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Background: In Italy, malaria was endemic until the 1970s, when it was declared eradicated by WHO. Nowadays, with the persistence of competent mosquito populations, the effect of climate change, and increased possibility of importing malaria parasites from endemic counties due to growing migration, a malaria resurgence in Italy has become more likely. Hence, enhancing the understanding of the current distribution of the Anopheles maculipennis complex and the factors that influence the presence of this malaria vector is crucial, especially in Northern Italy, characterised by a high density of both human population and livestock. Methods: To assess the presence and abundance of malaria vectors, a 4-year field survey in the plain areas of Lombardy and Emilia-Romagna region in Italy was conducted. Every sampling point was characterised in space by the land use in a 500-m radius and in time considering meteorological data collected in the short and long time periods before sampling. We combined the results of a linear regression model with a random forest analysis to understand the relative importance of the investigated niche dimensions in determining Anopheles mosquito presence and abundance. Results: The estimated normalised variable importance indicates that rice fields were the most important land use class explaining the presence of Anopheles, followed by transitional woodlands and shrubland. Farm buildings were the third variable in terms of importance, likely because of the presence of animal shelters, followed by urbanised land. The two most important meteorological variables influencing the abundance of Anopheles in our study area were mean temperature in the 24 h before the sampling date and the sum of degree-days with temperature between 18 °C and 30 °C in the 14 days before the sampling date. Conclusions: The results obtained in this study could be helpful in predicting the risk of autochthonous malaria transmission, based on local information on land cover classes that might facilitate the presence of malaria vectors and presence of short- and medium-term meteorological conditions favourable to mosquito development and activity. The results can support the design of vector control measures through environmental management. [ABSTRACT FROM AUTHOR]

This study endeavors to produce a comprehensive land cover map for Morocco, addressing the absence of such a detailed map in the country. Our research encompasses ecological and climatic aspects specific to Morocco, while the methods used can be adapted to various regions and countries, considering their unique climatic conditions and land cover types. A combination of MODIS and Landsat datasets was employed to create a 5 km resolution Land Use and Land Cover (LULC) map for the entire nation. The process involved the aggregation and advanced processing of these datasets using surface processes algorithms. The resulting LULC map is the first of its kind for Morocco, shedding light on land cover distribution nationwide. It shows that approximately 13.5% of the country is covered by forests, predominantly in the Atlas and Rif mountains, Rabat–Sale, and the southern regions. Grasslands occupy over 16% of the study area, mainly in the north-east and west. Urban areas, including major cities like Casablanca, Rabat, and Marrakech, span nearly 3400 km². Moreover, large areas of shrublands and bare lands are evident across the country, while agricultural lands account for almost 20% of the national territory, mainly in the interior plains and north-western Atlantic coast. This study forms a crucial basis for ecological and climatic research in Morocco and serves as a valuable reference for various disciplines such as agriculture, natural resource management, and climate modeling. The mapping of biophysical parameters for each land cover class is a key feature of our research, and these parameters will be instrumental in a subsequent study examining the impact of urban development on surface climate in Morocco. Overall, our study underscores the importance of understanding biophysical parameters in addressing environmental and societal challenges. [ABSTRACT FROM AUTHOR]

Population growth and environmental shifts have elevated the pressure on land use and cover (LULC), necessitating vital management and adaptive strategies to preserve the balance between ecosystem services and human well-being in watersheds. It's pivotal to understand the implications of human-induced shifts from natural to human-dominated surroundings This study utilized Google Earth Engine (GEE) to analyze 31 years of LULC changes in Letaba watershed. Using GEE's random forest, LULC classes were mapped with 93% to 99% accuracy across four timeframes (1990, 2000, 2010, and 2021). Trends revealed declining water bodies (-4%), bare surfaces (-2%), natural forest (-3%), and grassland (-3%), while shrublands, plantations, and built-up areas increased at annual rates of 41%, 24%, and 47% respectively. This transformation reflects population-driven shifts, necessitating adaptive strategies. Given the importance of plantations for income, embracing climate-smart agriculture could ensure long-term food and environmental security, thus addressing the evolving dynamics in Letaba watershed. [ABSTRACT FROM AUTHOR]

• DOM molecular compositions in multi-interface runoffs were quantified by FT-ICR MS. • Soil-epikarst structures controlled the DOM quantity and molecular compositions. • High microbially-derived carbon was detected in epikarst flow. • Flow rate and discharge effected DOM quantity and quality during runoff process. • Soil thicknesses affected runoff DOM dynamics by impacting hydrological processes. Understanding the chemical composition, origin, and molecular structure of dissolved organic matter (DOM) in multi-interface runoff is essential for comprehending the fate of laterally transported DOM in complex soil-epikarst systems of karst hillslopes. Limited information, however, is available for the optical properties and molecular compositions of the transported OM in multiple runoff components on the karst hillslope in relation to land-uses and soil thicknesses. In this study, we conducted a study to observe the changes in the quantity and quality of DOM in multiple interface flow (surface, subsurface, and epikarst) during natural rainfall events in 2022 in karst hillslopes that are covered by different land uses (cropland and shrubland) and soil thicknesses (with mean depths of 66.0 cm for deeper soil and 35.4 cm for shallower soil) in the karst region of southwest China. chemcial compositions of runoff DOM were determined by optical analysis and microbial compositions in runoff were inferred with high-throughput sequencing. The results showed that the soil-epikarst structure was controlling the runoff DOM quantity and quality during rainfall events. A decrease in the aromaticity, humification, unsaturation, and oxidation degree and an increase in carbohydrate, aminosugars, protein, and lipid compounds were found from surface to epikarst flow, indicating that plant-and soil-derived carbon decreased, while the microbially-derived carbon increased. The results were further comfirmed by the higher bacterial richness and diversity, along with fungal diversity in the epikarst flow compared to other runoff components. The bio-labile protein materials (C2) were the most important component of runoff DOM output in karst hillslopes. In surface and subsurface flow, rainfall amount, runoff rate, and discharge significantly affected the DOM concentration and quality during rainfalls, indicating that the dynamics of DOM in runoff from karst hillslopes were predominantly influenced by hydrological processes. Furthermore, the runoff DOM quality in cropland was dominated by lower unsaturation and oxidation degrees and higher protein component, compared to those in shrubland. The compositions of DOM in runoff from hillslope plots with thicker soils were primarily characterized by microbially-derived materials. Our findings were conducive to understanding the mechanism governing the migration of DOM quality and quantity in discharge during multi-interface hydrological processes on karst hillslopes. [ABSTRACT FROM AUTHOR]

Land use and land cover change (LULCC) impacts local and regional climates through various biogeophysical processes. Accurate representation of land surface parameters in land surface models (LSMs) is essential to accurately predict these LULCC-induced climate signals. In this work, we test the applicability of the default Noah, Noah-MP, and Community Land Model (CLM) LSMs in the Weather Research and Forecasting (WRF) model over Sub-Saharan Africa. We find that the default WRF LSMs do not accurately represent surface albedo, leaf area index, and surface roughness in this region due to various flawed assumptions, including the treatment of the MODIS woody savanna land use and land cover (LULC) category as closed shrubland. Consequently, we developed a WRF CLM version with more accurate African land surface parameters (CLM-AF), designed such that it can be used to evaluate the influence of LULCC. We evaluate meteorological performance for the default LSMs and CLM-AF against observational datasets, gridded products, and satellite estimates. Further, we conduct LULCC experiments with each LSM to determine if differences in land surface parameters impact the LULCC-induced climate responses. Despite clear deficiencies in surface parameters, all LSMs reasonably capture the spatial pattern and magnitude of near-surface temperature and precipitation. However, in the LULCC experiments, inaccuracies in the default LSMs result in illogical localized temperature and precipitation changes. Differences in thermal changes between Noah-MP and CLM-AF indicate that the temperature impacts from LULCC are dependent on the sensitivity of evapotranspiration to LULCC in Sub-Saharan Africa. Errors in land surface parameters indicate that the default WRF LSMs considered are not suitable for LULCC experiments in tropical or Southern Hemisphere regions and that proficient meteorological model performance can mask these issues. We find CLM-AF to be suitable for use in Sub-Saharan Africa LULCC studies, but more work is needed by the WRF community to improve its applicability to other tropical and Southern Hemisphere climates. [ABSTRACT FROM AUTHOR]

