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

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%

Remote Sensing Information Sciences Research Group, Santa Barbara Information Sciences Research Group, year 3

Research continues to focus on improving the type, quantity, and quality of information which can be derived from remotely sensed data. The focus is on remote sensing and application for the Earth Observing System (Eos) and Space Station, including associated polar and co-orbiting platforms. The remote sensing research activities are being expanded, integrated, and extended into the areas of global science, georeferenced information systems, machine assissted information extraction from image data, and artificial intelligence. The accomplishments in these areas are examined

Integration of remote sensing and GIS in studying vegetation trends and conditions in the gum arabic belt in North Kordofan, Sudan

The gum arabic belt in Sudan plays a significant role in environmental, social and economical aspects. The belt has suffered from deforestation and degradation due to natural hazards and human activities. This research was conducted in North Kordofan State, which is affected by modifications in conditions and composition of vegetation cover trends in the gum arabic belt as in the rest of the Sahelian Sudan zone. The application of remote sensing, geographical information system and satellites imageries with multi-temporal and spatial analysis of land use land cover provides the land managers with current and improved data for the purposes of effective management of natural resources in the gum arabic belt. This research investigated the possibility of identification, monitoring and mapping of the land use land cover changes and dynamics in the gum arabic belt during the last 35 years. Also a newly approach of object-based classification was applied for image classification. Additionally, the study elaborated the integration of conventional forest inventory with satellite imagery for Acacia senegal stands. The study used imageries from different satellites (Landsat and ASTER) and multi-temporal dates (MSS 1972, TM 1985, ETM+ 1999 and ASTER 2007) acquired in dry season (November). The imageries were geo-referenced and radiometrically corrected by using ENVI-FLAASH software. Image classification (pixel-based and object-based), post-classification change detection, 2x2 and 3x3 pixel windows and accuracy assessment were applied. A total of 47 field samples were inventoried for Acacia senegal tree’s variables in Elhemmaria forest. Three areas were selected and distributed along the gum arabic belt. Regression method analysis was applied to study the relationship between forest attributes and the ASTER imagery. Application of multi-temporal remote sensing data in gum arabic belt demonstrated successfully the identification and mapping of land use land cover into five main classes. Also NDVI categorisation provided a consistent method for land use land cover stratification and mapping. Forest dominated by Acacia senegal class was separated covering an area of 21% and 24% in the year 2007 for areas A and B, respectively. The land use land cover structure in the gum arabic belt has obvious changes and reciprocal conversions between the classes indicating the trends and conditions caused by the human interventions as well as ecological impacts on Acacia senegal trees. The study revealed a drastic loss of Acacia senegal cover by 25% during the period of 1972 to 2007.The results of the study revealed to a significant correlation (p ≤ 0.05) between the ASTER bands (VNIR) and vegetation indices (NDVI, SAVI, RVI) with stand density, volume, crown area and basal area of Acacia senegal trees. The derived 2x2 and 3x3 pixel windows methods successfully extracted the spectral reflectance of Acacia senegal trees from ASTER imagery. Four equations were developed and could be widely used and applied for monitoring the stand density, volume, basal area and crown area of Acacia senegal trees in the gum arabic belt considering the similarity between the selected areas. The pixel-based approach performed slightly better than the object-based approach in land use land cover classification in the gum arabic belt. The study come out with some valuable recommendations and comments which could contribute positively in using remotely sensed imagery and GIS techniques to explore management tools of Acacia senegal stands in order to maintain the tree component in the farming and the land use systems in the gum arabic belt

Desertification in Europe: mitigation strategies, land use planning: Proceedings of the advanced study course held in Alghero, Sardinia, Italy from 31 May to 10 June 1999

The present volume is based on lectures given at the course held in Alghero, Sardinia, Italy, from 31 May to 10 June 1999 on ‘Desertification in Europe: Mitigation Strategies, Land Use Planning’. It also contains presentations, given by the participating students, on their own research activities and interests. With the adoption of the International Convention to Combat Desertification, which represents a follow up of the Rio recommendations, this publication is timely. It highlights the specific situation of the Southern European regions and provides a comprehensive and state-of-the-art review of this complex issue

Detection of land cover changes in El Rawashda forest, Sudan: A systematic comparison: Detection of land cover changes in El Rawashda forest, Sudan: A systematic comparison

