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Accurate and timely information on farmland size is crucial for agricultural development, resource management, and other related fields. However, there is currently no mature method for estimating farmland size in smallholder farming areas. This is due to the small size of farmland plots in these areas, which have unclear boundaries in medium and high-resolution satellite imagery, and irregular shapes that make it difficult to extract complete boundaries using morphological rules. Automatic farmland mapping algorithms using remote sensing data also perform poorly in small-scale farming areas. To address this issue, this study proposes a farmland size evaluation index based on edge frequency (ECR). The algorithm utilizes the high temporal resolution of Sentinel-2 satellite imagery to compensate for its spatial resolution limitations. First, all Sentinel-2 images from one year are used to calculate edge frequencies, which can divide farmland areas into low-value farmland interior regions, medium-value non-permanent edges, and high-value permanent edges (PE). Next, the Otsu's thresholding algorithm is iteratively applied twice to the edge frequencies to first extract edges and then permanent edges. The ratio of PE to cropland (ECR) is then calculated. Using the North China Plain and Northeast China Plain as study areas, and comparing with existing farmland size datasets, the appropriate estimation radius for ECR was determined to be 1600 m. The study found that the peak ECR value for the Northeast China Plain was 0.085, and the peak value for the North China Plain was 0.105. The overall distribution was consistent with the reference dataset. [ABSTRACT FROM AUTHOR]

China repeatedly surpasses international fertilizer safety limits, resulting in significant fertilizer nonpoint source pollution (denoted as FNSP), which adversely affects food security and agricultural sustainability. Simultaneously, farmland transfer has emerged as a pivotal strategy for transitioning between agricultural production methods. The present study aims to investigate the relationship between farmland transfer and FNSP. In line with the aim of the study, based on China's panel data from 2005 to 2020, the fixed-effect model, mediating-effect model, spatial Durbin model, and threshold regression model are employed. The findings reveal that farmland transfer exerts a significant inhibitory effect on FNSP. The reduction in FNSP through farmland transfer is facilitated by the decrease in fertilizer application intensity and increase in compound fertilizer application. Further, farmland transfer demonstrates a significant spatial spillover effect on FNSP, mitigating pollution levels within regions and influencing neighboring areas. Moreover, a nonlinear relationship between farmland transfer and FNSP is observed. These findings contribute to understanding the intricate dynamics between agricultural land management strategies and environmental sustainability, offering valuable insights for policymakers and stakeholders engaged in promoting green and sustainable agricultural practices. [ABSTRACT FROM AUTHOR]

Small-scale Acacia decurrens plantation forests, established by farmers on degraded lands, have become increasingly prevalent in the Northwestern Highlands of Ethiopia. This trend has been particularly notable in Fagita Lekoma District over the past few decades. Such plantations play a significant role in addressing concerns related to sustainable agricultural land use, mitigating the adverse effects of deforestation, and meeting the livelihood and energy requirements of a growing population. However, the spatial distribution of Acacia decurrens and the essential remote sensing and environmental variables that determine its distribution are not well understood. This study aimed to model the spatial distribution of Acacia decurrens plantation forests using PlanetScope data and environmental variables combined with a species distribution model (SDM). Employing 557 presence/absence points, noncollinear variables were identified and utilized as input for six SDM algorithms, with a 70:30 split between training and test data, and 10-fold bootstrap replication. The model performance was evaluated using the receiver operation characteristic curve (AUC) and true skill statics (TSS). The ensemble model, which combined results from six individual algorithms, was implemented to predict the spatial distribution of Acacia decurrens. The highest accuracy with the values of 0.93 (AUC) and 0.82 (TSS) was observed using random forest (RF), followed by SVM with values of 0.89 (AUC) and 0.71 (TSS), and BRT with values of 0.89 (AUC) and 0.7 (TSS). According to the ensemble model result, Acacia decurrens plantation forests cover 22.44% of the district, with the spatial distribution decreasing towards lower elevation areas in the northeastern and western parts of the district. The major determinant variables for identifying the species were vegetation indices, specifically CVI, ARVI, and GI, with AUC metric values of 39.3%, 16%, and 7.1%, respectively. The findings of this study indicate that the combination of high-resolution remote sensing-derived vegetation indices and environmental variables using SDM could play a vital role in identifying Acacia decurrens plantations, offering valuable insights for land use planning and management strategies. Moreover, comprehending the spatial distribution's extent is crucial baseline information for assessing its environmental implications at a local scale. [ABSTRACT FROM AUTHOR]

Regional studies of precipitation changes over Europe show that its eastern part is characterized by small changes in annual precipitation and insignificant aridity trends compared to central and southern Europe. However, a frequency analysis over the past 30 years showed statistically significant increasing dryness trends in eastern Europe and an increase in the occurrence of extremely high rainfall as well as prolonged no-rain intervals during the warm season. The largest increase in aridity was observed in the western and central parts of Belarus. During 1990–2020, the frequency of dry periods doubled in all river basins along the Black, Caspian, and Baltic Sea water divide areas of eastern Europe. From 1970 to 1990, there were high streamflow rates during the winter low-flow season. Consequently, over the past 50 years, in spring, we observed here a continued decrease in maximal discharges across all river basins. In summer, we detected a statistically significant increase in the number of days with anticyclonic weather over eastern Europe, a decrease in rainfall duration by 15–20%, an increase in daily precipitation maxima by 20–30%, and an increase in the number of days with a low relative humidity by 1–4 days per decade. [ABSTRACT FROM AUTHOR]

