contribution to the spatial disagreement among cropland products over Africa, followed by the high frequency of cloud cover, small and fragmented field size, and elevation complexity. To improve the accuracy of future cropland products for African regions, the data producers are encouraged to take a multi-classification approach and incorporate multi-sensors into their cropland mapping processes. [ABSTRACT FROM AUTHOR]

Land cover is a fundamental component of crucial importance in the earth sciences. To date, many excellent international teams have created a variety of land cover products covering the entire globe. To provide a reference for researchers studying the Arctic, this paper evaluates four commonly used land cover products. First, we compare and analyze the four land cover products from the perspectives of land cover type, distribution and spatial heterogeneity. Second, we evaluate the accuracy of such products by using two sets of sample points collected from the Arctic region. Finally, we obtain the spatial consistency distribution of the products by means of superposition analysis. The results show the following: (a) among the four land cover products, Climate Change Initiative Land Cover (CCI-LC) has the highest overall accuracy (63.5%) in the Arctic region, GlobeLand30 has an overall accuracy of 62.2% and the overall accuracy of the Global Land Cover by the National Mapping Organization (GLCNMO) is only 48.8%. When applied in the Arctic region, the overall accuracy of the Moderate Resolution Imaging Spectroradiometer (MODIS) is only 29.5% due to significant variances. Therefore, MODIS and GLCNMO are not recommended in Arctic-related research as their use may lead to major errors. (b) An evaluation of the consistency of the four products indicates that the classification of the large-scale homogeneous regions in the Arctic yields satisfactory results, whereas the classification results in the forest–tundra ecotone are unsatisfactory. The results serve as a reference for future research. (c) Among the four products, GlobeLand30 is the best choice for analyzing finely divided and unevenly distributed surface features such as waters, urban areas and cropland. Climate Change Initiative Land Cover (CCI-LC) has the highest overall accuracy, and its classification accuracy is relatively higher for forests, shrubs, sparse vegetation, snow/ice and water. GlobeLand30 and CCI-LC do not vary much from each other in terms of overall accuracy. They differ the most in the classification accuracy of shrub-covered land; CCI-LC performed better than GlobeLand30 in the classification of shrub-covered land, whereas the latter obtained higher accuracy than that of the former in the classification of urban areas and cropland. [ABSTRACT FROM AUTHOR]

Wide-coverage spatial information on irrigated croplands is a vital foundation for food security and water resources studies at the regional level. Several global irrigated-cropland maps have been released to the public over the past decade due to the efforts of the remote sensing community. However, the consistency and discrepancy between these maps is largely unknown because of a lack of comparative studies, limiting their use and improvement. To close this knowledge gap, we compared the latest four irrigated-cropland datasets (GMIA, GRIPC, GlobCover, and GFSAD) in mainland China. First, the four maps were compared quantitatively and neutral regional- and provincial-level statistics of the relative proportions of irrigated land were obtained through regression analysis. Second, we compared the similarities and discrepancies of the datasets on spatial grids. Furthermore, the contributions of mosaic cropland pixels in GlobCover and GFSAD were also analyzed because of their extensive distribution and ambiguous content. Results showed that GMIA has the lowest dispersion and best statistical correlation followed by GRIPC, while the corresponding features of GlobCover and GFSAD are approximately equal. Spatial agreement of the four maps is higher in eastern than western China, and disagreement is contributed mostly by GlobCover and GFSAD. However, divergence exists in the ratios of the different agreement levels, as well as their sources, on a regional scale. Mosaic pixels provide more than half of the irrigated areas for GlobCover and GFSAD, and they include both correct and incorrect information. Our results indicate a need for a uniform quantitative classification system and for greater focus on heterogeneous regions. Furthermore, the results demonstrate the advantage of numerical restriction in the calculations. Therefore, special attention should be paid to integrating databases and to exploring remote sensing features and methods for spatial reconstruction and identification of untypical irrigation areas., Competing Interests: The authors declare no conflicts of interest.