THE DEVELOPMENT OF A HOLISTIC EXPERT SYSTEM FOR INTEGRATED COASTAL ZONE MANAGEMENT

Coastal data and information comprise a massive and complex resource, which is vital to the practice of Integrated Coastal Zone Management (ICZM), an increasingly important application. ICZM is just as complex, but uses the holistic paradigm to deal with the sophistication. The application domain and its resource require a tool of matching characteristics, which is facilitated by the current wide availability of high performance computing. An object-oriented expert system, COAMES, has been constructed to prove this concept. The application of expert systems to ICZM in particular has been flagged as a viable challenge and yet very few have taken it up. COAMES uses the Dempster- Shafer theory of evidence to reason with uncertainty and importantly introduces the power of ignorance and integration to model the holistic approach. In addition, object orientation enables a modular approach, embodied in the inference engine - knowledge base separation. Two case studies have been developed to test COAMES. In both case studies, knowledge has been successfully used to drive data and actions using metadata. Thus a holism of data, information and knowledge has been achieved. Also, a technological holism has been proved through the effective classification of landforms on the rapidly eroding Holderness coast. A holism across disciplines and CZM institutions has been effected by intelligent metadata management of a Fal Estuary dataset. Finally, the differing spatial and temporal scales that the two case studies operate at implicitly demonstrate a holism of scale, though explicit means of managing scale were suggested. In all cases the same knowledge structure was used to effectively manage and disseminate coastal data, information and knowledge

Native forest replacement by exotic plantations in southern Chile (1985–2011) and partial compensation by natural regeneration

Although several studies have reported rates of deforestation and spatial patterns of native forest fragmentation, few have focused on the role of natural forest regeneration and exotic tree plantations on landscape dynamics. The objective of this study was to analyze the dynamics of land cover change in order to test the hypothesis that exotic tree plantations have caused a major transformation of temperate forest cover in southern Chile during the last three decades. We used three Landsat satellite images taken in 1985 (TM), 1999 (ETM+), and 2011 (TM) to quantify land cover change, together with a set of landscape indicators to describe the spatial configuration of land cover. Our results showed that the major changes were dynamic conversion among forest, exotic tree plantation and shrubland. During the study period, the area covered by exotic tree plantations increased by 168% (20,896–56,010 ha), at an annual rate of 3.8%, mostly at the expense of native forest and shrubland. There was a total gross loss of native forest of 30% (54,304 ha), but a net loss of initial cover of only 5.1% (9130 ha), at an annual net deforestation rate of 0.2%. The difference between gross and net loss of native forest was mostly the result of conversion of shrubland and agricultural and pasture land to secondary forest following natural regeneration. Over the course of the study period, exotic tree plantations showed a constant increase in patch density, total edge length, nearest-neighbor distance, and largest patch index; maximum mean patch size occurred in the middle of the study period. Native forest exhibited an increase and then a decrease in patch density and total edge length, whereas mean patch size and largest patch index were lowest in the middle of the period. Overall, the observed trends indicate expansion of exotic tree plantations and increase in native forest loss and fragmentation, particularly between 1985 and 1999. Forest loss included both old-growth and secondary forests, while native forest established after secondary succession differed in diversity, structure, and functionality from old-growth and old growth/secondary forests. Since different successional stages influence the provision of ecosystem services, the changes observed in our study are likely to have consequences for humans that extend beyond immediate changes in land use patterns

Remote sensing methods for biodiversity monitoring with emphasis on vegetation height estimation and habitat classification

Biodiversity is a principal factor for ecosystem stability and functioning, and the need for its protection has been identified as imperative globally. Remote sensing can contribute to timely and accurate monitoring of various elements related to biodiversity, but knowledge gap with user communities hinders its widespread operational use. This study advances biodiversity monitoring through earth observation data by initially identifying, reviewing, and proposing state-of-the-art remote sensing methods which can be used for the extraction of a number of widely adopted indicators of global biodiversity assessment. Then, a cost and resource effective approach is proposed for vegetation height estimation, using satellite imagery from very high resolution passive sensors. A number of texture features are extracted, based on local variance, entropy, and local binary patterns, and processed through several data processing, dimensionality reduction, and classification techniques. The approach manages to discriminate six vegetation height categories, useful for ecological studies, with accuracies over 90%. Thus, it offers an effective approach for landscape analysis, and habitat and land use monitoring, extending previous approaches as far as the range of height and vegetation species, synergies of multi-date imagery, data processing, and resource economy are regarded. Finally, two approaches are introduced to advance the state of the art in habitat classification using remote sensing data and pre-existing land cover information. The first proposes a methodology to express land cover information as numerical features and a supervised classification framework, automating the previous labour- and time-consuming rule-based approach used as reference. The second advances the state of the art incorporating Dempster–Shafer evidential theory and fuzzy sets, and proves successful in handling uncertainties from missing data or vague rules and offering wide user defined parameterization potential. Both approaches outperform the reference study in classification accuracy, proving promising for biodiversity monitoring, ecosystem preservation, and sustainability management tasks.Open Acces

Characterization of the spectral distribution of hyperspectral imagery for improved exploitation