Global land cover (GLC) information with fine spatial resolution is a fundamental data input for studies on biogeochemical cycles of the Earth system and global climate change. Although there are several public GLC products with 30 m resolution, considerable inconsistencies were found among them, especially in fragmented regions and transition zones, which brings great uncertainties to various application tasks. In this paper, we developed an improved global land cover map in 2015 with 30 m resolution (GLC-2015) by fusing multiple existing land cover (LC) products based on the Dempster–Shafer theory of evidence (DSET). Firstly, we used more than 160 000 global point-based samples to locally evaluate the reliability of the input products for each land cover class within each 4 ∘ × 4 ∘ geographical grid for the establishment of the basic probability assignment (BPA) function. Then, Dempster's rule of combination was used for each 30 m pixel to derive the combined probability mass of each possible land cover class from all the candidate maps. Finally, each pixel was determined with a land cover class based on a decision rule. Through this fusing process, each pixel is expected to be assigned the land cover class that contributes to achieving a higher accuracy. We assessed our product separately with 34 711 global point-based samples and 201 global patch-based samples. Results show that the GLC-2015 map achieved the highest mapping performance globally, continentally, and ecoregionally compared with the existing 30 m GLC maps, with an overall accuracy of 79.5 % (83.6 %) and a kappa coefficient of 0.757 (0.566) against the point-based (patch-based) validation samples. Additionally, we found that the GLC-2015 map showed substantial outperformance in the areas of inconsistency, with an accuracy improvement of 19.3 %–28.0 % in areas of moderate inconsistency and 27.5 %–29.7 % in areas of high inconsistency. Hopefully, this improved GLC-2015 product can be applied to reduce uncertainties in the research on global environmental changes, ecosystem service assessments, and hazard damage evaluations. The GLC-2015 map developed in this study is available at 10.6084/m9.figshare.22358143.v2 (Li et al., 2023). [ABSTRACT FROM AUTHOR]

Context: Declining biodiversity in agricultural landscapes has increased the need for research and monitoring of insect abundance and diversity at the landscape level. Objectives: We investigated the accuracy of different spatial sampling designs in estimating landscape-level abundance and species richness of carabids in agricultural landscapes and, further, which sample size per landscape section was required and whether dominating land use or landscape subdivision affected accuracy and required sample size. Methods: We developed a simulation model that created raster maps of agricultural landscapes, compiled local carabid communities (species composition and abundances) within raster cells and simulated the sampling of carabids with pitfall traps using different spatial sampling designs and sample sizes between 4 and 49 sampling points per landscape section. Spatial sampling designs included random, systematic grid-based, stratified and clustered schemes. Results: To estimate landscape-level abundance, area-proportional stratified random sampling was most accurate followed by systematic grid-based designs. A sample size of 25 appeared to be the best trade-off between accuracy and sampling cost. Accuracy was not affected substantially by landscape characteristics in most cases. With respect to species richness, all designs except for clustered sampling had comparable accuracies, but even 49 samples were not sufficient to detect 80% of the species. Conclusion: Systematic grid-based designs are generally recommendable for sampling of carabids in agricultural landscapes and, in case a carabid-specific habitat classification is available, area-proportional stratified random sampling provides optimal accuracy for estimating landscape-level abundance. For assessment of species richness, large sample size is more important than spatial sampling design. [ABSTRACT FROM AUTHOR]

With billions of active users, social media platforms now generate spatial data on a massive scale, which presents researchers with opportunities to use this data in new and innovative ways. There is a body of literature within which location-based social media data have been used as a means to spatially analyze certain phenomenon. Due to the low cost, high availability, and extreme ranges of content and geographic coverage, location-based social media data have the potential to play a major role in GIS research. To this end, our review maps the field of evidence as it relates to the methods used by GIS researchers to collect, spatially analyze, and cartographically visualize location-based social media data. The results are informed by 222 articles that utilize spatial social media for a wide variety of purposes and GIS-based analyses. Findings were organized according to a typical GIS workflow and are presented to give the reader an operationalized understanding of each step in the workflow. Collection strongly favoured platforms like Twitter and Flickr, and similar to methods analysis were dictated either by content or locational data attributes. Data visualization was achieved through discrete, continuous, and transmission surfaces. [ABSTRACT FROM AUTHOR]

Rotational grazing in paddocks is a strong indicator of intensive grassland management. Uncertainty in the extent of grassland management intensity is a reported source of uncertainty in greenhouse gas budgeting. This article outlines a method of detecting paddock locations in Sentinel 2 satellite imagery using a statistical multimodality test. The test was compared to three machine learning algorithms (support vector machine, random forest and extreme gradient boosting). Photographic records of the Eurostat 2018 LUCAS survey were used as ground truth data to confirm the presence or absence of paddocks. The multimodality test achieved an overall accuracy of 88.4% versus the best machine learning accuracy of 82.4%. The test was also used to map paddock occurrence at a regional scale in the Republic of Ireland. Overall map accuracy was 85.7% versus validation data. The test can be applied in temperate grasslands with pre-mapped field boundaries where rotational grazing is practiced. [ABSTRACT FROM AUTHOR]

