Manifestation of an advanced fuzzy logic model coupled with Geo-information techniques to landslide susceptibility mapping and their comparison with logistic regression modelling

Landslides are very common natural problems in the Selangor area of Malaysia due to the improper use of landcover and tropical rainfall. There are many landslide susceptibility analyses such as statistical, bivariate and data mining approaches exist in the literature. This paper presents the use of fuzzy logic relations for landslide susceptibility mapping on part of Selangor area, Malaysia, using a Geographic Information System (GIS) and remote sensing data. At first, landslide locations were identified in the study area from the interpretation of aerial photographs and satellite images, supported by extensive field surveys. Topographic and geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. Thirteen landslide conditioning factors such as slope gradient, slope exposure, plan curvature, altitude, stream power index, topographic wetness index, distance from drainage, distance from road, lithology, distance from faults, soil, landcover and normalized difference vegetation index (ndvi) were extracted from the spatial database. These factors were analyzed using fuzzy logic relations to produce the landslide susceptibility maps. Using the landslide conditioning factors and the identified landslides, the fuzzy membership values were calculated. Then fuzzy algebraic operators were applied to the fuzzy membership values for landslide susceptibility mapping. Finally, the ROC curves for all landslide susceptibility models were drawn and the area under curve values were calculated. Landslide locations were used to validate results of the landslide susceptibility maps and the validation results showed 94% accuracy for the fuzzy gamma operator employing all parameters produced in the present study as the landslide conditioning factors. Results showed that, among the fuzzy relations, in the case in which the gamma operator (λ =  0.975) showed the best accuracy (94.73%) while the case in which the fuzzy algebraic Or was applied showed the worst accuracy (84.76%). The landslide susceptibility maps produced by the fuzzy gamma operators shows similar trends as those obtained by applying logistic regression procedure by the same author and indicate that fuzzy relations results perform slightly better than the earlier method. Qualitatively, the model yields reasonable results which can be used for preliminary land-use planning purposes.     

Scale-dependent relations in land cover biophysical dynamics

The exploration of the relationships between plant biotic dynamics and scale can reveal important information on ecosystem spatial organization by addressing preservation of information integrity in upscaling/downscaling procedures of land-surface parameterization for environmental modeling applications. Scale-dependent relations of vegetation dynamics are investigated in this study by using emergent biophysical characteristics obtained through a predictive multidimensional model of vegetation anomalies derived from remote-sensing observations. In particular, the analysis is focused on the spatial organization of some phenological parameters including deterministic variations (seasonal range, interannual variability, jump discontinuities) and stochastic components (plant memory, spatial correlations). The analysis is performed using MODIS-based Normalized Difference Vegetation Index (NDVI) 16-day composites for the period from March 2000 to December 2006 over Italy at different levels of spatial aggregation (1-8 km). Scale-dependences of the statistical moments of the phenological parameters are quantified through simple power laws for five distinct vegetated land covers. Results suggest that some biophysical characteristics, especially deterministic components, show no preferential spatial scale for important coverage. In particular, broad-leaved forests and natural grasslands are characterized by deterministic and low-distance spatial components well explained by scale relationships, which are modulated by possible spatiotemporal dynamics of climatic drivers. Agricultural lands show high scale-dependent relations on short-term biophysical memory sources and low-distance spatial components of phenology likely related to hierarchical interactions of anthropogenic and ecological processes; whereas mixed patterns of croplands and natural areas generally present no consistent scaling relations.     

沙地退化植被恢复过程中植被的空间异质性

通过野外取样和室内分析,应用地统计学分析方法研究了科尔沁沙地退化植被恢复过程中不同封育年限（0、11和20年）的流动沙丘的植被盖度和丰富度特征及其空间异质性规律。结果表明,随着流动沙丘的固定和封育年限的增加,植被盖度和丰富度逐渐增加。在流动沙丘植被恢复过程中,植被盖度和丰富度具有明显的空间自相关性,其空间自相关范围从封育0年的流动沙丘（46.05m和33.63m）、封育11的流动沙丘（21.63m和17.25m）到封育20年的流动沙丘（26.12m和24.18m）先减小后增加,但均未超出我们的研究尺度50m,表现出不同大小的斑块形式分布的小尺度分布格局。由半方差函数及其参数和空间分布格局图分析得出,随着沙丘植被的恢复,植被特征的空间异质性在所研究的尺度上表现出先增大（封育0年到封育11年）后减小（封育11年到封育20年）的变化特点。     