• Land-use types, seasons, slope position significantly affected SOC and SWC. • Revegetation increased SWS consumption and SOC accumulation. • There was a positive linear relationship between SOC and SWC. • Planting grass and shrubs is recommended in the semi-arid hilly and gully regions. Understanding the response of soil organic carbon (SOC) and moisture to different land-use types, slope positions, seasonal variations, and their interactions has great significance for land use and management. However, the coupling effects of SOC and soil moisture in typical hilly and gully regions are still elusive. Therefore, in this study, five different land-use types, including cropland, abandoned land, grassland, shrubland, and forestland were selected in a typical Chinese loess hilly-gully region, and soil samples were collected from 0 to 200 cm profiles at the upper, middle, and lower slope positions in four seasons. The results demonstrated that the distributions of SOC and soil moisture were jointly significantly affected by land-use types, seasons, and slope positions (P < 0.01). Average SOC content was found to be highest in the shrubland, followed by grassland, abandoned land, forestland, and cropland. SOC storage at the lower slope position was 10.97% higher than that at the upper slope position. SOC storage in winter was 45.47% higher than that in summer. SOC storages of abandoned land, grassland, shrubland, and forestland were 17.38%, 33.08%, 52.52% and 1.26% more than that of cropland, respectively. Soil moisture fluctuated greatly in 0–100 cm layers and was stable beneath the 100 cm layers, which was highest in autumn, and lowest in summer. Whereas, under different land-use types, the soil moisture of cropland was the highest, especially significantly higher than all revegetated treatments in the 60–200 cm soil layers. Compared with cropland, soil water storage at other land-use types was averagely 36.45 % lower in the entire 200 cm soil profile. SOC was positively correlated with soil moisture under different factors. Thus, we recommend planting shrub and grass in watersheds with serious soil erosion in the semi-arid hilly and gully region, because they can sequestrate more SOC and retain more soil water. [ABSTRACT FROM AUTHOR]

Land use and land cover change (LULCC) impacts local and regional climates through various biogeophysical processes. Accurate representation of land surface parameters in land surface models (LSMs) is essential to accurately predict these LULCC-induced climate signals. In this work, we test the applicability of the default Noah, Noah-MP, and CLM LSMs in the Weather Research and Forecasting Model (WRF) over Sub-Saharan Africa. We find that the default WRF LSMs do not accurately represent surface albedo, leaf area index, and surface roughness in this region due to various flawed assumptions, including the treatment of the MODIS woody savanna LULC category as closed shrubland. Consequently, we developed a WRF CLM version with more accurate African land surface parameters (CLM-AF), designed such that it can be used to evaluate the influence of LULCC. We evaluate meteorological performance for the default LSMs and CLM-AF against observational datasets, gridded products, and satellite estimates. Further, we conduct LULCC experiments with each LSM to determine if differences in land surface parameters impact the LULCC-induced climate signals. Despite clear deficiencies in surface parameters, all LSMs reasonably capture the spatial pattern and magnitude of near surface temperature and precipitation. However in the LULCC experiments, inaccuracies in the default LSMs result in illogical localized temperature and precipitation climate signals. Differences in thermal climate signals between Noah-MP and CLM-AF indicate that the temperature impacts from LULCC are dependent on the sensitivity of evapotranspiration to LULCC in Sub-Saharan Africa. Errors in land surface parameters indicate that the default WRF LSMs considered are not suitable for LULCC experiments in tropical or Southern Hemisphere regions, and that proficient meteorological model performance can mask these issues. We find CLM-AF to be suitable for use in Sub-Saharan Africa LULCC studies, but more work is needed by the WRF community to improve its applicability to other tropical and Southern Hemisphere climates. [ABSTRACT FROM AUTHOR]

A comprehensive understanding of the relationship between land cover, climate change and disturbance dynamics is needed to inform scenarios of vegetation change on the African continent. Although significant advances have been made, large uncertainties exist in projections of future biodiversity and ecosystem change for the world's largest tropical landmass. To better illustrate the effects of climate–disturbance–ecosystem interactions on continental‐scale vegetation change, we apply a novel statistical multivariate envelope approach to subfossil pollen data and climate model outputs (TraCE‐21ka). We target paleoenvironmental records across continental Africa, from the African Humid Period (AHP: ca 14 700–5500 yr BP) – an interval of spatially and temporally variable hydroclimatic conditions – until recent times, to improve our understanding of overarching vegetation trends and to compare changes between forest and grassy biomes (savanna and grassland). Our results suggest that although climate variability was the dominant driver of change, forest and grassy biomes responded asymmetrically: 1) the climatic envelope of grassy biomes expanded, or persisted in increasingly diverse climatic conditions, during the second half of the AHP whilst that of forest did not; 2) forest retreat occurred much more slowly during the mid to late Holocene compared to the early AHP forest expansion; and 3) as forest and grassy biomes diverged during the second half of the AHP, their ecological relationship (envelope overlap) fundamentally changed. Based on these asymmetries and associated changes in human land use, we propose and discuss three hypotheses about the influence of anthropogenic disturbance on continental‐scale vegetation change. [ABSTRACT FROM AUTHOR]

Land use-induced land cover change (LUCC) is an important anthropogenic driving force of global change that has influenced, and is still influencing, many aspects of regional and global environments. Accurate historical global land use/cover datasets are essential for a better understanding of the impacts of LUCC on global change. However, there are not only evident inconsistencies in current historical global land use/cover datasets, but inaccuracies in the data in these global dataset revealed by historical record-based reconstructed regional data throughout the world. A focus in historical LUCC and global change research relates to how the accuracy of historical global land cover datasets can be improved. A methodology for assessing the credibility of existing historical global land cover datasets that addresses temporal as well as spatial changes in the amount and distribution of land cover is therefore needed. Theoretically, the credibility of a global land cover dataset could be assessed by comparing similarities or differences in the data according to actual land cover data (the "true value"). However, it is extremely difficult to obtain historical evidence for assessing the credibility of historical global land cover datasets, which cannot be verified through field sampling like contemporary global land cover datasets. We proposed a methodological framework for assessing the credibility of global land cover datasets. Considering the types and characteristics of the available evidence used for assessments, we outlined four methodological approaches: (1) accuracy assessment based on regional quantitative reconstructed land cover data, (2) rationality assessment based on regional historical facts, (3) rationality assessment based on expertise, and (4) likelihood assessment based on the consistency of multiple datasets. These methods were illustrated through five case studies of credibility assessments of historical cropland cover data. This framework can also be applied in assessments of other land cover types, such as forest and grassland. [ABSTRACT FROM AUTHOR]

Previously, applications of intensity analysis (IA) on land use and land cover change (LULCC) studies have focused on deviations from uniform intensity (UI) and failed to quantify the reasons behind these deviations. This study presents the application of IA with hypothetical errors that could explain non-uniform LULCC in the context of IA at four-time points. LULCC in the Ashi watershed was examined using Landsat images from 1990, 2000, 2010 and 2014 showing the classes: Urban, water, agriculture, close canopy, open canopy and other vegetation. Matrices were created to statistically examine LULCC using IA. The results reveal that the seeming LULCC intensities are not uniform with respect to the interval, category and transition levels of IA. Error analysis indicates that, hypothetical errors in 13%, 19% and 11.2% of the 2000, 2010 and 2014 maps respectively could account for all differences between the observed gain intensities and the UI; while errors in 12%, 21%, and 11% of the 1990, 2000 and 2010 maps respectively could account for all differences between the observed loss intensities and the UI. A hypothetical error in 0.6% and 1.6% of the 1990 map; 1.5% and 4% of the 2000 map; 1.2% and 2.1% of the 2010 map could explain divergences from uniform transitions given URB gain and AGR gain during 1990–2000, 2000–2010 and 2010–2014 respectively. Evidence for a specific deviation from the relevant hypothesized UI is either strong or weak depending on the size of these errors. We recommend that users of IA concept consider assessing their map errors, since limited ground information on past time point data exist. These errors will indicate strength of evidence for deviations and reveals patterns that increase researcher's insight on LULCC processes. [ABSTRACT FROM AUTHOR]

