Challenges and opportunities in mapping land use intensity globally

Land-use intensification dominates China's land provisioning services: From the perspective of land system science

A pressing challenge to global sustainability is meeting the escalating needs of a growing population while safeguarding land resources from degradation. In recent decades, China's rapid growth, expanding population, urban sprawl, and diminishing high-quality farmland have presented a compelling case suitable for exploring solutions and challenges related to this critical issue. Therefore, there is an urgent need for comprehensive and detailed information regarding land systems. Here, we developed the first fine-scale dataset of the China Land System at a spatial resolution of 1 km, covering the period from 2000 to 2015. By leveraging this comprehensive land information, we identified five primary types of land systems and their respective subsystems, thereby delineating distinct patterns of human-environmental interaction. Land system dynamics followed diverse developmental trajectories characterized by incremental shifts toward more functionally centralized systems. Land use intensification played a significant role in increasing the population capacity and food production in China, contributing nearly 93.94% and 84.99%, respectively. In contrast, land cover changes accounted for only 4.69% and 11.43%, respectively. These findings underscore the tendency of previous studies to overestimate the impact of land cover change and underestimate the influence of land use intensification in meeting the growing demands of land-based production. This study emphasizes the importance of transcending traditional land cover-based approaches and integrating land systems into land representation and global land change scenario simulations to promote sustainability.

Grassland vertical height heterogeneity predicts flower and bee diversity: an UAV photogrammetric approach

The ecosystem services offered by pollinators are vital for supporting agriculture and ecosystem functioning, with bees standing out as especially valuable contributors among these insects. Threats such as habitat fragmentation, intensive agriculture, and climate change are contributing to the decline of natural bee populations. Remote sensing could be a useful tool to identify sites of high diversity before investing into more expensive field survey. In this study, the ability of Unoccupied Aerial Vehicles (UAV) images to estimate biodiversity at a local scale has been assessed while testing the concept of the Height Variation Hypothesis (HVH). This hypothesis states that the higher the vegetation height heterogeneity (HH) measured by remote sensing information, the higher the vegetation vertical complexity and the associated species diversity. In this study, the concept has been further developed to understand if vegetation HH can also be considered a proxy for bee diversity and abundance. We tested this approach in 30 grasslands in the South of the Netherlands, where an intensive field data campaign (collection of flower and bee diversity and abundance) was carried out in 2021, along with a UAV campaign (collection of true color-RGB-images at high spatial resolution). Canopy Height Models (CHM) of the grasslands were derived using the photogrammetry technique "Structure from Motion" (SfM) with horizontal resolution (spatial) of 10 cm, 25 cm, and 50 cm. The accuracy of the CHM derived from UAV photogrammetry was assessed by comparing them through linear regression against local CHM LiDAR (Light Detection and Ranging) data derived from an Airborne Laser Scanner campaign completed in 2020/2021, yielding an [Formula: see text] of 0.71. Subsequently, the HH assessed on the CHMs at the three spatial resolutions, using four different heterogeneity indices (Rao's Q, Coefficient of Variation, Berger-Parker index, and Simpson's D index), was correlated with the ground-based flower and bee diversity and bee abundance data. The Rao's Q index was the most effective heterogeneity index, reaching high correlations with the ground-based data (0.44 for flower diversity, 0.47 for bee diversity, and 0.34 for bee abundance). Interestingly, the correlations were not significantly influenced by the spatial resolution of the CHM derived from UAV photogrammetry. Our results suggest that vegetation height heterogeneity can be used as a proxy for large-scale, standardized, and cost-effective inference of flower diversity and habitat quality for bees.

Both long-term grasslands and crop diversity are needed to limit pest and weed infestations in agricultural landscapes

Increasing landscape heterogeneity has been suggested to be an important strategy to strengthen natural pest control in crops, especially through enhancing the amount of seminatural habitats. Increasing crop diversity is also a promising strategy to complement or replace seminatural habitat when seminatural habitat is scarce. However, their relative or possibly interactive effects on pest and weed infestation remain poorly investigated, and the role of different types of seminatural habitats has been understudied. Using an extensive sampling effort in 974 arable fields across 7 y, we evaluated the separate and interactive effects of crop diversity (seven arable crop types) and the amount of four types of seminatural habitats (meadows, hay, forests, and hedgerows) in the landscape on pest and weed control. Meadows and crop diversity, respectively, supported insect pest and weed control services in agricultural landscapes through a complementarity effect. Crop diversity increased weed seed predation rate (by 16%) and reduced weed infestation (by 6%), whereas long-term grasslands (to a much higher degree than hay or woody habitats) increased insect pest predation rates (by 23%) and reduced pest infestation (by 19%) in most arable crops. Our results demonstrate that diversification of the agricultural landscape requires long-term grasslands as well as improved crop diversity to ensure the delivery of efficient pest and weed control services.

Impacts of anthropogenic disturbance on forest vegetation cover, health, and diversity within Doma forest reserve, Nigeria

Forest encroachment is a common practice that has led to the destruction of canopy trees in the Guinea savanna part of Nigeria. This study investigated the influence of human activities on vegetation health and species composition of Doma forest reserve located in Nasarawa State, Nigeria. Landsat satellite data from 1986 to 2021 were utilized to assess forest cover change, land surface temperature (LST), and vegetation indices (VIs). The results show that dense woodland vegetation in the Doma forest reserve depreciated between 1991 and 1999 by 17.82% before increasing by 7.37% between 1999 and 2021. Similarly, vegetation greenness (measured by the Normalized Difference Vegetation Index (NDVI), Green Chlorophyll Vegetation Index (GCVI), and leaf area index (LAI)) of the forest mirrored the changes observed in the forest cover. The LST extracted for each year was correlated with all VIs, and an inverse relationship was observed in all relationships analyzed. The decline in greenness between 1999 and 2011 was attributed to increasing lumbering, bush burning, and sand dredging activities. Results also showed the current diversity state (H1 = 0.23), evenness (0.63), and the volume of tree (1.31 m3) species in the heart of the Doma forest reserve. However, a high (25%) native tree species in the Fabaceae family correlated with a dramatic increase in the VIs and an increase in dense woodland cover indicating the importance of Fabaceae in forest ecosystem regeneration.

Spatial differences and influence mechanisms of construction land development intensity in China, 2002-2020

Construction land development intensity is a spatial mapping of modern urbanization level, which integrally reflects urban development strategy, land use efficiency, and population carrying intensity. This article analyzed the spatial and temporal evolution of construction land development intensity using panel data of 31 provincial administrative divisions in China from 2002 to 2020, with the application of the Theil index and spatial autocorrelation. To further investigate the relationship between human activities and land development, the article used geographic detectors to analyze the influencing mechanisms. The results showed that: (1) The average intensity of construction land development of Chinese provinces from 2002 to 2020 showed a trend of "steady increase, a short decline, and then a steady increase," and there were significant differences in the characteristics of construction land development intensity changes in different regions. (2) The regional differences in construction land development intensity between provinces showed a decreasing trend. There were uneven differences among regions, with more minor regional differences in Central, South, and North China but more significant differences in Northwest, East, Southwest, and Northeast China. (3) The spatial agglomeration of construction land development intensity in the region increased initially and then decreased during the study period. The overall pattern was "small agglomeration and large dispersion." (4) Economic development factors such as GDP per land, industrial structure, and fixed asset investment completion significantly affect land development intensity. The interaction between the factors was apparent, and the effect of "1 + 1 > 2" was produced. Based on the study's results, it is suggested that scientific regional development planning, guiding inter-provincial factor flow, and rational control of land development efforts are the key to promoting sustainable regional development.