Representation of critical natural capital in China

Traditional means of assessing representativeness of conservation value in protected areas depend on measures of structural biodiversity. The effectiveness of priority conservation areas at representing critical natural capital (CNC) (i.e., an essential and renewable subset of natural capital) remains largely unknown. We analyzed the representativeness of CNC‐conservation priority areas in national nature reserves (i.e., nature reserves under jurisdiction of the central government with large spatial distribution across the provinces) in China with a new biophysical‐based composite indicator approach. With this approach, we integrated the net primary production of vegetation, topography, soil, and climate variables to map and rank terrestrial ecosystems capacities to generate CNC. National nature reserves accounted for 6.7% of CNC‐conservation priority areas across China. Considerable gaps (35.2%) existed between overall (or potential) CNC representativeness nationally and CNC representation in national reserves, and there was significant spatial heterogeneity of representativeness in CNC‐conservation priority areas at the regional and provincial levels. For example, the best and worst representations were, respectively, 13.0% and 1.6% regionally and 28.9% and 0.0% provincially. Policy in China is transitioning toward the goal of an ecologically sustainable civilization. We identified CNC‐conservation priority areas and conservation gaps and thus contribute to the policy goals of optimization of the national nature reserve network and the demarcation of areas critical to improving the representativeness and conservation of highly functioning areas of natural capital. Moreover, our method for assessing representation of CNC can be easily adapted to other large‐scale networks of conservation areas because few data are needed, and our model is relatively simple.     

Landscape evaluation in conservation: molecular sampling and habitat modeling for the Iberian lynx.

Conservation of endangered species requires comprehensive understanding of their distribution and habitat requirements, in order to implement better management strategies. Unfortunately, this understanding is often difficult to gather at the short term required by rapidly declining populations of many rare vertebrates. We present a spatial habitat modeling approach that integrates a molecular technique for species detection with landscape information to assess habitat requirements of a critically endangered mammalian carnivore, the Iberian lynx (Lynx pardinus), in a poorly known population in Spain. We formulated a set of model hypotheses for habitat selection at the spatial scale of home ranges, based on previous information on lynx requirements of space, vegetation, and prey. To obtain the required data for model selection, we designed a sampling protocol based on surveys of feces and their molecular analysis for species identification. After comparing candidate models, we selected a parsimonious one that allowed (1) reliable assessment of lynx habitat requirements at the scale of home ranges, (2) prediction of lynx distribution and potential population size, and (3) identification of landscape management priorities for habitat conservation. This model predicted that the species was more likely to occur in landscapes with a higher percentage of rocky areas and higher cover of bushes typical of mature mediterranean shrubland mosaics. Its accuracy for discriminating lynx presence was approximately 85%, indicating high predictive performance. Mapping model predictions showed that only 16% of the studied areas constitute potential habitat for lynx, even though the region is dominated by large extents of well-preserved native vegetation with low human interference. Habitat was mostly clumped in two nearby patches connected by vegetation adequate for lynx dispersal and had a capacity for 28-62 potential breeding territories. The lynx population in Sierra Morena is probably the largest persisting today, but it is still critically small for optimism about its long-term persistence. Model results suggest habitat conservation and restoration actions needed for preserving the species, including reconciliation of hunting management with preservation of mature shrubland over large areas (particularly in rocky landscapes). The approach presented here can be applied to many other species for which the ecological information needed to develop sound habitat conservation strategies is lacking.     

Mapping opportunities and challenges for rewilding in Europe

Farmland abandonment takes place across the world due to socio‐economic and ecological drivers. In Europe agricultural and environmental policies aim to prevent abandonment and halt ecological succession. Ecological rewilding has been recently proposed as an alternative strategy. We developed a framework to assess opportunities for rewilding across different dimensions of wilderness in Europe. We mapped artificial light, human accessibility based on transport infrastructure, proportion of harvested primary productivity (i.e., ecosystem productivity appropriated by humans through agriculture or forestry), and deviation from potential natural vegetation in areas projected to be abandoned by 2040. At the continental level, the levels of artificial light were low and the deviation from potential natural vegetation was high in areas of abandonment. The relative importance of wilderness metrics differed regionally and was strongly connected to local environmental and socio‐economic contexts. Large areas of projected abandonment were often located in or around Natura 2000 sites. Based on these results, we argue that management should be tailored to restore the aspects of wilderness that are lacking in each region. There are many remaining challenges regarding biodiversity in Europe, but megafauna species are already recovering. To further potentiate large‐scale rewilding, Natura 2000 management would need to incorporate rewilding approaches. Our framework can be applied to assessing rewilding opportunities and challenges in other world regions, and our results could guide redirection of subsidies to manage social‐ecological systems.     