In the present study, the effect of land use land cover information on the simulation of seasonal planetary boundary layer (PBL) flow‐field variables over the Singapore region is analysed using the Advanced Research Weather Research and Forecasting (ARW) mesoscale model. The results achieved are compared using the standard ARW Moderate Resolution Imaging Spectroradiometer (ARW‐MODIS) global land cover data and a regional land cover map produced at the Centre for Remote Imaging, Sensing and Processing (ARW‐CRISP), Singapore. The high‐resolution simulations are conducted with triple‐nested domains (horizontal grid spacing 27, 9 and 3 km) and 51 unequally spaced vertical sigma levels. The evolution of PBL variables and rainfall are modelled for four seasons (northeast monsoon season, first inter‐monsoon period, southwest monsoon season and second inter‐monsoon period). For the purpose of numerical simulations, 30 days in each season were considered and the model‐simulated variables were validated with in situ available meteorological observations and rainfall at four stations over Singapore. The simulation results highlight the broad variation of the low‐level flow‐field in different seasons. The ARW mesoscale model simulated diurnal trends in surface meteorological variables and rainfall are in better agreement with observations by using the ARW‐CRISP regional land cover map compared to ARW‐MODIS global land cover data. Stronger winds are found during monsoon seasons compared with inter‐monsoon periods. Overall, the results of the present study indicate that regional level air quality monitoring and prediction capabilities around Singapore can be improved by using up‐to‐date land cover products designed to take into account regional land cover characteristics. [ABSTRACT FROM AUTHOR]

After the year 2010, a century of rapid decline in Philippine forest cover was reversed due to a deliberate National Greening Program (NGP). Drivers that can sustain or counter this increase can be better investigated through the help of land change models. However, such models are not yet in mainstream use for planning in the Philippines. Hence, this study used two models – FOREST-SAGE and GEOMOD – to simulate forest conditions based on anthropogenic drivers and to evaluate model applicability in the Philippines. The performance of each model was assessed using the root-mean-square error (RMSE), mean absolute error (MAE), Kappa, national average tree cover percentage, and common deforestation hotspots with reference datasets. Validation with Climate Change Initiative Land Cover dataset (CCI-LC) yielded similar results between the models: 2015 tree cover maps with RMSE of 22–25% tree cover, MAE of 10–12% tree cover, and moderate agreement with reference map based on Kappa (0.4–0.6); and 2010–2015 change maps with RMSE of 8–9% tree cover, MAE of 1–2% tree cover, and agreement due to chance based on Kappa (0.01–0.03). Validation with MODerate Resolution Imaging Spectroradiometer-Vegetation Continuous Field (MODIS-VCF) maps yielded similar MAE results (10% tree cover) between the models. Validating FOREST-SAGE end-time and change maps with MODIS-VCF yielded better RMSE results than GEOMOD (RMSE of 13% tree cover for FOREST-SAGE; 22% tree cover for GEOMOD). However, GEOMOD tree cover maps yielded better Kappa than FOREST-SAGE (0.60 for GEOMOD; 0.01 for FOREST-SAGE). Results suggest that FOREST-SAGE is more applicable in the Philippines for provincial extent studies that aim to quantitively track forest cover change, while GEOMOD is more applicable for national extent studies that use categorical data. Results also suggest that input parameter settings must be improved to simulate spatial distribution of forest cover. [ABSTRACT FROM AUTHOR]

We conducted a quantitative assessment of forage biomass in Harshin district to determine its annual productive potential, carrying capacity, and stocking rates. The dominant Land Use and Land Cover include woodland (35.5%), shrubs (28.3%), grassland (10.6%), and bare land (25.5%). The region has browse‐rich shrubland that is edible to dromedary and goats, as well as massive grassland plains for sheep and cattle. The interannual rainfall variation is 16.5% which implies that the rangeland is a subsistence equilibrium system. The range of forage production is between 105 and 2,310 kg/ha, whereas the average productivity of the district is 742.6 kg/ha. The result indicates that the average carrying capacity of the district is 0.3 TLU ha−1 year−1 (4.9 ha TLU−1 year−1) while the existing stocking rate is 5.4 TLU ha−1 year−1 (0.18 ha TLU−1 year−1). This implies that the grazing intensity in the district is much higher than its carrying capacity (recommended rate), which has seen overstocking or grazing pressure excesses of 5.1 TLU/ha (7.2 cattle/ha). Thus, it clearly signals the risk of overgrazing in the district. If this trend continues, the grazing will not be sustainable and there will be shortage of forage as well as expansion of land degradation (due to overgrazing) in the near future. [ABSTRACT FROM AUTHOR]

A microgravity investigation on bedrock topography was conducted at Maluri park reference level in Kuala Lumpur, Malaysia. The study aim to mapping the near-surface structure and soil and land cover distribution for geography and geophysics surveys. Two types of crosssection modeling of the residual anomaly generated the MaluriBouguer Anomaly model for site-1 and site-2 at Maluri Park. The 2D microgravity models produced the contour map, displaying the characterization due to density contrast in rock types while mapping the subsurface geological structure at different depths. Moreover, a synthetic model was initiated with the assumption of lateral distance on the left and right sides taken at 50 m and a depth of 60 m. The results of modeling confirmed that the soil and rock type composition on both models site tests are topsoil (1.1 to 1.92 g/cm³), soil (1.8 g/cm³), clay (1.63 g/cm³), gravel (1.7 g/cm³), sand (2.0 g/cm³), shale (2.4 g/cm³), sandstone (2.76 g/cm³), and limestone (2.9 g/cm³). The 2D gravity modeling using two model site tests obtained a correspondence with the observed microgravity data. [ABSTRACT FROM AUTHOR]

Land cover information at national or regional scale is essential for science, monitoring, reporting, and policy making. CORINE Land Cover (CLC) is the most consistent land cover map for the entire European territory, with four repetitions during the period 1985–2012. The long-term consistency of CLC maps is the most appreciated strength and it should be guaranteed. Beyond some common general guidelines, the mapping approaches used in individual countries differ and change over time, leading to inconsistencies that should be known and reported. Through a series of metrics over the Spanish CLC most recent layers (CLC2006 and CLC2012) and comparisons with eight other countries CLC statistics, we demonstrate that the methodological changes recently implemented have introduced some discrepancies with previous CLC versions. The most affected classes in Spain were transitional woodland-shrub, complex cultivation patterns, artificial, grasslands, and forests. Users should be aware of the important implications these discrepancies may have in land use and land cover change studies, trend analysis, and reports. [ABSTRACT FROM AUTHOR]

In this study, the Weather Research and Forecasting (WRF) model is used to quantify the effects of land use and cover change (LUCC) on the near-surface wind speed (SWS). The simulated results show that the SWS based on land use cover data for the 2010s (LUC10) is lower than that based on land use cover data for the 1980s (LUC80) by a difference of 0.17 m s−1; the LUCC effects also result in a decrease of 9.0% of the probability of strong wind. The LUCC effects induce significant alteration of the roughness length, causing changes in the drag coefficient and friction velocity, and thereby decrease SWS. A 0.1 m increase in roughness length could cause a 0.003 increase in drag coefficient and a 0.015 m s−1 increase in friction velocity. The contributions of LUCC to the SWS changes vary among different regions. The increase of SWS in Northeastern China is caused by the changes from deciduous broadleaf to deciduous needleleaf forests, mixed forests and croplands, and these changes decrease the surface roughness length, drag coefficient and friction velocity. The significant decrease of SWS over the middle reaches of the Yangtze River is induced by the changes from closed shrubland and cropland/natural vegetation mosaic to evergreen broadleaf and deciduous broadleaf forest. The slowdown of SWS over the Shandong Peninsula, the Beijing–Tianjin–Hebei region, the Yangtze River Delta, and the Pearl River Delta can be attributed to the extension of urban and built-up areas and the decrease of croplands and the cropland/natural vegetation mosaic. The slowdown in SWS caused by LUCC is also revealed by the friction wind model (FWM); however, the FWM presented more significant effects of LUCC on decrease in SWS. [ABSTRACT FROM AUTHOR]

Simulation modelling is a useful method for the explanation and prediction of land use and land cover changes, which are fundamentally interconnected with broader social and natural elements. Significant land cover changes, predominantly land abandonment, occurred in peripheral rural areas across Central and Eastern Europe after the collapse of socialism. One such area is Požega-Slavonia County in Croatia, for which we modelled land cover changes from 1985 to 2027. The prevailing change from 1985 to 2013 was secondary succession. The simulation model of future land cover changes was based on cellular automata and artificial neural networks, and was implemented in the MOLUSCE plugin for QGIS. Validation of the test models showed that they more successfully predicted the quantity (rather than the location) of the changes, and the post-simulation combination of individual results slightly improved their agreement with reference data. The final model predicted that forests would cover over 60% of the whole area by 2027 if the current negative demographic and economic trends continue into the future. [ABSTRACT FROM AUTHOR]