Mapping abandoned cropland using Within-Year Sentinel-2 time series

Against the background of the COVID-19 pandemic and various armed conflicts, the world is experiencing an unprecedented food crisis. The reclamation of abandoned cropland with food production potential may increase the global food supply in a short period of time, ensuring food security. At present, the extraction of abandoned cropland is mainly based on low- and medium-resolution remote sensing image data, making it difficult to extract fragmented areas in mountainous regions and to distinguish between abandoned cropland and transitional classes (such as fallow cropland). We developed a change-detection method based on within-year Sentinel-2 time series to extract cropland abandoned from 2018 to 2021 and defined four types of croplands, namely spontaneously abandoned, induced abandoned, fallow, and lost cropland, using Linxia County in mountainous China as the study region. First, cropland objects were generated from multi-temporal Sentinel-2 images using the multi-resolution segmentation method, and the land use map of Linxia County from 2017 to 2021 was drawn using random forest classifier. Second, through defining and identifying different cropland types, the interannual dynamic changes in cropland from 2018 to 2021 were extracted by analyzing the annual land use change trajectory. Third, by analyzing the normalized difference vegetation index (NDVI) time series of cropland within-year, the active and cultivated cropland sites within-year were extracted by threshold segmentation. Finally, the changes in the four cropland types were extracted by intersecting the two result types. Our method captured the object level changes well (overall mapping accuracy = 93 ± 5 %), and the extraction accuracy of abandoned cropland reached 81 ± 2 %. Abandoned cropland was mostly located in areas of medium quality and with a moderate distance from rural settlements. Reclamation can potentially increase the grain production in Linxia County by at least 3.6 % and needs to be combined with the local natural geography and human activities. Our method is a robust method for extracting abandoned cropland and may be applied to other research related to land use change.

Beyond pastures, look at plastic: Using Sentinel-2 imagery to map silage bags to improve understanding of cattle intensity

Cattle ranching has increased globally in the last decades, and although pasture expansion is well documented across different regions, there is little understanding of the intensity at which cattle operate in these areas. With freely available Sentinel-2 satellite imagery, we mapped for the first time polyethylene silage bags used for forage conservation in a year with the Random Forest algorithm, and proposed them as a spatial indicator of cattle intensity. For this, we combined monthly silage area with land cover and climatic variables in a regression framework to understand cattle intensity metrics at regional and farm scales throughout 20 million hectares in the Dry Chaco. In addition, we explored the impact of using maize silage supplementation on productive and environmental metrics at the farm scale in a precipitation gradient. We validated our models using a spatially explicit database of cattle distribution. Our results highlight that silage bags are accurate mappable objects with Sentinel-2, which can contribute to the understanding of cattle density, and heifer and steer density in pasture contexts at farm and regional scales. Finally, our whole-farm simulations support the idea that incorporating silage supplementation in cattle ranching regional analyses conducts to significant differences on environmental or productive estimations, which should be considered. The amount of stored forage that is used in supplementation has strong implications for the performance of cattle ranching, but remains difficult to quantify at the regional level with remote sensing. Silage bag mapping is thus an opportunity to improve the overall understanding of livestock intensification and its productive and environmental impacts, particularly in highly seasonal rangelands. Following this metric could be a valuable indicator of the cattle ranching performance in terms of it resilience, production increase and impacts over natural ecosystems (related to Sustainable Development Goal 2-zero hunger and also in the 15-life on land).

Classification of land use/land cover using artificial intelligence (ANN-RF)

Because deep learning has various downsides, such as complexity, expense, and the need to wait longer for results, this creates a significant incentive and impetus to invent and adopt the notion of developing machine learning because it is simple. This study intended to increase the accuracy of machine-learning approaches for land use/land cover classification using Sentinel-2A, and Landsat-8 satellites. This study aimed to implement a proposed method, neural-based with object-based, to produce a model addressed by artificial neural networks (limited parameters) with random forest (hyperparameter) called ANN_RF. This study used multispectral satellite images (Sentinel-2A and Landsat-8) and a normalized digital elevation model as input datasets for the Sana'a city map of 2016. The results showed that the accuracy of the proposed model (ANN_RF) is better than the ANN classifier with the Sentinel-2A and Landsat-8 satellites individually, which may contribute to the development of machine learning through newer researchers and specialists; it also conventionally developed traditional artificial neural networks with seven to ten layers but with access to 1,000's and millions of simulated neurons without resorting to deep learning techniques (ANN_RF).

Distinguishing the impacts of land use change in intensity and type on ecosystem services trade-offs

Changes in land use intensity and types can affect the structure and function of ecosystems, and thus ecosystem services (ESs) as well as their interactions. However, the impacts of changes in land use intensity on ESs remain poorly understood. Through four different land use scenarios, we distinguished the independent contribution of changes in agricultural land use intensity and types to grain production (GP), water purification (WP), and their trade-offs in the Dongting Lake Basin. The results showed that from 1990 to 2015, GP increased across 58.07% of the total area, but WP decreased across 64.81% of the study area. The two ESs simultaneously increased or decreased across 41.93% of the total area. Watersheds covering 48.72% of the study area where GP increased and WP decreased were mainly distributed in areas with increased land use intensity. The other regions where GP decreased and WP increased were mainly distributed in areas with decreased land use intensity. The scenario analysis of GP, WP, and their trade-offs showed that the areas where agricultural land use intensity was the dominant factor were as large as 1.95 times, 2.38 times, and 2.43 times those dominated by land use type respectively, under the same climate conditions. This study highlighted the importance of changes in agricultural land use intensity on ES, which provided further supporting to ES-based land use management.

Identifying Land-Use Related Potential Disaster Risk Drivers in the Ayeyarwady Delta (Myanmar) during the Last 50 Years (1974–2021) Using a Hybrid Ensemble Learning Model

Land-use and land-cover change (LULCC) dynamics significantly impact deltas, which are among the world’s most valuable but also vulnerable habitats. Non-risk-oriented LULCCs can act as disaster risk drivers by increasing levels of exposure and vulnerability or by reducing capacity. Making thematically detailed long-term LULCC data available is crucial to improving understanding of those dynamics interlinked at different spatiotemporal scales. For the Ayeyarwady Delta, one of the least studied mega-deltas, such comprehensive information is still lacking. This study used 50 Landsat and Sentinel-1A images spanning five decades from 1974 to 2021 in five-year intervals. A hybrid ensemble model consisting of six machine-learning classifiers was employed to generate land-cover maps from the images, achieving accuracies of about 90%. The major identified potential risk-relevant LULCC dynamics include urban growth towards low-lying areas, mangrove deforestation, and the expansion of irrigated agricultural areas and cultivated aquatic surfaces. The novel area-wide LULCC products achieved through the analyses provide a basis to support future risk-sensitive development decisions and can be used for regionally adapted disaster risk management plans and models. Developed with freely available data and open-source software, they hold great potential to increase research activity in the Ayeyarwady Delta and will be shared upon request.