Impacts of residential development on vegetation cover for a remote coastal barrier in the Outer Banks of North Carolina, USA

Coastal barrier environments are heavily influenced by human activities yet there are few examples of landscape ecological work investigating human dimensions of settlement disturbance patterns and processes. We investigated the impacts of residential development on vegetation cover for a remote roadless coastal barrier in Carova, North Carolina that is subject to policies from the federal to local levels and addressed three research questions: How has the region’s the policy history influenced patterns of residential parcel development? What are the spatial and temporal patterns of parcel development? How has development impacted patterns of barrier vegetation cover? We traced the influences of the federal 1982 Coastal Barrier Resources Act (CBRA) designed to discourage development in risky coastal areas as well as state/local coastal policies and employed remote sensing change detection, NDVI analysis and spatial analysis and regression techniques. Results showed an acceleration of new housing structures since 1990, contrary to the intended effects of CBRA. An estimated vegetation cover loss of 437 m² was associated with each newly developed parcel. NDVI varied along spatial and temporal gradients with more recent development having lower NDVI than older development. Recently developed parcels were larger in area, closer to the beach, and contained houses with larger footprints compared to older developed parcels. Our approach represents a place-based analytical framework for coastal barrier landscapes. Beyond the Carova case study, adopting such an approach coupling natural and human systems for the entire eastern US barrier system requires defining a comprehensive set of coastal barrier spatial units to enable typological classification and subsequent systematic investigation to inform debates regarding coastal ecosystem services and sustainability.     

Fragmentation profiles for real and simulated landscapes

When a natural landscape is represented by a series of categorical raster maps of varying resolution, a multiresolution characterization of spatial pattern can be obtained in which entropy is computed at each resolution conditional on the next coarser resolution. The series of entropy values is plotted as a function of resolution, resulting in a multiresolution profile of fragmentation pattern in the landscape. If a categorical raster map is available at a single resolution only, a series of degraded maps at increasingly coarser resolutions is generated and the fragmentation profile is computed for this series. An algorithm has been developed for obtaining the profile directly from the single resolution map without having to generate and store the coarser resolution maps. A hierarchical stochastic model is described for simulating categorical raster maps and the fragmentation profile of the generating process is obtained in terms of the model parameters. These process profiles provide benchmarks for assessing empirical profiles obtained from raster maps of actual landscapes. Methods of the paper are applied to several watersheds of Pennsylvania using landcover maps derived from satellite imagery. These examples indicate that characteristic landscape types induce characteristic features in their fragmentation profiles.     

Modeling and interpreting the accuracy assessment error matrix for a doubly classified map

This paper considers two maps having the same spatial extent and the same mapping categories but where each map is subject to classification error. An overlay of the maps yields a (dis)similarity matrix whose (i, j)-entry is the areal proportion placed into category i by the first map and into category j by the second map. A parametric model, called the latent truth model, is proposed which specifies the dissimilarity matrix in terms of the true (but unknown) proportions for the mapping categories as well as the unknown error rates for the two maps. The number of parameters in the model exceeds the degrees of freedom in the dissimilarity matrix. However, a method of regularization is applied to effectively reduce the dimension of the parameter space and to permit model fitting. From the fitted model, one obtains estimates for the true mapping proportions as well as estimated error matrices for each of the maps. Accuracy assessment characteristics for each map (such as user's accuracy, producer's accuracy, overall accuracy, and the kappa coefficient) can be computed from the estimated error matrices. Methods are illustrated with two landcover maps of Wicomico County, Maryland.     