Cropland expansion is expected to increase across sub-Saharan African (SSA) countries in the next thirty years to meet growing food needs across the continent. These land transformations will have cascading social and ecological impacts that can be monitored using novel Earth observation techniques that produce datasets complementary to national cropland surveys. In this study, we present a flexible Bayesian data synthesis workflow on Google Earth Engine (GEE) that can be used to fuse optical and synthetic aperture radar data and demonstrate its ability to track agricultural change at national scales. We adapted the previously developed Bayesian Updating of Land Cover (Unsupervised) algorithm (BULC-U) by integrating a shapelet and slope thresholding algorithm to identify the locations and dates of cropland expansion and implemented a tiling scheme to allow the processing of large volumes of imagery. We apply this approach to map annual cropland change from 2000 to 2015 for Zambia (750,000 km2), a country that is experiencing rapid growth in agricultural land. We applied our cropland mapping approach to a time series of unsupervised classifications developed from Landsat 5, 7, 8, Sentinel-1, and ALOS PALSAR within 1476 tiles covering Zambia. The annual cropland changes maps reveal active cropland expansion between 2000 to 2015 in Zambia, especially in the Southern, Central, and Eastern provinces. Our accuracy assessment estimates that we have identified 27.5% to 69.6% of the total cropland expansion from 2000 to 2015 in Zambia (commission errors between 6.1% to 37.6%), depending on the slope threshold. Our results demonstrate the usefulness of Bayesian data fusion and shapelet, slope-based thresholding to synthesize optical and synthetic aperture radar for monitoring agricultural changes in situations where training data are scarce. In addition, the annual cropland maps provide one of the first spatially continuous, annually incremented accounts of cropland growth in this region. Our flexible, cloud-based workflow using GEE enables multi-sensor, national-scale agricultural change monitoring at low cost for users. [ABSTRACT FROM AUTHOR]

Farms are not homogeneous. Smaller farms generally have different planted crops, yields, agricultural inputs, and irrigation applications compared to larger farms. However, gridded farm-size-specific data that are moreover crop specific, are currently lacking. This obscures our understanding of differences between small-scale and large-scale farms, e.g., with respect to climate change adaptation and mitigation strategies, contribution to (local) food security, and water consumption patterns. This study fills a significant part of the current data gap, by developing high-resolution gridded, simultaneously farm-size-specific and crop-specific datasets of harvested areas for 56 countries (i.e., covering about half the global cropland). Hereto, we downscaled the most complete global direct measurements of farm size and crop type by compiling state of the art datasets, including crop maps, cropland extent maps, and dominant field size distribution, representative for the year 2010. Using two different crop map sources, we were able to produce two new 5 arcmin gridded datasets on simultaneously derived farm-size-specific and crop-specific harvested areas: one for 11 farm sizes, 27 crops, and 2 farming systems, and one for 11 farm sizes, 42 crops, and 4 farming systems. In line with previous findings, our resulting datasets show major differences in planted crops and irrigated area (%) between farm sizes. Consistency between our resulting datasets and (i) observations from satellite images on farm-size-specific oil palm, (ii) household surveys on the farm-size-specific irrigated area (%), and (iii) previous studies that mapped noncrop-specific farm sizes and support the validity of our datasets. Although at grid level some uncertainties remain to be overcome, particularly those stemming from uncertainties in crop maps, results at country level seem robust. Source data, code, and resulting datasets are open access and freely available at 10.5281/zenodo.6976249 (Su et al., 2022). [ABSTRACT FROM AUTHOR]

Aim: Agriculture is one of the greatest pressures on biodiversity. Regional studies have shown that the presence of natural habitat and landscape heterogeneity are beneficial for biodiversity in agriculture, but it remains unclear whether their importance varies geographically. Here, we use local biodiversity data to determine which local and landscape variables are most associated with biodiversity patterns and whether their association varies between tropical and non‐tropical regions. Location: Global terrestrial area in forest biomes. Major taxa studied: More than 21,000 species of vertebrates, invertebrates, plants and other taxa. Methods: We used generalized linear mixed‐effects models to analyse the relationships between either community total abundance or species richness (derived from the PREDICTS database) and a number of site‐level (predominant land use and land‐use intensity) and landscape‐level variables (distance to forest, the percentage of natural habitat in the surrounding landscape, landscape homogeneity, the number of land‐cover types in the landscape, and total fertilizer application). We compared the associations of these variables with biodiversity in tropical and non‐tropical regions. Results: In most cases, changes in biodiversity associated with landscape‐level variables were greater than those associated with local land use and land‐use intensity. Increased natural habitat availability was associated with the most consistent increases in biodiversity. Landscape homogeneity was also important but showed different directions of biodiversity change between regions. Associations with fertilizer application or the number of land‐cover types were generally weaker, although still of greater magnitude than for the local land‐use measures. Main conclusions: Our results highlight similarities and differences in the association of local‐ and landscape‐scale variables with local biodiversity in tropical and non‐tropical regions. Landscape natural habitat availability had a consistent positive association with biodiversity, highlighting the key role of landscape management in the maintenance of biodiversity in croplands. Landscape‐scale variables were almost always associated with greater changes in biodiversity than the local‐scale measures. [ABSTRACT FROM AUTHOR]

Citizen science has become an increasingly popular approach to scientific data collection, where classification tasks involving visual interpretation of images is one prominent area of application, e.g., to support the production of land cover and land-use maps. Achieving a minimum accuracy in these classification tasks at a minimum cost is the subject of this study. A Bayesian approach provides an intuitive and reasonably straightforward solution to achieve this objective. However, its application requires additional information, such as the relative frequency of the classes and the accuracy of each user. While the former is often available, the latter requires additional data collection. In this paper, we present a two-stage approach to gathering this additional information. We demonstrate its application using a hypothetical two-class example and then apply it to an actual crowdsourced dataset with five classes, which was taken from a previous Geo-Wiki crowdsourcing campaign on identifying the size of agricultural fields from very high-resolution satellite imagery. We also attach the R code for the implementation of the newly presented approach. [ABSTRACT FROM AUTHOR]