Highlights • GlobeLand30 maps show gross land change from 2000 to 2010 on 9.8% of Asia. • Difference Components reveal gross change is 4.4 times larger than net change. • Grass has largest loss; Barren has largest gain; Shrub has most intense changes. • Artificial's gain targets Cultivated; Cultivated's gain targets Artificial. • Researchers need error assessment concerning temporal change for GlobeLand30. Abstract This article uses the GlobeLand30 maps of land cover to characterize the difference between years 2000 and 2010 in Asia. Methods of Intensity Analysis and Difference Components dissect the transition matrix for nine categories: Barren, Grass, Cultivated, Forest, Shrub, Water, Artificial, Wetland and Ice. Results show that Barren, Grass, Cultivated, and Forest each account for more than 21% of Asia at both 2000 and 2010, while transitions among those four categories account for more than half of the temporal difference. Nearly ten percent of Asia shows overall temporal difference, which is the sum of three components: quantity, exchange and shift. Quantity accounts for less than a quarter of the temporal difference, while exchange accounts for three quarters of the temporal difference. The largest quantity components at the category level are a net gain of Barren and net losses of Grass and Shrub. Shrub demonstrates the most intensive loss and gain relative to a category's size. The largest and most intensive transitions to Barren are from Grass and Shrub. The largest and most intensive transition to Artificial is from Cultivated. Error information is not available for GlobeLand30 concerning 2000 or temporal change, but a confusion matrix is available for the global extent at 2010. This article applies methods to interpret the difference between two time points when a confusion matrix is available for only the latter time point. If the 2010 global confusion matrix reflects errors in Asia, then such errors could help to explain some of the gross gain of Barren and the counter-intuitive loss of Artificial. If the GlobeLand30 data indicate true change, then gross change in Asia is 4.4 times larger than net change. [ABSTRACT FROM AUTHOR]

Abstract The impact of land use/land cover and climate change on the hydrology are global issues affecting the whole catchment of the river basin. The main objective of this study was to investigate runoff response to land use/land cover change in Aynalem catchment, located in Tigray regional state, northern Ethiopia. The study analyzed the historical land cover changes from 1995 to 2015 that have taken place in the catchment and its effect on the runoff of the catchment. The land cover changes within the catchment were examined through classification of satellite images with integrated use of ERDAS Imagine (Version 9.2) and ArcGIS software. Land cover change analysis between the periods of 1995 and 2015 has shown that the urban area has increased from 1.39% to 7.50%, Cropland from 54.03% to 65.69% and Water bodies from 0.14% to 0.42%, while Planted Forest area has decreased from 3.61% to 2.92%, Grassland 2.22%–1.90% and Open Shrub lands from 38.61% to 21.53%. Further, Water and Energy Transfer between Soil, Plants, and Atmosphere (WetSpa) model was used to investigate the runoff response to a land cover change on the study area. The model was calibrated (1994–1998) and validated (1999–2001) against the observed daily stream flow data. WetSpa model parameters sensitivity analysis result shown that the groundwater recession coefficient (Kg), Initial active groundwater storage (Go) and Surface runoff exponent (K_run) are the most sensitive parameters affecting the hydrology of the catchment. The WetSpa model application in Aynalem catchment performances very good in reproducing the hydrological process of the catchment with coefficient of determination (R2), Nash and Sutcliff efficiency (NSE) values of 0.82 and 0.90 for calibration, 0.78 and 0.88 for validation, respectively. The WetSpa model application with land useland cover maps of 1995, 2003 and 2015 shows annual runoff increased by 21.1% from the year of 1995–2003 and 23.5% from 2003 to 2015 and also, 44.6% from 1995 to 2015. The increment in runoff is likely to have been caused by the change of the land use/land cover from shrubs and grass lands to urban and cultivated lands. Highlights • Built-up area and cropland increased on the expense of planted forest and grassland. • WetSpa model performed well and can be used for runoff estimation in the catchment. • Annual surface runoff increased in response to land use/landcover changes. [ABSTRACT FROM AUTHOR]

Abstract Land use and land cover change (LUCC) is not only a vital form of global change, but through biogeophysical and biogeochemical mechanisms, it also has a strong impact on the global and regional climate and environment change. The existing historical LUCC datasets still have many uncertainties that cause errors in climate and environment simulations. Reconstruction of historical LUCC on the regional scale is essential for improving the precision of global and national LUCC. In this study, we combined existing datasets with historical statistics and survey data to reconstruct cropland change in North China Plain Area over the past 300 years. Following systematic steps, a range of original methods from data sources and methods comparison, data re-calibration, time slices selection, data supplement, to cropland area fraction calculation were used. The cropland area at the county level was allocated into grid cells of 10 km × 10 km so as to achieve a high spatial resolution and precision. The results show that the cropland area change in North China Plain Area can be classified into two main stages: steady increase before 1954 and rapid decrease after 1954. The spatial distribution of cropland change over the past 300 years corresponded to the patterns of the total cropland change in North China Plain Area. From the late 17th century to the 1980s, the cropland area fraction in the plains increased and the cropland expanded to the hilly and coastal areas gradually. After that, the cropland area fraction in the plains decreased, especially in the southeastern of North China Plain Area. The total cropland area in North China Plain Area of our study is close to the results of most previous studies but the maps generated based on our dataset present local difference of cropland distribution more clearly than a recent important global dataset HYDE3.2. Our dataset of cropland area at 10 km × 10 km grid scale can also be used directly in climate and environment simulations to improve the data precision in North China Plain Area. Highlights • The cropland dataset at 10 km × 10 km grid scale in North China Plain Area was produced. • The cropland increased steadily before 1954 and decreased rapidly after 1954. • The change in the number of counties with high cropland area fraction corresponds to the change in the total cropland area. • Our total cropland area data are close to most of the results of other studies. • Regarding the spatial distribution of cropland, our study shows local difference more clearly than HYDE3.2. [ABSTRACT FROM AUTHOR]

Land cover change (LCC) is an important part of global change study and sustainable development research. Accurately describing the process of land surface change at the national scale is a necessary step in the study of the relationship between man and nature. The analysis of land cover change of Democratic People's Republic of Korea (DPRK), a hot developing country of concern to the international community, will be a key point to lay a foundation for the sustainable utilization of land resources and ecological environment protection in Northeast Asia and cross-boundary region of China. In this study, Landsat TM/OLI images of DPRK in 1990 and 2015 were used to extract the information of land cover by using object-based images analysis (OBIA) and some statistical data were used to analyze the driving forces of land use change in DPRK. The results showed that forestland (-4976.1 km2) and cropland (+4821.5 km2) were the most obviously changed land cover of DPRK in the 25 years. Wetland decreased by 212.2 km2, while built-up land increased by 82.8 km2. Among various land cover changes of DPRK, the expansion of cropland converted mainly from montane forest (94.6%) were essential and fast. The conversion between forestland and cropland significantly occured in sloping fields with relatively high elevations (100-1000 m) and intermediate slope gradients (8°-35°). In the meantime, the changed area of wetland and built-up land were mainly found at lower elevation (0-100 m and 0-200 m) areas which were nearly flat (0-8° ). North Hwanghae, with a change rate of 17.3%, was the most obviously changed district, followed by South Pyongan. Land cover change of Ryanggang was least apparent with a change rate of 2.2%. DPRK has been suffering from the economic downturn and worsening natural environment since the 1990s, and its land cover changes were influenced mostly by human factors such as the growth of population, the deterioration of economic environment and the regulation of national macropolicy. [ABSTRACT FROM AUTHOR]

A land use/land cover map is an important input for different applications. However, the accuracy of land cover maps remains a great uncertainty and mapping accuracy assessment is not well-documented. The objective of this paper is to examine the relationship between overall accuracy and the number of classification classes by conducting a literature review of land cover/land use studies. The results revealed a weak negative correlation between the map's accuracy and the number of classes. The paper suggests a decrease of 0.77% map's overall accuracy with respect to the increase of 1 land cover class. The average overall accuracy produced by 05 sensor types does not show the big difference. In addition, high spatial resolution sensor such as Airborne might not be always advantageous for producing high overall accuracy map since its accuracy depends on several factors including the number of land cover classes. [ABSTRACT FROM AUTHOR]

