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The climate change has turned out to be a determining factor in the development of forest in Spain. Production systems have emitted polluting gases and other particles into the atmosphere, for which some plants have not yet developed adaptation systems. Among the most harmful pollutants for the environment are gases such as nitrous oxides, ozone, particulate matter.

However, this condition is not the same in Peninsular Spain, and the Balearic Islands since the plant compositions differ in the territory and the bioclimatic, topographic, and anthropic characteristics. Monitoring the vegetation with sufficient spatial and temporal resolution, studying variables conditioning plant health is a challenge from the nature of the variables and the amount of data to be handled. 

The Mediterranean forest is one of the most ecosystem affected by climate change because of usually experimented long periods of drought that, in combination with increased temperatures, can drastically reduce the photosynthetic activity of trees and therefore the biomass of forests.

That is why the application of environmental technologies based on Remote Sensing (which provide plant health indices from passive sensors on satellite platforms and other variables of interest), Geographic Information Systems (to integrate, process, analyze spatial and temporal data) and machine learning models (which facilitate the extraction of relationships between variables, conditioning factors and predict patterns). 

In this regard, this work's objective is to evaluate the possible effect that different pollutants have on the health of the vegetation, measured from the annual values of the Normalized Difference Vegetation Index (NDVI), in the Mediterranean forests of Peninsular Spain. To achieve this, we are used machine learning techniques using the Random Forest algorithm. The study has also been done with various climatic, topographic, and anthropic variables that characterize the forest to carry it out. 

The results showed that certain variables such as the aridity index had generated the NDVI values and therefore plant development, while others are limiting factors such as the concentration of certain pollutants and the direct relationship between them particulates and NOx. This study can verify how the Random Forest algorithm offers reliable results, even when working with heterogeneous variables. 

How to cite: García Bruzón, A., Arrogante Funes, P., and Muñoz Moral, L.: Effects of air quality on the health of Mediterranean forests, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16171, https://doi.org/10.5194/egusphere-egu21-16171, 2021.

Rangelands cover 70% of the world’s land surface, and provide critical ecosystem services of primary production, soil carbon storage, and nutrient cycling. These ecosystem services are governed by very fine-scale spatial patterning of soil carbon, nutrients, and plant species at the centimeter-to-meter scales, a phenomenon known as “islands of fertility”. Such fine-scale dynamics are challenging to detect with most satellite and manned airborne platforms. Remote sensing from unmanned aerial vehicles (UAVs) provides an alternative option for detecting fine-scale soil nutrient and plant species changes in rangelands over smaller extents than typically imaged with satellite and manned airborne platforms. We demonstrate that a model incorporating the fusion of UAV multispectral and structure-from-motion photogrammetry classifies plant functional types and bare soil cover with an overall accuracy of 95% in rangelands degraded by shrub encroachment and disturbed by fire. We further demonstrate that employing UAV hyperspectral and LiDAR (light detection and ranging) fusion greatly improves upon these results by classifying 9 different plant species and soil fertility microsite types (SFMT) with an overall accuracy of 87%. Creosote bush (Larrea tridentata) and black grama (Bouteloua eriopoda) are the most important native species in the rangeland and have the highest producer’s accuracies at 98% and 94%, respectively. The integration of UAV LiDAR-derived plant height differences was critical in these improvements. Finally, we use synthesis of the UAV datasets with ground-based LiDAR surveys and lab characterization of soils to estimate that the burned rangeland potentially lost 1,474 kg/ha of C and 113 kg/ha of N owing to soil erosion processes during the first year after a prescribed fire. However, during the second-year post-fire, grass and plant-interspace SFMT functioned as net sinks for sediment and nutrients and gained approximately 175 kg/ha C and 14 kg/ha N, combined. These results provide important site-specific insight that is relevant to the 423 Mha of grasslands and shrublands that are burned globally each year. While fire, and specifically post-fire erosion, can degrade some rangelands, post-fire plant-soil-nutrient dynamics might provide a competitive advantage to grasses in rangelands degraded by shrub encroachment. These novel UAV and ground-based LiDAR remote sensing approaches thus provide important details towards more accurate accounting of the carbon and nutrients in the soil surface of rangelands.

