Grassland Cover Research Articles

Operational AOD Retrieval at Subkilometer Resolution Using OceanSat‐2 OCM Over Land: SAER Algorithm, Uncertainties, Validation & Inter‐Sensor Comparison

AbstractThe ocean color monitor (OCM) sensor onboard OceanSat‐2 is providing data in visible and near Infrared (NIR) bands. Due to the limited spectral coverage of OCM, the widely used dark‐target and deep‐blue aerosol algorithms cannot be adapted. Here, a new aerosol optical depth (AOD) retrieval algorithm for OCM (or similar sensors) over land, termed Space Applications Center AErosol Retrieval (SAER) is described. It utilizes two blue bands for the AOD inversion and, Red and NIR band to characterize the surface in visible bands without assuming red and NIR bands transparent to aerosols. Unlike the dark‐target algorithm, the SAER algorithm can retrieve AOD over bright arid and urban areas too. The uncertainty analysis of SAER suggests a theoretically expected error (EE) envelope of ±(0.06 + 0.26 × AOD) for typical retrieval conditions. OCM AOD over land is retrieved operationally for the first time over Indian and neighboring countries' landmass at the finest spatial resolution of 0.007°. The SAER algorithm is validated against in‐situ AOD measurements during the years 2016–2018 at 21 Aerosol Robotic Network stations located in south‐Asia. Overall validation using 1900 match‐up points shows correlations exceeding 0.8 with 74% of retrievals within EE. The retrievals over cropland, grassland, and mixed land cover types show high (low) correlation (bias), while over bright urban areas somewhat low (high) correlation (bias) is observed. Excluding monsoon season, OCM AOD retrievals show good performance over the year. The performance of MODerate Resolution Imaging Spectroradiometer dark‐target and OCM AOD, against common in‐situ, is close to each other. The study shows that OCM AOD can be used for air quality monitoring/modeling at high spatial resolution.

Spatial–Temporal Changes and Prediction of Carbon Storage in the Tibetan Plateau Based on PLUS-InVEST Model

The changes in the recent and future spatial–temporal patterns of carbon storage of the Tibetan Plateau and its dominant factors in different periods were unclear, and were conducive to optimizing the spatial layout of land. Exploring the spatial and temporal changes in terrestrial ecosystem carbon storage and their influencing factors during a long study period had important theoretical and practical significance for achieving the goal of carbon neutrality. In this study, the Integrated Valuation of Ecosystem Services and Trade-offs model (InVEST) was used to analyze the changes in carbon storage based on vegetation-type data during 2000–2020. The Path-generating Land-Use Simulation model (PLUS) was then used to predict the spatial distribution of carbon storage in the Tibetan Plateau in 2030 and 2060 under inertial development, farmland protection and ecology priority scenarios. The results showed that: (1) The degradation of vegetation types reduced the carbon storage during the study period. During 2000–2020, the desert shrub and non-vegetation area expanded by 63.21% and 13.35%, respectively, while the deciduous scrub, mixed forest and low coverage grassland decreased accordingly. The carbon storage of the Tibetan Plateau showed a decreasing trend by 0.37 × 106 t. (2) The spatial distribution patterns of carbon storage were consistent with that of the vegetation types. (3) In 2030 and 2060, under the constraint of the ecological priority development, the reduction in carbon storage was the smallest, at 0.01 × 106 t and 0.16 × 106 t, respectively. Under the constraint of the inertial development, carbon storage had the largest reduction, at 0.12 × 106 t and 0.43 × 106 t, respectively. (4) During 2000–2020, the dominant single factor that had the greatest impacts on the changes in carbon storage was FVC (vegetation coverage), with q values of 0.616, 0.619 and 0.567, respectively. The interactive dominant effects were mainly nonlinear enhancement and double-factor enhancement. The interactive dominant factors that had the greatest impact were FVC and the DEM (Digital Elevation Model), with q values of 0.94, 0.92 and 0.90, respectively. Therefore, ecological land with a high FVC should be protected and the expansion of non-vegetation areas should be restricted in future planning to improve the carbon storage level of the Tibetan Plateau and achieve the goal of carbon neutrality.

