Modelling agricultural land abandonment in a fine spatial resolution multi-level land-use model: An application for the EU

Machine Learning Algorithms of Remote Sensing Data Processing for Mapping Changes in Land Cover Types over Central Apennines, Italy

This work presents the use of remote sensing data for land cover mapping with a case of Central Apennines, Italy. The data include 8 Landsat 8-9 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) satellite images in six-year period (2018-2024). The operational workflow included satellite image processing which were classified into raster maps with automatically detected 10 classes of land cover types over the tested study. The approach was implemented by using a set of modules in Geographic Resources Analysis Support System (GRASS) Geographic Information System (GIS). To classify remote sensing (RS) data, two types of approaches were carried out. The first is unsupervised classification based on the MaxLike approach and clustering which extracted Digital Numbers (DN) of landscape feature based on the spectral reflectance of signals, and the second is supervised classification performed using several methods of Machine Learning (ML), technically realised in GRASS GIS scripting software. The latter included four ML algorithms embedded from the Python's Scikit-Learn library. These classifiers have been implemented to detect subtle changes in land cover types as derived from the satellite images showing different vegetation conditions in spring and autumn periods in central Apennines, northern Italy.

Soil Microbiome Inoculation for Resilient and Multifunctional New Forests in Post-Agricultural Landscapes

Afforestation is increasingly recognized as a critical strategy to restore ecosystems and enhance biodiversity on post-agricultural landscapes. However, agricultural legacies, such as altered soil structure, nutrient imbalances, and depleted microbial diversity, can slow down forest establishment or cause ecosystems to deviate from expected successional trajectories. In this opinion paper, we explore the potential of soil inoculations as a tool to overcome these challenges by introducing beneficial microbial communities that can accelerate ecosystem recovery and forest development. Restoring soil biodiversity is a crucial aspect of this process that drives broader ecosystem functionality and resilience. We highlight the need to carefully consider the type and timing of inoculations and to ensure compatibility between the inoculum and recipient site characteristics to optimize the establishment of introduced species. While tree productivity is often a central focus of afforestation efforts, the restoration of soil biodiversity, which will also contribute to increased ecosystem-level functions, should also be a priority for long-term forest resilience. Agricultural legacies add complexities to the restoration process, creating unique challenges that need to be addressed in restoration planning. Thus, successful inoculation strategies require a thorough understanding of both donor and recipient site characteristics, also in relation to potential mismatches related to soil physiochemical properties to avoid unintended consequences such as the non-establishment of introduced species. Additionally, we call for the re-evaluation of afforestation targets and the development of standardized monitoring protocols that track the success of inoculation efforts, particularly regarding soil health, microbial community establishment, and biodiversity recovery. By integrating inoculation practices within a broader restoration framework, we can enhance the resilience, biodiversity, and ecosystem functionality of newly afforested landscapes. Ultimately, this approach may play a critical role in ensuring the success of large-scale afforestation projects.

Modelling Alternative Economic Incentive Schemes for Semi-Natural Grassland Conservation in Estonia

Semi-natural grasslands (SNGLs) in Estonia are threatened by abandonment. This threat is leading to concerns about the degradation of biodiversity within grassland communities. Despite the high relevance of economic incentives in this context, how such incentives influence land managers' decision-making regarding the agricultural use of SNGLs has not been investigated. To obtain its socio-ecological implications for policy-making, we developed regionally specific agricultural scenarios (compensation payments, livestock capacity, hey export, and bioenergy production) and an interdisciplinary modelling approach that made it possible to simulate agricultural land use changes through land managers' responses to varied economic conditions. Through this approach, we found that some economic factors hampered the use of SNGLs: the moderate profitability of beef production, labour shortages, and the relatively high profitability of mulching. We observed a positive relationship between SNGLs and habitat suitability for breeding and feeding birds. However, due to the high maintenance costs of SNGLs, the modelling results indicated that increasing the use of SNGLs through public budgets caused crowding-out effects, i.e., the deteriorating market integration of regional agriculture. This study emphasises the need for policy measures aimed at cost-effective, labour-efficient management practices for SNGLs.