Land Change Science and the STEPLand Framework: An Assessment of Its Progress

This contribution assesses a new term that is proposed to be established within Land Change Science: Spatio-TEmporal Patterns of Land (‘STEPLand’). It refers to a specific workflow for analyzing land-use/land cover (LUC) patterns, identifying and modeling driving forces of LUC changes, assessing socio-environmental consequences, and contributing to defining future scenarios of land transformations. In this article, we define this framework based on a comprehensive meta-analysis of 250 selected articles published in international scientific journals from 2000 to 2019. The empirical results demonstrate that STEPLand is a consolidated protocol applied globally, and the large diversity of journals, disciplines, and countries involved shows that it is becoming ubiquitous. In this paper, the main characteristics of STEPLand are provided and discussed, demonstrating that the operational procedure can facilitate the interaction among researchers from different fields, and communication between researchers and policy makers.

Driving Mechanism of Habitat Quality at Different Grid-Scales in a Metropolitan City

Urban ecosystem dysfunction, habitat fragmentation, and biodiversity loss caused by rapid urbanization have threatened sustainable urban development. Urban habitat quality is one of the important indicators for assessing the urban ecological environment. Therefore, it is of great practical significance to carry out a study on the driving mechanism of urban habitat quality and integrate the results into urban planning. In this study, taking Zhengzhou, China, as an example, the InVEST model was used to analyze the spatial differentiation characteristics of urban habitat quality and Geodetector software was adopted to explore the driving mechanism of habitat quality at different grid-scales. The results show the following: (1) LUCC, altitude, slope, surface roughness, relief amplitude, population, nighttime light, and NDVI are the dominant factors affecting the spatial differentiation of habitat quality. Among them, the impacts of slope, surface roughness, population, nighttime light, and NDVI on habitat quality are highly sensitive to varying grid-scales. At the grid-scale of 1000 to 1250 m, the impacts of the dominant factors on habitat quality is closer to the mean level of multiple scales. (2) The impact of each factor on the spatial distribution of habitat quality is different, and the difference between most factors has always been significant regardless of the variation of grid-scales. The superimposed impact of two factors on the spatial distribution of habitat quality is greater than the impact of the single factor. (3) Combined with the research results and the local conditions of Zhengzhou, we put forward some directions of habitat protection around adjusting urban land use structure, applying nature-based solutions and establishing a systematic thinking model for multi-level urban habitat sustainability.

Relative effects of land conversion and land-use intensity on terrestrial vertebrate diversity

Land-use has transformed ecosystems over three quarters of the terrestrial surface, with massive repercussions on biodiversity. Land-use intensity is known to contribute to the effects of land-use on biodiversity, but the magnitude of this contribution remains uncertain. Here, we use a modified countryside species-area model to compute a global account of the impending biodiversity loss caused by current land-use patterns, explicitly addressing the role of land-use intensity based on two sets of intensity indicators. We find that land-use entails the loss of ~15% of terrestrial vertebrate species from the average 5 × 5 arcmin-landscape outside remaining wilderness areas and ~14% of their average native area-of-habitat, with a risk of global extinction for 556 individual species. Given the large fraction of global land currently used under low land-use intensity, we find its contribution to biodiversity loss to be substantial (~25%). While both sets of intensity indicators yield similar global average results, we find regional differences between them and discuss data gaps. Our results support calls for improved sustainable intensification strategies and demand-side actions to reduce trade-offs between food security and biodiversity conservation.

Land use intensification increasingly drives the spatiotemporal patterns of the global human appropriation of net primary production in the last century

Land use has greatly transformed Earth's surface. While spatial reconstructions of how the extent of land cover and land-use types have changed during the last century are available, much less information exists about changes in land-use intensity. In particular, global reconstructions that consistently cover land-use intensity across land-use types and ecosystems are missing. We, therefore, lack understanding of how changes in land-use intensity interfere with the natural processes in land systems. To address this research gap, we map land-cover and land-use intensity changes between 1910 and 2010 for 9 points in time. We rely on the indicator framework of human appropriation of net primary production (HANPP) to quantify and map land-use-induced alterations of the carbon flows in ecosystems. We find that, while at the global aggregate level HANPP growth slowed down during the century, the spatial dynamics of changes in HANPP were increasing, with the highest change rates observed in the most recent past. Across all biomes, the importance of changes in land-use areas has declined, with the exception of the tropical biomes. In contrast, increases in land-use intensity became the most important driver of HANPP across all biomes and settings. We conducted uncertainty analyses by modulating input data and assumptions, which indicate that the spatial patterns of land use and potential net primary production are the most critical factors, while spatial allocation rules and uncertainties in overall harvest values play a smaller role. Highlighting the increasing role of land-use intensity compared to changes in the areal extent of land uses, our study supports calls for better integration of the intensity dimension into global analyses and models. On top of that, we provide important empirical input for further analyses of the sustainability of the global land system.

An Outcome-Oriented, Social–Ecological Framework for Assessing Protected Area Effectiveness

Both the number and the extent of protected areas have grown considerably in recent years, but evaluations of their effectiveness remain partial and are hard to compare across cases. To overcome this situation, first, we suggest reserving the term effectiveness solely for assessing protected area outcomes, to clearly distinguish this from management assessments (e.g., sound planning). Second, we propose a multidimensional conceptual framework, rooted in social–ecological theory, to assess effectiveness along three complementary dimensions: ecological outcomes (e.g., biodiversity), social outcomes (e.g., well-being), and social–ecological interactions (e.g., reduced human pressures). Effectiveness indicators can subsequently be evaluated against contextual and management elements (e.g., design and planning) to shed light on management performance (e.g., cost-effectiveness). We summarize steps to operationalize our framework to foster more holistic effectiveness assessments while improving comparability across protected areas. All of this can ensure that protected areas make real contributions toward conservation and sustainability goals.

Land-use change and biodiversity: Challenges for assembling evidence on the greatest threat to nature

Land-use change is considered the greatest threat to nature, having caused worldwide declines in the abundance, diversity, and health of species and ecosystems. Despite increasing research on this global change driver, there are still challenges to forming an effective synthesis. The estimated impact of land-use change on biodiversity can depend on location, research methods, and taxonomic focus, with recent global meta-analyses reaching disparate conclusions. Here, we critically appraise this research body and our ability to reach a reliable consensus. We employ named entity recognition to analyze more than 4000 abstracts, alongside full reading of 100 randomly selected papers. We highlight the broad range of study designs and methodologies used; the most common being local space-for-time comparisons that classify land use in situ. Species metrics including abundance, distribution, and diversity were measured more frequently than complex responses such as demography, vital rates, and behavior. We identified taxonomic biases, with vertebrates well represented while detritivores were largely missing. Omitting this group may hinder our understanding of how land-use change affects ecosystem feedback. Research was heavily biased toward temperate forested biomes in North America and Europe, with warmer regions being acutely underrepresented despite offering potential insights into the future effects of land-use change under novel climates. Various land-use histories were covered, although more research in understudied regions including Africa and the Middle East is required to capture regional differences in the form of current and historical land-use practices. Failure to address these challenges will impede our global understanding of land-use change impacts on biodiversity, limit the reliability of future projections and have repercussions for the conservation of threatened species. Beyond identifying literature biases, we highlight the research priorities and data gaps that need urgent attention and offer perspectives on how to move forward.