Assessing the exposure of UK habitats to 20th‐ and 21st‐century climate change, and its representation in ecological monitoring schemes

Abstract Climate change is a significant driver of contemporary biodiversity change. Ecological monitoring schemes can be crucial in highlighting its consequences, but connecting and interpreting observed climatic and ecological changes demands an understanding of monitored locations' exposure to climate change. Generalising from trends in monitored sites to habitats also requires an assessment of how closely sampled locations' climate change trajectories mirror those of wider ecosystems. Such assessments are rare but vital for drawing robust ecological conclusions. Focusing on the UK, we generated a metric of climate change exposure by quantifying the change in observed historical (1901–2019) and predicted future (2021–2080, pessimistic emissions scenario) conditions. We then assessed habitat‐specific climate change exposure by overlaying the resulting data with maps of contemporary (2019) land cover. Finally, we compared patterns of climate change exposure in locations sampled by ecological monitoring schemes to random samples from wider habitats. The UK's climate changed significantly between the early 20th century and the last decade, and is predicted to undergo even greater changes (including the development of Iberian/Mediterranean climate types in places) into the 21st century. Climate change exposure is unevenly distributed: regionally, it falls more in southern, central and eastern England; locally, it is greater at higher‐elevation locations than nearby areas at lower elevations. Areas with contemporary arable and horticulture, urban, calcareous grassland and suburban land cover are predicted to experience the greatest overall climatic change, though other habitats experienced relatively greater change than these in the first half of the 20th century. The extent to which locations sampled by ecological monitoring schemes represent broader habitat‐level gradients of climate change exposure varies. Monitored sites' coverage of wider trends is heterogeneous across habitats, time periods and schemes. Policy implications. UK ecological monitoring schemes can effectively, though variably, capture the effects of climate change on habitats. To improve their performance, climate change could be explicitly included in the design of such programmes. Additionally, our findings on how effectively different datasets represent wider patterns of climate change are crucial for informing syntheses of ecological change connected to shifting atmospheric conditions.

Multi-annual grassland mowing dynamics in Germany: spatio-temporal patterns and the influence of climate, topographic and socio-political conditions

Introduction: Grasslands cover one third of the agricultural area in Germany and are mainly used for fodder production. However, grasslands fulfill many other ecosystem functions, like carbon storage, water filtration and the provision of habitats. In Germany, grasslands are mown and/or grazed multiple times during the year. The type and timing of management activities and the use intensity vary strongly, however co-determine grassland functions. Large-scale spatial information on grassland activities and use intensity in Germany is limited and not openly provided. In addition, the cause for patterns of varying mowing intensity are usually not known on a spatial scale as data on the incentives of farmers behind grassland management decisions is not available.Methods: We applied an algorithm based on a thresholding approach utilizing Sentinel-2 time series to detect grassland mowing events to investigate mowing dynamics in Germany in 2018–2021. The detected mowing events were validated with an independent dataset based on the examination of public webcam images. We analyzed spatial and temporal patterns of the mowing dynamics and relationships to climatic, topographic, soil or socio-political conditions.Results: We found that most intensively used grasslands can be found in southern/south-eastern Germany, followed by areas in northern Germany. This pattern stays the same among the investigated years, but we found variations on smaller scales. The mowing event detection shows higher accuracies in 2019 and 2020 (F1 = 0.64 and 0.63) compared to 2018 and 2021 (F1 = 0.52 and 0.50). We found a significant but weak (R2 of 0–0.13) relationship for a spatial correlation of mowing frequency and climate as well as topographic variables for the grassland areas in Germany. Further results indicate a clear value range of topographic and climatic conditions, characteristic for intensive grassland use. Extensive grassland use takes place everywhere in Germany and on the entire spectrum of topographic and climatic conditions in Germany. Natura 2000 grasslands are used less intensive but this pattern is not consistent among all sites.Discussion: Our findings on mowing dynamics and relationships to abiotic and socio-political conditions in Germany reveal important aspects of grassland management, including incentives of farmers.