Scale dependency of conservation outcomes in a forest-offsetting scheme

Offset schemes help avoid or revert habitat loss through protection of existing habitat (avoided deforestation), through the restoration of degraded areas (natural regrowth), or both. The spatial scale of an offset scheme may influence which of these 2 outcomes is favored and is an important aspect of the scheme's design. However, how spatial scale influences the trade-offs between the preservation of existing habitat and restoration of degraded areas is poorly understood. We used the largest forest offset scheme in the world, which is part of the Brazilian Forest Code, to explore how implementation at different spatial scales may affect the outcome in terms of the area of avoided deforestation and area of regrowth. We employed a numerical simulation of trade between buyers (i.e., those who need to offset past deforestation) and sellers (i.e., landowners with exceeding native vegetation) in the Brazilian Amazon to estimate potential avoided deforestation and regrowth at different spatial scales of implementation. Allowing offsets over large spatial scales led to an area of avoided deforestation 12 times greater than regrowth, whereas restricting offsets to small spatial scales led to an area of regrowth twice as large as avoided deforestation. The greatest total area (avoided deforestation and regrowth combined) was conserved when the spatial scale of the scheme was small, especially in locations that were highly deforested. To maximize conservation gains from avoided deforestation and regrowth, the design of the Brazilian forest-offset scheme should focus on restricting the spatial scale in which offsets occur. Such a strategy could help ensure conservation benefits are localized and promote the recovery of degraded areas in the most threatened forest landscapes.     

Mapping Conflicts Between Biodiversity and Human Needs in Langtang National Park, Nepal

We mapped areas of congruence and conflict between the objectives of protecting red pandas ( Ailurus fulgens ) and meeting the claims and needs of people who live in and around Langtang National Park, Nepal. Semi-structured interviews were used to solicit information on land-use practices, and spatial information technology (sketch maps, satellite images, geographical information systems) was used to place these practices in a spatial context and to model the effects of grazing. Spatial information technology was useful for delineating areas where conflicts occur between the objectives of preserving biodiversity and meeting the needs of local residents. Despite the fact that villagers recognize pasture boundaries, rules and regulations govern pasture management, and sanctions are imposed on violators, over 60% of the red panda's habitat is heavily grazed, and all available land within the study site suitable for grazing is already being used. The study suggests that common property management of natural resources to protect biodiversity (i.e., red pandas) and meet the needs of local people at the same time will be difficult.     

Restoration planning to guide Aichi targets in a megadiverse country

Ecological restoration has become an important strategy to conserve biodiversity and ecosystems services. To restore 15% of degraded ecosystems as stipulated by the Convention on Biological Diversity Aichi target 15, we developed a prioritization framework to identify potential priority sites for restoration in Mexico, a megadiverse country. We used the most current biological and environmental data on Mexico to assess areas of biological importance and restoration feasibility at national scale and engaged stakeholders and experts throughout the process. We integrated 8 criteria into 2 components (i.e., biological importance and restoration feasibility) in a spatial multicriteria analysis and generated 11 scenarios to test the effect of assigning different component weights. The priority restoration sites were distributed across all terrestrial ecosystems of Mexico; 64.1% were in degraded natural vegetation and 6% were in protected areas. Our results provide a spatial guide to where restoration could enhance the persistence of species of conservation concern and vulnerable ecosystems while maximizing the likelihood of restoration success. Such spatial prioritization is a first step in informing policy makers and restoration planners where to focus local and large‐scale restoration efforts, which should additionally incorporate social and monetary cost–benefit considerations.     

Assessment of the environmental conditions of the Sarno river basin (south Italy): a stream sediment approach

The Sarno river basin covers an area of 500 km² collecting the waters of Solofrana and Cavaiola tributaries. Originally it manly represents a source of livelihood for inhabitants by fishing and transporting goods; currently, the Sarno river, still partially used for irrigation, is affected by an extreme environmental degradation as a result of uncontrolled outflow of industrial waste. Within the framework of a wider geochemical prospecting project aiming at characterizing the whole territory of the Campania region, 89 stream sediment samples with a sampling density of 1 sample per 5 km² were collected in the river basin and analyzed by means of inductively coupled plasma-mass spectrometry in order to assess the environmental conditions at a regional scale. A GIS-aided technique, based on both the actual distribution of potentially harmful elements and their regional background values, was used to generate the maps of the contamination factors and of the contamination degrees for As, Cd, Cr, Cu, Hg, Pb and Zn. Furthermore, a factor analysis was performed to assess the nature and the extent of contamination sources for the river sediments. Results showed that the Sarno river basin could be divided in two “environmental status” units: one, low contaminated, corresponding to the hilly and mountain areas, and the second, from moderately to very highly contaminated, corresponding to the economically developed areas of the valley floor characterized by a high population density. This work was developed within a project that aims to investigate the relationships between environmental pollution and human health by analyzing environmental media (stream sediments, water, soil and vegetation) together with human hair of resident population. In this context, the spatial correlation between the extremely compromised environmental conditions of developed areas and the incidence rate of liver cancer in the same area was also explored posing the need of a careful costs/benefits analysis to assess whether the deterioration of the environment, that could adversely affect the conditions of public health, is worth the economic development.     

