Land Parcel Identification System Research Articles

Prediction of Canopy Cover for Agricultural Land Classification in Land Parcel Identification System (LPIS) Data Using Planet-Scope Multispectral Images: A Case of Gelendost District

Prediction of Canopy Cover for Agricultural Land Classification in Land Parcel Identification System (LPIS) Data Using Planet-Scope Multispectral Images: A Case of Gelendost District

The first map of crop sequence types in Europe over 2012–2018

Abstract. Crop diversification is considered a key element of agroecological transition, whereas current dominant cropping systems are known to rely on only a few crop species – like cereals in Europe. To assess the benefits of crop diversification at a large scale, an accurate description of current crop sequences is required as a baseline. However, such a description is lacking at the scale of Europe. Here, we developed the first map of dominant crop sequences in Europe for the period 2012–2018. We used the Land Use Cover Area frame statistical Survey (LUCAS) dataset that provides temporally incomplete (2012, 2015 and 2018) land cover information from a stable grid of points covering Europe. Eight crop sequence types were identified using hierarchical clustering implemented on LUCAS data and mapped over Europe. We show, in France, that the relative importance of these eight crop sequence types (as estimated from LUCAS data) was highly consistent with those derived from an almost spatially exhaustive temporally complete national dataset (the French Land Parcel Identification System) for the same period, thus validating the method and typology for this country. Land use (i.e. crop production area) derived from our map of dominant crop sequences was also highly consistent with land use reported by official statistics at both national and European levels, validating the approach at a European scale. This first map of dominant crop sequences in Europe should be useful for future studies dealing with agricultural issues that are sensitive to crop rotations. The map of dominant crop sequence types in Europe derived from our work is available at https://doi.org/10.5281/zenodo.7016986 (Ballot et al., 2022).

Nationwide indicators reflecting the current problems of the agricultural landscape in Slovakia: large agricultural parcels, farm size structure and share of woody vegetation

The main problems of the agricultural landscape in Slovakia are the largest parcels in the EU, the share of agricultural land managed by large agro-holdings, and the low proportion of woody vegetation. The authors have analysed these issues using agricultural beneficiary data and remote sensing products. The parcel size and farm sizes were analysed from agricultural beneficiaries’ data, woody vegetation was analysed from the combined layer of Copernicus products and data from the Land Parcel Identification System. Regarding all three indicators, the situation is more problematic in the lowlands and partially in hilly areas. The proposed indicators could be applied to assess the impact of the new CAP (2023-2027), which supports the reduction of parcel size, improves conditions for small and medium farmers, and improves the share of green infrastructure in agricultural landscapes.

Land Parcel Identification System (LPIS) data allows identification of crop sequence patterns and diversity in organic and conventional farming systems.

Farmers grow crops in specific sequences to lower disease pressure and boost crop productivity, particularly in organic farming where artificial pesticides and chemical fertilisers are prohibited. Knowledge about crop sequences used in organic and conventional farming will aid the development of future farming systems through optimising crop diversity and pre-crop effects for improved resource efficiency. This study aims to investigate crop diversity and patterns in organic and conventional crop sequences in Sweden. Large-scale LPIS field data managed by the European Union (EU) Integrated Administration and Control System (IACS) were used to monitor crop sequences on arable land in Sweden over 10 consecutive years (2005–2014). Individual fields (land parcels) could be followed on 40% of Sweden’s total arable area (349,891 fields extracted) over the 10 years. The LPIS data was combined with information from a database on which fields were farmed organically. Crop distribution, diversity of crop sequences and pre-crops to the main cereal crops (winter wheat, spring barley) were analysed in organic and conventional farming systems in the five agricultural productivity zones of Sweden. The results showed that in the most productive zone in southernmost Sweden, small-grain cereals (particularly winter wheat) were the most common crops (62%), followed by oilseeds (11%), ley and forage crops (9%) and sugar beet (8%), when excluding permanent grassland. In the least productive zone (at higher altitudes and/or latitudes), ley and forage crops dominated (67%), followed by spring cereals (barley, oats) (23%). Crop diversity was higher in the two more productive zones (mean 4.6 crop types) than in two less productive zones (3.4) and organic farms showed 9% higher crop diversity than conventional farms in the most productive zones. Overall, in all zones, the pre-crop to winter wheat was generally a different crop type (3 out of 5 times) e.g., young ley (1–2 years) or grain legume, while the pre-crop to spring barley was most often (4 out of 5 times) another cereal. For both these crops, pre-crop type was more diverse in organic than conventional systems. These findings demonstrate that LPIS data can offer valuable insights into agronomic trends and on-farm practices regarding crop choice and that analysis of field-level LPIS data on crop sequences at large scale can reveal information about organic and conventional cropping in different productivity zones across countries. This information can be used to understand the practical limitations in the use of crop diversity to maximise pre-crop effects. This could in turn support advisory service and policy makers to facilitate more sustainable, productive and resource efficient crop production.

