Sustainable soil management is recognised as a pivotal solution for addressing current and future global challenges, but existing global and national soil property maps often lack the fine-scale resolution required for local or intra-field assessments. Here, we aimed to develop an open access framework to downscale soil property maps using remote and proximal sensor data and test it for predicting soil organic carbon (SOC) and clay across different regions of Europe. To facilitate the dissemination of this framework, we developed the R package "soilscaler", which contains integrated functions for producing downscaled soil maps. This approach uses coarse resolution maps as a baseline, incorporating sensor data and soil observations to train a model explaining local variation of soil properties. We tested the framework in Denmark, Northern Ireland, Lithuania, The Netherlands, and Turkey. For comparison, we also created high-resolution maps using a conventional digital soil mapping (DSM) approach for each field independently. We found that the downscaling performance depends on the quality of the coarse-resolution soil maps, the spatial variability of soil properties within a given field, and the range of inter-field variations in each country. Although the downscaling process showed lower performance than the conventional DSM approach, the results indicate that the downscaled maps better represent local variability than existing national and global soil maps. Additionally, we found that remote sensing sensors generally better represent the spatial distribution of SOC, while proximal soil sensors better capture clay contents. Future studies should focus on gathering more sensor data and correlating it with soil properties to improve predictions based solely on sensor data.

This report is the result of a review on the possibilities of remote sensing for applications in the nature domain, with emphasis on Natura 2000 habitat monitoring. In recent years, enormous progress has been made in the availability and processing of high-resolution satellite and drone images. This increases the potential application for answering all kinds of policy and nature management questions. We demonstrate that remote sensing can have much added value for the monitoring of habitat distribution and habitat quality across a wide range of nature areas. We also demonstrate that higher spatial resolution of remotely sensed imagery often results in better classification accuracies. Deep learning techniques are also becoming popular since they are able to consider the contextual information and not only the spectral information from the imagery in classifying or identifying objects (from habitats to individual plant species). However, the amount of training data can have a large impact on classification accuracies, much more than for more conventional classification methods. This, then, requires a large investment in the collection of in-situ (field) data as well. Another finding is that including LiDAR and hyperspectral data can significantly improve detailed habitat mapping. In summary, the resource of remote sensing data and techniques should be selected depending on the relevant nature types, research questions and nature targets at a specific local, regional or national scale. It requires more communication between remote sensing researchers and ecologists. If nature goals and remote sensing technologies are brought together at an early stage, many applications will be possible. For the Netherlands, the remote sensing community should focus especially on monitoring the structure and function of habitat types. Also, such large-scale and long-term remote sensing monitoring should become part of a national nature monitoring programme.

This report provides a complete methodological description and background information of the Dutch National System for Greenhouse gas reporting of the LULUCF sector. It provides detailed description of the methodologies, activity data and emission factors that were used. Each of the reporting categories Forest Land, Cropland, Grassland, Wetlands, Settlements, Other Land and Harvested Wood Products are described in a separate chapter.

Monitoring traits of cattle with an Unmanned Aerial Vehicle (UAV) could assist farmers in monitoring the status and health of their herd without actually visiting them. This feasibility study presents 3D methods to manually estimate body dimensions, such as height and weight, of individual Holstein Friesian cows with stereo RGB imagery, video and LiDAR data from UAVs. In total, 25 different cows over 3 years were monitored and 4,611 images, ∼10 videos, and a LiDAR dataset were analysed. The methods used were estimating weight by analysing 3D models made with RGB imagery and video from UAV, and estimating height and weight from LiDAR data. Software used in this study to process the UAV data and to extract 3D models are Agisoft Metashape Professional, CloudCompare, RiPROCESS 1.8.4 Riegl Software and Potree Desktop. LiDAR showed accurate results for estimating withers height (mean error 6cm) and weight (mean error 38 kg). Estimating weight with 3D models extracted with Structure for Motion (SfM) from overlapping RGB imagery resulted in less accurate results (mean error 62kg). The latter result improved to a mean error of 31 kg when three outliers were removed which exceeded twice the standard deviation. These results suggest that the use of UAVs is promising for estimating body dimensions in extensive beef production systems. The main challenge remains to record individual cows from all sides without too much movement or motion from the cow to enable 3D modelling. Another challenge was to decrease the mean error in weight estimations so that changes in body weight over time can be monitored. Future research should focus on improving the overall mean error of cattle weight estimation, finding solutions for moving cattle and shifting from manual to automated processing of UAV imagery into 3D models.