How to cite: Sankey, T., Sankey, J., Li, J., Ravi, S., Wang, G., Caster, J., and Kasprak, A.: Quantifying plant-soil-nutrient dynamics in rangelands: Fusion of UAV hyperspectral-LiDAR, UAV multispectral-photogrammetry, and ground-based LiDAR-digital photography in a shrub-encroached desert grassland, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-749, https://doi.org/10.5194/egusphere-egu21-749, 2021.

Metrics such as Leaf Area Index (LAI) are key factors in agricultural monitoring to understand the health and predictive yield of crops. Knowing the spatial distribution and variability in more detail increases the precision of fertilizer and irrigation practices. Unmanned Aircraft Systems (UAS) provide a means to carry sensors at a low altitude below the clouds providing a much higher spatial and temporal resolution than previously seen with satellite remote sensing while also providing more spatially complete data as compared to ground methods. Being that LiDAR is an active sensor and does not depend on solar reflectance and its corresponding zenith angle like commonly used passive optical sensors, it can further improve upon these UAS characteristics. It can also sense further into the canopy as the laser signals can pass through small gaps and are not affected by the shadowing of plant features created by the canopy itself. Evaluating the penetration of these signals and investigating the gap fraction (GF) that relates to canopy density, we are able to retrieve LAI. However, as LiDAR is sensing all above-ground plant elements it may present the ability to estimate Plant Area Index (PAI) rather than LAI when monitoring an entire growing season for a cereal crop like winter wheat that begins browning during senescence. This study investigates the feasibility of using LiDAR to estimate LAI or similar crop canopy density metrics. As LiDAR sensors for UAS are just becoming more accessible, studies related to this topic are scarcely seen.

In this study, a winter wheat field in Selhausen, Germany (~10 ha in size) was monitored throughout the growing season using the following methods: [1] air campaigns with a DJI Matrice 600 UAS with a YellowScan Surveyor LiDAR system, [2] a DJI Matrice 600 UAS with a Micasense RedEdge-M (five band) multispectral sensor, and [3] ground measurements using a SS1 SunScan ceptometer. The resulting LAI type metrics of the UAS LiDAR methods used were compared to methods commonly used with multispectral (MS) and ground instruments to assess the proposed method’s potential. Additionally, because both products are spatially complete unlike the ground measurements, the LiDAR and multispectral methods were compared for similarities in spatial patterns.

The results showed promise in using UAS LiDAR to estimate metrics that relate to LAI. Pearson correlation coefficient between the LiDAR and multispectral methods were moderate to high (R= 0.39 – 0.66) over the growing season. The comparison of UAS LiDAR towards the ground reference was within a 3% difference at times before senescence. Later in the growing season, the discrepancy increased between LiDAR and MS sensor retrievals mainly because of plant browning related to changes in plant chlorophyll content. This study covers the benefits of using UAS mounted LiDAR for LAI related measurements and its potential for improving crop health monitoring for precision farming.

How to cite: Bates, J., Montzka, C., Schmidt, M., and Jonard, F.: UAS mounted LiDAR for Estimating LAI Type Metrics for Winter Wheat, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5982, https://doi.org/10.5194/egusphere-egu21-5982, 2021.

