Remote Sensing Applications to Support Locust Management and Research: Evaluating the Potential of Earth Observation for Locust Outbreaks in Different Regions

This dissertation focuses on satellite remote sensing applications for locust management and additional contributions to locust research. Specifically, the remote sensing-based characterization and interpretation of land surface cover and its dynamics are addressed with a special emphasis on the requirements of different locust species. At first, the aim of this dissertation is to provide a holistic overview of the existing applications using satellite data focusing on different locust species and thus, to present current and new opportunities. Furthermore, remote sensing and geospatial datasets are used in a model to categorize areas with ideal and less than ideal conditions for locust outbreaks. The benefit of up-to-date remote sensing data for preventive locust management is demonstrated using time-series-based Sentinel-2 land cover classification. Due to the diversity of the numerous locust species and their spatial distribution in different geographical locations, this research focuses mainly on two locust species, the Italian locust (Calliptamus italicus) and the Moroccan locust (Dociostaurus maroccanus), as well as on selected study areas within their extensive habitats, respectively. Both selected locust species caused numerous damages in Europe, the Caucasus, Central Asia and North Africa in the past. For both species, there is only a limited number of publications exploiting the capabilities of remote sensing methods. Therefore, this dissertation aims to explore the potential approaches of Earth observation datasets to support preventive locust management and research for both species.Die vorliegende Dissertation beschäftigt sich mit dem Einsatz der Satellitenfernerkundung im Bereich Heuschreckenmanagement und -forschung. Die fernerkundungsbasierte Charakterisierung und Interpretation der Landoberflächen-bedeckung und deren Dynamik stehen dabei - mit Fokus auf die Anforderungen der verschiedenen Heuschreckenarten - im Vordergrund. Ziel dieser Dissertation ist es zunächst, einen ganzheitlichen Überblick über vorhandene Anwendungen von Satellitendaten im Kontext Heuschreckenmanagement zu erarbeiten. Des Weiteren werden fernerkundungs- und geobasierten Datensätzen in einem Model verwendet, um Flächen mit idealen bzw. weniger idealen Bedingungen für Heuschreckenausbrüche zu kategorisieren. Der Vorteil von aktuellen Fernerkundungsdaten für präventives Heuschreckenmanagement wird anhand zeitreihenbasierten Sentinel-2 Landbedeckungsklassifikation demonstriert. Aufgrund der Vielfältigkeit der zahlreichen Heuschreckenarten und deren räumlicher Verteilung in verschiedenen geographischen Lagen, konzentriert sich diese Arbeit im Wesentlichen auf zwei Heuschreckenarten, die Italienische Schönschrecke (Calliptamus italicus) und die Marokkanische Wanderheuschrecke (Dociostaurus maroccanus), sowie auf ausgewählte Studiengebiete innerhalb deren weiträumigen Habitaten. Beide Heuschreckenarten verursachten zahlreiche Ausbrüche in der Vergangenheit mit Schäden in Europa, dem Kaukasus, Zentralasien und Nordafrika. Für beide Heuschreckenarten existieren nur wenige Forschungsarbeiten, die sich mit der Anwendung von Fern-erkundungsdaten auseinandersetzen. Vor diesem Hintergrund zielt diese Dissertation auf die Entwicklung von relevanten Methoden unter Einsatz von Fernerkundungsdaten für beide Heuschreckenarten ab, um präventives Heuschreckenmanagement und -forschung zu unterstützen.Данная диссертация раскрывает тему применения спутникового дистанционного зондирования для контроля саранчовых и проведения дополнительных исследований саранчи. В частности, особое внимание уделяется изучению потребностей различных видов саранчовых при описании характеристик земного покрова и его динамики на основе данных дистанционного зондирования. Первостепенная цель данной диссертации состоит в том, чтобы предоставить целостный обзор существующих приложений, использующих спутниковые данные, в разрезе различных видов саранчовых для того, чтобы раскрыть текущие и потенциальные возможности. Кроме того, дистанционное зондирование и наборы геопространственных данных используются для классификации территорий с идеальными и не идеальными условиями для нашествий саранчи. исследование сосредоточено в основном на двух видах саранчи, итальянского пруса (Calliptamus italicus) и марокканской саранче (Dociostaurus maroccanus), а также на определенных территориях, в пределах их обширногo местообитаний