Land cover changes with the development of anaerobic digestion for biogas production in France

AbstractAnaerobic digestion is developing in various countries worldwide to produce renewable energy. In addition, the resulting digestates provide readily available nutrients when applied to cropping systems as fertilizers. The introduction of a biogas plant on a farm can induce land cover changes, in relation to the production of feedstock for the biogas plant and/or to the modification of the farming system. The aim of this study was therefore to characterize and quantify the land cover changes in farms associated with biogas plants in France. We combined two national spatialized databases: the Land Parcel Identification System (yearly French land cover at the parcel scale with farm identifier per parcel) and the SINOE database (biogas plant location and year of start‐up). We showed that, on average, the changes were limited, with an increase in maize areas (+3.4% of the total farm areas) compensated by a decrease in wheat and rapeseed areas (−1.8% and −1.9%, respectively), but with a certain variability. The French regulation and market limiting the use of dedicated energy crops seems to have limited land cover changes in France compared to other countries. However, we elaborated a typology of land cover changes and characterized five clusters of farms across the country. The main one (67% of the farms) corresponded to unchanged land cover after the introduction of a biogas plant. The four other clusters showed contrasting changes, for example, an increase or a decrease in grassland areas, a strong increase in maize areas, or a replacement of winter wheat by winter barley. The diversity and the driving factors behind these changes deserve to be better studied and understood through further farmer surveys.

Land use classification over smallholding areas in the European Common Agricultural Policy framework

Land use (LU) monitoring and mapping from remote sensing (RS) data are relevant topics in Earth observation (EO). LU monitoring requires powerful/robust classification approaches able to provide reliable identifications in decision-making applications, even over challenging areas. The European Common Agricultural Policy (CAP) promotes the use of remote sensing for LU monitoring in Europe useful in the compliance control of the subsidy payments. In this context, the present study proposes a novel classification chain to identify ten land uses, namely: forest (FOR), pasture with trees (TRE), pasture with shrubs (SHR), pastureland (PAS), vineyard (VIN), arable land (ARL), fruit trees (FRU), nut trees (NUT), citrus (CIT), and olive grove (OLI), over three areas in the Valencian Autonomous Region (Spain) where smallholding farming predominates. The approach exploits the multitemporal information provided by Sentinel-2 data using a novel spatial strategy specifically designed to deal with heterogeneous agricultural areas. More precisely, we implemented a deep learning algorithm based on bidirectional recurrent neural networks to account for complex temporal dynamics. The classification results over the three areas showed accuracies higher than 95.5% over validation sets composed of in-situ checks never used during the training process. The proposed methodology outperformed standard approaches that not consider the spatial variability of the training samples, and revealed very good agreement concerning the Land Parcel Identification System (LPIS) of Spain. In addition, the developed chain proposes the novelty of using the kernel normalized difference vegetation index (kNDVI) as a predictor in a LU classification processing chain. Including the kNDVI instead of the traditional NDVI outperformed the classification accuracy in all metrics and classes. Ultimately, the obtained classifications were used for assessing the 2020 farmers’ declarations in the three study areas. The declarations showed a level of agreement concerning the proposed approach near 99%.