The mapping of field boundaries can provide important information for increasing food production and security in agricultural systems across the globe. Remote sensing can provide a viable way to map field boundaries across large geographic extents, yet few studies have used satellite imagery to map boundaries in systems where field sizes are small, heterogeneous, and irregularly shaped. Here we used very-high-resolution WorldView-3 satellite imagery (0.5 m) and a mask region-based convolutional neural network (Mask R-CNN) to delineate smallholder field boundaries in Northeast India. We found that our models had overall moderate accuracy, with average precision values greater than 0.67 and F1 Scores greater than 0.72. We also found that our model performed equally well when applied to another site in India for which no data were used in the calibration step, suggesting that Mask R-CNN may be a generalizable way to map field boundaries at scale. Our results highlight the ability of Mask R-CNN and very-high-resolution imagery to accurately map field boundaries in smallholder systems. [ABSTRACT FROM AUTHOR]

Pollinators have been declining in recent years due to changes in land use, and the negative impacts on food production are concern. Wildflowers play an important role in maintaining pollinators and pollination services in agricultural landscapes, but little is known about the effects of wildflowers at field margins on crop pollinators. We aimed to clarify the relationship between crop pollination and the species richness of wildflowers at field margins through sharing of flower visitors. We focused on insects that visit the flowers of buckwheat, Fagopyrum esculentum, in fields in Japan. Field surveys were conducted to investigate flower‐visiting insect communities in both the pre‐blooming and blooming periods of buckwheat in summer and autumn. The community structure of flower‐visiting insects differed between buckwheat and wildflowers in both seasons, but buckwheat shared beetles with co‐flowering plants in autumn. The abundance of insects visiting buckwheat increased with species richness of co‐flowering plants in autumn but was not related to wildflowers in summer. The morphological similarity of floral traits would not be important for these positive effects of wildflowers. Thus, co‐flowering plants have positive or neutral effects on the insects visiting buckwheat, which may contribute to the enhancement of buckwheat yields. [ABSTRACT FROM AUTHOR]

Many global climate change mitigation pathways presented in IPCC assessment reports rely heavily on the deployment of bioenergy, often used in conjunction with carbon capture and storage. We review the literature on bioenergy use for climate change mitigation, including studies that use top‐down integrated assessment models or bottom‐up modelling, and studies that do not rely on modelling. We summarize the state of knowledge concerning potential co‐benefits and adverse side effects of bioenergy systems and discuss limitations of modelling studies used to analyse consequences of bioenergy expansion. The implications of bioenergy supply on mitigation and other sustainability criteria are context dependent and influenced by feedstock, management regime, climatic region, scale of deployment and how bioenergy alters energy systems and land use. Depending on previous land use, widespread deployment of monoculture plantations may contribute to mitigation but can cause negative impacts across a range of other sustainability criteria. Strategic integration of new biomass supply systems into existing agriculture and forest landscapes may result in less mitigation but can contribute positively to other sustainability objectives. There is considerable variation in evaluations of how sustainability challenges evolve as the scale of bioenergy deployment increases, due to limitations of existing models, and uncertainty over the future context with respect to the many variables that influence alternative uses of biomass and land. Integrative policies, coordinated institutions and improved governance mechanisms to enhance co‐benefits and minimize adverse side effects can reduce the risks of large‐scale deployment of bioenergy. Further, conservation and efficiency measures for energy, land and biomass can support greater flexibility in achieving climate change mitigation and adaptation. [ABSTRACT FROM AUTHOR]

Context: While land use change is the main driver of biodiversity loss, most biodiversity assessments either ignore it or use a simple land cover representation. Land cover representations lack the representation of land use and landscape characteristics relevant to biodiversity modeling. Objectives: We developed a comprehensive and high-resolution representation of European land systems on a 1-km2 grid integrating important land use and landscape characteristics. Methods: Combining the recent data on land cover and land use intensities, we applied an expert-based hierarchical classification approach and identified land systems that are common in Europe and meaningful for studying biodiversity. We tested the benefits of using this map as compared to land cover information to predict the distribution of bird species having different vulnerability to landscape and land use change. Results: Next to landscapes dominated by one land cover, mosaic landscapes cover 14.5% of European terrestrial surface. When using the land system map, species distribution models demonstrate substantially higher predictive ability (up to 19% higher) as compared to models based on land cover maps. Our map consistently contributes more to the spatial distribution of the tested species than the use of land cover data (3.9 to 39.1% higher). Conclusions: A land systems classification including essential aspects of landscape and land management into a consistent classification can improve upon traditional land cover maps in large-scale biodiversity assessment. The classification balances data availability at continental scale with vital information needs for various ecological studies. [ABSTRACT FROM AUTHOR]