Surface soil moisture estimate from Sentinel-1 and Sentinel-2 data in agricultural fields in areas of high vulnerability to climate variations: the Marche region (Italy) case study

Surface soil moisture is a key hydrologic state variable that greatly influences the global environment and human society. Its significant decrease in the Mediterranean region, registered since the 1950s, and expected to continue in the next century, threatens soil health and crops. Microwave remote sensing techniques are becoming a key tool for the implementation of climate-smart agriculture, as a means for surface soil moisture retrieval that exploits the correlation between liquid water and the dielectric properties of soil. In this study, a workflow in Google Earth Engine was developed to estimate surface soil moisture in the agricultural fields of the Marche region (Italy) through Synthetic Aperture Radar data. Firstly, agricultural areas were extracted with both Sentinel-2 optical and Sentinel-1 radar satellites, investigating the use of Dual-Polarimetric Entropy-Alpha decomposition's bands to improve the accuracy of radar data classification. The results show that Entropy and Alpha bands improve the kappa index obtained from the radar data only by 4% (K = 0.818), exceeding optical accuracy in urban and water areas. However, they still did not allow to reach the overall optical accuracy (K = 0.927). The best classification results are reached with the total dataset (K = 0.949). Subsequently, Water Cloud and Tu Wien models were implemented on the crop areas using calibration parameters derived from literature, to test if an acceptable accuracy is reached without in situ observation. While the first model’s accuracy was inadequate (RMSD = 12.3), the extraction of surface soil moisture using Tu Wien change detection method was found to have acceptable accuracy (RMSD = 9.4)

Application of Remote Sensing Data for Locust Research and Management-A Review

Recently, locust outbreaks around the world have destroyed agricultural and natural vegetation and caused massive damage endangering food security. Unusual heavy rainfalls in habitats of the desert locust (Schistocerca gregaria) and lack of monitoring due to political conflicts or inaccessibility of those habitats lead to massive desert locust outbreaks and swarms migrating over the Arabian Peninsula, East Africa, India and Pakistan. At the same time, swarms of the Moroccan locust (Dociostaurus maroccanus) in some Central Asian countries and swarms of the Italian locust (Calliptamus italicus) in Russia and China destroyed crops despite developed and ongoing monitoring and control measurements. These recent events underline that the risk and damage caused by locust pests is as present as ever and affects 100 million of human lives despite technical progress in locust monitoring, prediction and control approaches. Remote sensing has become one of the most important data sources in locust management. Since the 1980s, remote sensing data and applications have accompanied many locust management activities and contributed to an improved and more effective control of locust outbreaks and plagues. Recently, open-access remote sensing data archives as well as progress in cloud computing provide unprecedented opportunity for remote sensing-based locust management and research. Additionally, unmanned aerial vehicle (UAV) systems bring up new prospects for a more effective and faster locust control. Nevertheless, the full capacity of available remote sensing applications and possibilities have not been exploited yet. This review paper provides a comprehensive and quantitative overview of international research articles focusing on remote sensing application for locust management and research. We reviewed 110 articles published over the last four decades, and categorized them into different aspects and main research topics to summarize achievements and gaps for further research and application development. The results reveal a strong focus on three species-the desert locust, the migratory locust (Locusta migratoria), and the Australian plague locust (Chortoicetes terminifera)-and corresponding regions of interest. There is still a lack of international studies for other pest species such as the Italian locust, the Moroccan locust, the Central American locust (Schistocerca piceifrons), the South American locust (Schistocerca cancellata), the brown locust (Locustana pardalina) and the red locust (Nomadacris septemfasciata). In terms of applied sensors, most studies utilized Advanced Very-High-Resolution Radiometer (AVHRR), Satellite Pour l'Observation de la Terre VEGETATION (SPOT-VGT), Moderate-Resolution Imaging Spectroradiometer (MODIS) as well as Landsat data focusing mainly on vegetation monitoring or land cover mapping. Application of geomorphological metrics as well as radar-based soil moisture data is comparably rare despite previous acknowledgement of their importance for locust outbreaks. Despite great advance and usage of available remote sensing resources, we identify several gaps and potential for future research to further improve the understanding and capacities of the use of remote sensing in supporting locust outbreak- research and management

A Complete Meteo/Hydro/Hydraulic Chain Application to Support Early Warning and Monitoring Systems: The Apollo Medicane Use Case