Global Assessment of Agricultural Productivity Losses from Soil Compaction and Water Erosion

To guide us toward a sustainable future, the impacts of human activities on natural resources need to be understood and quantified. In this study on global agriculture, we use a Life Cycle Assessment framework to estimate potential long-term soil productivity losses caused by soil compaction and water erosion due to agricultural crop production. We combine several data sets to model spatially resolved Life Cycle Inventory information at the global level and multiply results with characterization factors from a previous publication. The global picture shows a compaction-stressed "Global North" and an erosion-stressed "Global South", with some countries and regions in between, for example, China and parts of South America. Results show that both compaction and water erosion impacts matter at the global level and that overall potential long-term productivity losses of 10-20% can be expected, with high relative impacts on low input production systems. These losses might limit long-term agricultural productivity and lead to additional land use change. Our work adds to and extends the discussion of global assessments of soil degradation. Furthermore, we prove the suggested framework to be applicable and useful for Life Cycle Assessments and other studies and provide results that can be used in such global assessments.

Modelling land system evolution and dynamics of terrestrial carbon stocks in the Luanhe River Basin, China: a scenario analysis of trade-offs and synergies between sustainable development goals

A more holistic understanding of land use and land cover (LULC) will help minimise trade-offs and maximise synergies, and lead to improved future land use management strategies for the attainment of Sustainable Development Goals (SDGs). However, current assessments of future LULC changes rarely focus on the multiple demands for goods and services, which are related to the synergies and trade-offs between SDGs and their targets. In this study, the land system (combinations of land cover and land use intensity) evolution trajectories of the Luanhe River Basin (LRB), China, and major challenges that the LRB may face in 2030, were explored by applying the CLUMondo and InVEST models. The results indicate that the LRB is likely to experience agricultural intensification and urban growth under all four scenarios that were explored. The cropland intensity and the urban growth rate were much higher under the historical trend (Trend) scenario compared to those with more planning interventions (Expansion, Sustainability, and Conservation scenarios). Unless the forest area and biodiversity conservation targets are implemented (Conservation scenario), the forest areas are projected to decrease by 2030. The results indicate that water scarcity in the LRB is likely to increase under all scenarios, and the carbon storage will increase under the Conservation scenario but decrease under all other scenarios by 2030. Our methodological framework and findings can guide regional sustainable development in the LRB and other large river basins in China, and will be valuable for policy and planning purposes to the pursuance of SDGs at the sub-national scale.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11625-021-01004-y.

Detect, Consolidate, Delineate: Scalable Mapping of Field Boundaries Using Satellite Images

Digital agriculture services can greatly assist growers to monitor their fields and optimize their use throughout the growing season. Thus, knowing the exact location of fields and their boundaries is a prerequisite. Unlike property boundaries, which are recorded in local council or title records, field boundaries are not historically recorded. As a result, digital services currently ask their users to manually draw their field, which is time-consuming and creates disincentives. Here, we present a generalized method, hereafter referred to as DECODE (DEtect, COnsolidate, and DElinetate), that automatically extracts accurate field boundary data from satellite imagery using deep learning based on spatial, spectral, and temporal cues. We introduce a new convolutional neural network (FracTAL ResUNet) as well as two uncertainty metrics to characterize the confidence of the field detection and field delineation processes. We finally propose a new methodology to compare and summarize field-based accuracy metrics. To demonstrate the performance and scalability of our method, we extracted fields across the Australian grains zone with a pixel-based accuracy of 0.87 and a field-based accuracy of up to 0.88 depending on the metric. We also trained a model on data from South Africa instead of Australia and found it transferred well to unseen Australian landscapes. We conclude that the accuracy, scalability and transferability of DECODE shows that large-scale field boundary extraction based on deep learning has reached operational maturity. This opens the door to new agricultural services that provide routine, near-real time field-based analytics.

Distinguishing anthropogenic and natural contributions to coproduction of national crop yields globally

Crop production is a crucial ecosystem service that requires a combination of natural and anthropogenic contributions to high and stable yields, which is a coproduction process. We analysed this coproduction based on nationally aggregated data for 15 major crops for 67 countries and the European Union with data for four time steps (2000, 2006, 2010, 2014). We found strong increases in fertilizer use, net capital stock and manure use intensity for lower-middle-income countries and stagnation or decrease of these for high-income countries. We used a multiple linear regression model predicting yield to distinguish the effect of anthropogenic contributions (crop-specific fertilizer use intensity, net capital stock intensity, manure use intensity) and natural contributions (crop-specific agricultural suitability, including soil characteristics, topography and climate). We found that in particular fertilizer use intensity, manure use intensity and agricultural suitability explained variation in yields to a considerable degree (R² = 0.62).

Agricultural Land Use Change in Chongqing and the Policy Rationale behind It: A Multiscale Perspective

Agricultural land resources have been the central issue for the Chinese government in its attempts to secure food and agricultural sustainability. Yet strict land use control does not protect the agricultural land from erosion by urban expansion. Identifying the specific patterns and mechanisms of the agricultural land conversion, thus, is critical for land management and related decision making. Based on the annual nominal 30 m land use/land cover datasets (called CLUD-A), this study goes below the national/regional level to examine agricultural land conversion in Chongqing from a multiscale perspective. At the metropolis and its subdivision’s scales, the volume of the conversion area has been generally increasing, from 122.40 km2 in 1980–1990, 162.26 km2 in 1990–2000, and 706.14 km2 in 2000–2010, to 684.83 km2 in 2010–2015. Such a conversion in the main city area and its surroundings far outweighed that in the rural outskirts, as 68.9% (1990–2000), 92.2% (2000–2010), and 82.7% (2010–2015) of the conversion happened in the former. Moreover, values of Gini coefficients and coefficient of variation (CV) based on the county/district scale (Gini [0.46, 0.64], CV [0.69, 0.99] throughout the four periods) are much lower than those based on the town/village scale (Gini [0.88, 0.94], CV [3.18, 4.47] throughout the four periods), suggesting the uneven extent of spatial distribution of the agricultural land conversion trickles down along with the downscale of administration: the lower the administrative level, the more severe the unbalance. The policy rationale behind this transition is also discussed. This research argues for tangible approaches to a sustainable rural-urban transformation.

Post-Soviet shifts in grazing and fire regimes changed the functional plant community composition on the Eurasian steppe

Globally, grasslands are shaped by grazing and fire, and grassland plants are adapted to these disturbances. However, temperate grasslands have been hotspots of land-use change, and how such changes affect interrelations between herbivory, fire and vegetation are poorly understood. Such land-use changes are widespread on the Eurasian steppe, where the dissolution of the Soviet Union in 1991 triggered the abandonment of cropland and pasture on globally relevant scales. Thus, to determine how relationships between plant functional composition, grazing and fire patterns changed after the Soviet Union dissolved, we studied a 358,000 km² region in the dry steppe of Kazakhstan, combining a large field dataset on plant functional traits with multi-scale satellite data. We found that increases in burned area corresponded to decreases in livestock grazing across large areas. Furthermore, fires occurred more often with high cover of grasses with high leaf dry matter content and thus higher flammability, whereas higher grazing pressure favoured grazing-tolerant woody forbs and ruderal plants with high specific leaf area. The current situation of low grazing pressure represents a historically exceptional, potentially non-analogue state. We suggest that the dissolution of the Soviet Union caused the disturbance regime to shift from grazer to fire control. As grazing and fire each result in different plant functional compositions, we propose that this led to widespread increases in grasses and associated changes in steppe plant community structure. These changes have potentially occurred across an area of more than 2 million km² , representing much of the world's largest temperate grassland area, with globally relevant, yet poorly understood implications for biodiversity and ecosystem functions such as carbon cycling. Additionally, future steppe management must also consider positive implications of abandonment ('rewilding') because reverting the regime shift in disturbance and associated changes in vegetation would require grazing animals to be reintroduced across vast areas.