Mapping lock-ins and enabling environments for agri-food sustainability transitions in Europe

European agri-food systems must overcome structural lock-ins to achieve more sustainable modes of production and consumption. Yet European regions are highly diverse, and we lack understanding of how different regional characteristics may enable or inhibit sustainability transitions. This hinders the development of context-tailored governance strategies. In this paper, we identify and apply sets of spatial indicators to map the regional potentials for agri-food transitions. We first analyse the strength of lock-in to the incumbent agro-industrial paradigm. We then map the enabling environments for two alternative agri-food networks-multifunctional value chains and civic food networks-that each embed distinct social-ecological qualities of agriculture and food. Results demonstrate a large spatial diversity in transition potential, with stronger lock-ins throughout North and Western Europe and stronger enabling environments for agri-food transitions in Italy, France, Switzerland, and Southwest Germany. We find that lock-ins are strongest in livestock-dominated regions and are associated with higher GHG emissions and excess nitrogen levels. Our study demonstrates the need for coordinated public policies that (1) leverage region-specific transition potentials and (2) enable complementary innovations in market-based and community-led networks.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11625-024-01480-y.

Bayesian areal disaggregation regression to predict wildlife distribution and relative density with low-resolution data

For species of conservation concern and human-wildlife conflict, it is imperative that spatial population data be available to design adaptive-management strategies and be prepared to meet challenges such as land use and climate change, disease outbreaks, and invasive species spread. This can be difficult, perhaps impossible, if spatially explicit wildlife data are not available. Low-resolution areal counts, however, are common in wildlife monitoring, that is, the number of animals reported for a region, usually corresponding to administrative subdivisions, for example, region, province, county, departments, or cantons. Bayesian areal disaggregation regression is a solution to exploit areal counts and provide conservation biologists with high-resolution species distribution predictive models. This method originated in epidemiology but lacks experimentation in ecology. It provides a plethora of applications to change the way we collect and analyze data for wildlife populations. Based on high-resolution environmental rasters, the disaggregation method disaggregates the number of individuals observed in a region and distributes them at the pixel level (e.g., 5 × 5 km or finer resolution), thereby converting low-resolution data into a high-resolution distribution and indices of relative density. In our demonstrative study, we disaggregated areal count data from hunting bag returns to disentangle the changing distribution and population dynamics of three deer species (red, sika, and fallow) in Ireland from 2000 to 2018. We show an application of the Bayesian areal disaggregation regression method and document marked increases in relative population density and extensive range expansion for each of the three deer species across Ireland. We challenged our disaggregated model predictions by correlating them with independent deer surveys carried out in field sites and alternative deer distribution models built using presence-only and presence-absence data. Finding a high correlation with both independent data sets, we highlighted the ability of Bayesian areal disaggregation regression to accurately capture fine-scale spatial patterns of animal distribution. This study uncovers new scenarios for wildlife managers and conservation biologists to reliably use regional count data disregarded so far in species distribution modeling. Thus, it represents a step forward in our ability to monitor wildlife population and meet challenges in our changing world.

Integrated Spatial Simulation of Population and Urban Land Use: a Pan-European Model Validation