Current efforts to enhance the understanding of global carbon (C) cycle rely on novel monitoring campaigns of C sequestration in terrestrial ecosystems.The successful outcome of such efforts will be relevant to sectors ranging from climate change and land use studies (global scale) to precision agriculture and land management consultancy (local scale).To that end, current investigations apply recently developed scientific instrumentation - e.g.  Light detection and Ranging (LiDAR) -  and computational methods - e.g. Machine Learning (ML). Near-field remote sensing - i.e.  Unmanned Aerial Vehicle (UAV)-LiDAR -, can provide high resolution LiDAR data, increasing the monitoring accuracy of C stocks estimates and biophysical variables at the ecosystem scale. In contrast to previous approaches (e.g. image-derived vegetation indices), UAV-LiDAR provides a true 3D description of the canopy vertical structure. In order to evaluate the potential of new approaches towards precise C stock quantification in an agricultural field of Denmark (13 ha.), using near-field remote sensed data, we compare the results based on using 3D canopy metrics - derived from UAV-LiDAR - against the well-established multispectral image based metrics. Then, the performance of six different machine learning (ML) models  - two Random Forest variations, KNN, AdaBoost, ElasticNet, Support Vector, and Linear regression - designed to predict above ground biomass (AGB) based on a set of features derived from (i) UAV-LiDAR point cloud data (PCD), and (ii) multispectral imagery is evaluated. Their prediction quality are tested against unseen data from the same species, and sampling campaigns. Also, the sources of uncertainty are assessed as well as the importance of each predicting feature. The field work was conducted within the footprint of an Integrated Carbon Observation System (ICOS) class 1 station site, facilitating ecosystem traits monitoring in real time. The aerial and biomass sampling campaigns have been operated at 15-days frequency during the crops' growing period, in which, simultaneously, UAV-LiDAR and multispectral image data as well as ground truth biomass data were collected. By means of laboratory analysis, C and nutrient content in the crops' biomass was also determined. Based on arithmetic and morphological methods, the PCD were pre-processed to remove noise and classify them to ground and vegetation points. By means of the methods described, we demonstrate that UAV-LiDAR combined with multispectral data and ML methods can be used to accurately estimate AGB, 3D ecosystem structure as well as C-stocks in agricultural ecosystems. 

How to cite: Caballer Revenga, J., Trepekli, K., Oehmcke, S., Gieseke, F., Igel, C., Jensen, R., and Friborg, T.: Prediction of above ground biomass and C-stocks based on UAV-LiDAR,multispectral imagery and machine learning methods., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15708, https://doi.org/10.5194/egusphere-egu21-15708, 2021.

The Sentinel 2 (S2) constellation mission was designed to facilitate the systematic mapping canopy biophysical variables at medium resolution on a global basis and in a free and open manner.  The mission concept requires the development of downstream services to map variables such as the fraction of absorbed photosynthetically active radiation (fAPAR), fraction of canopy cover (fCOVER) and leaf area index (LAI) using Level 2A surface reflectance inputs from the S2 ground segment.  Currently, free and open products generation can be performed using the Simplified Level 2 Prototype Processor (SL2P) applied on a product granule basis.  Considering that the processor is a prototype this study addresses three questions: 1) Can the SL2P algorithm, or subsequent versions, be engineered to facilitate systematic product generation over large extents in a free and open manner? 2) What is the uncertainty of SL2P products over North America during the growing season? 3) Can the uncertainty be reduced by changing the calibration database used within SL2P?  

To facilitate validation and product generation, SL2P was ported to a Google Earth Engine application (the Landscape Evolution and Forecasting Toolbox).  This now allows mapping of up to one million square kilometers in near real time using either the original SL2P algorithm or updated versions.  SL2P uncertainty was quantified over North America using direct comparison to 20 in-situ sites within the National Environmental Observing Network in the continental United States of America and within a Canada wide field campaign over forests and shrublands conducted by Canada Centre for Remote Sensing. SL2P outputs were also compared to MODIS and Copernicus Global Land Service products over the Belmanip II regional sites and 30 additional forested regions in North America.  Results from NEON validation indicate SL2P is generally within uncertainty requirements except for forests; where it underestimates fAPAR, fCOVER and LAI.  Results for other sites will also be presented.  To address the forest bias, SL2P was recalibrated using simulations from the FLIGHT 3D radiative transfer model representative of North American forests.  The uncertainty of the recalibrated SL2P algorithm will be compared to baseline SL2P estimates to determine if increased model complexity is warranted.

How to cite: Fernandes, R., Baret, F., Brown, L., Canisius, F., Dash, J., Djamai, N., Hong, G., MacDougall, C., Shah, H., Weiss, M., and Zhong, D.: Improvements to the Simplified Level 2 Prototype Processor for Retrieving Canopy Biophysical Variables from Sentinel 2 Multispectral Instrument Data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-510, https://doi.org/10.5194/egusphere-egu21-510, 2021.