Sensitivity of Grassland Coverage to Climate across Environmental Gradients on the Qinghai-Tibet Plateau

Grassland cover is strongly influenced by climate change. The response of grassland cover to climate change becomes complex with background climate. There have been some advances in research on the sensitivity of grassland vegetation to climate change around the world, but the differences in climate sensitivity among grassland types are still unclear in alpine grassland. Therefore, we applied MODIS NDVI data and trend analysis methods to quantify the spatial and temporal variation of grassland vegetation cover on the Qinghai-Tibet Plateau. Then, we used multiple regression models to analyze the sensitivity of fractional vegetation cover (FVC) to climatic factors (Temperature, Precipitation, Solar radiation, Palmer drought severity index) and summarized the potential mechanisms of vegetation sensitivity to different climatic gradients. Our results showed (1) a significant increasing trend in alpine desert FVC from 2000–2018 (1.12 × 10−3/a, R2 = 0.56, p < 0.001) but no significant trend in other grassland types. (2) FVC sensitivity to climatic factors varied among grassland types, especially in the alpine desert, which had over 60% of the area with positive sensitivity to temperature, precipitation and PDSI. (3) The sensitivity of grassland FVC to heat factors decreases with rising ambient temperature while the sensitivity to moisture increases. Similarly, the sensitivity to moisture decreases while the sensitivity to thermal factors increases along the moisture gradient. Furthermore, the results suggest that future climate warming will promote grassland in cold and wet areas of the Qinghai-Tibet Plateau and may suppress vegetation in warmer areas. In contrast, the response of the alpine desert to future climate is more stable. Studying the impact of climate variation at a regional scale could enhance the adaptability of vegetation in future global climates.

SMLFire1.0: a stochastic machine learning (SML) model for wildfire activity in the western United States

Abstract. The annual area burned due to wildfires in the western United States (WUS) increased by more than 300 % between 1984 and 2020. However, accounting for the nonlinear, spatially heterogeneous interactions between climate, vegetation, and human predictors driving the trends in fire frequency and sizes at different spatial scales remains a challenging problem for statistical fire models. Here we introduce a novel stochastic machine learning (SML) framework, SMLFire1.0, to model observed fire frequencies and sizes in 12 km × 12 km grid cells across the WUS. This framework is implemented using mixture density networks trained on a wide suite of input predictors. The modeled WUS fire frequency matches observations at both monthly (r=0.94) and annual (r=0.85) timescales, as do the monthly (r=0.90) and annual (r=0.88) area burned. Moreover, the modeled annual time series of both fire variables exhibit strong correlations (r≥0.6) with observations in 16 out of 18 ecoregions. Our ML model captures the interannual variability and the distinct multidecade increases in annual area burned for both forested and non-forested ecoregions. Evaluating predictor importance with Shapley additive explanations, we find that fire-month vapor pressure deficit (VPD) is the dominant driver of fire frequencies and sizes across the WUS, followed by 1000 h dead fuel moisture (FM1000), total monthly precipitation (Prec), mean daily maximum temperature (Tmax), and fraction of grassland cover in a grid cell. Our findings serve as a promising use case of ML techniques for wildfire prediction in particular and extreme event modeling more broadly. They also highlight the power of ML-driven parameterizations for potential implementation in fire modules of dynamic global vegetation models (DGVMs) and earth system models (ESMs).

Spatio-Temporal Changes in the Grass Land Cover of Manas National Park

Spatio-Temporal Changes in the Grass Land Cover of Manas National Park

Beyond "greening" and "browning": Trends in grassland ground cover fractions across Eurasia that account for spatial and temporal autocorrelation.