Trace element atmospheric pollution in South Albania studied by the moss technique and inductively coupled plasma–atomic emission spectrometry

Monitoring hazardous air pollutants is needed for understanding their spatial and temporal distribution and ultimately to minimize their harmful effects. For the first time, the moss biomonitoring technique has been applied to air pollution monitoring in South Albania. Moss samples were collected during the period of September–October 2010, and were analyzed for total concentration of the elements Al, As, Ba, Ca, Cd, Cr, Cu, Fe, K, Li, Mg, Mn, Na, Ni, P, Pb, Sr, V, and Zn by inductively coupled plasma–atomic emission spectrometry. Geographical distribution maps of the elements over the sampled territory were constructed using geographic information systems technology. Multivariate statistical analysis was applied to distinguish elements mainly of anthropogenic origin from those predominantly originating from natural sources. Four factors were identified: Factor 1 reflects wind-blown mineral particles or local emissions from industry (Al, As, Ba, Ca, Cr, Cu, Fe, Mn, Ni, V, Zn); Factor 2 is related to long-range atmospheric transport of elements or local emissions from industry (Cd, Pb); Factor 3 (Na, Mg) and Factor 4 (K) reflect the natural origin of elements as crustal, marine, and vegetation components.     

Identification of Conservation-Worthy Areas in Northern Zululand, South Africa

Abstract: We used spatial and statistical analyses to identify and prioritize broad areas for conservation attention in the northern Zululand region of KwaZulu-Natal, South Africa. We attempted to identify conservation-worthy areas based on species, vegetation types, ecological processes, and threats to biodiversity. Information on species was limited and so could not form the basis of the analysis. Priority vegetation types were identified by degree of endemicity, extent of protection and transformation, and degree of fragmentation. These priority vegetation types and threats to biodiversity were used to define broad linkages between existing protected areas. We set a goal of 10% protection for each vegetation type and 25% for each species. We identified several important (endemic or threatened) animal species and predicted their ranges using a simple model. Species ranges and their hotspots were compared with the distribution of protected areas and the suggested linkages to evaluate increased species representation. Generally, the eastern part of the study area was well protected. Unprotected conservation-worthy areas under greatest threat lay in the west, and protecting these areas is a priority. Furthermore, several vegetation types were not protected by provincial authorities, a situation that also needs to be addressed. The findings of our study need to be reassessed at a finer land-parcel scale, and implementation of a range of land-use options considered.     

Spatial modelling of socioeconomic data to understand patterns of human-caused wildfire ignition risk in the SW of Madrid (central Spain)

The majority of wildfires in the Mediterranean Basin are caused directly or indirectly by human activity. Many biophysical and socioeconomic factors have been used in quantitative analyses of wildfire risk. However, the importance and effects of socioeconomic factors in spatial modelling have been given inadequate attention. In this paper, we use different approaches to spatially model our data to examine the influence of human activity on wildfire ignition in the south west of the Madrid region, central Spain. We examine the utility of choropleth and dasymetric mapping with both Euclidean and functional distance surfaces for two differently defined wildfire seasons. We use a method from Bayesian statistics, the Weights of Evidence model, and produce ten predictive maps of wildfire risk: (1) five maps for a two-month fire season combining datasets of evidence variables and (2) five maps for the four-month fire season using the same dataset combinations. We find that the models produced from a choropleth mapping approach with spatial variables using Euclidian and functional distance surfaces are the best of the ten models. Results indicate that spatial patterns of wildfire ignition are strongly associated with human access to the natural landscape. We suggest the methods and results presented will be useful to optimize wildfire prevention resources in areas where human activity and the urban-forest interface are important factors for wildfire ignition.     