The association of climatic variables and climatic oscillations with wheat yield across 26 canal command areas (CCAs) in Punjab, Pakistan, is studied, for recent 34 years (1982–2015). Climatic variables include precipitation, temperature, dry/wet spell lengths, hot/cold spell lengths, wet/dry spell ratio along with climatic oscillations, and satellite-based vegetation index for the months of January to April (Rabi season). During Rabi season, temperature increase rate was 0.14 °C decade−1, whereas precipitation trends were spatially inhomogeneous and varied between – 2.7 mm decade−1 and + 5.4 mm decade−1. The precipitation, temperature, cold spell length, smoothed NDVI (ND), and standard deviation of ND (SDND) displayed significant correlations (0.31–0.96) with wheat yield at varying time scales. The inter-annual wheat yield variability displayed a gain of 415.7 kg ha−1 under present climatic conditions for all CCAs. Worse to extreme climatic conditions based on percentiles obtained from present climate displayed a reduction of wheat yield by 252.0 to 954.9 kg ha−1, respectively. The good to best climate in comparison to present climatic conditions added up 108.1 to 251.9 kg ha−1, respectively. The 11-year period of 1993–2003 was the most drought prone period with consecutive five years of drought (1998–2002), having decreased precipitation, small wet/dry spell ratio and increased temperature, and long dry/hot spell length during wheat growing season. The wheat yield is predicted based on multiple regression models with sizable skill (having an explained variance of 0.79 and a root mean squared error of 121.3 kg ha−1). [ABSTRACT FROM AUTHOR]

Ecosystem monitoring is fundamental to our understanding of how ecosystem change is impacting our natural resources and is vital for developing evidence‐based policy and management. However, the different types of ecosystem monitoring, along with their recommended applications, are often poorly understood and contentious. Varying definitions and strict adherence to a specific monitoring type can inhibit effective ecosystem monitoring, leading to poor program development, implementation and outcomes. In an effort to develop a more consistent and clear understanding of ecosystem monitoring programs, we here review the main types of monitoring and recommend the widespread adoption of three classifications of monitoring, namely, targeted, surveillance and landscape monitoring. Landscape monitoring is conducted over large areas, provides spatial data, and enables questions relating to where and when ecosystem change is occurring to be addressed. Surveillance monitoring uses standardised field methods to inform on what is changing in our environments and the direction and magnitude of that change, whilst targeted monitoring is designed around testable hypotheses over defined areas and is the best approach for determining the causes of ecosystem change. The classification system is flexible and can incorporate different interests, objectives, targets and characteristics as well as different spatial scales and temporal frequencies, while also providing valuable structure and consistency across distinct ecosystem monitoring programs. To support our argument, we examine the ability of each monitoring type to inform on six key types of questions that are routinely posed for ecosystem monitoring programs, such as where and when change is occurring, what is the magnitude of change, and how can the change be managed? As we demonstrate, each type of ecosystem monitoring has its own strengths and weaknesses, which should be carefully considered relative to the desired results. Using this scheme, scientists and land managers can design programs best suited to their needs. Finally, we assert that for our most serious environmental challenges, it is essential that we include information from each of these monitoring scales to inform on all facets of ecosystem change, and this is best achieved through close collaboration between the scales. With a renewed understanding of the importance of each monitoring type, along with greater commitment to monitor cooperatively, we will be well placed to address some of our greatest environmental challenges. [ABSTRACT FROM AUTHOR]

Water erosion on arable land can reduce soil fertility and agricultural productivity. Despite the impact of water erosion on crops, it is typically neglected in global crop yield projections. Furthermore, previous efforts to quantify global water erosion have paid little attention to the effects of field management on the magnitude of water erosion. In this study, we analyse the robustness of simulated water erosion estimates in maize and wheat fields between the years 1980 and 2010 based on daily model outputs from a global gridded version of the Environmental Policy Integrated Climate (EPIC) crop model. By using the MUSS water erosion equation and country-specific and environmental indicators determining different intensities in tillage, residue handling and cover crops, we obtained the global median water erosion rates of 7 t ha -1 a -1 in maize fields and 5 t ha -1 a -1 in wheat fields. A comparison of our simulation results with field data demonstrates an overlap of simulated and measured water erosion values for the majority of global cropland. Slope inclination and daily precipitation are key factors in determining the agreement between simulated and measured erosion values and are the most critical input parameters controlling all water erosion equations included in EPIC. The many differences between field management methods worldwide, the varying water erosion estimates from different equations and the complex distribution of cropland in mountainous regions add uncertainty to the simulation results. To reduce the uncertainties in global water erosion estimates, it is necessary to gather more data on global farming techniques to reduce the uncertainty in global land-use maps and to collect more data on soil erosion rates representing the diversity of environmental conditions where crops are grown. [ABSTRACT FROM AUTHOR]

Despite the fact that at the global level, half the planet's wildlife population has declined since 1970, this trend is not homogeneous across species and areas. Indeed, focusing on the ungulates, their increasing population and density has become worrisome in several rural areas. Favoured by economic and social changes often related to human activities, ungulates have conquered areas where coexistence with humans is difficult to maintain as a result of the damage that ungulates cause to agricultural activities and forests. This work aims to analyse the relationship between the number of ungulates and the characteristics of a specific area they inhabit. Applying a Geographically Weighted Regression analysis (GWR), we analysed and tested the spatial non-stationarity of the relationship between ungulates and human activities. Mugello, an area in central Italy, was selected for this study. This area was chosen due to the presence of a high number of ungulates that interact in different territorial scenarios, including urban agglomerations located in the flat zone, agricultural areas in the central-northern part and forested areas in the northern part of Mugello. This article looks at the way the number of ungulates is directly related to human activities in a specific territory. This contributes to the literature by providing useful information to stakeholders for future planning and wildlife management in agricultural areas within the limits of sustainability. Moreover, the social and economic implications are significant, especially considering such agricultural areas are at risk of being damaged by the presence of ungulates. The result of the analysis has validated the use of GWR, highlighting the relationship of selected variables and the number of ungulates. [ABSTRACT FROM AUTHOR]