Grassland ecosystems cover up to 40% of the global land area and provide many ecosystem services directly supporting the livelihoods of over 1 billion people. Monitoring long-term changes in grasslands is crucial for food security, biodiversity conservation, achieving Land Degradation Neutrality goals, and modeling the global carbon budget. Although long-term grassland monitoring using remote sensing is extensive, it is typically based on a single vegetation index and does not account for temporal and spatial autocorrelation, which means that some trends are falsely identified while others are missed. Our goal was to analyze trends in grasslands in Eurasia, the largest continuous grassland ecosystems on Earth. To do so, we calculated Cumulative Endmember Fractions (annual sums of monthly ground cover fractions) derived from MODIS 2002-2020 time series, and applied a new statistical approach PARTS that explicitly accounts for temporal and spatial autocorrelation in trends. We examined trends in green vegetation, non-photosynthetic vegetation, and soil ground cover fractions considering their independent change trajectories and relations among fractions over time. We derived temporally uncorrelated pixel-based trend maps and statistically tested whether observed trends could be explained by elevation, land cover, SPEI3, climate, country, and their combinations, all while accounting for spatial autocorrelation. We found no statistical evidence for a decrease in vegetation cover in grasslands in Eurasia. Instead, there was a significant map-level increase in non-photosynthetic vegetation across the region and local increases in green vegetation with a concomitant decrease in soil fraction. Independent environmental variables affected trends significantly, but effects varied by region. Overall, our analyses show in a statistically robust manner that Eurasian grasslands have changed considerably over the past two decades. Our approach enhances remote sensing-based monitoring of trends in grasslands so that underlying processes can be discerned.

Soil fertility and landscape surrounding former arable fields drive the ecological resilience of Mediterranean dry grassland plant communities

IntroductionSemi-natural dry grasslands are among the species-richest and most diverse habitats in Europe especially in the Mediterranean Basin. They evolved with long-term severe environmental constraints, either biotic (e.g., grazing or mowing) or abiotic (e.g., dryness or oligotrophy). Their recovery after sever anthropogenic disturbance, such as cultivation is often very slow and incomplete. A deeper understanding of the processes driving the recovery would be of great benefit to better restore or conserve these grasslands. We used the filter model as a framework to assess the relative importance of dispersion, abiotic and biotic filters as drivers of this recovery.MethodsWe studied the medium-term dynamics of 37 Mediterranean dry grassland plant communities after an episode of cultivation in the plain of La Crau (Southern France) by two census: after 25 years of abandonment, and revisited 10 years later (35 years of abandonment). Our aims were to characterize the temporal medium-term dynamics of these communities, to measure their taxonomic resilience and to identify the drivers. We performed structural equation modeling to measure the importance of the various drivers, with cover of remnant dry grasslands surrounding each studied community as a proxy for the dispersion filter, soil pH and phosphorus content as proxies for the abiotic filter and cover of the most dominant species as a proxy for the biotic filter.Results and DiscussionOur results show that former arable field vegetation do not recover after more than 40 years. The three filters drive the recovery of dry grassland vegetation: former arable fields that have lower phosphorus content (abiotic filter) have a lower cover of dominant species (biotic filter), a higher species richness and a higher similarity with remnant dry grassland plant communities, especially if the former arable fields are surrounded by remnant dry grasslands (dispersion filter).

Assessing the vulnerability to climate change of a semi-arid pastoral social–ecological system: A case study in Hulunbuir, China

Covering approximately 15% of the Earth's land surface and home to 14% of the total global population, semi-arid pastoral areas are characterized by high climatic variability and ecological vulnerability. Recurring extreme weather prodoundly shapes decision-making in semi-arid pastoral social–ecological systems (SAPSES) and their traditional natural-resource-based livelihoods. This study developed a conceptual framework of climate-livelihood vulnerability in SAPSES and evaluated the vulnerability of SAPSES to climate change effects and environmental pressure in Hulunbuir, China using a questionnaire survey and remote sensing monitoring data. Results showed that the study area were impacted substantially by climate change (0.68). However, the vulnerability was relatively low because of the moderate adaptability (0.59) and medium sensitivity of the local socio–ecological system (0.61). Natural factors, including climate variation, natural disasters and ecosystem sensitivity, were the main causes of vulnerability. The increasing vulnerability had been largely slowed by adaptive capital, the traditional cultural organization system and the collective action mechanism. Degraded grassland cover, lack of technical training and high habitation stickiness were the main reasons for increased sensitivity. Natural capital and financial capital were important factors restricting regional adaptive capacity. Therefore, promoting local livestock cooperatives, improving the provision of public services and strengthening ecological governance can help to enhance the climate change resilience and increase the stability and sustainability of SAPSES.