Precision agriculture has drawn much attention in the last few years because of the benefits it has on reducing farming costs while maximizing the harvest obtained. Yield prediction is of importance for farmers to fertilize accordingly to reach the potential yield. However, this task is still relying on manual work, which is expensive and time-consuming. Instance segmentation has been implemented in the last years for fruit detection and yield estimation, obtaining state-of-the-art metrics, and reducing the labor required. This research presents a novel approach for spinach seed yield estimation for seed production purposes, that consists of correlating the number of plants and two phenotyping variables (plant area and canopy cover percentage) with the number of harvested seeds and the thousand seed weight. Mask R-CNN is applied to count the number of detections of spinach plants and obtain the object mask from which the plant area is derived. The results show that there is a high linear correlation between a multivariate linear mixed model of the three variables and the number of seeds, with an R2adj of 0.80. Furthermore, 77.42% of the variation in the weight of thousand seeds can be explained by the number of plants. For future studies, the algorithm should be trained with more spinach images from different locations and under varying weather conditions to allow it to generalize for the crop worldwide. It can be concluded, until further research, that Mask R-CNN can be applied for spinach counting and the computation of its individual plant area, with promising results.

This study investigates the association between having a domestic garden or not and the amount of greenery in this garden, and the prevalence of several types of diseases and disorders, as known by one’s general practitioner. Data on garden ownership and the amount of garden greenery, as well as on health, are available for about 800,000 people, all living within city limits. In the statistical analyses, the associations are corrected for, among other things, the socioeconomic status of the individual and its neighbourhood, but also for the local air quality and noise exposure. The results show that for a large number of diseases and disorders having a garden, and especially having more greenery in that garden, is associated with a lower prevalence.

Forest fires and post-fire management practices (PFMP) cause changes in the hydrological response of a hillslope. This study evaluates the effect of log erosion barriers (LB) and Easy-Barriers (R) (EB) on the spatial patterns and values of structural sediment connectivity (SC) in a Mediterranean mountainous pine forest affected by an arson fire in August 2017. A drone flight was done in July 2019 (23 months after the fire and 11 months after the PFMP) to obtain a high-resolution orthomosaic and DEM (at 0.05 m). Two contrasted areas, with and without PFMP, were selected along the same hillslope and 26 small basins were identified: 16 in the treated area (mean area, slope and vegetation recovery of 916 m(2), 60% and 25%; with 94 LB and 39 EB) and 10 in the untreated area (1952 m(2), 75% and 20%). The aggregated index of sediment connectivity (AIC) was chosen to compute SC in three temporal scenarios: Before and just after the fire and when all PFMP were implemented including the incipient vegetation recovery. Output normalization allowed the comparison of the non-nested basins among them. After accounting the intrinsic differences among the basins and areas, and the temporal changes of SC between the three scenarios, the contribution of the barriers was estimated in 27% from the total decrease of SC in the treated area (8.5%). The remaining 73% was explained by the vegetation recovery. The effectiveness of the LB (11.3% on average) and EB (13.4%) did not diminish with increasing slope gradients. These percentages become relevant considering the small area affected by the LB (2.8%) and EB (1.3%). Independent metrics (convergence index, flow width, flat areas and LS factor) also reported clear differences between the two areas-higher soil erosive intensity in the untreated area- and in accordance with the AIC results.

Constructed salt marshes as a Nature-Based Solution for coastal defense offer additional benefits over conventional engineering, but project realization is often hampered by practical and governmental obstacles. We assessed the execution of a local-scale salt marsh construction project as a Nature-Based Solution (NBS) with respect to the regional-scale Social-Ecological System (SES) in an explicitly linked NBS-SES framework. A local municipality came up with various plans to develop its waterfront but these proved unrealizable without wider stakeholder participation. Crucial for success was that the local initiative was turned into an NBS integrating livability, biodiversity and flood safety, and that it was linked to the governance systems and actors in a regional SES. The chosen NBS consists of a city beach and two salt marshes, a salt marsh park that is open to the public and a pioneer salt marsh that is only accessible for research. The pioneer salt marsh was constructed by raising the seabed to around mean high tide with sand obtained from a capital dredging project. It was used as a large-scale natural experiment in salt marsh construction. To test the effect of enrichment with silt and clay on initial salt marsh development, six hectare-scale compartments were created in which mud was mixed with sand in the top 1.0 m of the bed to three mud contents of on average 8%, 25% and 48%. Heavy machinery was needed to mix mud through the upper meter of the sandy bed. Mixing mud was softening the sediment causing the machines to sink into the 48% mud enriched bed. To test whether seeding with a pioneer plant species accelerates salt marsh development, fragments of Salicornia procumbens plants were seeded in half of three compartments. Field observations between November 2018 to September 2020 showed that seeding of Salicornia plant fragments resulted in significant differences in vegetation cover and species richness in the first growing season. Mud content showed significant positive effects on vegetation cover and species richness in the two monitored growing seasons, where the compartments with on average 7–9% mud had the lowest vegetation cover and species numbers. When constructing a salt marsh by raising sand and mixing mud, a mud content of 25% is practically feasible and results in high vegetation cover and species richness.