The PM2.5 as one of the main pollutants in Tehran city has a devastating effect on human health. Knowing the key parameters associated with PM2.5 concentration is essential to take effective actions to reduce the concentration of these particles. This study assesses the relationship between meteorological (humidity, pressure, temperature, precipitation, and wind speed) and environmental parameters (normalize difference vegetation index and land surface temperature of MODIS satellite data) on PM2.5 concentration in Tehran city. The Geographically Weighted Regression (GWR) was employed to assess the impact of key parameters on PM2.5 concentrations in winter and summer. For this purpose, first the seasonal average of meteorological data were extracted and synchronized to satellite data. Then, using the ordinary least square model, the important parameters related to PM2.5 concentration were determined and evaluated. Finally, using the GWR model, the relationships between parameters related to PM2.5 concentration were analyzed. The results of this study indicate that meteorological and environmental parameters in winter season (71%) have a much higher ability to explain PM2.5 concentration than summer season (40%). In winter, PM2.5 concentration has a negative correlation with vegetation at most parts of the study area, a negative correlation with LST in the western and a positive correlation in the eastern part of the study area, a positive correlation with temperature, and a negative correlation with wind speed in the northeastern part of the study area. Precipitation has a positive correlation with PM2.5 concentration in most parts of the study area in both seasons. But, it was investigated in case of higher precipitation (more than 2 mm), PM2.5 concentration decreases. But, there is no negative relationship in any of the dependent parameters with PM2.5 concentration in summer. In this season, the air temperature parameter showed a high correlation with PM2.5 concentration. Also, spatial variations of the local coefficients for all parameters are higher in winter than in summer. [ABSTRACT FROM AUTHOR]

The agro-environmental policies included in rural development plans are getting increasing importance in European Community strategies. These policies represent the meeting point between demand and supply of positive externalities. The difficulty of assessing real environmental efficiency is one of the elements characterizing agro-environmental measures. This difficulty is related to the identification of suitable parameters for evaluating farms according to their impact on the territory. This impact is mainly related both to chemical inputs and to the territorial characteristics of the farm. Different types of fertilizers, pesticides and herbicides are currently used in production processes; however, the analysis has focused only on nitrates, as they represent the most critical types of chemicals related to soil pollution. A case study is provided by analysis of agro-environmental measures in Tuscany for the reduction of nitrates in organic and integrated farms. Using spatial multicriteria analysis, integrated and organic farms were classified according to their geographical locations and their release of nitrates into the soil. This classification permits the highlighting of farms that make the greatest economic efforts to reduce pollution and therefore it could determine environmental benefits. Considering that the trend of policy strategies is toward a reduction of monetary resources, the classification could help decision makers choose the right allocation of future resources. [ABSTRACT FROM AUTHOR]

The cultivation of bioenergy plants in fertile, arable lands increasingly results in new land use conflicts with food production and cannot be considered as sustainable. Marginal lands have been frequently considered as potential alternatives for producing bioenergy from biomass. However, clear definitions and assessment methods for selecting marginal lands and for calculating potentials are still widely missing. The project "SEEMLA" aims at triggering the exploitation of currently underused marginal lands for biomass production for energy purposes. Study sites have been selected in different European countries: Germany, Greece, and Ukraine. The selected sites represent a wide variety of different types of marginal lands. Based on a soil assessment set given by the Muencheberg Soil Quality Rating (SQR) system potentially "marginal" sites have been investigated. The SQR system allows for clearly distinguishing between soils of higher and lower quality. Soils with SQR scores below 40 are regarded as "marginal". They can be classified into different groups with regard to the importance of soil hazard indicators as evaluated by the SQR approach. The calculated SQR scores correlate significantly with biomass yields of bioenergy plants. Further, the SQR method was adapted for use in a GIS study on marginal-land potentials in Europe. Thus, 46 % of the investigated European area could be classified as "marginal" with SQR scores below 40. From that area 22.6 % can be considered as potentially suitable for producing renewable resources after eliminating protected sites or other places not suitable for any kind of land use. Taking the ecological demands of selected bioenergy plants into account it is possible to give first preliminary recommendations for regional crop cultivation. It can be concluded that Europe offers a large potential for renewable resources from marginal sites. However, the implementation into practice is often impeded by missing or varying policies and regulations. A proper implementation needs clear regulations and also incentives for farmers at the European level. [ABSTRACT FROM AUTHOR]

There is an increasing amount of open Earth observation (EO) data available, offering solutions to map, assess and monitor natural resources and to obtain answers to global and local societal challenges. With the help of free and open source software (FOSS) and open access cloud computing resources, the remote sensing community can take the full advantage of these vast geospatial data repositories. To empower developing societies, support should be given to higher education institutions (HEIs) to train professionals in using the open data, software and tools. In this paper, we describe a participatory mapping methodology, which utilizes open source software Open Foris and QGIS, various open Earth observation data catalogues, and computing capacity of the free Google Earth Engine cloud platform. Using this methodology, we arranged a collaborative data collection event, Mapathon, in Tanzania, followed by a training of the related FOSS tools for HEIs' teaching staff. We collected feedback from the Mapathon participants about their learning experiences and from teachers about the usability of the methodology in remote sensing training in Tanzania. Based on our experiences and the received feedback, using a participatory mapping campaign as a training method can offer effective learning about environmental remote sensing through a real-world example, as well as networking and knowledge sharing possibilities for the participating group. [ABSTRACT FROM AUTHOR]

