Quantifying landscape-level land-use intensity patterns through radar-based remote sensing

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.

High resolution mass spectrometric suspect screening, wide-scope target analysis of emerging contaminants and determination of legacy pollutants in adult black-tailed godwit Limosa limosa limosa in the Netherlands - A pilot study

The Dutch breeding population of the black-tailed godwit Limosa limosa limosa has declined substantially over recent decades; the role of contaminants is unknown. We analysed liver samples from 11 adult birds found dead on their breeding grounds in SW Friesland 2016-2020, six from extensive, herb-rich grasslands, five from intensive grasslands. We carried out LC and GC wide-scope target analysis of more than 2400 substances, LC suspect screening for more than 60,000 substances, target analysis for Cd, Hg, Ni and Pb, organo-phosphate flame retardants (OPFRs), dechlorane plus compounds and selected polybrominated diphenyl ether flame retardants (PBDEs), and bioassay for polybrominated dibenzo-p-dioxins and dibenzofurans (PBDDs/PDBFs) and dioxin-like polychlorinated biphenyls (dl-PCBs). Residues of 29 emerging contaminants (ECs) were determined through wide-scope target analysis. Another 20 were tentatively identified through suspect screening. These contaminants include industrial chemicals (personal care products, surfactants, PAHs and others), plant protection products (PPPs) and pharmaceuticals and their transformation products. Total contaminant load detected by wide-scope target analysis ranged from c. 155 to c. 1400 ng g^-1 and was generally lower in birds from extensive grasslands. Heatmaps suggest that birds from intensive grasslands have a greater mix and higher residue concentrations of PPPs, while birds from extensive grasslands have a greater mix and higher residue concentrations of per- and polyfluoroalkyl substances (PFAS). All four metals and two OPFRs were detected. All tested PBDEs were below the respective LODs. Bioassay revealed presence of PBDDs, PBDFs and dl-PCBs. Further research is required to elucidate potential health risks to godwits and contaminant sources.

Spatiotemporal analysis of land use changes and their trade-offs on the northern slope of the Tianshan Mountains, China

The unprecedented urbanization recently has inevitably intensified the changes in land use morphology. However, current studies on land use primarily analyze a single morphology, ignoring the relationships between different land use morphologies. Taking the northern slope of the Tianshan Mountains (NSTM) as the study area, this article quantifies the spatiotemporal pattern of land use change, and estimates trade-offs and synergies between dominant (patch density, largest patch index, and landscape shape index) and recessive (land use efficiency, land use intensity, and agricultural non-point source pollution) morphologies to fully understand the dynamic characteristics of land use. Results showed bare areas and grassland were always predominant land use types, and land use change from 1990 to 2020 was characterized by the increase of impervious surfaces and the decrease of bare areas. The strongest trade-off was found between largest patch index and land use intensity, while the synergy between landscape shape index and land use intensity was strongest. There are significant disparities in terms of temporal and spatial patterns of trade-offs/synergies. The correlation coefficients in different study periods were much smaller than their estimations in the whole region, and the trade-offs/synergies in the eastern NSTM were basically identical with the whole relationships. The findings reveal the interactions among various land use characteristics, and provide significant references for coordinated land management and regional high-quality development.

Changes in surface water drive the movements of Shoebills

Animal movement is mainly determined by spatial and temporal changes in resource availability. For wetland specialists, the seasonal availability of surface water may be a major determinant of their movement patterns. This study is the first to examine the movements of Shoebills (Balaeniceps rex), an iconic and vulnerable bird species. Using GPS transmitters deployed on six immature and one adult Shoebills over a 5-year period, during which four immatures matured into adults, we analyse their home ranges and distances moved in the Bangweulu Wetlands, Zambia. We relate their movements at the start of the rainy season (October to December) to changes in Normalized Difference Water Index (NDWI), a proxy for surface water. We show that Shoebills stay in the Bangweulu Wetlands all year round, moving less than 3 km per day on 81% of days. However, average annual home ranges were large, with high individual variability, but were similar between age classes. Immature and adult Shoebills responded differently to changes in surface water; sites that adults abandoned became drier, while sites abandoned by immatures became wetter. However, there were no differences in NDWI of areas used by Shoebills before abandonment and newly selected sites, suggesting that Shoebills select areas with similar surface water. We hypothesise that the different responses to changes in surface water by immature and adult Shoebills are related to age-specific optimal foraging conditions and fishing techniques. Our study highlights the need to understand the movements of Shoebills throughout their life cycle to design successful conservation actions for this emblematic, yet poorly known, species.