Drones offer a flexible way to collect data for monitoring of natural areas. Changes in land cover can be seen in the RGB imagery and changes in the terrain are detectable from the LiDAR data. Differences in the Digital Terrain Models from 2017 and 2020 show the hotspots where blowouts and deposition occur and the Lidar profiles show the dynamics of the terrain in high detail. The results indicate significant morphological changes in the dunes that were not directly recognizable in the field. This clearly indicates to the terrain manager that the quality of the ecosystem is afresh and functions as a (near) natural

Post-fire practices (PFP) aim to reduce soil erosion and favour vegetation recovery, but their effectiveness is spatially heterogeneous and under debate because of the economic and environmental costs. This study evaluates the different changes (Δ) of canopy cover (CC), sediment connectivity (SC) and local topography in four areas affected by the Pinet fire in eastern Spain (August 8th, 2018) and managed with: totally burnt with tree removal and long log erosion barriers (LEBs) (Pinet-1), partially burnt without PFP (Pinet-2), totally burnt with tree removal and short LEBs (Pinet-3), and totally burnt without PFP (Pinet-4). An unburnt nearby area was used as control site (Pinet-5). High-resolution images obtained before the fire and during two drone flights after the fire (10.5 and 5.5 months after the fire and PFP; and 18 and 13 months after the fire and PFP) were analysed; and LiDAR- and SfM-derived digital elevation models used to compute the Aggregated Index of SC (AICv2). After correcting calculations, because of the different input sources, and excluding the forest roads (x¯=3.6% of the total surface), CC in the first post-fire scenario was of 25.5% (−40.4% with respect to the pre-fire scenario), 14.5% (−68.4%), 23.8% (−43.7%), 26.9% (−26.5%) and 29.6% (−32.7%) in Pinet-1, P-2_totally_burnt, P-2_partially_burnt, P-3 and P-4; and ΔCC among the drone flights were of +2.45%, +0.02% and +10.54% in Pinet-1, Pinet-3 and Pinet-4. The annual CC recovery rate decrease from 27.5% to 19.1% per year between the first and the second post-fire scenario, indicating a quick vegetation recovery, especially in the first year, and considering the surface area covered by rocks (x¯=16.3%). Topographic changes indicated that not install LEBs favoured shorter flow length pathways after the fire, and thus, runoff will flow faster to cover the same area, achieving higher velocity and thus soil detachment capacity. Sediment connectivity increased in all burnt sub-sites after the fire (Δ1¯=+32.4%), but the increments in the two sub-sites with LEBs were 36% lower than the increase in the sub-sites without LEBs. The increase of connectivity in the first and second post-fire scenarios was −32% and −45% in the sub-site with long LEBs compared with the sub-site with short LEBs. Overall, LEBs effectively favoured vegetation recovery, lengthened overland flow pathways, and reduced sediment transport in the early months, but their usefulness was not as pronounced during the second post-fire year, although these results may be influenced by the Mediterranean conditions of the site.

Knowing before harvesting how many plants have emerged and how they are growing is key in optimizing labour and efficient use of resources. Unmanned aerial vehicles (UAV) are a useful tool for fast and cost efficient data acquisition. However, imagery need to be converted into operational spatial products that can be further used by crop producers to have insight in the spatial distribution of the number of plants in the field. In this research, an automated method for counting plants from very high-resolution UAV imagery is addressed. The proposed method uses machine vision—Excess Green Index and Otsu’s method—and transfer learning using convolutional neural networks to identify and count plants. The integrated methods have been implemented to count 10 weeks old spinach plants in an experimental field with a surface area of 3.2 ha. Validation data of plant counts were available for 1/8 of the surface area. The results showed that the proposed methodology can count plants with an accuracy of 95% for a spatial resolution of 8 mm/pixel in an area up to 172 m 2 . Moreover, when the spatial resolution decreases with 50%, the maximum additional counting error achieved is 0.7%. Finally, a total amount of 170 000 plants in an area of 3.5 ha with an error of 42.5% was computed. The study shows that it is feasible to count individual plants using UAV-based off-the-shelf products and that via machine vision/learning algorithms it is possible to translate image data in non-expert practical information.