With the growth of the Internet, crowdsourcing has become a popular way to perform intelligence tasks that hitherto would be either performed internally within an organization or not undertaken due to prohibitive costs and the lack of an appropriate communications infrastructure. In crowdsourcing systems, whereby multiple agents are not under the direct control of a system designer, it cannot be assumed that agents will act in a manner that is consistent with the objectives of the system designer or principal agent. In situations whereby agents’ goals are to maximize their return in crowdsourcing systems that offer financial or other rewards, strategies will be adopted by agents to game the system if appropriate mitigating measures are not put in place. The motivational and incentivization research space is quite large; it incorporates diverse techniques from a variety of different disciplines including behavioural economics, incentive theory, and game theory. This paper specifically focusses on game theoretic approaches to the problem in the crowdsourcing domain and places it in the context of the wider research landscape. It provides a survey of incentive engineering techniques that enable the creation of apt incentive structures in a range of different scenarios. [ABSTRACT FROM AUTHOR]

Crowdsourcing can efficiently complete tasks that are difficult to automate, but the quality of crowdsourced data is tricky to evaluate. Algorithms to grade volunteer work often assume that all tasks are similarly difficult, an assumption that is frequently false. We use a cropland identification game with over 2,600 participants and 165,000 unique tasks to investigate how best to evaluate the difficulty of crowdsourced tasks and to what extent this is possible based on volunteer responses alone. Inter-volunteer agreement exceeded 90% for about 80% of the images and was negatively correlated with volunteer-expressed uncertainty about image classification. A total of 343 relatively difficult images were independently classified as cropland, non-cropland or impossible by two experts. The experts disagreed weakly (one said impossible while the other rated as cropland or non-cropland) on 27% of the images, but disagreed strongly (cropland vs. non-cropland) on only 7%. Inter-volunteer disagreement increased significantly with inter-expert disagreement. While volunteers agreed with expert classifications for most images, over 20% would have been mis-categorized if only the volunteers' majority vote was used. We end with a series of recommendations for managing the challenges posed by heterogeneous tasks in crowdsourcing campaigns. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Cropland fields are the basic spatial units for agricultural management, and information about their distribution is critical for analyzing agricultural investments and management. However, the extraction of cropland fields of smallholder farms is a challenging task because of their irregular shapes and diverse spectrum. In this paper, we proposed a new object-based Genetic Programming (GP) approach to extract cropland fields. The proposed approach used the multiresolution segmentation (MRS) method to acquire objects from a very high resolution (VHR) image, and extracted spectral, shape and texture features as inputs for GP. Then GP was used to automatically evolve the optimal classifier to extract cropland fields. The results show that the proposed approach has obtained high accuracy in two areas with different landscape complexities. Further analysis show that the GP approach significantly outperforms five commonly used classifiers, including K-Nearest Neighbor (KNN), Decision Tree (DT), Naïve Bayes (NB), Support Vector Machine (SVM), and Random Forest (RF). By using different numbers of training samples, GP can maintain high accuracy with any volume of samples compared to other classifiers. [ABSTRACT FROM AUTHOR]

Accurate and timely crop type mapping and rotation monitoring play a critical role in crop yield estimation, soil management, and food supplies. To date, to our knowledge, accurate mapping of crop types remains challenging due to the intra-class variability of crops and labyrinthine natural conditions. The challenge is further complicated for smallholder farming systems in mountainous areas where field sizes are small and crop types are very diverse. This bottleneck issue makes it difficult and sometimes impossible to use remote sensing in monitoring crop rotation, a desired and required farm management policy in parts of China. This study integrated Sentinel-1 and Sentinel-2 images for crop type mapping and rotation monitoring in Inner Mongolia, China, with an extensive field-based survey dataset. We accomplished this work on the Google Earth Engine (GEE) platform. The results indicated that most crop types were mapped fairly accurately with an F1-score around 0.9 and a clear separation of crop types from one another. Sentinel-1 polarization achieved a better performance in wheat and rapeseed classification among different feature combinations, and Sentinel-2 spectral bands exhibited superiority in soybean and corn identification. Using the accurate crop type classification results, we identified crop fields, changed or unchanged, from 2017 to 2018. These findings suggest that the combination of Sentinel-1 and Sentinel-2 proved effective in crop type mapping and crop rotation monitoring of smallholder farms in labyrinthine mountain areas, allowing practical monitoring of crop rotations. [ABSTRACT FROM AUTHOR]

Dramatic improvements in data availability and quality are needed to meet the challenge of monitoring and analyzing food systems, so that appropriate policies and actions to improve human and planetary health can be identified and data-informed accountability mechanisms put in place to strengthen food systems governance. Studying food systems is complex due to diverse actors and interlinking processes that operate on multiple spatial and temporal scales, and their multiple outcomes, which may be subject to hidden feedback mechanisms and tradeoffs. However, descriptive research to characterize food system components and make comparisons across geography, income groups, and population groups is an important foundation. The first part of this article details a series of critical data gaps and limitations that are currently hindering food systems learning and accountability, also comparing these gaps across regions and income groups. The second part of the article introduces the Food Systems Dashboard, a new data visualization tool that aims to improve access to and usage of food systems-related data, thus strengthening the data value chain and better informing policies and actions intended to improve diets, nutrition, livelihoods, and environmental sustainability. [ABSTRACT FROM AUTHOR]