Spatiotemporal evolution and attribution analysis of grassland NPP in the Yellow River source region, China

The protection of the environment and promotion of sustainable development in the Yellow River Basin (YRB) is a major national strategy in China. Madoi County (MC), at the headwaters of the YRB, plays a crucial role in ensuring the ecological security and sustainable development of the middle and lower reaches of the river. Vegetation net primary productivity (NPP) is a critical indicator for assessing the structure and function of the ecosystem. Therefore, studying the spatiotemporal evolutionary characteristics of grassland net primary productivity (GNPP) in MC and its driving mechanisms is of the utmost importance. We employed linear regression analysis, optimal parameters-based geographical detector (OPGD), multiscale geographic weighted regression (MGWR), and residual analysis to investigate the spatiotemporal change of GNPP in MC under the influence of climate change (CC) and policies in different periods based on MOD17A3 NPP data. Additionally, we explored the natural and anthropogenic factors affecting GNPP and their spatiotemporal processes on global and grid scales. Lastly, we quantified the relative contributions of CC and human activity (HA) to the observed changes in GNPP. The results were as follows: 1) GNPP in MC has demonstrated a fluctuating and increasing trend since 2000, with an average annual growth rate of 1.58 gC·m−2·a−1 and a maximum growth rate of 6.29 gC·m−2·a−1 in 2017–2021. 2) Spatial variation in GNPP has been evident since 2000, with a gradual increase in GNPP from the northwest to southeast. Regions with notable increases in grassland were predominantly located in areas with eco-engineering. 3) The OPGD was used to identify key driving factors (DFs), revealing that climate factors had stronger explanatory powers for the GNPP at each phase. MGWR results indicated that the effect of key DFs exhibited clear spatiotemporal heterogeneity. Annual cumulative solar radiation and annual cumulative precipitation had the most significant effect on GNPP. Regions with positive effects (PEs) of grassland area change and grassland cover change were mostly located in eco-engineering zones. The PEs of DFs on GNPP increased. 4) The contribution of CC to GNPP changes and the dominant area share were greater than those of HA. However, areas with PEs of HA increased gradually. The synergies between eco-protection policies and natural regulation are therefore critical to the eco-environmental protection in the YRB.

Industrial forestry negatively affects small-farms honey production in south-central Chile: evidence from a landscape scale

Land use and cover change have a high influence on bee species, where honey bees (Apis mellifera) could be affected in terms of resources, dispersal, and habitat. However, the influence of landscape context on honey productivity has been scarcely explored in comparison with population parameters, which is an essential topic for the economic development of rural communities based on apiculture. Here, we tested the hypothesis if honey production decreases with the increase of exotic forest plantations in the landscape as the cover of native forest decreases, vegetation and soils become increasingly degraded. We compiled data on honey productivity for two years (2018 and 2019) of small-scale honey production in 17 landscapes of 1 km radii (Curanilahue state in south-central Chile). Then, the landscapes were characterized by using remote sensing techniques, including biophysical and compositional landscape variables. Finally, Generalized Linear Mixed Models were implemented to explore the relationship between landscape variables and honey productivity. We identified a positive relationship between honey productivity with built cover and grassland cover. On the other hand, a negative relationship was found between honey production and net primary production, exotic forest plantations, and tree senescence index. Our results provide new insights into the landscape-scale drivers of hive productivity in rural landscapes of south-central Chile.