This report provides a complete methodological description and background information of the Dutch National System for Greenhouse gas reporting of the LULUCF sector. It provides detailed description of the methodologies, activity data and emission factors that were used. Each of the reporting categories Forest Land, Cropland, Grassland, Wetlands, Settlements, Other land and Harvested Wood Products are described in a separate chapter. Additionally it gives a table-by-table elaboration of the choices and motivations for filling the CRF tables for KP-LULUCF.

This report provides a complete methodological description and background information of the Dutch National System for Greenhouse Gas Reporting of the LULUCF sector. It provides detailed description of the methodologies, activity data and emission factors that were used. Each of the reporting categories Forest Land, Cropland, Grassland, Wetlands, Settlements, Other land and Harvested Wood Products are described in a separate chapter. Additionally it gives a table-by-table elaboration of the choices and motivations for filling the CRF tables for KP-LULUCF.

RESEARCH HIGHLIGHTS1.Both RB and RF classification methods performed well in vegetation structure mapping, respectively, 84.1% and 86.4%. 2. RF was preferred over RB, since the former was better able to handle the complexity of the rules needed to distinguish many classes.3. Exploitation of digital aerial photographs in semi-automatic classification processes remains challenging, due to inaccurate calibration of the reflectance values and the limited number of spectral bands in aerial photographs.4. High resolution satellite imagery is a good alternative if aerial photographs are not available.

waardering van het landelijke gebied in Nederland.Op basis van de antwoorden van ruim 45.000 respondenten concludeert het onderzoek dat de deelnemers de aantrekkelijkheid van het landelijk gebied in Nederland waarderen met gemiddeld een 7,5.Er zijn grote regionale verschillen.In de directe nabijheid van de drie grootste steden in de Randstad is de score het laagst, tot onder de 6.De respondenten maken zich zorgen over toekomstige ontwikkelingen, waarbij het verdwijnen van bloemen, vogels en insecten als grootste bedreiging wordt gezien.Er bestaat een brede steun voor het financieel ondersteunen van boeren voor maatregelen die landschap en natuur versterken.Over de plaatsing van windturbines en zonnepanelen zijn de meningen verdeeld, al is er overeenstemming dat verstedelijkte locaties (en voor windturbines de plaatsing in zee) het meest geschiktst zijn.Participants of the National Landscape Survey appreciate the attractiveness of the rural area in the Netherlands with an average score of 7.5.There are large regional differences.In the direct vicinity of the three largest cities in the Randstad, the score is lowest, under 6.The respondents are worried about future developments, where the disappearance of flowers, birds and insects is seen as the biggest threat.There is broad support for financial support for farmers.Opinions are divided on the placement of wind turbines and solar panels, although there is agreement that urbanized locations and for wind turbines the sea are the most suitable.

Increased soil salinity is a significant agricultural problem that decreases yields for common agricultural crops. Its dynamics require cost and labour effective measurement techniques and widely acknowledged methods are not present yet. We investigated the potential of Unmanned Aerial Vehicle (UAV) remote sensing to measure salt stress in quinoa plants. Three different UAV sensors were used: a WIRIS thermal camera, a Rikola hyperspectral camera and a Riegl VUX-SYS Light Detection and Ranging (LiDAR) scanner. Several vegetation indices, canopy temperature and LiDAR measured plant height were derived from the remote sensing data and their relation with ground measured parameters like salt treatment, stomatal conductance and actual plant height is analysed. The results show that widely used multispectral vegetation indices are not efficient in discriminating between salt affected and control quinoa plants. The hyperspectral Physiological Reflectance Index (PRI) performed best and showed a clear distinction between salt affected and treated plants. This distinction is also visible for LiDAR measured plant height, where salt treated plants were on average 10 cm shorter than control plants. Canopy temperature was significantly affected, though detection of this required an additional step in analysis - Normalised Difference Vegetation Index (NDVI) clustering. This step assured temperature comparison for equally vegetated pixels. Data combination of all three sensors in a Multiple Linear Regression model increased the prediction power and for the whole dataset R-2 reached 0.46, with some subgroups reaching an R-2 of 0.64. We conclude that UAV borne remote sensing is useful for measuring salt stress in plants and a combination of multiple measurement techniques is advised to increase the accuracy.