Widely used methods of target, anomaly, and change detection when applied to spectral imagery provide less than desirable results due to the complex nature of the data. In the case of hyperspectral data, dimension reduction techniques are employed to reduce the amount of data used in the detection algorithms in order to produce better results and/or decreased computation time. This essentially ignores a significant amount of the data collected in k unique spectral bands. Methods presented in this work explore using the distribution of the collected data in the full k dimensions in order to identify regions of interest contained in spatial tiles of the scene. Here, interest is defined as small and large scale manmade activity. The algorithms developed in this research are primarily data driven with a limited number of assumptions. These algorithms will individually be applied to spatial subsets or tiles of the full scene to indicate the amount of interest contained. Each tile is put through a series of tests using the algorithms based on the full distribution of the data in the hyperspace. The scores from each test will be combined in such a way that each tile is labeled as either interesting or not interesting. This provides a cueing mechanism for image analysts to visually inspect locations within a hyperspectral scene with a high likelihood of containing manmade activity

Modelling cropland expansion and its drivers in Trans Nzoia County, Kenya

Population growth and increasing demand for agricultural production continue to drive global cropland expansions. These expansions lead to the overexploitation of fragile ecosystems, propagating land degradation, and the loss of natural diversity. This study aimed to identify the factors driving land use/land cover changes (LULCCs) and subsequent cropland expansion in Trans Nzoia County in Kenya. Landsat images were used to characterize the temporal LULCCs in 30 years and to derive cropland expansions using change detection. Logistic regression (LR), boosted regression trees (BRTs), and evidence belief functions (EBFs) were used to model the potential drivers of cropland expansion. The candidate variables included proximity and biophysical, climatic, and socioeconomic factors. The results showed that croplands replaced other natural land covers, expanding by 38% between 1990 and 2020. The expansion in croplands has been at the expense of forestland, wetland, and grassland losses, which declined in coverage by 33%, 71%, and 50%, respectively. All the models predicted elevation, proximity to rivers, and soil pH as the critical drivers of cropland expansion. Cropland expansions dominated areas bordering the Mt. Elgon forest and Cherangany hills ecosystems. The results further revealed that the logistic regression model achieved the highest accuracy, with an area under the curve (AUC) of 0.96. In contrast, EBF and the BRT models depicted AUC values of 0.86 and 0.77, respectively. The findings exemplify the relationships between different potential drivers of cropland expansion and contribute to developing appropriate strategies that balance food production and environmental conservation

Mapping Flood Vulnerability by Applying EBF And AHP Methods, in the Iraqi Mountain Region

Flood hazards are a member of the world's catastrophic events with a hydrological climate origin. They are referred to as a situation in which the river flow and water level increase suddenly and cause human and financial losses. This research aims to determine flood-prone zones and evaluate the efficacy of RS and GIS-based evidence-based belief function (EBF) and hierarchical analysis (AHP) models in flood-prone area mapping. Using the Rezan River basin in the Mergasor area of Erbil governorate, Iraq, as an example, 11 factors such as slope, slope direction, land use, distance to the stream, distance to the road, elevation, soil, rainfall, geology, NDVI, and drainage density were utilized for flood moderation. The prediction rates of the EBF and AHP models were also analyzed to be 0.869% and 0.836%, respectively, indicating that these two models are better predictors. The findings of the study area revealed that 32% of the study area is under very high to high flooding hazard zones for the EBF method and 22% for the AHP method. This research’s conclusions are crucial for flood-prone region management, decision-making, and local administrative planning

Natural forests loss and tree plantations: large-scale tree cover loss differentiation in a threatened biodiversity hotspot

Distinguishing between natural forests from exotic tree plantations is essential to get an accurate picture of the world's state of forests. Most exotic tree plantations support lower levels of biodiversity and have less potential for ecosystem services supply than natural forests, and differencing them is still a challenge using standard tools. We use a novel approach in south-central of Chile to differentiate tree cover dynamics among natural forests and exotic tree plantations. Chile has one of the world's most competitive forestry industry and the region is a global biodiversity hotspot. Our collaborative visual interpretation method combined a global database of tree cover change, remote sensing from high-resolution satellite images and expert knowledge. By distinguishing exotic tree plantation and natural forest loss, we fit spatially explicit models to estimate tree-cover loss across 40 millions of ha between 2000 and 2016. We were able to distinguish natural forests from exotic tree plantations with an overall accuracy of 99% and predicted forest loss. Total tree cover loss was continuous over time, and the disaggregation revealed that 1 549 909 ha of tree plantations were lost (mean = 96 869 ha year(-1)), while 206 142 ha corresponded to natural forest loss (mean = 12 884 ha year(-1)). Mostly of tree plantations lost returned to be plantation (51%). Natural forests were converted mainly (75%) to transitional land covers (e.g. shrubland, bare land, grassland), and an important proportion of these may finish as tree plantation. This replacement may undermine objectives of increasedcarbon storage and biodiversity. Tree planting as a solution has gained increased attention in recen years with ambitious commitments to mitigate the effects of climate change. However, negative outcomes for the environment could result if strategies incentivize the replacement of natural forests into other land covers. Initiatives to reduce carbon emissions should encourage differentiating natural forests from exotic tree plantations and pay more attention on protecting and managing sustainably the former