Identifying Agricultural Frontiers for Modeling Global Cropland Expansion

The increasing expansion of cropland is major driver of global carbon emissions and biodiversity loss. However, predicting plausible future global distributions of croplands remains challenging. Here, we show that, in general, existing global data aligned with classical economic theories of expansion explain the current (1992) global extent of cropland reasonably well, but not recent expansion (1992-2015). Deviations from models of cropland extent in 1992 ("frontierness") can be used to improve global models of recent expansion, most likely as these deviations are a proxy for cropland expansion under frontier conditions where classical economic theories of expansion are less applicable. Frontierness is insensitive to the land cover dataset used and is particularly effective in improving models that include mosaic land cover classes and the largely smallholder-driven frontier expansion occurring in such areas. Our findings have important implications as the frontierness approach offers a straightforward way to improve global land use change models.

Improving Predictions of Climate Change-Land Use Change Interactions

Climate change and land use change often interact, altering biodiversity in unexpected ways. Research into climate change-land use change (CC-LUC) interactions has so far focused on quantifying biodiversity outcomes, rather than identifying the underlying ecological mechanisms, making it difficult to predict interactions and design appropriate conservation responses. We propose a risk-based framework to further our understanding of CC-LUC interactions. By identifying the factors driving the exposure and vulnerability of biodiversity to land use change, and then examining how these factors are altered by climate change (or vice versa), this framework will allow the effects of different interaction mechanisms to be compared across geographic and ecological contexts, supporting efforts to reduce biodiversity loss from interacting stressors.

Remote Sensing of Grassland Production and Management—A Review

Grasslands cover one third of the earth’s terrestrial surface and are mainly used for livestock production. The usage type, use intensity and condition of grasslands are often unclear. Remote sensing enables the analysis of grassland production and management on large spatial scales and with high temporal resolution. Despite growing numbers of studies in the field, remote sensing applications in grassland biomes are underrepresented in literature and less streamlined compared to other vegetation types. By reviewing articles within research on satellite-based remote sensing of grassland production traits and management, we describe and evaluate methods and results and reveal spatial and temporal patterns of existing work. In addition, we highlight research gaps and suggest research opportunities. The focus is on managed grasslands and pastures and special emphasize is given to the assessment of studies on grazing intensity and mowing detection based on earth observation data. Grazing and mowing highly influence the production and ecology of grassland and are major grassland management types. In total, 253 research articles were reviewed. The majority of these studies focused on grassland production traits and only 80 articles were about grassland management and use intensity. While the remote sensing-based analysis of grassland production heavily relied on empirical relationships between ground-truth and satellite data or radiation transfer models, the used methods to detect and investigate grassland management differed. In addition, this review identified that studies on grassland production traits with satellite data often lacked including spatial management information into the analyses. Studies focusing on grassland management and use intensity mostly investigated rather small study areas with homogeneous intensity levels among the grassland parcels. Combining grassland production estimations with management information, while accounting for the variability among grasslands, is recommended to facilitate the development of large-scale continuous monitoring and remote sensing grassland products, which have been rare thus far.

Decoupling of Land Use Intensity and Ecological Environment in Gansu Province, China

Land is the carrier of the production and living activities of human society and the basis of survival and development of all living organisms. With the continuous development of the social economy, the unreasonable use of land is becoming more and more serious, aggravating the deterioration of the ecological environment. Most studies in this field have mainly focused on land use changes and the corresponding impacts on the ecological environment, but relatively few studies have delinked the relationship between land use intensity and the ecological environment. Based on data on these two factors for Gansu Province from 1998 to 2017, we used the Tapio decoupling model to evaluate the decoupling relationship between land use intensity and ecological environment. From 1998 to 2017, the comprehensive land use intensity in Gansu province increased by 107.77%, and the comprehensive ecological environment index increased by 63.76%. In general, the relationship between land use intensity and ecological environment experienced five states, namely weak decoupling, strong negative decoupling, strong decoupling, expansive negative decoupling, and declining decoupling. During 1999–2013 and 2013–2016, land use intensity and ecological environment had decoupled, and the main reasons were as follows: (1) The Chinese government introduced a series of farmland protection policies and measures, controlled the area of newly added construction land, and reduced urban land expansion; (2) ecological restoration projects for mountains, forests, fields, lakes, and grassland strengthened the environmental protection in Gansu Province; and (3) in the process of economic development, the increased investment of technology and capital improved the land use efficiency, finally realizing the “double growth” of land use intensity and environmental quality. Based on these results, land use intensity and environmental quality are not necessarily contradictory, and a moderate improvement of land use efficiency and environmental protection can probably result in increased land use intensity and higher environmental quality.

Assessing the Link between Human Modification and Changes in Land Surface Temperature in Hainan, China Using Image Archives from Google Earth Engine

In many areas of the world, population growth and land development have increased demand for land and other natural resources. Coastal areas are particularly susceptible since they are conducive for marine transportation, energy production, aquaculture, marine tourism and other activities. Anthropogenic activities in the coastal areas have triggered unprecedented land use change, depletion of coastal wetlands, loss of biodiversity, and degradation of other vital ecosystem services. The changes can be particularly drastic for small coastal islands with rich biodiversity. In this study, the influence of human modification on land surface temperature (LST) for the coastal island Hainan in Southern China was investigated. We hypothesize that for this island, footprints of human activities are linked to the variation of land surface temperature, which could indicate environmental degradation. To test this hypothesis, we estimated LST changes between 2000 and 2016 and computed the spatio-temporal correlation between LST and human modification. Specifically, we classified temperature data for the four years 2000, 2006, 2012 and 2016 into 5 temperature zones based on their respective mean and standard deviation values. We then assessed the correlation between each temperature zone and a human modification index computed for the year 2016. Apart from this, we estimated mean, maximum and the standard deviation of annual temperature for each pixel in the 17 years to assess the links with human modification. The results showed that: (1) The mean LST temperature in Hainan Island increased with fluctuations from 2000 to 2016. (2) The moderate temperature zones were dominant in the island during the four years included in this study. (3) A strong positive correlation of 0.72 between human modification index and mean and maximum LST temperature indicated a potential link between human modification and mean and maximum LST temperatures over the 17 years of analysis. (4) The mean value of human modification index in the temperature zones in 2016 showed a progressive rise with 0.24 in the low temperature zone, 0.33 in the secondary moderate, 0.45 in the moderate, 0.54 in the secondary high and 0.61 in the high temperature zones. This work highlighted the potential value of using large and multi-temporal earth observation datasets from cloud platforms to assess the influence of human activities in sensitive ecosystems. The results could contribute to the development of sustainable management and coastal ecosystems conservation plans.