Species accumulation in novel grassland habitats is linked to land cover history

Novel grassland habitats along linear infrastructure have gained attention as potential supplementary or replacement habitats for species suffering from the widespread loss of traditionally managed semi‐natural grasslands. However, it can take time for species to colonise new habitats, and both the historical and the current landscape composition can affect colonisation rates, particularly for grassland specialist species. We investigated the influence of more than 100 years of landscape history on present‐day species richness of plants, butterflies and bumblebees in two types of linear infrastructure habitats: road verges and power line corridors. We analysed the effects of time since habitat establishment and the amount of surrounding grassland cover in different time periods on species richness of the three taxa. Our results suggest a long time lag in the establishment of plant species in novel grassland habitats, with richness of butterflies and bumblebees largely dependent on the richness of plant species. There was no direct relationship between habitat age and the richness of butterflies or bumblebees, but we found unexpected legacy effects of grassland area on present‐day species richness. Higher richness of bumblebee species in road verges located in landscapes with larger surrounding grassland cover in the past is most likely a consequence of those landscapes containing a mosaic of afforested and abandoned grassland habitats today. Furthermore, we found that power line corridors might be important replacement habitats for butterflies in landscapes with little other grassland area left. Provided that road verges and power‐line corridors are managed appropriately, our results show that their species richness can be expected to increase in the future.

Slope Runoff Process and Regulation Threshold under the Dual Effects of Rainfall and Vegetation in Loess Hilly and Gully Region

The rainfall in loess hilly and gully area is concentrated, and mostly comes in the form of rainstorms. The runoff on the slope caused by rainstorms is the main cause of serious soil and water loss in the loess hilly area, and the grassland vegetation has a good inhibitory effect on the runoff on the slope. Therefore, it is of great significance to reveal the role of grassland vegetation in the process of runoff generation, and the mechanisms for controlling soil erosion in this area. In this study, typical grassland slopes in hilly and gully regions of the loess plateau were taken as research objects. Through artificial rainfall in the field, the response rules of the slope rainfall-runoff process to different grass coverage were explored. The results show that: (1) With the increase in rainfall intensity, the inhibitory effect of grassland vegetation on slope runoff decreased, which was mainly reflected in the gradual decrease in runoff rate and runoff coefficient, and the time required to reach stability gradually shortened. (2) Under 60 mm/h rainfall intensity, the sensitivity of runoff coefficient to 31.5% of grass cover change is the lowest, and the cost performance of grass cover with 55% coverage is the highest. (3) Grass coverage inhibited slope runoff by changing the hydraulic characteristics of the slope, but this effect was only obvious in low rainfall intensity and early rainfall. Rainfall in the loess hilly area is characterized by intense rain. The regulating effect of grass cover on slope runoff is not particularly significant under high intensity rainfall. If only considering the regulation of grassland vegetation on slopes, more than 60% grassland coverage is more efficient in inhibiting slope runoff under medium and low intensity rainfall.

Land use in the Prairie Pothole Region influences the soil bacterial community composition and relative abundance of nitrogen cycling genes

The undulating topography of Prairie Pothole Region of North America creates spatial and temporal variability in soil moisture and nutrient levels, affecting microbial community processes and greenhouse gas emissions. By identifying differences in soil bacterial and archaeal community composition and the abundance of nitrogen cycling genes in permanent cover versus annual crop land over two growing seasons (2017 and 2018), we were able to assess the effects of topography and land use on the functional capacity of the soil microbiome. Permanent grassland cover was associated with higher bacterial diversity in upland positions and lower diversity in low-lying depressions. Bacterial community composition was also significantly different between cultivated and permanent cover at all points along the topographic slope, with the largest effects seen in the footslope and backslope positions. Compared to permanent cover, soil from annual cropland had consistently more abundant nitrifiers, including Nitrospira in the toeslope and backslope, and Nitrososphaeraceae in the shoulder and knoll samples while soils from permanent cover had a greater abundance of several Alphaproteobacteria from Rhodospirillales and Hyphomicrobiaceae across multiple upland positions. Upland soils from annual cropland also had consistently higher abundance of both bacterial and archaeal ammonia oxidizing ( amoA) genes and a higher ratio of nirK: nirS genes compared to those from permanent cover. These differences in microbial community composition were associated with higher N2O and CO2 emissions in upland soils in annual cropland; however, there were no differences in GHG emissions between the two systems in low-lying positions.

