Integrated Applications of Geo-Information in Environmental Monitoring

This book focuses on fundamental and applied research on geo-information technology, notably optical and radar remote sensing and algorithm improvements, and their applications in environmental monitoring. This Special Issue presents ten high-quality research papers covering up-to-date research in land cover change and desertification analyses, geo-disaster risk and damage evaluation, mining area restoration assessments, the improvement and development of algorithms, and coastal environmental monitoring and object targeting. The purpose of this Special Issue is to promote exchanges, communications and share the research outcomes of scientists worldwide and to bridge the gap between scientific research and its applications for advancing and improving society

QUANTIFYING GRASSLAND NON-PHOTOSYNTHETIC VEGETATION BIOMASS USING REMOTE SENSING DATA

Non-photosynthetic vegetation (NPV) refers to vegetation that cannot perform a photosynthetic function. NPV, including standing dead vegetation and surface plant litter, plays a vital role in maintaining ecosystem function through controlling carbon, water and nutrient uptake as well as natural fire frequency and intensity in diverse ecosystems such as forest, savannah, wetland, cropland, and grassland. Due to its ecological importance, NPV has been selected as an indicator of grassland ecosystem health by the Alberta Public Lands Administration in Canada. The ecological importance of NPV has driven considerable research on quantifying NPV biomass with remote sensing approaches in various ecosystems. Although remote images, especially hyperspectral images, have demonstrated potential for use in NPV estimation, there has not been a way to quantify NPV biomass in semiarid grasslands where NPV biomass is affected by green vegetation (PV), bare soil and biological soil crust (BSC). The purpose of this research is to find a solution to quantitatively estimate NPV biomass with remote sensing approaches in semiarid mixed grasslands. Research was conducted in Grasslands National Park (GNP), a parcel of semiarid mixed prairie grassland in southern Saskatchewan, Canada. Multispectral images, including newly operational Landsat 8 Operational Land Imager (OLI) and Sentinel-2A Multi-spectral Instrument (MSIs) images and fine Quad-pol Radarsat-2 images were used for estimating NPV biomass in early, middle, and peak growing seasons via a simple linear regression approach. The results indicate that multispectral Landsat 8 OLI and Sentinel-2A MSIs have potential to quantify NPV biomass in peak and early senescence growing seasons. Radarsat-2 can also provide a solution for NPV biomass estimation. However, the performance of Radarsat-2 images is greatly affected by incidence angle of the image acquisition. This research filled a critical gap in applying remote sensing approaches to quantify NPV biomass in grassland ecosystems. NPV biomass estimates and approaches for estimating NPV biomass will contribute to grassland ecosystem health assessment (EHA) and natural resource (i.e. land, soil, water, plant, and animal) management

Study of land degradation and desertification dynamics in North Africa areas using remote sensing techniques

In fragile-ecosystem arid and semi-arid land, climatic variations, water scarcity and human pressure accelerate ongoing degradation of natural resources. In order to implement sustainable management, the ecological state of the land must be known and diachronic studies to monitor and assess desertification processes are indispensable in this respect. The present study is developed in the frame of WADIS-MAR (www.wadismar.eu). This is one of the five Demonstration Projects implemented within the Regional Programme “Sustainable Water Integrated Management (SWIM)” (www.swim-sm.eu ), funded by the European Commission and which aims to contribute to the effective implementation and extensive dissemination of sustainable water management policies and practices in the Southern Mediterranean Region. The WADIS-MAR Project concerns the realization of an integrated water harvesting and artificial aquifer recharge techniques in two watersheds in Maghreb Region: Oued Biskra in Algeria and wadi Oum Zessar in Tunisia. The WADIS MAR Project is coordinated by the Desertification Research Center of the University of Sassari in partnership with the University of Barcelona (Spain), Institut des Régions Arides (Tunisia) and Agence Nationale des Ressources Hydrauliques (Algeria) and the international organization Observatorie du Sahara et du Sahel. The project is coordinated by Prof. Giorgio Ghiglieri. The project aims at the promotion of an integrated, sustainable water harvesting and agriculture management in two watersheds in Tunisia and Algeria. As agriculture and animal husbandry are the two main economic activities in these areas, demand and pressure on natural resources increase in order to cope with increasing population’s needs. In arid and semiarid study areas of Algeria and Tunisia, sustainable development of agriculture and resources management require the understanding of these dynamics as it withstands monitoring of desertification processes. Vegetation is the first indicator of decay in the ecosystem functions as it is sensitive to any disturbance, as well as soil characteristics and dynamics as it is edaphically related to the former. Satellite remote sensing of land affected by sand encroachment and salinity is a useful tool for decision support through detection and evaluation of desertification indicating features. Land cover, land use, soil salinization and sand encroachment are examples of such indicators that if integrated in a diachronic assessment, can provide quantitative and qualitative information on the ecological state of the land, particularly degradation tendencies. In recent literature, detecting and mapping features in saline and sandy environments with remotely sensed imagery has been reported successful through the use of both multispectral and hyperspectral imagery, yet the limitations to both image types maintain “no agreed-on best approach to this technology for monitoring and mapping soil salinity and sand encroachment”. Problems regarding the image classification of features in these particular areas have been reported by several researchers, either with statistical or neural/connectionist algorithms for both fuzzy and hard classifications methods. In this research, salt and sand features were assessed through both visual interpretation and automated classification approaches, employing historical and present Landsat imagery (from 1984 to 2015). The decision tree analysis was chosen because of its high flexibility of input data range and type, the easiness of class extraction through non-parametric, multi-stage classification. It makes no a priori assumption on class distribution, unlike traditional statistical classifiers. The visual interpretation mapping of land cover and land use was undergone according to acknowledged standard nomenclature and methodology, such as CORINE land cover or AFRICOVER 2000, Global Land Cove 2000 etc. The automated one implies a decision tree (DT) classifier and an unsupervised classification applied to the principal components (PC) extracted from Knepper ratios composite in order to assess their validity for the change detection analysis. In the Tunisian study area, it was possible to conduct a thorough ground truth survey resulting in a record of 400 ground truth points containing several information layers (ground survey sheet information on various land components, photographs, reports in various file formats) stored within the a shareable standalone geodatabase. Spectral data were also acquired in situ using the handheld ASD FieldSpec 3 Jr. Full Range (350 – 2500 nm) spectroradiometer and samples were taken for X-ray diffraction analysis. The sampling sites were chosen on the basis of a geomorphological analysis, ancillary data and the previously interpreted land cover/land use map, specifically generated for this study employing Landsat 7 and 8 imagery. The spectral campaign has enabled the acquisition of spectral reflectance measurements of 34 points, of which 14 points for saline surfaces (9 samples); 10 points for sand encroachment areas (10 samples); 3 points for typical vegetation (halophyte and psammophyte) and 7 points for mixed surfaces. Five of the eleven indices employed in the Decision Tree construction were constructed throughout the current study, among which we propose also a salinity index (SMI) for the extraction of highly saline areas. Their application have resulted in an accuracy of more than 80%. For the error estimation phase, the interpreted land cover/use map (both areas) and ground truth data (Oum Zessar area only) supported the results of the 1984 to 2014 salt – affected areas diachronic analysis obtained through both automatic methods. Although IsoDATA classification maps applied to Knepper ratios Principal Component Analysis has proven its good potential as an approach of fast automated, user-independent classifier, accuracy assessment has shown that decision tree outstood it and was proven to have a substantial advantage over the former. The employment of the Decision Tree classifier has proven to be more flexible and adequate for the extraction of highly and moderately saline areas and major land cover types, as it allows multi-source information and higher user control, with an accuracy of more than 80%. Integrating results with ancillary spatial data, we could argue driving forces, anthropic vs natural, as well as source areas, and understand and estimate the metrics of desertification processes. In the Biskra area (Algeria), results indicate that the expansion of irrigated farmland in the past three decades contributes to an ongoing secondary salinization of soils, with an increase of over 75%. In the Oum Zessar area (Tunisia), there was substantial change in several landscape components in the last decades, related to increased anthropic pressure and settlement, agricultural policies and national development strategies. One of the most concerning aspects is the expansion of sand encroached areas over the last three decades of around 27%