Assessment of yield gaps on global grazed-only permanent pasture using climate binning

To meet rising demands for agricultural products, existing agricultural lands must either produce more or expand in area. Yield gaps (YGs)-the difference between current and potential yield of agricultural systems-indicate the ability to increase output while holding land area constant. Here, we assess YGs in global grazed-only permanent pasture lands using a climate binning approach. We create a snapshot of circa 2000 empirical yields for meat and milk production from cattle, sheep, and goats by sorting pastures into climate bins defined by total annual precipitation and growing degree-days. We then estimate YGs from intra-bin yield comparisons. We evaluate YG patterns across three FAO definitions of grazed livestock agroecosystems (arid, humid, and temperate), and groups of animal production systems that vary in animal types and animal products. For all subcategories of grazed-only permanent pasture assessed, we find potential to increase productivity several-fold over current levels. However, because productivity of grazed pasture systems is generally low, even large relative increases in yield translated to small absolute gains in global protein production. In our dataset, milk-focused production systems were found to be seven times as productive as meat-focused production systems regardless of animal type, while cattle were four times as productive as sheep and goats regardless of animal output type. Sustainable intensification of pasture is most promising for local development, where large relative increases in production can substantially increase incomes or "spare" large amounts of land for other uses. Our results motivate the need for further studies to target agroecological and economic limitations on productivity to improve YG estimates and identify sustainable pathways toward intensification.

Quantifying Land Use/Land Cover and Landscape Pattern Changes and Impacts on Ecosystem Services

Based on satellite remote sensing image, GIS and Fragstats, this study modeled and calculated the dynamic changes of land use, land cover and landscape patterns in Guizhou Province, China, and calculated the changes of ecosystem service values (ESVs). The impacts of the evolution of landscape patterns on the ESVs were analyzed, and reasonable policy recommendations were made. The findings are as follows: (1) In the past two decades, the area of cropland and grassland has decreased; the area of water bodies, urban and rural, industrial and mining, and residential areas has increased; the area of forestland has increased first and then decreased. (2) The two major types of landscapes, cropland and grassland, are clearly being replaced by two land types, forest land and water bodies. (3) Overall, the degree of landscape aggregation and adjacency has decreased, and the landscape heterogeneity has increased. (4) The total amount of ESV in 2000, 2008, 2013 and 2017 was 2574 × 108 Yuan RMB, 2605 × 108 Yuan RMB, 2618 × 108 Yuan RMB and 2612 × 108 Yuan RMB, respectively. The changes of landscape patterns had important impacts on the ESVs. In order to solve the problems caused by the increasingly prominent changes in the landscape patterns and improve the ESVs, it is necessary to rationally plan and allocate land resources, optimize the industrial structures, and develop effective regulatory policies.

Use of Intensity Analysis to Characterize Land Use/Cover Change in the Biggest Island of Persian Gulf, Qeshm Island, Iran

In this study, land use/cover change was systematically investigated in the Qeshm Island to understand how human and nature interact in the largest island of Persian Gulf. Land-use maps were prepared for 1996, 2002, 2008, and 2014 using Landsat satellite imagery in six classes including agriculture, bare-land, built-up, dense-vegetation, mangrove, and water-body, and then dynamic of changes in the classes was evaluated using intensity analysis at three levels: interval, category, and transition. Results illustrated that, while the land changes were fast over the first and third time intervals (1996–2002 and 2008–2014), the trend of changes was slow in the second period (2002–2008). Driven by high demand for construction and population growth, the built-up class was identified as an active gainer in all the three time intervals. The class of bare-land was the main supplier of the land for other classes especially for built-up area, while built-up did not act as the active supplier of the land for other classes. The dense-vegetation class was active in all three time intervals. As for the mangrove class, drought and cutting by residents had negative effects, while setting up protected areas can effectively maintain this valuable ecosystem. High demands were observed for land change in relation to built-up and agriculture classes among other classes. The findings of this study can advance our understanding of the relationship and behavior of land use/cover classes among each other over 18 years in a coastal island with arid climate.

Spatiotemporal Differentiation and the Factors of Ecological Vulnerability in the Toutun River Basin Based on Remote Sensing Data

Ecological vulnerability assessment increases the knowledge of ecological status and contributes to formulating local plans of sustainable development. A methodology based on remote sensing data and spatial principal component analysis was introduced to discuss ecological vulnerability in the Toutun River Basin (TRB). Exploratory spatial data analysis and a geo-detector were employed to evaluate the spatial and temporal distribution characteristics of ecological vulnerability and detect the driving factors. Four results were presented: (1) During 2003 and 2017, the average values of humidity, greenness, and heat in TRB increased by 49.71%, 11.63%, and 6.51% respectively, and the average values of dryness decreased by 165.24%. However, the extreme differences in greenness, dryness, and heat tended to be obvious. (2) The study area was mainly dominated by a high and extreme vulnerability grade, and the ecological vulnerability grades showed the distribution pattern that the northern desert area was more vulnerable than the central artificial oasis, and the central artificial oasis was more vulnerable than the southern mountainous area. (3) Ecological vulnerability in TRB showed significant spatial autocorrelation characteristics, and the trend was enhanced. The spatial distribution of hot/cold spots presented the characteristics of “hot spot—cold spot—secondary hot spot—cold spot” from north to south. (4) The explanatory power of each factor of ecological vulnerability was temperature (0.5955) > land use (0.5701) > precipitation (0.5289) > elevation (0.4879) > slope (0.3660) > administrative division (0.1541). The interactions of any two factors showed a non-linear strengthening effect, among which, land use type ∩ elevation (0.7899), land use type ∩ precipitation (0.7867), and land use type ∩ temperature (0.7791) were the significant interaction for ecological vulnerability. Overall, remote sensing data contribute to realizing a quick and objective evaluation of ecological vulnerability and provide valuable information for decision making concerning ecology management and region development.

Conceptual Framework for Biodiversity Assessments in Global Value Chains

Land use and land use change are among the main drivers of the ongoing loss of biodiversity at a global-scale. Although there are already Life Cycle Impact Assessment (LCIA) methods to measure this impact, they are still rarely used by companies and municipalities in the life cycle assessment of products and processes. Therefore, this paper highlights four main requirements for a biodiversity methodological framework within LCIA in order to facilitate biodiversity assessments: first, to consider the global uneven distribution of biodiversity and its risks with respect to vulnerability and irreplaceability; second, to account for the need to regionalize the impacts of land use; third, to consider the specific impacts that different land use types have on biodiversity; and fourth, to analyze the biodiversity impacts of different land use management parameters and their influence on the intensity of land use. To this end, we provided a review of existing methods in respect to conformity and research gaps. The present publication describes the development of a new methodological framework that builds on these requirements in a three-level hierarchical framework, which enables the assessment of biodiversity in LCA at a global-scale. This publication reveals research gaps regarding the inclusion of proactive and reactive conservation concepts as well as methods of land management into LCIA methodology. The main objective of this concept paper is therefore to describe a new methodological framework for the assessment of biodiversity in the LCA that could fill some of the research gaps, including compilation and suggestion of suitable data sets. The conclusion discusses both the benefits and limitations of this framework.

A Regional Sustainable Intensive Land Use Evaluation Based on Ecological Constraints: A Case Study in Jinan City

Intensive development is a sign of human social progress, and moderate intensification is a continuously pursued goal. However, how to conduct a moderately intensive land use evaluation remains a challenge. To solve this problem, this research proposes the concept of sustainable intensification variable and develops a sustainable intensification variable model to determine the appropriate interval of regional intensive land use and evaluate the intensification of land use. The evaluation method of the sustainable intensification variable model is based on the principle and method of the intensification variable, and the regional sustainable development evaluation factors in the model are revised based on rational land use and ecological constraints. To verify the rationality of the model and systematically evaluate the intensification of land use in the city of Jinan, this method was tested using land use data and social economic data on Jinan from 2001, 2011, and 2015. The results show that the model has a high accuracy in judging the moderately intensive interval of regional land use and evaluating intensive land use, and has important reference value for regional sustainable development decision-making.