Because of the ongoing changing climate, extreme rainfall events’ frequency at the global scale is expected to increase, thus resulting in high social and economic impacts. A Meteo/Hydro/Hydraulic forecasting chain combining heterogeneous observational data sources is a crucial component for an Early Warning System and is a fundamental asset for Civil Protection Authorities to correctly predict these events, their effects, and put in place anticipatory actions. During the last week of October 2021 an intense Mediterranean hurricane (Apollo) affected many Mediterranean countries (Tunisia, Algeria, Malta, and Italy) with a death toll of seven people. The CIMA Meteo/Hydro/Hydraulic forecasting chain, including the WRF model, the hydrological model Continuum, the automatic system for water detection (AUTOWADE), and the hydraulic model TELEMAC-2D, was operated in real-time to predict the Apollo weather evolution as well as its hydrological and hydraulic impacts, in support of the early warning activities of the Italian Civil Protection Department. The WRF model assimilating radar data and in situ weather stations showed very good predictive capability for rainfall timing and location over eastern Sicily, thus supporting accurate river flow peak forecasting with the hydrological model Continuum. Based on WRF predictions, the daily automatic system for water detection (AUTOWADE) run using Sentinel 1 data was anticipated with respect to the scheduled timing to quickly produce a flood monitoring map. Ad hoc tasking of the COSMO-SkyMed satellite constellation was also performed to overcome the S1 data latency in eastern Sicily. The resulting automated operational mapping of floods and inland waters was integrated with the subsequent execution of the hydraulic model TELEMAC-2D to have a complete representation of the flooded area with water depth and water velocity

Effect of the ingestion in the WRF model of different Sentinel-derived and GNSS-derived products: analysis of the forecasts of a high impact weather event

This paper presents the first experimental results of a study on the ingestion in the Weather Research and Forecasting (WRF) model, of Sentinel satellites and Global Navigation Satellite Systems (GNSS) derived products. The experiments concern a flash-floodevent occurred in Tuscany (Central Italy) in September 2017. The rationale is that numerical weather prediction (NWP) models are presently able to produce forecasts with a km scale spatial resolution, but the poor knowledge of the initial state of the atmosphere may imply an inaccurate simulation of the weather phenomena. Hence, to fully exploit the advances in numerical weather modelling, it is necessary to feed them with high spatiotemporal resolution information over the surface boundary and the atmospheric column. In this context, the Copernicus Sentinel satellites represent an important source of data, because they can provide a set of high-resolution observations of physical variables (e.g. soil moisture, land/sea surface temperature, wind speed) used in NWP models runs. The possible availability of a spatially dense network of GNSS stations is also exploited to assimilate water vapour content. Results show that the assimilation of Sentinel-1 derived wind field and GNSS-derivedwater vapour data produce the most positive effects on the performance of the forecast

Effect of the ingestion in the WRF model of different Sentinel-derived and GNSS-derived products: analysis of the forecasts of a high impact weather event

This paper presents the first experimental results of a study on the ingestion in the Weather Research and Forecasting (WRF) model, of Sentinel satellites and Global Navigation Satellite Systems (GNSS) derived products. The experiments concern a flash-floodevent occurred in Tuscany (Central Italy) in September 2017. The rationale is that numerical weather prediction (NWP) models are presently able to produce forecasts with a km scale  spatial resolution, but the poor knowledge of the initial state of the atmosphere may imply an inaccurate simulation of the weather phenomena. Hence, to fully exploit the advances in numerical weather modelling, it is necessary to feed them with high spatiotemporal resolution information over the surface boundary and the atmospheric column. In this context, the Copernicus Sentinel satellites represent an important source of data, because they can provide a set of high-resolution observations of physical variables (e.g. soil moisture, land/sea surface temperature, wind speed) used in NWP models runs. The possible availability of a spatially dense network of GNSS stations is also exploited to assimilate water vapour content. Results show that the assimilation of Sentinel-1 derived wind field and GNSS-derivedwater vapour data produce the most positive effects on the performance of the forecast