Coastal sand dunes provide a large variety of ecosystem services, among which the inland protection from marine floods. Nowadays, this protection is fundamental, and its importance will further increase in the future due to the rise of the sea level and storm violence induced by climate change. Despite the crucial role of coastal dunes and their potential application in mitigation strategies, the phenomenon of the coastal squeeze, which is mainly caused by the urban sprawl, is progressively reducing the extents of the areas where dune can freely undergo their dynamics, thus dramatically impairing their capability of providing ecosystem services.

Aiming to embed the use of satellite images in the study of coastal foredune and beach dynamics, we developed a classification algorithm that uses the satellite images and server-side functions of Google Earth Engine (GEE). The algorithm runs on the GEE Python API and allows the user to retrieve all the available images for the study site and the chosen time period from the selected sensor collection. The algorithm also filters the cloudy and saturated pixels and creates a percentile-composite image over which it applies a random forest classification algorithm. The classification is finally refined by defining a mask for land pixels only. 

According to the provided training data and sensor selection, the algorithm can give different outcomes, ranging from sand and vegetation maps, beach width measurements, and shoreline time evolution visualization. This very versatile tool that can be used in a great variety of applications within the monitoring and understanding of the dune-beach systems and associated coastal ecosystem services. For instance, we show how this algorithm, combined with machine learning techniques and the assimilation of real data, can support the calibration of a coastal model that gives the natural extent of the beach width and that can be, therefore, used to plan restoration activities. 

How to cite: Latella, M., Luijendijk, A., and Camporeale, C.: Regional-scale analysis of dune-beach systems using Google Earth Engine, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12923, https://doi.org/10.5194/egusphere-egu21-12923, 2021.

Studying of the optical properties of agricultural vegetation is one of the methods for plants condition estimation, prediction of their development and changes influenced by natural and anthropogenic factors.

The work is dedicated to the investigation of spectral reflectance function of agricultural Brassica napus taking into account the degree of soil moisture. When most of the agricultural lands in Belarus are covered with vegetation in summer, employing the optical properties of agricultural vegetation for deciphering the soil depends on the degree of soil moisture. Insufficient numbers of days in year when the soil is not covered by vegetation or is in a plowed state requires in-situ optical measurements, because there are more than 50 % cloudy conditions in the year, especially in spring and autumn time.

The study has been carried out near the Minsk 11.06.2020 (53.837004º N, 27.487597º E) in clear, cloudless day. The relief for investigated field is hilly-ridge, characterized by a predominance of elevation marks from 250 to 300 m and it is actively sown field. During the spectrometric measurements, the field has been sown with Brassica napus in the phenological phase of pod formation.

When studying the spectral reflectance of Brassica napus, in-situ spectrometric measurements and analysis of a multispectral image have been carried out. Spectrometric measurements have been carried out by FSR-02 spectrometer (spectral range 400-900 nm, spectral resolution 4.3 nm) aiming to retrieve spectral reflectance function.

The normalized vegetation index NDVI has been used for analyzing the multispectral image from Landsat 8 OLI system with a spatial resolution of 30 m. The results of a study of the correlation between the reflection coefficient of Brassica napus and the area of observed soils will be presented. In addition, the results of the analysis of quasi-synchronous values of the NDVI index and in-situ measurements of the spectral reflectance of Brassica napus will be discussed.

How to cite: Davidovich, Y.: Spectral reflectivity variations of Brassica napus depending on degree of soil moisture, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15781, https://doi.org/10.5194/egusphere-egu21-15781, 2021.