Spatial models jointly simulating population and land-use change provide support for policy-making, by allowing to explore territorial developments under alternative scenarios and resulting impacts in the environment, economy and society. However, their ability to reproduce observed spatial patterns is rarely evaluated through model validation. This lack of insight prevents researchers and policy-makers of fully grasping the ability of existing models to provide sensible projections of future land use and population density. In this article, we address this gap by performing a model validation of the LUISA Territorial Modelling Platform, a spatial model jointly simulating population and land use at a fine resolution (100 m) in the European Union and United Kingdom. In particular, we compare observed and simulated patterns of population and urban residential land-use change for the period of 1990-2015, and evaluate the model performance according to different degrees of urbanisation. The results show that model performance can vary depending on the context, even when the same data and methods are uniformly applied. The model performed consistently well in urban areas characterized by compact urban growth, but poorly where residential development occurred predominantly in scattered patterns across rural areas. Overall, the model tends to favour the formation of densely populated, highly accessible urban conglomerations, which often do not entirely correspond to the observed patterns. Based on the validation results, we propose directions for further model improvement and development. Model validation should be regarded as a critical step, and an integral part, in the process of developing models for policy support.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12061-023-09518-x.

Risk assessment and validation of farmland abandonment based on time series change detection

Farmland abandonment, a widespread phenomenon during land-use transition, leads to a cycling or vanishing evolution of farmland resources. As urbanization advances, an increasing number of agricultural laborers migrate from rural to urban areas, causing ongoing farmland abandonment. However, in contrast to the abandoned information extraction and driving mechanisms revelation, the potential risk of farmland abandonment has received insufficient attention. This study took Yangtze River Economic Belt of China as study area, selected multiple aspects to construct a risk assessment system for farmland abandonment, and applied time series change detection to verify the results. The results showed that (1) farmland abandonment risk, with a regional average value of 0.0978, has strong spatial heterogeneity, with high values clustering in Yunnan-Guizhou and Sichuan-Chongqing mountainous areas and low values distributed in the midstream and downstream plains and the Sichuan Basin. (2) The proportion of farmland area gradually decreased as the risk grade increased. Farmland, with low abandonment risk, occupied an area of 204,837 km², constituting the highest percentage of 35.18% among the overall farmland, and was mainly distributed in the provinces of Jiangsu and Anhui. The area of farmland with high risk was 16,458 km², only accounting for 2.83%, the majority of which was clustered in Sichuan and Yunnan provinces. (3) The Normalized Difference Vegetation Index (NDVI) time series change detection validated the reliability of the risk assessment system. Samples of farmland having low abandonment risk indeed had the lowest abandonment rate of 10%, and those which indicated high risk had the highest abandonment rate of 32%. We propose differentiated managements for farmland resources with high and low abandonment risk from the perspective of sustainable use. This study provides a more reasonable and scientific system for farmland abandonment risk assessment and helps to fill the research gap.

Dynamic response relationship between cultivated land marginalisation and rural labour out-migration in mountainous areas in China: evidence from a vector autoregressive model

Understanding the dynamic interaction between cultivated land marginalisation (CLM) and rural labour out-migration (RLM) is vital for the sustainable utilisation of cultivated land, particularly in mountainous areas. Most previous research focused on unilateral CLM or RLM in mountainous areas, with limited research on the dynamic response between these two factors. To address this gap, we identified the characteristics of CLM and analysed the changing trends in RLM in 19 counties of western Hubei Province, China, from 2000 to 2018. The dynamic response relationship between the two phenomena was identified using a vector autoregressive model. CLM showed a volatile trend throughout the study area, with fluctuations most evident during 2004-2007 and 2009-2015. The rural labour population showed an inverted U-shaped trend with an increase during 2003-2015 and a decrease afterward, which is consistent with the trends in socioeconomic development. The dynamic response between the two factors showed large fluctuations in the short term but a stable relationship in the long term. These findings have important implications for differentiated land management, comprehensive land improvement, and rural land use policies and indicate that the added value of agricultural products from mountainous areas should be strengthened.