Alternaria spores are a common component of the bioaerosol. Many Alternaria species are plant pathogens, and their conidia are catalogued as important aeroallergens. Several aerobiological studies showing a strong relationship between concentrations of airborne spore and meteorological parameters have consequently been developed. However, the Alternaria airborne load variation has not been thoroughly investigated because it is difficult to assess their sources, as they are a very common and widely established phytopathogen. The objective of this study is to estimate the impact of vegetation and land uses as potential sources on airborne spore load and to know their influence, particularly, in cases of long-medium distance transport. The daily airborne spore concentration was studied over a 5-year period in León and Valladolid, two localities of Castilla y León (Spain), with differences in their bioclimatic and land use aspects. Moreover, the land use analysis carried out within a 30 km radius of each monitoring station was combined with air mass data in order to search for potential emission sources. The results showed a great spatial variation between the two areas, which are relatively close to each other. The fact that the spore concentrations recorded in Valladolid were higher than those in León was owing to prevailing winds originating from large areas covered by cereal crops, especially during the harvest period. However, the prevailing winds in León came from areas dominated by forest and shrubland, which explains the low airborne spore load, since the main Alternaria sources were the grasslands located next to the trap. Furthermore, the risk days in this location presented an unusual wind direction. This study reveals the importance of land cover and wind speed and direction data for establishing potential airborne routes of spore transport in order to improve the Alternaria forecasting models. The importance of conducting Alternaria aerobiological studies at a local level is also highlighted. [Display omitted] • There is a great spatial variation in Alternaria airborne load between nearby areas. • The analysis of land use around each trap is crucial to explain spore variation. • High frequencies of low wind speeds from Alternaria sources cause high spore levels. • Cereal crops and grasslands are the most important Alternaria emission sources. • Harvest season with favorable spore transport conditions is the period of greatest risk. [ABSTRACT FROM AUTHOR]

The simultaneous study of demography and behavior might provide a more comprehensive understanding about animal responses to anthropogenic disturbances. The White-winged Dove (Zenaida asiatica) is a frequent dweller of urban and agricultural habitats in North America, Central America, and the Caribbean. Still, research on the species is heavily biased to the USA. We analyzed the response of the species to human disturbances during winter across an urban-agricultural landscape in northwestern Mexico through a demographic and behavioral approach. We modeled the effect of disturbance types (pedestrians, cars, pavement and building cover) on the density of the species. Also, we investigated if surveillance behavior varied across the landscape by estimating and comparing perching proportion among land uses (urban grey areas, urban green areas, cropfields, shrublands). We did not detect the species within cropfields, possibly because food resources were not available for the dove during winter in this land use. Pavement cover limited species' density, as it might reduce the surface of the preferred feeding substrates of the dove. Pedestrians and building cover increased the density of the species, as they might provide food and shelter for the dove, respectively. Surveillance behavior peaked in urban grey areas, whereas it dropped in urban green areas. Differences on threat levels, threat type, and protective cover among land uses might influence such behavioral pattern. Our approach contributes to the knowledge of the species and demonstrates that both demographic and behavioral cues provide complementary evidence for analyzing the impact of human disturbances on animals. [ABSTRACT FROM AUTHOR]

We describe a simulation model representing the most important human and natural factors driving land use and cover changes (LUCC) in southern Chile. We evaluate the model by examining its ability to simulate LUCC observed over the past three decades, conduct a sensitivity analysis of simulated trends to changes in important model parameters, and use the model to project likely landscape transformations over the next decade under “as usual,” “pessimistic,” and four “optimistic” scenarios. The model consists of five submodels representing LUCC on two distinct soil formations (volcanic ash and gleysols) and four major land use categories: native forest, agricultural land, shrubland, and urban land. Land use and cover sub-categories include old growth forests, secondary forests, and low and flooded shrubland. The model simulated well general historic trends in forest cover, agricultural land, shrubland, and urban land: from a forest-dominated landscape in 1976 to a landscape dominated by shrubland and agricultural land by 2007. Forest loss, forest degradation by logging and clearing for agriculture were the most important direct drivers of LUCC: forest logging and clearing were most important from 1976 to 1985, whereas after 1985 logging for firewood, driven by population growth, was most important. Sensitivity analysis indicated that model projections of general trends in the main land use and cover categories were not overly sensitive to changes in important model parameters, although further study is necessary to improve our estimates of the proportion of pasture requirements supplied by clearing low shrubland. Projections of LUCC suggested that a reduced amount of secondary forest would be left by 2017 if no actions are taken to reduce forest loss (“as usual”). Increasing population (“pessimistic scenario”) resulted in similar trajectories than those predicted by the as usual scenario, whereas reducing logging for firewood and increasing forest recruitment from shrubland could reduce loss of native forest by nearly one-third (“optimistic scenarios”). Surprisingly, shrubland exhibited the most complex and influential dynamics in all scenarios, being the immediate outcome of forest loss and the main long-term source of land for agriculture, urban expansion, and forest recovery. Few studies in Chile, or elsewhere, have considered the importance of this intermediate successional stage. Of the scenarios simulated, financial incentives targeted toward channeling shrubland into regenerated forest seemed most promising, although obstacles to such a management strategy exist. [Copyright &y& Elsevier]

PlanetScope satellite data with a 3-m resolution and near-daily global coverage have been increasingly used for land surface monitoring, ranging from land cover change detection to vegetative biophysics characterization and ecological assessments. Similar to other satellite data, effective screening of clouds and cloud shadows in PlanetScope images is a prerequisite for these applications, yet remains challenging as PlanetScope has 1) fewer spectral bands than other satellites hindering the use of traditional methods, and 2) inconsistent radiometric calibration across satellite sensors making the cloud/shadow detection using fixed thresholds unrealistic. To address these challenges, we developed a SpatioTemporal Integration approach for Automatic Cloud and Shadow Screening ('STI-ACSS'), including two steps: (1) generating initial masks of clouds/shadows by integrating both spatial (i.e. cloud/shadow indices of an individual PlanetScope image) and temporal (i.e. reflectance outliers in PlanetScope image time series) information with an adaptive threshold approach; (2) a two-step fine-tuning on these initial masks to derive final masks by integrating morphological processing with an object-based cloud and cloud shadow matching. We tested STI-ACSS at six tropical sites representative of different land cover types (e.g. forest, urban, cropland, savannah, and shrubland). For each site, we evaluated the performance of STI-ACSS with reference to the manual masks of clouds/shadows, and compared it with four state-of-the-art methods, namely Function of mask (Fmask), Automatic Time-Series Analysis (ATSA), Iterative Haze Optimized Transformation (IHOT) and the default PlanetScope quality control layer. Our results show that, across all sites, STI-ACSS 1) has the highest average overall accuracy (98.03%), 2) generates an average producer accuracy of 95.53% for clouds and 89.48% for cloud shadows, and 3) is robust across sites and seasons. These results suggest the effectiveness of using STI-ACSS for cloud/shadow detection for PlanetScope satellites in the tropics, with potential to be extended to other satellite sensors with limited spectral bands. • An automatic cloud and cloud shadow screening method was developed for PlanetScope. • The method was rigorously evaluated across diverse land cover types in the tropics. • The method was compared with the other four state-of-the-art methods. • Our method has the highest accuracy and is insensitive to land cover types. [ABSTRACT FROM AUTHOR]

• Afforestation with Pinus radiata will intensify the decrease in water yield. • Conservative scenarios with native forest could partially mitigate the effect of climate change. • Pine areas under drier climate conditions revealed a larger vulnerability than shrublands. Climate change (CC) along with Land Use and Land Cover Change (LULCC) have a strong influence in water availability in already fragile Mediterranean ecosystems. In this work the Soil and Water Assessment Tool (SWAT) was implemented for the 2006–2018 period in a rainfed catchment of central Chile (36°) to test the hypothesis that adaptive plantation strategies could mitigate the impacts of climate change and increase streamflow. We also hypothesize that afforestation with exotic tree plantations will reduce water availability in Mediterranean catchments, acting in synergy with climate change. Five LULCC scenarios are analyzed: i) current long-term national Forest Policy (FP), ii) extreme scenario (EX) with large afforestation surfaces, both including the replacement of native shrublands with Pinus radiata ; iii) adaptive plantation management scenario (FM), with lower planting density, iv) forced land displacement scenario (FLD), where plantations at the headwaters are moved to lowland areas and replaced with native shrublands, and v) pristine scenario (PR), with only native vegetation. Each LULCC scenario was run with present climate and with projections of different CMIP5 climate models under the RCP 8.5 scenario for the period 2037–2050, and then compared against simulations based on the present land cover and climate. Simulations with the five LULCC scenarios (FP, EX, FM, FLD and PR) with present climate resulted in variations of −2.5, −17.3, 0, 2.3 and 10.9% on mean annual streamflow (Q), while simulations with the current land cover and CC projections produced a 32.1% decrease in mean annual Q. The joint impact of CC and LULCC leads to changes in mean annual Q ranging from −46.2% (EX) to –23.3% (PR). Afforestation with exotic pines will intensify the reduction in water yield, while conservative scenarios focused on native forests protection and restoration could partially mitigate the effect of CC. We make a strong call to rethink current and future land management strategies to cope with lower water availability in a drier future. [ABSTRACT FROM AUTHOR]