Long-term trends in functional crop diversity across Swedish farms

The diversity of cultivated crops is relevant on various spatial scales, from the field and farm to the landscape. We apply a decomposition of the Shannon diversity index that allows the differentiation of functional diversity of production. The decomposition separates diversity of functional crop groups from related diversity, which shows the species diversity within the crop groups. Using population-based field and farm-level data from Sweden 2001–2018, we are able to study the development of overall (Shannon), functional and related crop diversity among a total of 83770 farms. Crop diversity indices are calculated by farm and year based on the Swedish Land Parcel Identification system (LPIS). We find that functional crop diversity has declined among Swedish farms over the period. Related crop diversity has declined but regained in recent years. Accounting for farm size and pedoclimatic conditions, organic farms have a higher functional diversity, and the uptake of organic practices leads to an increase in functional crop diversity over the period.

Land cover classification using Land Parcel Identification System (LPIS) data and open source Eo-Learn library

Depending on the development of remote sensing technologies, data at various spatial, temporal and spectral resolutions are obtained and play a key role in Earth investigation. Land cover/use maps are also created using these data. These data play an important role as a base for determining land cover and land use classes, detecting changes, monitoring forest, agriculture and wetlands, as well as updating the LPIS data used by countries. As well as being reference data in the LPIS update, current land cover and land use classification results with satellite images also provide statistical information about which class has changed over the years and how much the class or classes need to be updated, and will also provide the planning of the update to be made on a national scale. In this study, land cover classification for 2020 covering Çivril-Baklan Plain, which is between the borders of Denizli Province (Turkey), Baklan, Çal and Çivril Districts, was performed. The LPIS classes of the study area drawn in 2015 and the land cover classes as a result of the 2020 classification were compared as a result of the classification process, and particularly the areas with change were referenced in the LPIS update. In the classification process, the open source Eo-Learn library, which uses machine learning and deep learning algorithms in remote sensing studies and Sentinel-2 images that can be accessed directly in this library were used. The open source Eo-Learn library has been preferred because it provides great convenience to users in the classification process with ready-made machine learning models and easy access to satellite images, image processing steps (cloud masking, calculate index, feature extraction, temporal interpolation) are carried out in a certain workflow. Instead of land cover classification from a single image, 17 different time (multi-temporal) images between 01.02.2020 and 31.11.2020 were used. In the classification process, 12 different features were used for the image taken on each date, including NDVI, NDWI, NDBI indices and Tasseled Cap transformations, as well as (Blue) B02, (Green) B03, (Red) B04, (NIR) B08, (SWIR-1) B11, (SWIR-2) B12 bands. In the classification process, the Light Gradient Boosting Machines (LightGBM) algorithm in the Eo-Learn library and the physical blocks produced within the scope of the Land Parcel Identification System (LPIS) project were used as ground truth data; arable land, bare land, forest, artificial surface, shrubland, tree crops and water classes were created. The classification results were evaluated using the K fold (k = 5) cross validation method, with F1 score, recall, and precision values calculated for each class, and the overall accuracy %92.2. When the classification result is compared with the LPIS data, it is seen that the arable land, shrub land, and water classes have changed. This study determined that these classes, in particular, need to be updated.

Modeling drivers of farming system trajectories in Mediterranean peri-urban regions: Two case studies in Avignon (France) and Pisa (Italy)