Biodiversity is rapidly declining and monitoring biodiversity change is thus of key importance to prevent the destabilization of ecosystems and their services. A key component of monitoring biodiversity change is the development of Essential Biodiversity Variables (EBVs) which facilitate the harmonization and standardization of raw data from disparate sources. In this context, consistent and adequate geospatial information needs to be available to ecologists and policymakers around the world, even for countries in which comprehensive in-situ biodiversity measurements cannot be taken on a regular basis. Satellite remote sensing (SRS) currently represents the only tool which allows to acquiree spatially contiguous and temporally replicated observations for monitoring biodiversity over continental or (near-)global spatial extents. Observations from SRS already provide a wealth of information on the distribution, structure and functioning of ecosystems, but user requirements of ecologists and policymakers have not been systematically quantified for allowing the development of roadmaps by SRS experts.

In response, we performed a top-down user requirement analysis combined with a bottom-up technical review to highlight (i) how currently available remote sensing products can contribute to biodiversity monitoring, and (ii) which immature SRS products could be prioritized for further development. We performed a systematic review of the Post2020 goals (for 2050) and biodiversity targets (for 2030) of the Convention on Biological Diversity (CBD) and their corresponding biodiversity indicators. Subsequently we evaluated SRS products according to relevance (to biodiversity indicators), (im)maturity, feasibility, and suitability for provisioning user-adequate spatio-temporal information. We found that currently existing CBD-relevant biodiversity indicators mainly use EBV-related information on ecosystem structure and distribution (e.g. available from remote sensing products of landcover and Leaf Area Index, LAI) or on species populations (predominantly acquired from in-situ biodiversity measurements because current SRS products are too limited in the spatio-temporal resolutions of their sensors). Moreover, only few biodiversity indicators derived from SRS currently focus on species traits or community composition EBVs, as both the identification of individual species and the quantification of species traits such as LAI and foliar nitrogen, phosphorus, kalium and chlorophyll content remain challenging. We outline how further advances in data-science techniques (e.g. merging SRS observations of high spectral and high spatial resolution) provide tremendous opportunities for advancing community composition and species-focused EBVs for global biodiversity monitoring.

How to cite: Timmermans, J. and Kissling, D.: Challenges and opportunities of remote sensing for monitoring biodiversity change, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2630, https://doi.org/10.5194/egusphere-egu21-2630, 2021.

Decisions about preventing further climate change involve human means for obtaining an equal and fair result for all humans. In consequence we have to make room for a complete different discours balancing between natural facts obeying natural laws and social behavior according to Duhem-Quine´s principle. In order to explain complex phenomena such as global warming and the isostatic uplift one should reject any one-hypothesis claim according to Duhem-Quine´s principle.

Considering climatologic problems as only pure positive scientific matter for making decisions for mankind how to deal with, is a pure essentialist and substantivistic conception of decision making. While climatologic models explain changes globally, the more unpredictable weather concerns rather local scales, implying that decisions must be adapted to the local situation. The unpredictability of some processes is universal but the consequences can be very local and form the boundary conditions of living. Stated otherwise, we do not just live on the planet Earth, but we live in a specific village/town in a specific region/country. 

Contrary to the widely accepted dominant paradigm decision making should not be based on the slogan ‘think global, act local’, but from the device ‘think local, manage the local effects of global warming’. Indeed, worldwide climate change will generally cause raising oceans, but more locally it will restore the former water balance of uplifted shear costs in Sweden and Finland and it affects the small fisher and farmer societies.

Here we suggest that knowledge of climate changes does not intervene in terms of their universal value such as truth, but under the local horizon of the social practices, artefacts and hierarchical relations with which they are associated. We advocate to reverse the former dominant technical code monopolized by technocracy from dominance of nature to creation of progress of the encroaching new ecosystems that develops out of the original shear coast in south-western Finland. We show based on Landsat imagery that this coast is rapidly changing due to the uplift. Furthermore, we demonstrate that the Eco-Development paradigm may rebalance nature, environment, humans and culture and that it is a valid alternative against the past and present-day socio-economical approach that have accelerated the change of the Earth’s climate, provided the global technocracy´s codes of the dominant paradigm are converted into local adapted social, economic and political codes.

How to cite: Verstraeten, G. J. M. and Verstraeten, W. W.: Beyond the technocratic truth to establish eco-development in the uplifted area of the Finnish southwestern shear coast as observed from space, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8298, https://doi.org/10.5194/egusphere-egu21-8298, 2021.