The degradation of soil resources is accelerating owing to water erosion induced by detrimental land use/land cover (LULC) practices. Although the relationship between affecting factors associated with LULC and soil erosion has been well-documented, a systematic review of LULC for erosion control is lacking. A quantitative synthesis of 1270 runoff and 1302 sediment observations from 79 references covering 57 sites was conducted to assess the effects of LULC on erosion control in the red soil hilly region (RSHR) of southern China. The results indicated that the maximum runoff and soil loss were found in bare land (BL), but the minimum runoff and soil loss were detected in cropland and grassland, respectively. Among the 16 land use subtypes, BL left for natural succession had the highest runoff, but the lowest runoff was found in orchard with contact cover; the soil loss was largest for cropland without conservation measures, while the minimum value was detected in natural shrubland. When the vegetation coverage exceeded 60%, runoff and soil loss both tended to reach a stable rate. Land use with a multi-layer vegetation structure or ground/contact cover or broadleaf plant had less runoff and lower sediment values. In addition, soil loss decreased sharply during the initial 3 y of vegetation restoration, and then slowly declined until reaching a relatively stable rate after 15 y of succession. Our results confirmed that land use (except for BL) has a limited influence on soil erosion provided that suitable land cover is established. This study revealed that sustainable land management measures should be adopted based on local conditions to reduce soil erosion induced by irrational LULC. These findings can serve as a scientific basis for policy makers and land managers with respect to water erosion control in the RSHR. • Effect of land use/cover on water erosion control was evaluated in southern China. • No land use is erosion-prone provided suitable land cover is established. • Soil loss tends to reach a stable rate when the vegetation coverage exceeded 60%. • Low erosion rates were found in land use with multi-layer/ground vegetation cover. • Soil and water loss attained a low level after 15 years' vegetation succession. [ABSTRACT FROM AUTHOR]

Summary The production of plant biomass on small farms and in woodland in densely populated locations in the Nigerian Sahelo-Sudan is estimated in order to test the hypothesis that conversion of land from natural vegetation to cultivation entails a loss in plant productivity. Estimates are given for reserved forest, woodland, shrubland and farmland at two sites varying in mean annual rainfall and population density. The results fail to support the hypothesis. The role of farmers in causing degradation through loss of plant productivity, and appropriate conservation strategies, may require re-evaluation. [ABSTRACT FROM AUTHOR]

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

Small islands face environmental issues directly or indirectly related to land cover/land use changes (LCLUC), such as natural hazards, climate change, loss of biodiversity and proliferation of invasive alien species, some of which are caused by direct human exploitation. A Land Cover/Land Use Change (LCLUC) detection approach based on PCA and vegetation indices derived from low cost high-resolution RapidEye multispectral satellite data and available vegetation maps was developed to assess vegetated/forested aboveground carbon stocks and their changes in Madeira Island, Portugal, for the period between December 2009 and August 2011 due to catastrophic events occurred in 2010. During this period, the identified LCLUC revealed a relevant decrease of vegetated areas (especially those dominated by native/endemic communities) substituted by increases of non-vegetated and human-managed vegetated/forested areas. In particular, there was a decrease of 2% of vegetated areas, 30% of which were represented by native/endemic vegetation. The largest and most accurate LCLUC detected were used to estimate changes in aboveground biomass carbon (AGC) stocks. In 2010 more than 25,000 Mg of AGC stocks may have been released. Both relevant LCLUC and AGC stocks depletion in such short period of time may have been strongly enhanced by two catastrophic events that affected Madeira in February (flashflood and landslides) and August 2010 (wildfires). This straightforward and cost-effective methodological approach may be successfully applied in remote territories such as islands or mountainous areas, where the logistic and economic costs associated to periodic and standard airborne remote sensing campaigns for mapping, assessing and monitoring aboveground biomass carbon stocks are generally unaffordable for most decision-makers and land managers in these territories. • RapidEye spectral-, thematic- and vegetation condition-based change detection • Methodology for carbon accounting in remote and impervious territories • 20,000 Mg C stock depletion in 2010 in Madeira Island, Portugal • 2010 wildfires and landslides contributed to C depletion in Madeira. • We address Sustainable Development Goals 13 and 15, namely targets 15.1, 15.5 and 15.8. [ABSTRACT FROM AUTHOR]

Over the last 20 years, substantial amounts of grassland have been converted to other land uses in the Northern Great Plains. Most of land cover/land use (LCLU) assessments in this region have been based on the U.S. Department of Agriculture - Cropland Data Layer (USDA - CDL), which may be inconsistent. Here, we demonstrate an approach to map land cover utilizing multi-temporal Earth Observation data from Landsat and MODIS. We first built an annual time series of accumulated growing degree-days (AGDD) from MODIS 8-day composites of land surface temperatures. Using the Enhanced Vegetation Index (EVI) derived from Landsat Collection 1's surface reflectance, we then fit at each pixel a downward convex quadratic model to each year's progression of AGDD (i.e. , EVI = α + β × AGDD − γ × AGDD2). Phenological metrics derived from fitted model and the goodness of fit then are submitted to a random forest classifier (RFC) to characterize LCLU for four sample counties in South Dakota in three years (2006, 2012, 2014) when reference point datasets are available for training and validation. To examine the sensitivity of the RFC to sample size and design, we performed classifications under different sample selection scenarios. The results indicate that our proposed method accurately mapped major crops in the study area but showed limited accuracy for non-vegetated land covers. Although all RFC models exhibit high accuracy, estimated land cover areas from alternative models could vary widely, suggesting the need for a careful examination of model stability in any future land cover supervised classification study. Among all sampling designs, the "same distribution" models (proportional distribution of the sample is like proportional distribution of the population) tend to yield best land cover prediction. RFC used only the most eight important variables (e.g. , three fitted parameter coefficients [α, β, and γ]; maximum modeled EVI; AGDD at maximum modeled EVI; the number of observations used to fit CxQ model; and the number of valid observations) have slightly higher accuracy compared to those using all variables. By summarizing annual image time series through land surface phenology modeling, LCLU classification can embrace both seasonality and interannual variability, thereby increasing the accuracy of LCLU change detection. • Land surface phenology (LSP) modeled using Landsat EVI and MODIS LST • Land cover from random forest classification (RFC) using only phenometrics as input • Combinations of reference data augmented by CDL used for training/validation • LSP-RFC accurately identified major crops compared to the Cropland Data Layer (CDL). • Caveat: alternative RFC training designs yielded widely varying area estimates. [ABSTRACT FROM AUTHOR]

Land use and land cover maps provide fundamental information that has been used in different kinds of studies, ranging from climate change to city planning. However, despite substantial efforts in recent decades, large-scale 30-m land cover maps still suffer from relatively low accuracy in terms of land cover type discrimination (especially for the vegetation and impervious types), due to limits in relation to the data, method, and design of the workflow. In this work, we improved the land cover classification accuracy by integrating free and public high-resolution Google Earth images (HR-GEI) with Landsat Operational Land Imager (OLI) and Enhanced Thematic Mapper Plus (ETM+) imagery. Our major innovation is a hybrid approach that includes three major components: (1) a deep convolutional neural network (CNN)-based classifier that extracts high-resolution features from Google Earth imagery; (2) traditional machine learning classifiers (i.e., Random Forest (RF) and Support Vector Machine (SVM)) that are based on spectral features extracted from 30-m Landsat data; and (3) an ensemble decision maker that takes all different features into account. Experimental results show that our proposed method achieves a classification accuracy of 84.40% on the entire validation dataset in China, improving the previous state-of-the-art accuracies obtained by RF and SVM by 4.50% and 4.20%, respectively. Moreover, our proposed method reduces misclassifications between certain vegetation types, and improves identification of the impervious type. Evaluation applied over an area of around 14,000 km2 confirms little improvement for land cover types (e.g., forest) of which the classification accuracies are already over 80% when using traditional machine learning approaches, yet improvements in accuracy of 7% for cropland and shrubland, 9% for grassland, 23% for impervious and 25% for wetlands were achieved when compared with traditional machine learning approaches. The results demonstrate the great potential of integrating features of datasets at different resolutions and the possibility to produce more reliable land cover maps. • Combining Google Earth images and Landsat data for land cover mapping • Fusing high-resolution spatial features and medium-resolution spectral features • Improving the previous highest OA from 80% to 84% on all samples in China • Reducing confusions among different vegetation and impervious types • Validating the method with 5 selected regions with a total area of about 14,000 km2 [ABSTRACT FROM AUTHOR]