CONTEXTMediterranean peri-urban regions have changed considerably in recent decades, leading to different farming system trajectories. The high level of heterogeneity and complexity of these areas are particularly challenging in terms of understanding the ongoing processes and their drivers, including the effects of spatial interactions, in order to make an initial assessment of the possible levers for territorial management. OBJECTIVEWe aim to show a new modeling approach to assess the factors explaining farming system trajectories on Mediterranean peri-urban areas, including an analysis of the spillover from neighboring areas, which is often neglected in these kinds of methods. The model was tested in two urban regions, located in Pisa (Italy) and Avignon (France), leading to very different perspectives, given the differences in terms of farming systems and environmental characteristics. Our model also highlights the factors acting on peri-urban farming system trajectories. METHODSWe first analyzed the farming systems characteristics and their trajectories, based on a land parcel identification system database and agricultural census. Then, we estimated explanatory factors at the farm level applying a spatially explicit model which takes into spatial autocorrelations into account: a Spatial Autoregressive Probit model. RESULTS AND CONCLUSIONSOur results provide new evidence supporting the hypothesis that peri-urban farming systems trajectories are influenced by other variables than the distance to the city center and the urbanization processes. Although urban sprawl characterizes medium-sized regions, this factor is not relevant on farming systems trajectories in the region of Pisa, where structural agro-pedoclimatic drivers contribute more significantly to the probability of change and, therefore, highlighting the greater stability of these farming systems. The region of Avignon reveals different aspects: a dense road infrastructure and favorable environmental conditions (availability of irrigation water during warm periods, adequate temperatures in winter, deep soils) increase the probability of intensification within the farming system. SIGNIFICANCEOur methodological approach, which is based on a spatial probit model, measures the effect of the spatial dependence of the dependent variables. In addition, the preliminary classification in terms of farming systems improves our understanding of the land trajectories. The results obtained provide new insights into the processes underway in Mediterranean peri-urban areas.

Mapping Crop Types of Germany by Combining Temporal Statistical Metrics of Sentinel-1 and Sentinel-2 Time Series with LPIS Data

Nationwide and consistent information on agricultural land use forms an important basis for sustainable land management maintaining food security, (agro)biodiversity, and soil fertility, especially as German agriculture has shown high vulnerability to climate change. Sentinel-1 and Sentinel-2 satellite data of the Copernicus program offer time series with temporal, spatial, radiometric, and spectral characteristics that have great potential for mapping and monitoring agricultural crops. This paper presents an approach which synergistically uses these multispectral and Synthetic Aperture Radar (SAR) time series for the classification of 17 crop classes at 10 m spatial resolution for Germany in the year 2018. Input data for the Random Forest (RF) classification are monthly statistics of Sentinel-1 and Sentinel-2 time series. This approach reduces the amount of input data and pre-processing steps while retaining phenological information, which is crucial for crop type discrimination. For training and validation, Land Parcel Identification System (LPIS) data were available covering 15 of the 16 German Federal States. An overall map accuracy of 75.5% was achieved, with class-specific F1-scores above 80% for winter wheat, maize, sugar beet, and rapeseed. By combining optical and SAR data, overall accuracies could be increased by 6% and 9%, respectively, compared to single sensor approaches. While no increase in overall accuracy could be achieved by stratifying the classification in natural landscape regions, the class-wise accuracies for all but the cereal classes could be improved, on average, by 7%. In comparison to census data, the crop areas could be approximated well with, on average, only 1% of deviation in class-specific acreages. Using this streamlined approach, similar accuracies for the most widespread crop types as well as for smaller permanent crop classes were reached as in other Germany-wide crop type studies, indicating its potential for repeated nationwide crop type mapping.

Assessment of the diversity of crop rotations based on network analysis indicators

CONTEXTMore diversified crop rotations are a key factor in reducing weed, disease and pest pressure while reducing the use of phytosanitary products. The increase in available data calls for the development of new methods and indicators to characterize crop rotations. OBJECTIVESThis paper presents an application of network analysis to assess the diversity of crop rotations from the Land Parcel Identification System, which now provides field boundaries and type of crops grown in a given year for farmers receiving subsidies from the European Common Agricultural Policy. Different indicators are presented to compare the diversity of crop rotations for the 22 regions of mainland France (corresponding to the boundaries of former administrative regions) and the influence of some methodological choices are discussed. METHODSUsing LPIS data, previous-following crop pairs have been identified for two thirds of the French fields for three crop successions (from 2017 to 2018, from 2018 to 2019 and from 2019 to 2020). These data were used to build crop rotation networks for each region. Crop rotations were simulated from those networks in order to estimate the return time of soft winter wheat, which is the most cultivated crop in the country. RESULTS AND CONCLUSIONCrop rotation networks were similar between the three crop successions compared, but differed among regions. Ignoring the most uncommon previous-following crop pairs, the mean number of precedents per crop ranged from three to nine depending on the region. The estimated return times for winter wheat increase when taking into account grasslands in crop rotation networks, but in any case the use of phytosanitary products was negatively correlated with the return time. SIGNIFICANCEThe methodology developed in this article provides some initial guidelines for developing relevant agronomic indicators from crop rotation network analysis. It has been shown that the estimated return time could be an indicator of the intensity of use of phytosanitary products, and could therefore be used to guide public policies aimed at reducing the use of these products.