Managing the middle: A shift in conservation priorities based on the global human modification gradient

An increasing number of international initiatives aim to reconcile development with conservation. Crucial to successful implementation of these initiatives is a comprehensive understanding of the current ecological condition of landscapes and their spatial distributions. Here, we provide a cumulative measure of human modification of terrestrial lands based on modeling the physical extents of 13 anthropogenic stressors and their estimated impacts using spatially explicit global datasets with a median year of 2016. We quantified the degree of land modification and the amount and spatial configuration of low modified lands (i.e., natural areas relatively free from human alteration) across all ecoregions and biomes. We identified that fewer unmodified lands remain than previously reported and that most of the world is in a state of intermediate modification, with 52% of ecoregions classified as moderately modified. Given that these moderately modified ecoregions fall within critical land use thresholds, we propose that they warrant elevated attention and require proactive spatial planning to maintain biodiversity and ecosystem function before important environmental values are lost.

Aligning research with policy and practice for sustainable agricultural land systems in Europe

Agriculture is widely recognized as critical to achieving the Sustainable Development Goals (SDGs), but researchers, policymakers, and practitioners have multiple, often conflicting yet poorly documented priorities on how agriculture could or should support achieving the SDGs. Here, we assess consensus and divergence in priorities for agricultural systems among research, policy, and practice perspectives and discuss the implications for research on trade-offs among competing goals. We analyzed the priorities given to 239 environmental and social drivers, management choices, and outcomes of agricultural systems from 69 research articles, the SDGs and four EU policies, and seven agricultural sustainability assessment tools aimed at farmers. We found all three perspectives recognize 32 variables as key to agricultural systems, providing a shared area of focus for agriculture's contribution to the SDGs. However, 207 variables appear in only one or two perspectives, implying that potential trade-offs may be overlooked if evaluated from only one perspective. We identified four approaches to agricultural land systems research in Europe that omit most of the variables considered important from policy and practice perspectives. We posit that the four approaches reflect prevailing paradigms of research design and data analysis and suggest future research design should consider including the 32 shared variables as a starting point for more policy- and practice-relevant research. Our identification of shared priorities from different perspectives and attention to environmental and social domains and the functional role of system components provide a concrete basis to encourage codesigned and systems-based research approaches to guide agriculture's contribution to the SDGs.

Analysis of High Temporal Resolution Land Use/Land Cover Trajectories

In this study, methods, originally developed to assess life course trajectories, are explored in order to evaluate land change through the analysis of sequences of land use/cover. Annual land cover maps which describe land use/land cover change for the 1985–2017 period for a large region in Northeast Brazil were analyzed. The most frequent sequences, the entropy and the turbulence of the land trajectories, and the average time of permanence were computed. Clusters of similar sequences were determined using different dissimilarity measures. The effect of some covariates such as slope and distance from roads on land trajectories was also evaluated. The obtained results show the potential of these techniques to analyze land cover sequences since the availability of multidate land cover data with both, high temporal and thematic resolutions, is continuously increasing and poses significant challenges to data analysis.

Uncertainty in ecosystem services maps: the case of carbon stocks in the Brazilian Amazon forest using regression analysis

Ecosystem Service (ES) mapping has become a key tool in scientific assessments of human-nature interactions and is being increasingly used in environmental planning and policy-making. However, the associated epistemic uncertainty underlying these maps often is not systematically considered. This paper proposes a basic procedure to present areas with lower statistical reliability in a map of an ES indicator, the vegetation carbon stock, when extrapolating field data to larger case study regions. To illustrate our approach, we use regression analyses to model the spatial distribution of vegetation carbon stock in the Brazilian Amazon forest in the State of Pará. In our analysis, we used field data measurements for the carbon stock in three study sites as the response variable and various land characteristics derived from remote sensing as explanatory variables for the ES indicator. We performed regression methods to map the carbon stocks and calculated three indicators of reliability: RMSE-Root-mean-square-error, R2-coefficient of determination - from an out-of-sample validation and prediction intervals. We obtained a map of carbon stocks and made explicit its associated uncertainty using a general indicator of reliability and a map presenting the areas where our prediction is the most uncertain. Finally, we highlighted the role of environmental factors on the range of uncertainty. The results have two implications. (1) Mapping prediction interval indicates areas where the map's reliability is the highest. This information increases the usefulness of ES maps in environmental planning and governance. (2) In the case of the studied indicator, the reliability of our prediction is very dependent on land cover type, on the site location and its biophysical, socioeconomic and political characteristics. A better understanding of the relationship between carbon stock and land-use classes would increase the reliability of the maps. Results of our analysis help to direct future research and fieldwork and to prevent decision-making based on unreliable maps.

An analysis of the spatial association between deforestation and agricultural field sizes in the tropics and subtropics

Tropical deforestation is one of the most pressing threats to biodiversity, and substantially reduces ecosystem services at the global scale. Little is known however about the global spatial distribution of the actors behind tropical deforestation. Newly available maps of global cropland field size offer an opportunity to gain understanding towards the spatial distribution of tropical deforestation actors. Here we use a map of global cropland field size and combine it with maps of forest loss to study the spatial association between field size and deforestation while accounting for other anthropogenic and geographical drivers of deforestation. We then use linear mixed–effects models and bootstrapping to determine what factors affect field sizes within deforested areas across all countries in the global tropics and subtropics. We find that field size within deforested areas is largely determined by country-level effects indicating the importance of socio-economic, cultural and institutional factors on the distribution of field sizes. Typically, small field sizes appear more commonly in deforested areas in Africa and Asia while the association was with larger field sizes in Australia and the Americas. In general, we find that smaller field sizes are associated with deforestation in protected areas and large field sizes with areas with lower agricultural value, although these results have low explanatory power. Our results suggest that the spatial patterns of actors behind deforestation are aggregated geographically which could help target conservation and sustainable land-use strategies.

Mapping Cropping Practices on a National Scale Using Intra-Annual Landsat Time Series Binning

Spatially explicit information on cropland use intensity is vital for monitoring land and water resource demands in agricultural systems. Cropping practices underlie substantial spatial and temporal variability, which can be captured through the analysis of image time series. Temporal binning helps to overcome limitations concerning operability and repeatability for mapping large areas and can improve the thematic detail and consistency of maps in agricultural systems. We here assessed the use of annual, quarterly, and eight-day temporal features for mapping five cropping practices on annual croplands across Turkey. We used 2403 atmospherically corrected and topographically normalized Landsat Collection 1 L1TP images of 2015 to compute quarterly best-pixel composites, quarterly and annual spectral-temporal metrics, as well as gap-filled eight-day time series of Tasseled Cap components. We tested 22 feature sets for binary cropland mapping, and subsequent discrimination of five cropping practices: Spring and winter cropping, summer cropping, semi-aquatic cropping, double cropping, and greenhouse cultivation. We evaluated area-adjusted accuracies and compared cropland area estimates at the province-level with official statistics. We achieved overall accuracies above 90%, when using either all quarterly features or the eight-day Tasseled Cap time series, indicating that temporal binning of intra-annual image time-series into multiple temporal features improves representations of cropping practices. Class accuracies of winter and spring, summer, and double cropping were robust, while omission errors for semi-aquatic cropping and greenhouse cultivation were high. Our mapped cropland extent was in good agreement with province-level statistics (r2 = 0.85, RMSE = 7.2%). Our results indicate that 71.3% (±2.3%) of Turkey’s annual croplands were cultivated during winter and spring, 15.8% (±2.2%) during summer, while 8.5% (±1.6%) were double-cropped, 4% (±1.9%) were cultivated under semi-aquatic conditions, and 0.32% (±0.2%) was greenhouse cultivation. Our study presents an open and readily available framework for detailed cropland mapping over large areas, which bears the potential to inform assessments of land use intensity, as well as land and water resource demands.