The Kaxgar River Basin, a key section of the Tarim River Basin, is a typical ecologically fragile region that has undergone rapid changes to its spatial patterns over the preceding few decades. In particular, the expansion of salinized land has posed a severe threat to ecological restoration and economic development. This study monitored the rates and patterns of land use and land cover (LULC) changes in the plain area of Aketao County in the middle reaches of the Kaxgar River Basin. Five Landsat images (captured in 1990, 1998, 2002, 2013, and 2018) were divided into seven LULC types: built-up land, cultivated land, woodland and grassland, light-moderate salinized land, heavy salinized land, water areas, and bare land. Subsequently, their dynamic processes were analyzed. The results revealed that in 1990, the dominant LULCs were cultivated land, woodland and grassland, and bare land. Throughout the study period (from 1990 to 2018), the coverage of built-up land, cultivated land, bare land, water areas, and light-moderate salinized land increased; by contrast, that of the other LULC types decreased. The most marked LULC changes were the expansion of light-moderate salinized land (by 6.2% of the study area) and the shrinkage of woodland and grassland (by 9.4% of the study area). Almost all the analyzed LULC types underwent conversion to other types; such conversion occurred most frequently between 1998 and 2018. The conversions of woodland and grassland into cultivated land and light-moderate salinized land were the most notable phenomena. Another highly evident change was the conversion of heavy salinized land into bare land. These results revealed that the expansion of salinized land and the shrinkage of woodland and grassland in the study area were the most severe environmental changes. Therefore, ecological protection and salinization control are urgently required to enable local economic development while not exceeding the environmental carrying capacity and ensuring the safety of the "green corridor" in the lower reaches of the Kaxgar River Basin. [ABSTRACT FROM AUTHOR]

This paper investigates the possibility of using fusion Sentinel-2 imageries (2016, ESA) and light detection and ranging (LiDAR) point clouds for the automation of land cover mapping with a primary focus on detecting and monitoring afforested areas and deriving precise information about the spatial (2D and 3D) characteristics of vegetation for reclaimed areas. The study was carried out for reclaimed areas – two former sulfur mines located in Southeast Poland, namely, Jeziórko, where 216.5 ha of afforested area was reclaimed after borehole exploitation, and Machów, where 871.7 ha of dump area was reclaimed after open cast strip mining. The current land use and land cover (LULC) classes at the Machów and Jeziórko former sulfur mines are derived based on Sentinel-2 image processing, and confirmed the applied type of reclamation for both analysed areas. The following LULC classes showed a significant spatial range: broad-leaved forest, coniferous forest, and transitional woodland shrub. The progress of afforested areas, not only in terms of the occupied area, but also in terms of the growth of trees and shrubs, was confirmed. The results of the study showed differences in vegetation parameters, namely, height and canopy cover. Various stages of vegetation growth were also observed. This indicates an ongoing process of vegetation development, as an effect of the reclamation treatment for these areas. [ABSTRACT FROM AUTHOR]

The Brahmaputra is a unique dynamic river in the world with intense braiding and critical bank erosion. Both erosion and deposition are continuous processes in the river in an attempt to reach a new equilibrium in channel geometry and morphology by the ever dynamic nature of flow. Erosion and deposition of the river have link to land use and land cover (LULC) as the land cover is under constant change in a dynamic landscape constantly shaped by continuous erosion and deposition. The objective of the present work is to evaluate the extent of erosion and deposition along the Brahmaputra river and change in the LULC of the Brahmaputra river in Assam, India. Remote sensing and geographic information system (GIS) techniques were utilised to extract information from Landsat images. Total area of erosion and deposition during 1973–2014 was 1557 and 204 km2, respectively. Increase in area (28%) of the Brahmaputra during 1973–2014 is not solely due to bank erosion, but also for the bifurcation of streams without the loss of land. LULC study has revealed that 29% area was occupied by active channels and 71% was occupied by bars in 2014. Maximum reaches experienced reduction of the submerged part in 2014 compared to 1994 in the post-monsoon months with an overall decrease from 37% to 29%. A reduction in natural grassland and forest has been observed with a corresponding increase in agricultural practices in different bars and islands of the Brahmaputra in Assam during 1994–2014. [ABSTRACT FROM AUTHOR]

High-precision land use/land cover classification mapping derived from remote sensing supplies essential datasets for scientific research on environmental assessment, climate change simulation, geographic condition monitoring, and environmental management at global and regional scales. It is an important issue in the study of earth system science, and the coastal area is a hot spot region in this field. In this paper, the coastal areas of the Maritime Silk Road were used as the research object and a fusion method based on agreement analysis and fuzzy-set theory was adopted to achieve the fusion of three land use/land cover datasets: MCD12Q1-2010, CCI-LC2010, and GlobeLand30-2010. The accuracy of the fusion results was analyzed using an error matrix, spatial confusion, average overall consistency, and average type-specific consistency. The main findings were as follows. (1) After the establishment of reference data based on Google Earth, both the producer accuracy and user accuracy of the fusion data were improved when compared with those of the three input data sources, and the fusion data had the highest overall accuracy and Kappa coefficient, with values of 90.37% and 0.8617, respectively. (2) Various input data sources differed in terms of the correctly classified contributions and misclassified influences of different land use/land cover types in the fusion data; furthermore, the overall accuracy and Kappa coefficient between the fusion data and any one of the input data sources were far higher than those between any two of the input data sources. (3) The average overall consistency of the fusion data was the highest at 89.29%, which was approximately 5% higher than that of the input data sources. (4) The average type-specific consistencies of cropland, forest, grassland, shrubland, wetland, artificial surfaces, bare land, and permanent snow and ice in the fusion data were the highest, with values of 69.95%, 74.41%, 21.24%, 34.22%, 97.62%, 51.83%, 84.39%, and 2.46%, respectively; compared with the input data sources, the average type-specific consistencies of the fusion data were 0.61–20.32% higher. This paper provides information and suggestions for the development and accuracy evaluation of future land use/land cover data in global and regional coastal areas. [ABSTRACT FROM AUTHOR]

Disasters are an unpredictable way to change land use and land cover. Improving the accuracy of mapping a disaster area at different time is an essential step to analyze the relationship between human activity and environment. The goals of this study were to test the performance of different processing procedures and examine the effect of adding normalized difference vegetation index (NDVI) as an additional classification feature for mapping land cover changes due to a disaster. Using Landsat ETM+ and OLI images of the Bento Rodrigues mine tailing disaster area, we created two datasets, one with six bands, and the other one with six bands plus the NDVI. We used support vector machine (SVM) and decision tree (DT) algorithms to build classifier models and validated models performance using 10-fold cross-validation, resulting in accuracies higher than 90%. The processed results indicated that the accuracy could reach or exceed 80%, and the support vector machine had a better performance than the decision tree. We also calculated each land cover type's sensitivity (true positive rate) and found that Agriculture, Forest and Mine sites had higher values but Bareland and Water had lower values. Then, we visualized land cover maps in 2000 and 2017 and found out the Mine sites areas have been expanded about twice of the size, but Forest decreased 12.43%. Our findings showed that it is feasible to create a training data pool and use machine learning algorithms to classify a different year's Landsat products and NDVI can improve the vegetation covered land classification. Furthermore, this approach can provide a venue to analyze land pattern change in a disaster area over time. [ABSTRACT FROM AUTHOR]