Alien plant species: environmental risks in agricultural and agro-forest landscapes under climate change

Alien plant species have been essential for farming and agro-forestry systems and for their supply of food, fiber, tannins, resins or wood from antiquity to the present. They also contributed to supporting functions and regulating services (water, soil, biodiversity) and to the design of landscapes with high cultural and scenic value. Some of those species were intentionally introduced, others arrived accidentally, and a small proportion escaped, naturalized and became invasive in natural ecosystems—these are known as invasive alien species (IAS). Here, invasive means that these species have some significant negative impact, either by spreading from human-controlled environments (e.g. fields, gardens) to natural ecosystems, where they can cause problems to native species, or to other production systems or urban areas, impacting on agricultural, forestry activities or human health. Socio-environmental impacts associated with plant invasions have been increasingly recognized worldwide and are expected to increase considerably under changing climate or land use. Early detection tools are key to anticipate IAS and to prevent and control their impacts. In this chapter, we focus on crop and non-crop alien plant species for which there is evidence or prediction of invasive behaviour and impacts. We provide insights on their history, patterns, risks, early detection, forecasting and management under climate change. Specifically, we start by providing a general overview on the history of alien plant species in agricultural and agroforestry systems worldwide. Then, we assess patterns, risks and impacts resulting from alien plants originally cultivated and that became invasive outside cultivation areas. Afterwards, we provide several considerations for managing the spread of invasive plant species in the landscape. Finally, we discuss challenges of alien plant invasions for agricultural and agroforest systems, in the light of climate change.Joana R. Vicente was supported by POPH/FSE and FCT (Post-Doc grant SFRH/BPD/84044/2012). Ana Sofia Vaz was supported by FSE/MEC and FCT (Ph.D. grant PD/ BD/52600/2014). Ana Isabel Queiroz supported by FCT—the Portuguese Foundation for Science and Technology [UID/HIS/04209/2013 and IF/00222/2013/CP1166/CT0001]. This work received financial support from the European Union (FEDER funds POCI-01-0145-FEDER-006821) and National Funds (FCT/MEC, Fundação para a Ciência e Tecnologia and Ministério da Educação e Ciência) under the Partnership Agreement PT2020 UID/BIA/50027/201

Zooplankton Diversity and Pelagic Food Webs

Zooplankton are of key importance in the structure and functioning of aquatic food webs. They contribute to a large part of the functional and structural biodiversity of predator and prey plankton communities. Promptly responding to long-term and seasonal changes in the physical and chemical environment, they are sensitive indicators of patterns and mechanisms of impact drivers, both natural and human induced. In this volume, we aim to present evidence for both long-term and seasonal changes in zooplankton community structure and dynamics, investigating different approaches from population dynamics to advanced molecular techniques and reconstructing past communities from subfossil remains in lake sediments