An evaluation of the potential of Sentinel 1 for improving flash flood predictions via soil moisture–data assimilation

open8siThe assimilation of satellite-derived soil moisture estimates (soil moisture–data assimilation, SM–DA) into hydrological models has the potential to reduce the uncertainty of streamflow simulations. The improved capacity to moni- tor the closeness to saturation of small catchments, such as those characterizing the Mediterranean region, can be exploited to enhance flash flood predictions. When compared to other microwave sensors that have been exploited for SM– DA in recent years (e.g. the Advanced SCATterometer – AS- CAT), characterized by low spatial/high temporal resolution, the Sentinel 1 (S1) mission provides an excellent opportu- nity to monitor systematically soil moisture (SM) at high spatial resolution and moderate temporal resolution. The aim of this research was thus to evaluate the impact of S1-based SM–DA for enhancing flash flood predictions of a hydro- logical model (Continuum) that is currently exploited for civil protection applications in Italy. The analysis was car- ried out in a representative Mediterranean catchment prone to flash floods, located in north-western Italy, during the time period October 2014–February 2015. It provided some important findings: (i) revealing the potential provided by S1- based SM–DA for improving discharge predictions, espe- cially for higher flows; (ii) suggesting a more appropriate pre-processing technique to be applied to S1 data before the assimilation; and (iii) highlighting that even though high spa- tial resolution does provide an important contribution in a SM–DA system, the temporal resolution has the most crucial role. S1-derived SM maps are still a relatively new product and, to our knowledge, this is the first work published in an international journal dealing with their assimilation within a hydrological model to improve continuous streamflow simulations and flash flood predictions. Even though the reported results were obtained by analysing a relatively short time pe- riod, and thus should be supported by further research activ- ities, we believe this research is timely in order to enhance our understanding of the potential contribution of the S1 data within the SM–DA framework for flash flood risk mitigation.openCenci, Luca; Pulvirenti, Luca; Boni, Giorgio; Chini, Marco; Matgen, Patrick; Gabellani, Simone; Squicciarino, Giuseppe; Pierdicca, NazzarenoCenci, Luca; Pulvirenti, Luca; Boni, Giorgio; Chini, Marco; Matgen, Patrick; Gabellani, Simone; Squicciarino, Giuseppe; Pierdicca, Nazzaren

Defining a Trade-Off Between Spatial and Temporal Resolution of a Geosynchronous SAR mission for Soil Moisture Monitoring

The next generation of synthetic aperture radar (SAR) systems could foresee satellite missions based on a geosynchronous orbit (GEO SAR). These systems are able to provide radar images with an unprecedented combination of spatial ( 641 km) and temporal ( 6412 h) resolutions. This paper investigates the GEO SAR potentialities for soil moisture (SM) mapping finalized to hydrological applications, and defines the best compromise, in terms of image spatio-temporal resolution, for SM monitoring. A synthetic soil moisture\u2013data assimilation (SM-DA) experiment was thus set up to evaluate the impact of the hydrological assimilation of different GEO SAR-like SM products, characterized by diverse spatio-temporal resolutions. The experiment was also designed to understand if GEO SAR-like SM maps could provide an added value with respect to SM products retrieved from SAR images acquired from satellites flying on a quasi-polar orbit, like Sentinel-1 (POLAR SAR). Findings showed that GEO SAR systems provide a valuable contribution for hydrological applications, especially if the possibility to generate many sub-daily observations is sacrificed in favor of higher spatial resolution. In the experiment, it was found that the assimilation of two GEO SAR-like observations a day, with a spatial resolution of 100 m, maximized the performances of the hydrological predictions, for both streamflow and SM state forecasts. Such improvements of the model performances were found to be 45% higher than the ones obtained by assimilating POLAR SAR-like SM maps

SAR Sentinel 1 imaging and detection of palaeo-landscape features in the mediterranean area

The use of satellite radar in landscape archaeology offers great potential for manifold applications, such as the detection of ancient landscape features and anthropogenic transformations. Compared to optical data, the use and interpretation of radar imaging for archaeological investigations is more complex, due to many reasons including that: (i) ancient landscape features and anthropogenic transformations provide subtle signals, which are (ii) often covered by noise; and, (iii) only detectable in specific soil characteristics, moisture content, vegetation phenomenology, and meteorological parameters. In this paper, we assessed the capability of SAR Sentinel 1 in the imaging and detection of palaeo-landscape features in the Mediterranean area of Tavoliere delle Puglie. For the purpose of our investigations, a significant test site (larger than 200 km2) was selected in the Foggia Province (South of Italy) as this area has been characterized for millennia by human frequentation starting from (at least) the Neolithic. The results from the Sentinel 1 (S-1) data were successfully compared with independent data sets, and the comparison clearly showed an excellent match between the S-1 based outputs and ancient anthropogenic transformations and landscape features