Local climate zone (LCZ) maps are increasingly being used to help understand and model the urban microclimate, but traditional land use/land cover map (LULC) accuracy assessment approaches do not convey the accuracy at which LCZ maps depict the local thermal environment. 17 types of LCZs exist, each having unique physical characteristics that affect the local microclimate. Many studies have focused on generating LCZ maps using remote sensing data, but nearly all have used traditional LULC map accuracy metrics, which penalize all map classification errors equally, to evaluate the accuracy of these maps. Here, we proposed a new accuracy assessment approach that better explains the accuracy of the physical properties (i.e., surface structure, land cover, and anthropogenic heat emissions) depicted in an LCZ map, which allows for a better understanding of the accuracy at which the map portrays the local thermal environment. [ABSTRACT FROM AUTHOR]

This study analyzes the impact of land use/land cover (LULC) changes on the hydrology of the dry Afromontane forest landscape in northern Ethiopia. Landsat satellite images of thematic mapper (TM) (1986), TM (2001), and Operational Land Imager (OLI) (2018) were employed to assess LULC. All of the images were classified while using the maximum likelihood image classification technique, and the changes were assessed by post-classification comparison. Seven LULC classes were defined with an overall accuracy 83–90% and a Kappa coefficient of 0.82–0.92. The classification result for 1986 revealed dominance of shrublands (48.5%), followed by cultivated land (42%). Between 1986 and 2018, cultivated land became the dominant (39.6%) LULC type, accompanied by a decrease in shrubland to 32.2%, as well as increases in forestland (from 4.8% to 21.4%) and bare land (from 0% to 0.96%). The soil conservation systems curve number model (SCS-CN) was consequently employed to simulate forest hydrological response to climatic variations and land-cover changes during three selected years. The observed changes in direct surface runoff, the runoff coefficient, and storage capacity of the soil were partially linked to the changes in LULC that were associated with expanding bare land and built-up areas. This change in land use aggravates the runoff potential of the study area by 31.6 mm per year on average. Runoff coefficients ranged from 25.3% to 47.2% with varied storm rainfall intensities of 26.1–45.4 mm/ha. The temporal variability of climate change and potential evapotranspiration increased by 1% during 1981–2018. The observed rainfall and modelled runoff showed a strong positive correlation (R2 = 0.78; p < 0.001). Regression analysis between runoff and rainfall intensity indicates their high and significant correlation (R2 = 0.89; p < 0.0001). Changes were also common along the slope gradient and agro-ecological zones at varying proportions. The observed changes in land degradation and surface runoff are highly linked to the change in LULC. Further study is suggested on climate scenario-based modeling of hydrological processes that are related to land use changes to understand the hydrological variability of the dry Afromontane forest ecosystems. [ABSTRACT FROM AUTHOR]

OpenStreetMap (OSM) is a free, open-access Volunteered geographic information (VGI) platform that has been widely used over the last decade as a source for Land Use Land Cover (LULC) mapping and visualization. However, it is known that the spatial coverage and accuracy of OSM data are not evenly distributed across all regions, with urban areas being likelier to have promising contributions (in both quantity and quality) than rural areas. The present study used OSM data history to generate LULC datasets with one-year timeframes as a way to support regional and rural multi-temporal LULC mapping. We evaluated the degree to which the different OSM datasets agreed with two existing reference datasets (CORINE Land Cover and the official Portuguese Land Cover Map). We also evaluated whether our OSM dataset was of sufficiently high quality (in terms of both completeness accuracy and thematic accuracy) to be used as a sampling data source for multi-temporal LULC maps. In addition, we used the near boundary tag accuracy criterion to assesses the fitness of the OSM data for producing training samples, with promising results. For each annual dataset, the completeness ratio of the coverage area for the selected study area was low. Nevertheless, we found high thematic accuracy values (ranged from 77.3% to 91.9%). Additionally, the training samples thematic accuracy improved as they moved away from the features' boundaries. Features with larger areas (>10 ha), e.g., Agriculture and Forest, had a steadily positive correlation between training samples accuracy and distance to feature boundaries. [ABSTRACT FROM AUTHOR]

Extreme peatland forest burning episodes in Indonesia, notably the 1997-98, 2002, 2006 and 2015 events, are a major source of global atmospheric greenhouse gases and have adverse effects on human health and ecosystems on local and regional scales. The re-occurrence of such episodes has refocused attention on the 2002 ASEAN Agreement on Transboundary Haze Pollution, signed and ratified by a total of 10 ASEAN states, including Indonesia since 2014. An operational fire early warning system based on seasonal forecasts has been identified by these states as a key requirement of the Agreement to help mitigate the impacts of widespread burning and haze. However, such a system currently does not exist. Moreover, to develop such a system, a sound understanding of how fire activity responds to climate and human-driven changes in the region is needed.Past research demonstrates that extreme burning events in Indonesia are induced by drought conditions associated with El-Niño and/or positive Indian Ocean Dipole conditions that disrupt the normal monsoonal rainfall patterns across the maritime continent. Several studies have reported clear correlations between antecedent fire weather and fire activity at regional scales. However, most present-day fires in Indonesia result from human-driven land use and land cover (LULC) change and practices associated with agricultural and plantation activities at local scales, and as a result relationships between fire weather and fire activity can be expected to vary depending on past LULC change. Consequently, any effort to develop an operational fire danger early warning system for Indonesia must account for both fire weather and LULC. However, the impact of the interaction of the two factors on fire activity has received comparatively little quantitative attention.This study addresses this research gap by statistically analysing relationships between fire occurrence, fire weather and LULC change across Indonesia from 2002 through 2018. The study uses individual MODIS active fire detections aggregated to 0.25o grid as a proxy for fire activity. Fire weather is characterised by the Canadian Forest Fire Danger Rating System (CFFDRS) indices; Fire Weather Index (FWI) and Drought Code (DC), which are calculated using ERA-5 climate reanalysis products at 0.25 degree resolution. LULC changes are obtained from South East Asia and Peatland land cover maps and forest cover loss datasets. Previous work conducted at (sub-)regional scales in Indonesia has used piecewise regression to establish threshold values for DC and FWI, above which a precipitous increase in fire activity occurs. We revisit these analyses at finer resolutions to determine how such thresholds shift for various LULC types.Our results indicate that relationships between CFFDRS indices and fire activity vary in space and depend on land cover type. The piecewise regression analyses suggest different threshold values for FWI and DC for different land cover types examined, including pristine versus degraded peatland forests, large-scale plantations and small-holder agriculture areas. These results reinforce the importance of considering LULC as part of any future fire early warning system in Indonesia based on near-time climate forecasts. [ABSTRACT FROM AUTHOR]

African wetlands in arid and semi-arid regions provide a multitude of ecosystem services andhave been described as the "heart of Sahelian life systems". The Khor Abu Habil alluvialwetlands (Khor is the name given to ephemeral rivers in Sudan) are situated in thesemi-arid zone in the southern part of the Sudan. These ephemeral wetlands are fedby seasonal wadi streams and remain for several months. They provide a habitatfor migratory water birds and play a fundamental role in supporting the humanpopulation (agriculture, grazing, fishing, hunting and construction). In this study weanalyse the Land Use and Land Cover (LULC) dynamics related to agriculturalactivities. We choose agriculture as an entry point because it is the main activity and themain LULC changing factor in the study area. A questionnaire and semi-structuredinterviews were conducted in the study area, documenting different agriculturalactivities and land uses. Additionally, remote sensing data and field data on LULC werecollected and analysed with Google Earth Engine, R and GIS software. Differenttypes of agriculture are practised depending on the soil type. The agriculture inthe wadi pathways depends on floods and the agriculture on the wadi banks is arain-fed. Accordingly, two agricultural calendars were created, namely a rain-fedagricultural calendar and a flood-depending one. Additionally maps illustrating the LULCchange over a year were created. The results helped us to understand the annualchanges in the LULC between the classes agriculture, vegetation, grassland, water andbare soil. Indeed, the same piece of land may be a grassland used by pastoralists aswell as an agricultural land used by farmers, depending on the period of the year.This is a challenge for the creation of classical LULC maps. Wadis or Khors areparticular ecosystems that occur in semi-arid regions. In our study area, the economicactivities show a relationship between land uses and different human communities: theagriculturalists and the pastoralists, who live in synergy with the natural cycles of the seasonsand the wadi waters. The agricultural activities and the different LULC classes aredynamic and vary over the year depending on the wadi flood waters. This characteristicdistinguishes the Khor Abu Habil ecosystem from other ecosystems where LULC classesremain stable most of the year. Our research is part of the FAO-supported projectRESSOURCE with the overall goal to localise and study wetlands of internationalimportance in the sub-saharan Africa and declare them as Ramsar protected zones. [ABSTRACT FROM AUTHOR]