Toward Best Practices for Developing Regional Connectivity Maps

Abstract: To conserve ecological connectivity (the ability to support animal movement, gene flow, range shifts, and other ecological and evolutionary processes that require large areas), conservation professionals need coarse‐grained maps to serve as decision‐support tools or vision statements and fine‐grained maps to prescribe site‐specific interventions. To date, research has focused primarily on fine‐grained maps (linkage designs) covering small areas. In contrast, we devised 7 steps to coarsely map dozens to hundreds of linkages over a large area, such as a nation, province, or ecoregion. We provide recommendations on how to perform each step on the basis of our experiences with 6 projects: California Missing Linkages (2001), Arizona Wildlife Linkage Assessment (2006), California Essential Habitat Connectivity (2010), Two Countries, One Forest (northeastern United States and southeastern Canada) (2010), Washington State Connected Landscapes (2010), and the Bhutan Biological Corridor Complex (2010). The 2 most difficult steps are mapping natural landscape blocks (areas whose conservation value derives from the species and ecological processes within them) and determining which pairs of blocks can feasibly be connected in a way that promotes conservation. Decision rules for mapping natural landscape blocks and determining which pairs of blocks to connect must reflect not only technical criteria, but also the values and priorities of stakeholders. We recommend blocks be mapped on the basis of a combination of naturalness, protection status, linear barriers, and habitat quality for selected species. We describe manual and automated procedures to identify currently functioning or restorable linkages. Once pairs of blocks have been identified, linkage polygons can be mapped by least‐cost modeling, other approaches from graph theory, or individual‐based movement models. The approaches we outline make assumptions explicit, have outputs that can be improved as underlying data are improved, and help implementers focus strictly on ecological connectivity.     

Knowing fire incidence through fuel phenology: A remotely sensed approach

Fire is a basic ecological factor that contributes to determine vegetation diversity and dynamics in time and space. Fuel characteristics play an essential role in fire ignition and propagation; at the landscape scale fuel availability and flammability are closely related to the vegetation phenology that directly affects wildfire pattern in time and space. In this view, the annual normalized difference vegetation index (NDVI) profiles derived from high temporal resolution satellites, like SPOT Vegetation, represent an effective tool for monitoring the coarse-scale vegetation seasonal timing. The objective of this study thus consists in quantifying the explanatory power of multitemporal NDVI profiles on the fire regime characteristics of the potential natural vegetation (PNV) types of Sardinia (Italy) over a 5-year period (2000-2004). The results obtained show a good association between the NDVI temporal dynamics of the PNV of Sardinia and the corresponding fire regime characteristics, emphasizing the role of the bioclimatic timing of the vegetation in controlling the coarse-scale wildfire spatio-temporal distribution of Sardinia. By providing a sound phytogeographical framework for describing different wildfire regimes, PNV maps can thus be considered helpful cartographic documents for fire management strategies at the landscape scale.     

Contrasting approved uses against actual uses at La Restinga Lagoon National Park, Margarita Island, Venezuela. A GPS and GIS method to improve management plans and rangers coverage

Protected areas are the core of efforts to conserve biological diversity and zoning uses, and they are used as a tool for their management. Global Positioning Systems (GPS) and Geographic Information System (GIS) have been used to evaluate conflicts between approved uses and actual uses in La Restinga Lagoon National Park in Venezuela (LRLNP). The park (188.6 km²), covers various ecosystems such as coastal lagoons, marine waters and xerophytic vegetation, it is visited by up to 260,000 people visit per year. A GIS, using a base map compiled from 1:25,000 maps was developed. Natural cover was mapped from LANDSAT VII TM images, orthophotomaps and aerial photographs. Spatial use data was collected by field GPS location of any use inside the park during 2 years. Thematic vector maps for each land cover, zoning area, and observed use were created. Maps of zoning and uses were overlapped and new maps for each use-zoning crossing were created. The park contain nine different zoning areas wherein 28 different uses were identified, 18 of them were direct consumptive and non-consumptive uses, occurring on 54% of the park. Tourism transit areas were the most used zones. More than 5,000 people use the park during a high season day. Superimposition of zoning maps with actual use data produced 13 uses taking place in not allowed areas (46% of park area). Most common prohibited use was commercial net fishing, occurring in nearly 40% of the park area. Therefore, identifying human use conflicts and its geographical distribution is a key issue to improve Management Plans as well as identifying hot sites. The GPS-GIS methodology presented here allows Park Rangers to select those areas which could benefit from enhanced supervision with the limited budget available.