Grassland Mowing Detection Using Sentinel-1 Time Series: Potential and Limitations

Grasslands encompass vast and diverse ecosystems that provide food, wildlife habitat and carbon storage. Their large range in land use intensity significantly impacts their ecological value and the balance between these goods and services. Mowing dates and frequencies are major aspects of grassland use intensity, which have an impact on their ecological value as habitats. Previous studies highlighted the feasibility of detecting mowing events based on remote sensing time series, a few of which using synthetic aperture radar (SAR) imagery. Although providing encouraging results, research on grassland mowing detection often lacks sufficient precise reference data for corroboration. The goal of the present study is to quantitatively and statistically assess the potential of Sentinel-1 C-band SAR for detecting mowing events in various agricultural grasslands, using a large and diverse reference data set collected in situ. Several mowing detection methods, based on SAR backscattering and interferometric coherence time series, were thoroughly evaluated. Results show that 54% of mowing events could be detected in hay meadows, based on coherence jumps. Grazing events were identified as a major confounding factor, as most false detections were made in pastures. Parcels with one mowing event in the summer were identified with the highest accuracy (71%). Overall, this study demonstrates that mowing events can be detected through Sentinel-1 coherence. However, the performances could probably be further enhanced by discriminating pastures beforehand and combining Sentinel-1 and Sentinel-2 data for mowing detection.

Agroecology in Large Scale Farming—A Research Agenda

Agroecology promises a third way between common global agriculture tradeoffs such as food production and nature conservation, environmental sustainability and ecosystem services. However, most successful examples of mainstreaming agroecology come from smallholder, family agriculture, that represents only about 30% of the world agricultural area. Mainstreaming agroecology among large scale farmers is urgently needed, but it requires addressing specific questions in research, technology and policy development to support sustainable transitions. Here we take stock of the existing knowledge on some key aspects necessary to support agroecological transitions in large scale farming, considering two contrasting starting points: highly subsidized and heavily taxed agricultural contexts, represented here by the examples of Western Europe and temperate South America. We summarize existing knowledge and gaps around service crops, arthropod-mediated functions, landscape and watershed regulation, graze-based livestock, nature-inclusive landscapes, and policy mechanisms to support transitions. We propose a research agenda for agroecology in large scale farming organized in five domains: (i) Breeding for diversity, (ii) Scalable complexity, (iii) Managing cycles beyond fields and farms, (iv) Sharing the cultivated landscape, and (v) Co-innovation with farmers, value chains and policy makers. Agroecology may result in a renewed impetus in large scale farming, to attract the youth, foster clean technological innovation, and to promote a new generation of large-scale farmers that take pride in contributing to feeding the world while serving the planet and its people.

Spatial-Temporal Differentiation Analysis of Agricultural Land Use Intensity and Its Driving Factors at the County Scale: A Case Study in Hubei Province, China

Scientifically characterizing the spatial-temporal distribution characteristics of agricultural land use intensity and analyzing its driving factors are of great significance to the formulation of relevant agricultural land use intensity management policies, the realization of food safety and health, and the achievement of sustainable development goals. Taking Hubei Province as an example, and taking counties as the basic evaluation unit, this paper establishes an agricultural land use intensity evaluation system, explores the spatial autocorrelation of agricultural land use intensity in each county and analyzes the driving factors of agricultural land use intensity. The results show that the agricultural land use intensity in Hubei Province increased as a whole from 2000 to 2016, and the spatial agglomeration about the agricultural land use intensity in Hubei Province experienced a process of continuous growth and a fluctuating decline; the maximum of the Global Moran’s I was 0.430174 (in 2007) and the minimum was 0.148651 (in 2001). In terms of Local Moran’s I, H-H agglomeration units were mainly concentrated in two regions: One comprising the cities of Huanggang, Huangshi and Ezhou, and the other the cities of Xiangyang and Suizhou; the phenomenon is particularly obvious after 2005. On the other hand, factors such as the multiple cropping index (MCI) that reflect farmers’ willingness to engage in agricultural production have a great impact on agricultural land use intensity, the influence of the structure of the industry on agricultural land use intensity varies with the degree of influence of different industries on farmers’ income, and agricultural fiscal expenditure (AFE) has not effectively promoted the intensification of agricultural land use. The present research has important significance for enhancing insights into the sustainable improvement of agricultural land use intensity and for realizing risk control of agricultural land use and development.

BESTMAP: behavioural, Ecological and Socio-economic Tools for Modelling Agricultural Policy

Half of the European Union (EU) land and the livelihood of 10 million farmers is threatened by unsustainable land-use intensification, land abandonment and climate change. Policy instruments, including the EU Common Agricultural Policy (CAP) have so far failed to stop this environmental degradation. BESTMAP will: 1) Develop a behavioural theoretical modelling framework to take into account complexity of farmers’ decision-making; 2) Develop, adapt and customize a suite of opensource, flexible, interoperable and customisable computer models linked to existing data e.g. LPIS/IACS and remote sensing e.g. Sentinel-2; 3) Link economic, individual-farm agent-based, biophysical ecosystem services and biodiversity and geostatistical socio-economic models; 4) Produce a simple-to-use dashboard to compare scenarios of Agri-Environmental Schemes adoption; 5) Improve the effectiveness of future EU rural policies’ design, monitoring and implementation.