Crop Detection Using Time Series of Sentinel-2 and Sentinel-1 and Existing Land Parcel Information Systems

Satellite crop detection technologies are focused on the detection of different types of crops in fields. The information of crop-type area is more useful for food security than the earlier phenology stage is. Currently, data obtained from remote sensing (RS) are used to solve tasks related to the identification of the type of agricultural crops; additionally, modern technologies using AI methods are desired in the postprocessing stage. In this paper, we develop a methodology for the supervised classification of time series of Sentinel-2 and Sentinel-1 data, compare the accuracies based on different input datasets and find how the accuracy of classification develops during the season. In the EU, a unified Land Parcel Identification System (LPIS) is available to provide essential field borders. To increase usability, we also provide a classification of the entire field. This field classification also improves overall accuracy.

Developing a high-resolution land use/land cover map by upgrading CORINE’s agricultural components using detailed national and pan-European datasets

The agricultural uses of the Coordination of Information on the Environment Land Cover (CLC) dataset suffer from limitations such as temporal stationarity, low spatial resolution, broad and rather simplified grouping of classes. The study attempts to address these shortcomings, using as test site the Sperchios River catchment, Central Greece. The Greek ‘branch’ of the Land Parcel Identification System, Beneficiaries’ Declarations (BD) and CLC inventories were utilized to develop hybrid layers, deriving from their harmonization, sequential incorporation and progressive update (BD → BD-ilot → BD-ilot-CLC). The final layer constitutes the new object-oriented Land Use/Land Cover map. Remote sensing data (Sentinel-2) was used to validate the accuracy of the BD, subject to the most frequent errors. The new map retains the key advantages of CLC yet is now characterized by highly detailed spatial resolution and the explicit description of the different cultivated farmlands included.

A Sentinel-2 Multiyear, Multicountry Benchmark Dataset for Crop Classification and Segmentation With Deep Learning

In this work, we introduce Sen4AgriNet, a Sentinel-2-based time series multicountry benchmark dataset, tailored for agricultural monitoring applications with machine and deep learning. Sen4AgriNet dataset is annotated from farmer declarations collected via the land parcel identification system (LPIS) for harmonizing country-wide labels. These declarations have only recently been made available as open data, allowing for the first time the labeling of satellite imagery from ground truth data. We proceed to propose and standardize a new crop type taxonomy across Europe that addresses common agriculture policy (CAP) needs, based on the Food and Agriculture Organization (FAO) indicative crop classification scheme. Sen4AgriNet is the only multicountry, multiyear dataset that includes all spectral information. It is constructed to cover the period 2016–2020 for Catalonia and France, while it can be extended to include additional countries. Currently, it contains 42.5 million parcels, which makes it significantly larger than other available archives. We extract two subdatasets to highlight its value for diverse deep learning applications—the object aggregated dataset (OAD) and the patches assembled dataset (PAD). OAD capitalizes zonal statistics of each parcel, thus creating a powerful label-to-features instance for classification algorithms. On the other hand, PAD structure generalizes the classification problem to parcel extraction and semantic segmentation and labeling. The PAD and OAD are examined under three different scenarios to showcase and model the effects of spatial and temporal variability across different years and different countries. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>