Carbon sequestration and water yield tradeoffs following restoration of abandoned agricultural lands in Mediterranean mountains

Abandoned cropland areas have the potential to contribute to climate change mitigation through natural revegetation and afforestation programs. These programs increase above and belowground carbon sequestration by expanding forest cover. However, this potential to mitigate climate change often involves tradeoffs between carbon sequestration and water availability. Particularly in a water limited environments such as the Mediterranean region, any loss of recharge to groundwater or streamflow can have critical societal consequences. In this study, we used an ecohydrologic model, Regional Hydro-Ecological Simulation System (RHESSys), to quantify these tradeoffs for land management plans in abandoned cropland areas in Mediterranean mountains. Changes to Net Ecosystem Production (NEP), water yield and Water-Use Efficiency (WUE) under different land management and climate scenarios were estimated for Arnás, a catchment with similar geology, vegetation and climate to many of the locations targeted for land abandonment restoration in the Spanish Pyrenees. Results showed significant changes to both carbon and water fluxes related to land management, while changes related to a warming scenario were not significant. Afforestation scenarios showed the highest average annual carbon sequestration rates (112 g C·m^-2·yr^-1) but were also associated with the lowest water yield (runoff coefficient of 26%) and water use efficiency (1.4 g C·mm^-1) compared to natural revegetation (-27 g C·m^-2·yr^-1, 50%, 1.7 g C·mm^-1 respectively). Under both restoration scenarios, results showed that the catchment ecosystem is a carbon sink during mid-February to July, coinciding with peak monthly transpiration and WUE, while during the rest of the year the catchment ecosystem is a carbon source. These results contribute to understanding carbon and water tradeoffs in Mediterranean mountains and can help adapt restoration plans to address both carbon sequestration and water management objectives.

Research on Construction Land Use Benefit and the Coupling Coordination Relationship Based on a Three-Dimensional Frame Model—A Case Study in the Lanzhou-Xining Urban Agglomeration

Coordinating the social, economic, and eco-environmental benefits of construction land use has become the key to the high-quality development of Lanzhou-Xining urban agglomerations (LXUA). Therefore, based on the coupling coordination connotation and interaction mechanism of construction land use benefit (CLUB), we measured the CLUB level and the coupling coordination degree (CCD) between its principal elements in LXUA from 2005 to 2018. Results showed that: (1) The construction land development intensity (CLDI) in the LXUA is generally low, and spatially presents a dual-core structure with Lanzhou and Xining urban areas as the core. (2) The comprehensive construction land use benefit has increased over time, but the overall level is not high. The spatial differentiation is obvious, and the core cities (Lanzhou and Xining) are significantly higher than other cities. (3) The regional differences in the subsystem benefit of construction land use are obvious. The social benefit and economic benefit showed a “convex” shape distribution pattern of “high in the middle and low in the east and west wings”, and regional differences of economic benefit vary greatly. The eco-environmental benefit was relatively high, showed a “concave” shape evolution in the east–west direction. (4) In addition, the CCD of the CLUB were still at a medium–low level. The higher the administrative level of the city, the better the economic foundation, and the higher or better the CCD of the social, economic, and eco-environmental benefits. (5) The CCD is inseparable from the influence of the three benefits of construction land use. Therefore, different regions should form their own targeted development paths to promote the coordinated and orderly development of LXUA.

Assessment of Land-Use Scenarios at a National Scale Using Intensity Analysis and Figure of Merit Components

To address the impacts of future land changes on biodiversity and ecosystem services, land-use scenarios have been developed at the national scale in Japan. However, the validation of land-use scenarios remains a challenge owing to the lack of an appropriate validation method. This research developed land-use maps for 10 land-use categories to calibrate a land-change model for the 1987–1998 period, simulate changes during the 1998–2014 period, and validate the simulation for the 1998–2014 period. Following an established method, this study assessed the three types of land change: (1) reference change during the calibration time interval, (2) simulation change during the validation time interval, and (3) reference change during the validation time interval, using intensity analysis and figure of merit components (hits, misses, and false alarms). The results revealed the cause of the low accuracy of the national scale land-use scenarios as well as priority solutions, such as aligning the underlying spatial vegetation maps and improving the model to reduce two types of disagreement between the simulation and reference maps. These findings should help to improve the accuracy of model predictions and help to better inform policymakers during the decision-making process.