Abundance of arthropods as food for meadow bird chicks in response to short- and long-term soil wetting in Dutch dairy grasslands

BACKGROUND

Throughout the world, intensive dairy farming has resulted in grasslands almost devoid of arthropods and birds. Meadow birds appear to be especially vulnerable during the chick-rearing period. So far, studies have focused mainly on describing population declines, but solutions to effectively stop these trends on the short-term are lacking. In this study at a single farm, we experimentally manipulated soil moisture through occasional irrigation, to mitigate against early season drainage and create favorable conditions for the emergence of above-ground arthropods during the meadow bird chick rearing phase.

METHODS

To guarantee the presence of at least a sizeable arthropod community for the measurement of effects of wetting, we selected a farm with low intensity management. The land use and intensity of the study site and surroundings were categorized according to the national land use database and quantified using remote sensing imagery. From May 1 to June 18, 2017, we compared a control situation, with no water added, to two wetting treatments, a "short-term" (3 weeks) treatment based on wetting on warm days with a sprinkler system and a "long-term" treatment next to a water pond with a consistently raised water table from 2010. We measured soil temperature, soil moisture and resistance as well as the biomass of arthropods at 3-day intervals. Flying arthropods were sampled by sticky traps and crawling arthropods by pitfall traps. Individual arthropods were identified to Order and their length recorded, to assess their relevance to meadow bird chicks.

RESULTS

The land use analysis confirmed that the selected dairy farm had very low intensity management. This was different from most of the surrounding area (20 km radius), characterized by (very) high intensity land use. The experiments showed that irrigation contributed to cooler soils during midday, and that his happened already in the early part of the season; the differences with the control increased with time. In the short- and long-term treatments, soil moisture increased and soil resistance decreased from the mid-measurement period onward. Compared with the control, cumulative arthropod biomass was higher in the long-term treatment, but showed no change in the irrigation treatment. We conclude that small-scale interventions, such as occasional irrigation, favorably affected local soil properties. However, the effects on above-ground arthropod abundance currently appear limited or overridden by negative landscape-scale processes on arthropods.

A Methodology to Monitor Urban Expansion and Green Space Change Using a Time Series of Multi-Sensor SPOT and Sentinel-2A Images

Monitoring urban expansion and greenspace change is an urgent need for planning and decision-making. This paper presents a methodology integrating Principal Component Analysis (PCA) and hybrid classifier to undertake this kind of work using a sequence of multi-sensor SPOT images (SPOT-2,3,5) and Sentinel-2A data from 1996 to 2016 in Hangzhou City, which is the central metropolis of the Yangtze River Delta in China. In this study, orthorectification was first applied on the SPOT and Sentinel-2A images to guarantee precise geometric correction which outperformed the conventional polynomial transformation method. After pre-processing, PCA and hybrid classifier were used together to enhance and extract change information. Accuracy assessment combining stratified random and user-defined plots sampling strategies was performed with 930 reference points. The results indicate reasonable high accuracies for four periods. It was further revealed that the proposed method yielded higher accuracy than that of the traditional post-classification comparison approach. On the whole, the developed methodology provides the effectiveness of monitoring urban expansion and green space change in this study, despite the existence of obvious confusions that resulted from compound factors.

Warming springs and habitat alteration interact to impact timing of breeding and population dynamics in a migratory bird

In seasonal environments, increasing spring temperatures lead many taxa to advance the timing of reproduction. Species that do not may suffer lower fitness. We investigated why black-tailed godwits (Limosa limosa limosa), a ground-breeding agricultural grassland shorebird, have not advanced timing of reproduction during the last three decades in the face of climate change and human-induced habitat degradation. We used data from an 11-year field study to parameterize an Integral Projection Model to predict how spring temperature and habitat quality simultaneously influence the timing of reproduction and population dynamics. We found apparent selection for earlier laying, but not a correlation between the laying dates of parents and their offspring. Nevertheless, in warmer springs, laying dates of adults show a stronger positive correlation with laying date in previous springs than in cooler ones, and this leads us to predict a slight advance in the timing of reproduction if spring temperatures continue to increase. We also show that only in landscapes with low agricultural activity, the population can continue to act as a source. This study shows how climate change and declining habitat quality may enhance extinction risk.