Grassland cover declined in Southern Africa but increased in other African subcontinents in early twenty-first century

The African continent has the most extensive grassland cover in the world, providing valuable ecosystem services. African grasslands, like other continental grasslands, are prone to various anthropogenic disturbances and climate, and require data-driven monitoring for efficient functioning and service delivery. Yet, knowledge of how the African grassland cover has changed in the past years is lacking, especially at the subcontinent level, due to lack of relevant long-term, Africa-wide observations and experiments. In this study, we used Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Type (MCD12Q1) data spanning 2001 to 2017 to conduct land use land cover (LULC) change analyses and map grassland distribution in Africa. Specifically, we assessed the changes in grassland cover across and within African subcontinents over three periods (2001-2013, 2013-2017, and 2001-2017). We found that the African grassland cover was 16,777,765.5 km2, 16,999,468.25 km2, and 16,968,304.25 km2 in 2001, 2013, and 2017, respectively. There were net gain (1.32%) and net loss (- 0.19%) during 2001-2013 and 2013-2017 periods, respectively, and the annual rate of change during these periods were 0.11% and - 0.05%, respectively. Generally, the African grassland cover increased by 1.14% (0.07% per annum) over the entire study period (2001-2017) at the expense of forestland, cropland, and built-up areas. The East and West African grassland cover reduced by 0.07% (- 0.02% per annum) and 1.35% (- 0.34% per annum), respectively from 2013 to 2017 but increased in other periods. On the other hand, the grassland cover in North and Central Africa increased throughout the three periods while that of Southern Africa decreased over the three periods. Overall, the net gains in the grassland cover of other African subcontinents offset the loss in Southern Africa and promoted the overall gain across Africa. This study underscores the need for continuous monitoring of African grasslands and the causes of their changes for efficient delivery of ecosystem services.

Habitat heterogeneity promotes bird diversity in agricultural landscapes: Insights from remote sensing data

Understanding the main drivers of biodiversity loss in Europe's agricultural landscapes has been a research priority in the last decades. One of the most important factors promoting biodiversity in farmed landscapes is habitat heterogeneity, which has often proved crucial for avian species and communities. Birds are highly sensitive to environmental changes and make use of a broad range of ecological niches, thus being exceptionally sensitive to the loss of habitat heterogeneity. Remote sensing data are particularly suited to quantify habitat heterogeneity at fine scales over relatively large extents, allowing to consider how different measures of heterogeneity may affect biotic communities at a regional scale. Here, we used airborne LiDAR (Light Detection And Ranging) and satellite multispectral data to derive vegetation canopy height and primary productivity for 118 sites in complex agricultural landscapes in a region in the Central Alps. We computed different bird diversity indices and classified bird species into guilds according to specific traits to analyse the relationship between avian communities and different facets of habitat heterogeneity. Results confirmed that habitat heterogeneity is essential in shaping rich and diverse bird communities, and it is particularly important for several farmland birds. By comparing the effects of canopy height, primary productivity, and specific vegetation features (e.g., cover of grassland, shrub, and tree layers), we showed how different habitat characteristics as well as landscape heterogeneity affected bird richness, diversity, functional entropy, and trait patterns. Landscape and height heterogeneity, estimated by NDVI and LiDAR Rao's Q indices, strongly influenced all response variables, for example, high NDVI values promoted species diversity and ground-understory nesters, and shrub layer was important for ground-understory nesters and forest specialists. Finally, we provide recommendations for conservation practices and mitigation measures to improve bird diversity in agricultural landscapes.