The primary objective of this research was to evaluate the potential for monitoring forest change using Landsat ETM and Aster data. This was accomplished by performing eight change detection algorithms: pixel post-classification comparison (PCC), image differencing Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Transformed Difference Vegetation Index (TDVI), principal component analysis (PCA), multivariate alteration detection (MAD), change vector analysis (CVA) and tasseled cap analysis (TCA). Methods, Post-Classification Comparison and vegetation indices are straightforward techniques and easy to apply. In this study the simplified classification with only 4 forest classes namely close forest, open forest, bare land and grass land was used The overall classification accuracy obtained were 88.4%, 91.9% and 92.1% for the years 2000, 2003 and 2006 respectively. The Tasseled Cap green layer (GTC) composite of the three images was proposed to detect the change in vegetation of the study area. We found that the RBG-TCG worked better than RGBNDVI. For instance, the RBG-TCG detected some areas of changes that RGB-NDVI failed to detect them, moreover RBG-TCG displayed different changed areas with more strong colours. Change vector analysis (CVA) based on Tasseled Cap transformation (TCT) was also applied for detecting and characterizing land cover change. The results support the CVA approach to change detection. The calculated date to date change vectors contained useful information, both in their magnitude and their direction. A powerful tool for time series analysis is the principal components analysis (PCA). This method was tested for change detection in the study area by two ways: Multitemporal PCA and Selective PCA. Both methods found to offer the potential for monitoring forest change detection. A recently proposed approach, the multivariate alteration detection (MAD), in combination with a posterior maximum autocorrelation factor transformation (MAF) was used to demonstrate visualization of vegetation changes in the study area. The MAD transformation provides a way of combining different data types that found to be useful in change detection. Accuracy assessment is an important final step addressed in the study to evaluate the different change detection techniques. A quantitative accuracy assessment at level of change/no change pixels was performed to determine the threshold value with the highest accuracy. Among the various accuracy assessment methods presented the highest accuracy was obtained using the post-classification comparison based on supervised classification of each two time periods (2000 -2003 and 2003-2006), which were 90.6% and 87% consequently

Desertification

IPCC SPECIAL REPORT ON CLIMATE CHANGE AND LAND (SRCCL) Chapter 3: Climate Change and Land: An IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystem

MANAGEMENT MATTER? EFFECTS OF CHARCOAL PRODUCTION MANAGEMENT ON WOODLAND REGENERATION IN SENEGAL

In Senegal, as in many parts of Africa, nearly 95% of its growing urban population depends on charcoal as their primary cooking energy. Extraction of wood for charcoal production is perceived to drive forest degradation. The Senegalese government and international donor agencies have created different forest management types with the ultimate goal of sustainably managing forests. This research combines local ecological knowledge, ecological surveys and remote sensing analysis to better understand questions related to how extraction for charcoal production and forest management affect Senegalese forests. Information derived from 36 semi-structured interviews suggests that the forests are degrading, but are depended on for income, grazing and energy. Interviewees understand the rules governing forest management types, but felt they had limited power or responsibility to enforce forest regulations. Ecological survey results confirmed that plots harvested for charcoal production are significantly different in forest structure and tree species composition than undisturbed sites. Across harvested and undisturbed and within forest management types the Combretum glutinosum species dominated (53% of all individuals and the primary species used for charcoal production) and demonstrated robust regenerative capacity. Few large, hardwood or fruiting trees were observed and had insufficient regenerative capacity to replace current populations. Species diversity was higher in co-managed areas, but declined after wood was harvested for charcoal production. Proximity to villages, roads and park edges in harvested and undisturbed plots and within forest management types had little impact on forest structure and tree diversity patterns with the harvesting of trees for charcoal spread consistently throughout the landscape. Remote sensing analysis with the MISR derived k(red) parameter demonstrated its ability to accurately classify broad land classes and showed potential when differentiating between pre- and post-harvest conditions over a three year time period, but could not accurately detect subtle changes in forest cover of known harvest time since last harvest in a single MISR scene. This research demonstrated the utility of multidisciplinary research in assessing the effects of charcoal production and forest management types on Senegalese forests; concluding that the effects of charcoal production on forest characteristics and regenerative capacity are consistent throughout all forest management types