Remote sensing, or Earth Observation (EO), is increasingly used to understand Earth system dynamics and create continuous and categorical maps of biophysical properties and land cover, especially based on recent advances in machine learning (ML). ML models typically require large, spatially explicit training datasets to make accurate predictions. Training data (TD) are typically generated by digitizing polygons on high spatial-resolution imagery, by collecting in situ data, or by using pre-existing datasets. TD are often assumed to accurately represent the truth, but in practice almost always have error, stemming from (1) sample design, and (2) sample collection errors. The latter is particularly relevant for image-interpreted TD, an increasingly commonly used method due to its practicality and the increasing training sample size requirements of modern ML algorithms. TD errors can cause substantial errors in the maps created using ML algorithms, which may impact map use and interpretation. Despite these potential errors and their real-world consequences for map-based decisions, TD error is often not accounted for or reported in EO research. Here we review the current practices for collecting and handling TD. We identify the sources of TD error, and illustrate their impacts using several case studies representing different EO applications (infrastructure mapping, global surface flux estimates, and agricultural monitoring), and provide guidelines for minimizing and accounting for TD errors. To harmonize terminology, we distinguish TD from three other classes of data that should be used to create and assess ML models: training reference data, used to assess the quality of TD during data generation; validation data, used to iteratively improve models; and map reference data, used only for final accuracy assessment. We focus primarily on TD, but our advice is generally applicable to all four classes, and we ground our review in established best practices for map accuracy assessment literature. EO researchers should start by determining the tolerable levels of map error and appropriate error metrics. Next, TD error should be minimized during sample design by choosing a representative spatio-temporal collection strategy, by using spatially and temporally relevant imagery and ancillary data sources during TD creation, and by selecting a set of legend definitions supported by the data. Furthermore, TD error can be minimized during the collection of individual samples by using consensus-based collection strategies, by directly comparing interpreted training observations against expert-generated training reference data to derive TD error metrics, and by providing image interpreters with thorough application-specific training. We strongly advise that TD error is incorporated in model outputs, either directly in bias and variance estimates or, at a minimum, by documenting the sources and implications of error. TD should be fully documented and made available via an open TD repository, allowing others to replicate and assess its use. To guide researchers in this process, we propose three tiers of TD error accounting standards. Finally, we advise researchers to clearly communicate the magnitude and impacts of TD error on map outputs, with specific consideration given to the likely map audience. [ABSTRACT FROM AUTHOR]

To produce enough food, smallholder farmers in developing countries apply fertilizer nitrogen (N) to cereals, sometimes even more than the local recommendations. During the last two decades, hand-held chlorophyll meters and canopy reflectance sensors, which can detect the N needs of the crop based on transmission and reflectance properties of leaves through proximal sensing, have been studied as tools for optimizing crop N status in cereals in developing countries. This review aims to describe the outcome of these studies. Chlorophyll meters are used to manage fertilizer N to maintain a threshold leaf chlorophyll content throughout the cropping season. Despite greater reliability of the sufficiency index approach, the fixed threshold chlorophyll content approach has been investigated more for using chlorophyll meters in rice and wheat. GreenSeeker and Crop Circle crop reflectance sensors take into account both N status and biomass of the crop to estimate additional fertilizer N requirement but only a few studies have been carried out in developing countries to develop N management strategies in rice, wheat and maize. Both chlorophyll meters and canopy reflectance sensors can increase fertilizer N use efficiency by reduction of N rates. Dedicated economic analysis of the proximal sensing strategies for managing fertilizer N in cereals in developing countries is not adequately available. [ABSTRACT FROM AUTHOR]

OSM2LULC is a software package developed to automatically convert OpenStreetMap (OSM) data into Land Use Land Cover (LULC) maps using Free and Open Source Software for Geospatial (FOSS4G) tools. It needs to be highly efficient given the increasing detail of OSM data and the need to apply it to large extent regions. In this article, a comparison between the implementation of OSM2LULC in different available GIS platforms is made using both vector and raster data structures, which resulted in different versions. A description of the differences of each version is made and, to assess their performance, they were applied to four different study areas with different characteristics, in terms of available OSM data and area size. The performance of each version was evaluated taking into account: the overall processing time required to obtain LULC maps; and differences in the results obtained when different data structures (vector and raster) were used. Results showed that the adoption of a strategy that favors interoperability between FOSS4G and the combined use of both vector and raster data promotes a performance increase. After analysing the topological relationships of OSM data, the conversion to raster data format and the execution of procedural parts with such data indicated significant performance gains, without any positional distortions that significantly compromise the applicability of the final result in further case scenarios. [ABSTRACT FROM AUTHOR]

Mapping the extent and location of field boundaries is critical to food security analysis but remains problematic in the Global South where such information is needed the most. The difficulty is due primarily to fragmentation in the landscape, small farm sizes, and irregular farm boundaries. Very high-resolution satellite imagery affords an opportunity to delineate such fields, but the challenge remains of determining such boundaries in a systematic and accurate way. In this paper, we compare a new crowd-driven manual digitization tool (Crop Land Extent) with two semi-automated methods (contour detection and multi-resolution segmentation) to determine farm boundaries from WorldView imagery in highly fragmented agricultural landscapes of Ethiopia. More than 7000 one square-kilometer image tiles were used for the analysis. The three methods were assessed using quantitative completeness and spatial correctness. Contour detection tended to under-segment when compared to manual digitization, resulting in better performance for larger (approaching 1 ha) sized fields. Multi-resolution segmentation on the other hand, tended to over-segment, resulting in better performance for small fields. Neither semi-automated method in their current realizations however are suitable for field boundary mapping in highly fragmented landscapes. Crowd-driven manual digitization is promising, but requires more oversight, quality control, and training than the current workflow could allow. [ABSTRACT FROM AUTHOR]