Topoedaphic constraints on woody plant cover in a semi-arid grassland

The proliferation of unpalatable woody plants at the expense of perennial grasses in recent decades has challenged our ability to manage rangelands. While there is substantial research documenting shrub proliferation, we know little about the maximum potential shrub cover for a given topoedaphic setting. To better understand the environmental controls over and constraints on shrub cover, we used high spatial resolution imagery to classify cover of a shrub (Prosopis velutina, velvet mesquite) proliferating in a Sonoran Desert grassland in southern Arizona, USA and explored how maximum shrub cover varies across ecological sites and topoedpahic settings. While the upper limit of shrub cover at the continental-scale is constrained by mean annual precipitation (MAP), our results show that this maxima has a wide range variously dictated by elevation, slope inclination/aspect, soil texture, and rainfall re-distribution. Within the watershed, maximum potential shrub cover ranged from < 3% to 45% with the magnitude and direction of topoedaphic influences varying significantly between landscape components. For example, topoedaphic properties enhanced precipitation (PPT) effectiveness and elevated maximum shrub cover above what might be predicted based on MAP alone on some ecological sites, but reduced PPT effectiveness and constrained shrub cover to levels below what would be predicted from MAP on other sites. Knowledge of upper limits of shrub cover at the within-watershed scale will strengthen dynamic vegetation models, serve as a basis to better design field and modeling experiments and decision support tools, and provide a spatial context indicators for prioritizing conservation/land management goals and objectives.

Mapping Alpine Grassland Fraction Coverage Using Zhuhai-1 OHS Imagery in the Three River Headwaters Region, China

The widely spread alpine grassland ecosystem in the Three River Headwaters Region (TRHR) plays an essential ecological role in carbon sequestration and soil and water conservation. In this study, we test the latest high spatial resolution hyperspectral (Zhuhai-1 OHS) remote sensing imagery to examine different alpine grassland coverage levels using Multiple Endmember Spectral Mixture Analysis (MESMA). Our results suggest that the 3-endmember (3-EM) MESMA model can provide the highest image pixel unmixing percentage, with a percentage exceeding 97% and 96% for pixel scale and landscape scale, respectively. The overall accuracy shows that Zhuhai-1 OHS imagery obtained the highest overall accuracy (83.7%, k = 0.77) in the landscape scale, but in the pixel scale, it is not as good as Landsat 8 OLI imagery. Overall, we can conclude that the hyperspectral imagery combined 3-EM MESMA model performs better in both pixel scale and landscape scale alpine grassland coverage mapping, while the multispectral imagery with the 3-EM MESMA model can satisfy requirements of alpine grassland coverage mapping at the pixel scale. The approaches and workflow to mapping alpine grassland in this study can help monitor alpine grassland degradation; not only in the Qinghai–Tibetan Plateau (QTP), but also in other grassland ecosystems.

Ecological network design based on optimizing ecosystem services:case study in the Huang-Huai-Hai region, China

In modern agricultural landscapes, constructing ‘ecological networks’ is regarded as an efficient way to conserve biodiversity and maintain ecosystem services. Here we aimed to develop an approach to design ecological corridor by employing the ecological source - resistance surface - ecological corridor framework in combination with semi-natural habitat planning and ecosystem service trade-off assessment. ‘Ecological source patches’ were identified based on a ‘Remote Sensing Ecological Index’ (RSEI) to objectively classify ecological and environmental conditions. Our resulting spatial resistance surface was further modified used based on the ‘Cultivated Land Use Intensity’ index, to derive a high accuracy and rationality of ecological corridor extraction in agriculture landscape. While planning the ecological network, key nodes and resulting semi-natural habitat (SNH) distribution were identified using Linkage Mapper tools and circuit theory. We constructed ecological network scenarios with different amounts of semi-natural habitats and calculated resulting regional ecosystem service values (ESV) using an equivalence factor method to explore optimal spatial layouts. The results showed, while regional ecosystem service values generally increased in line with semi-natural habitat area contained within the ecological network, ecological networks with forests covering 10% of the total area were predicted as an optimal scenario balancing ecosystem services with agricultural yield in the study region. Networks with mixed forest and grassland cover totaling 20% of the area represented an alternative choice that strongly enhanced regional ecosystem services while may still allowing for high agricultural productivity. In constructing corridors, identifying, restoring and protecting key ecological nodes using targeted management and habitat restoration, while protecting existing wetlands and other water bodies that support regional water cycle and supply services, should be prioritized. Regional policy measures furthermore need to promote targeted ecological network planning to help improve the overall sustainability of agricultural production.