Spatial Data Model for Rural Planning and Land Management in Turkey

In Turkey, in the areas of rural planning and land management, problems regarding data retrieval, data quality, implementation scenario and legal base (law or regulation) have long been experienced. In this study, in order to contribute to resolving such problems, a conceptual/semantic data model was designed which focuses on the definition of required data, determination of their basic qualities and also their relations. As the preparation step for the model development, interviews, and discussions with authorized people were carried out. In addition, for the definitions of the data in the model, the Land Parcel Identification System and Infrastructure for Spatial Information in the European Community (INSPIRE) are considered. For the model design, an object-oriented modelling method with the Unified Modelling Language (UML) notation was used. In the model, planning activities were focused on. It is envisaged that the model will guide work for the preparation of a technical regulation which may enable a standardized implementation throughout Turkey. It has also the potential to be an example for the implementation of laws related to spatial data both in Turkey and also worldwide.

Investigating operational country-level crop monitoring with Sentinel~1 and~2 imagery

ABSTRACT In this paper, we propose an operational solution for the yearly classification of crop parcels at national scale (namely France) for Land Parcel Identification System updating, under the Common Agricultural Policy (CAP) umbrella. Our pipeline is based on the open-source framework and fed with both time series of Sentinel-1 radar and Sentinel-2 optical images, with complementary contributions. Three conceivable scenarios are investigated with two sets of nomenclatures (17 and 43 classes): early, on-line, and late classifications. Experiments performed on 2017 show very satisfactory results (82–97%), locally almost on-par with state-of-the-art deep-based methods. We can conclude our framework offers a strong basis for country-scale operational deployment for 2020+ CAP.

Natural Carbon Sinks Linked to Pastoral Activity in S Spain: A Territorial Evaluation Methodology for Mediterranean Goat Grazing Systems

Exploring and developing new tools for the accounting and management of natural C sinks will provide a closer, more accurate option to remark the importance of such sinks in relation to livestock production, helping to support the persistence of some seriously endangered traditional, environmentally sustainable livestock farming. Following both precision and usability criteria, two main C sink databases covering the Andalusian region (S Spain) were developed from the Spanish Land Parcel Identification System (SIGPAC, coarse resolution) and the Spanish Information System on Land Cover (SIOSE, finer resolution) land use classes. Particular C sink factors based on growth rates for individual plant species were associated with detailed vegetation maps and, further, were linked to Land Use and Covers cartography across the region. In addition, eight ruminant farms were exhaustively studied in situ and used as a control. Results were compared with the obtained through the application of the developed C sink databases, and with the commonly used Petersen methodology. The sink capacity of vegetation associated with farms varied from 0.25 to 1.37 t CO2 ha−1 year−1, depending on the plant species composition and abundance. All the approaches showed significant differences from the control. C sink values were significantly higher when applying SIGPAC-based C sink database to farms, while values from the SIOSE and Petersen methodology approaches provided more moderate values, closer to the control. SIGPAC and Petersen approaches showed higher usability but presented lower precision due to a poor definition of plant cover. SIOSE-based C sink database provided suitable values able to be adapted to reality and used by farmers. In this regard, further research efforts to improve the adjustment of results and ease of use are required. The present approach means a methodological advance in the estimation of the C sink capacity associated with pastoral livestock farms, able to be incorporated into the CF calculation in contrasted areas worldwide, in the frame of the ‘eco-schemes’ being recently under development through the EU CAP.

Exploring the risk of hypospadias in children born from mothers living close to a vineyard.