Modeling the spatial distribution of grazing intensity in Kazakhstan

With increasing affluence in many developing countries, the demand for livestock products is rising and the increasing feed requirement contributes to pressure on land resources for food and energy production. However, there is currently a knowledge gap in our ability to assess the extent and intensity of the utilization of land by livestock, which is the single largest land use in the world. We developed a spatial model that combines fine-scale livestock numbers with their associated energy requirements to distribute livestock grazing demand onto a map of energy supply, with the aim of estimating where and to what degree pasture is being utilized. We applied our model to Kazakhstan, which contains large grassland areas that historically have been used for extensive livestock production but for which the current extent, and thus the potential for increasing livestock production, is unknown. We measured the grazing demand of Kazakh livestock in 2015 at 286 Petajoules, which was 25% of the estimated maximum sustainable energy supply that is available to livestock for grazing. The model resulted in a grazed area of 1.22 million km2, or 48% of the area theoretically available for grazing in Kazakhstan, with most utilized land grazed at low intensities (average off-take rate was 13% of total biomass energy production). Under a conservative scenario, our estimations showed a production potential of 0.13 million tons of beef additional to 2015 production (31% increase), and much more with utilization of distant pastures. This model is an important step forward in evaluating pasture use and available land resources, and can be adapted at any spatial scale for any region in the world.

Patterns and drivers of recent disturbances across the temperate forest biome

Increasing evidence indicates that forest disturbances are changing in response to global change, yet local variability in disturbance remains high. We quantified this considerable variability and analyzed whether recent disturbance episodes around the globe were consistently driven by climate, and if human influence modulates patterns of forest disturbance. We combined remote sensing data on recent (2001-2014) disturbances with in-depth local information for 50 protected landscapes and their surroundings across the temperate biome. Disturbance patterns are highly variable, and shaped by variation in disturbance agents and traits of prevailing tree species. However, high disturbance activity is consistently linked to warmer and drier than average conditions across the globe. Disturbances in protected areas are smaller and more complex in shape compared to their surroundings affected by human land use. This signal disappears in areas with high recent natural disturbance activity, underlining the potential of climate-mediated disturbance to transform forest landscapes.

Regional-scale management maps for forested areas of the Southeastern United States and the US Pacific Northwest

Forests in the United States are managed by multiple public and private entities making harmonization of available data and subsequent mapping of management challenging. We mapped four important types of forest management, production, ecological, passive, and preservation, at 250-meter spatial resolution in the Southeastern (SEUS) and Pacific Northwest (PNW) USA. Both ecologically and socio-economically dynamic regions, the SEUS and PNW forests represent, respectively, 22.0% and 10.4% of forests in the coterminous US. We built a random forest classifier using seasonal time-series analysis of 16 years of MODIS 16-day composite Enhanced Vegetation Index, and ancillary data containing forest ownership, roads, US Forest Service wilderness and forestry areas, proportion conifer and proportion riparian. The map accuracies for SEUS are 89% (10-fold cross-validation) and 67% (external validation) and PNW are 91% and 70% respectively with the same validation. The now publicly available forest management maps, probability surfaces for each management class and uncertainty layer for each region can be viewed and analysed in commercial and open-source GIS and remote sensing software.

Latent Drivers of Landscape Transformation in Eastern Europe: Past, Present and Future

Land-use changes in Europe have been influenced by social forces including economic, demographic, political, technological and cultural factors. Contributing to a refined conceptualization of multifaceted processes of landscape transformation in the European continent, the present study proposes an extensive review of land-use trends in Eastern Europe, focusing on past, present and future conditions that may characterize latent drivers of change. Three time periods with a specific institutional, political and socioeconomic context reflecting distinct processes of land-use change were identified including: (i) the rapid transition to a centralized political system since the early 1950s (up to the late 1980s); (ii) a progressive transition from communist regimes to parliamentary democracy in 1989–1990 (up to the early 2000s); and (iii) the subsequent accession of individual countries to the European Union (2004–2007) up to nowadays. The most recent land-use trends are increasingly influenced by European directives on the environment, while national policies continue to shape economic development in member states.

Pasture intensification is insufficient to relieve pressure on conservation priority areas in open agricultural markets

Agricultural expansion is a leading driver of biodiversity loss across the world, but little is known on how future land-use change may encroach on remaining natural vegetation. This uncertainty is, in part, due to unknown levels of future agricultural intensification and international trade. Using an economic land-use model, we assessed potential future losses of natural vegetation with a focus on how these may threaten biodiversity hotspots and intact forest landscapes. We analysed agricultural expansion under proactive and reactive biodiversity protection scenarios, and for different rates of pasture intensification. We found growing food demand to lead to a significant expansion of cropland at the expense of pastures and natural vegetation. In our reference scenario, global cropland area increased by more than 400 Mha between 2015 and 2050, mostly in Africa and Latin America. Grazing intensification was a main determinant of future land-use change. In Africa, higher rates of pasture intensification resulted in smaller losses of natural vegetation, and reduced pressure on biodiversity hotspots and intact forest landscapes. Investments into raising pasture productivity in conjunction with proactive land-use planning appear essential in Africa to reduce further losses of areas with high conservation value. In Latin America, in contrast, higher pasture productivity resulted in increased livestock exports, highlighting that unchecked trade can reduce the land savings of pasture intensification. Reactive protection of sensitive areas significantly reduced the conversion of natural ecosystems in Latin America. We conclude that protection strategies need to adapt to region-specific trade positions. In regions with a high involvement in international trade, area-based conservation measures should be preferred over strategies aimed at increasing pasture productivity, which by themselves might not be sufficient to protect biodiversity effectively.

Models meet data: Challenges and opportunities in implementing land management in Earth system models

As the applications of Earth system models (ESMs) move from general climate projections toward questions of mitigation and adaptation, the inclusion of land management practices in these models becomes crucial. We carried out a survey among modeling groups to show an evolution from models able only to deal with land-cover change to more sophisticated approaches that allow also for the partial integration of land management changes. For the longer term a comprehensive land management representation can be anticipated for all major models. To guide the prioritization of implementation, we evaluate ten land management practices-forestry harvest, tree species selection, grazing and mowing harvest, crop harvest, crop species selection, irrigation, wetland drainage, fertilization, tillage, and fire-for (1) their importance on the Earth system, (2) the possibility of implementing them in state-of-the-art ESMs, and (3) availability of required input data. Matching these criteria, we identify "low-hanging fruits" for the inclusion in ESMs, such as basic implementations of crop and forestry harvest and fertilization. We also identify research requirements for specific communities to address the remaining land management practices. Data availability severely hampers modeling the most extensive land management practice, grazing and mowing harvest, and is a limiting factor for a comprehensive implementation of most other practices. Inadequate process understanding hampers even a basic assessment of crop species selection and tillage effects. The need for multiple advanced model structures will be the challenge for a comprehensive implementation of most practices but considerable synergy can be gained using the same structures for different practices. A continuous and closer collaboration of the modeling, Earth observation, and land system science communities is thus required to achieve the inclusion of land management in ESMs.