Hypospadias (H) is a common birth defect affecting the male urinary tract. It has been suggested that exposure to endocrine disrupting chemicals might increase the risk of H by altering urethral development. However, whether H risk is increased in places heavily exposed to agricultural pesticides, such as vineyards, remains debated and difficult to ascertain. The objective of the work is to test the possible association of H with residential proximity to vineyards. Residential address at birth of 8,766 H cases born 1980–2011 was taken from 17 specialized surgery centers. The geographical distribution of vineyards was obtained from the European Land Parcel Identification System (LPIS) and the distance of address to the nearest vineyard was computed. A first estimate of the variation of H relative risk with distance to vineyards was obtained using as controls 13,105 cryptorchidism (C) cases operated during the same period in the same centers. A separate estimate was obtained from a case-control study using “virtual controls” (VC) defined as points of the map sampled to match the demographic distribution of births within the recruitment territories of the study centers. Non-exposed patients were defined as those with a residence between 5,000 and 10,000 m from the closest vineyard. The residential distance to vineyard was smaller for H than for C cases (p<10−4). We found 42/8766 H cases (0.48%) and 50/13,105 C cases (0.38%) born to mothers living within 20 m of a vineyard. The odds ratios for H were 2.48 (CI: 1.0 to 5.1) and 2.4 (CI: 1.3 to 4.4), vs C or vs VC, respectively, when pregnant mothers lived 10–20 m from a vineyard. In conclusion, our study supports that children born to mothers living close to a vineyard have a two-fold increased risk of H. For environmental research, the use of VC provides an alternative to classical case control technique.

Monitoring and evaluating the contribution of the rural development program to high nature value farmland dominated by traditional mosaic landscape in Slovakia

This paper focuses on designing an approach to monitor the effects of the Rural Development Program (RDP) on High Nature Value Farmland type 2 – farmland dominated by traditional mosaics of cultivated land and small-scale features (HNV2) – and its application to farmland in Slovakia. Based on the identification of HNV2 on the national level and selection of monitoring sites, we carried out monitoring for two periods: 1st monitoring of RDP 2007–2013 and 2nd monitoring of RDP 2014–2020. We developed a set of indicators for monitoring of HNV2, which were mapped in the field: management intensity, categories of land cover and type of management, share of semi-natural elements, management activities. The most significant changes were observed in the proportion of semi-natural vegetation and management intensity. We defined five categories of changes in management intensity: either positive – 1) improved, or 2) persistent regular management – or negative – 3) continued abandonment, 4) decrease in management intensity, or 5) intensification causing decrease of landscape diversity. The evaluation of land cover changes on the basis of landscape indices shows higher landscape diversity on sites without subsidies. This shows that subsidies are linked to farmland with less diverse landscape structure. Statistical analyses showed semi-natural vegetation to be dominant on both subsidised and unsubsidised sites. The total area of HNV2, which is one of the basic indicators evaluated under the RDP, is decreasing: partly due to updates and refinements of the Land Parcel Identification System (LPIS), and exclusion of HNV2 sites from LPIS due to a presence of woody elements, but also partly due to abandonment. The results of our study showed a slightly positive contribution of RDP to maintenance and sustainable management of HNV2.

Mapping Aboveground Woody Biomass on Abandoned Agricultural Land Based on Airborne Laser Scanning Data

Mapping aboveground woody biomass (AGB) on abandoned agricultural land (AAL) is required by relevant stakeholders to monitor the spatial dynamics of farmland afforestation, to assess the carbon sequestration, and to set the appropriate management of natural resources. The objective of this study was, therefore, to present and assess a workflow consisting of (1) the spatial identification of AAL based on a combination of airborne laser scanning (ALS) data, cadastral data, and Land Parcel Identification System data, and (2) the prediction of AGB on AAL using an area-based approach and a nonparametric random forest (RF) model based on a combination of field and ALS data. Part of the second objective was also to evaluate the applicability of (1) the author-developed algorithm for the calculation of ALS metrics and (2) a single comprehensive RF model for the whole area of interest. The study was conducted in the forest management unit Vígľaš (Slovakia, Central Europe) covering a total area of 12,472 ha. Specifically, five reference areas consisting of 11,194 reference points were used to assess the accuracy of the spatial identification of AAL, and seventy-five ground reference plots were used for the development of the ALS-based AGB model and for assessing the accuracy of the AGB map. The overall accuracy of the spatial identification of AAL was found to be 93.00% (Cohen’s kappa = 0.82). The difference between ALS-predicted and ground-observed AGB reached a relative root mean square error (RMSE) at 26.1%, 33.1%, and 21.3% for the whole sample size, plots dominated by shrub species, and plots dominated by tree species, respectively.