Spatial Model Approach for Deforestation

Java is very densely populated since it is inhabited by more than 60% of the total population of Indonesia. Based on data from the Ministry of Forestry, forest loss between 2000-2005 in Java was about 800,000 hectares. Regardless of the debate on whether the different methodologies of forest inventory applied in 2005 have resulted in an underestimation of the figure of forest loss or not, the decrease of forest cover in Java is obvious and needs immediate response. Spatial modeling of the deforestation will assist the policy makers in understanding this process and in taking it into consideration, when decisions are made on the issue. Moreover, the results can be used as data input to solve environmental problems resulting from deforestation. The authors of this chapter modeled the deforestation in Java by using logistic regression. Percentage of deforested area was considered as the response variable, whilst biophysical and socioeconomic factors, that explain the current spatial pattern in deforestation, were assigned as explanatory variables. Furthermore, the authors predicted the future deforestation process, and then, for the case of Java, it was validated with the actual deforestation derived from MODIS satellite imageries from 2000 to 2008. Results of the study showed that the impacts of population density, road density, and slope are significant. Population density and road density have negative impacts on deforestation, while slope has positive impact. Deforestation on Java Island tends to occur in remote areas with limited access, low density population and relatively steep slopes. Implication of the model is that the government should pay more attention to remote rural areas and develop good access to accelerate and create alternative non agricultural jobs in order to reduce pressure on the forest.

Modeling of current and future state of biodiversity in Central America using GLOBIO3 methodology

The modeling of the state of biodiversity in Central America using GLOBIO3 methodology was carried out by the Regional Biodiversity Institute for the Central American Commission on Environment and Development. For each country, current and future states of biodiversity under three socio-economic scenarios were explored. The country results were integrated into one regional assessment. The aim of this chapter is to explain how GLOBIO3 was adapted to the national scale. The main issues and the approaches adopted to solve them are described. The results from the Central American experience are presented followed by a discussion on main model limitations and derived recommendations. Finally, the challenges countries face to integrate the results into their government agendas are analyzed. This chapter is expected to be helpful for potential users of GLOBIO3 who are interested in the application of the methodology on a national and sub regional scale.

Modeling Species Distribution

The results show that among the three approaches, the potentially suitable habitats derived from cartographic overlay cover the largest area and are likely to overestimate existing occurrence areas. The logistic regression model predicts approximately 56% as suitable area, while maximum entropy results covers approximately 9% of the sanctuary. Although the results show large differences in the suitable areas, it should not be concluded that any one method always proves better than the others. Utilization of any method is dependent on the situation and available information. If species observations are limited, the cartographic overlay or habitat suitability is recommended. The logistic regression method is recommended when adequate presence and absence data are available. If presence-only data is available, a niche-based model or the maximum entropy method (MAXENT) is highly recommended.

Applying GLOBIO at Different Geographical Levels

Biodiversity is decreasing at high rates due to a number of human impacts. The GLOBIO3 model has been developed to assess human-induced changes in terrestrial biodiversity at national, regional, and global levels. Recently, GLOBIO-aquatic has been developed for inland aquatic ecosystems. These models are built on simple cause–effect relationships between environmental drivers and biodiversity, based on meta-analyses of literature data. The mean abundance of original species relative to their abundance in undisturbed ecosystems (MSA) is used as the indicator for biodiversity. Changes in drivers are derived from the IMAGE 2.4 model. Drivers considered are land-cover change, land-use intensity, fragmentation, climate change, atmospheric nitrogen deposition, excess of nutrients, infrastructure development, and river flow deviation. GLOBIO addresses (1) the impacts of environmental drivers on MSA and their relative importance; (2) expected trends under various future scenarios; and (3) the likely effects of various policy-response options. The changes in biodiversity can be assessed by the GLOBIO model at different geographical levels. The application depends largely on the availability of future projections of drivers. From the different analyses at the different geographical levels, it can be seen that biodiversity loss, in terms of MSA, will continue, and current policies may only reduce the rate of loss.

Consequences of Deforestation and Climate Change on Biodiversity

Ever since their evolution, forests have been interacting with the Earth’s climate. Species diversity is particularly high in forests of stable moist tropical climates, but patterns of diversity differ among various taxa. Species richness typically implies high ecosystem resilience to ecosystem disturbances; many species are present to fill in newly created niches and facilitate regeneration. Species loss, on the other hand, often entails environmental degradation and erosion of essential ecosystem services. Until now species extinction rates have been highest on tropical islands which are characterized by a high degree of species endemism but comparatively low species richness (and therefore high vulnerability to invasive species). Deforestation and forest degradation in many countries has lead to forest fragmentation with similar effects on increasingly insularized and vulnerable forest habitat patches. If forest fragments are becoming too small to support important keystone species, further extinctions may occur in cascading ways, and the vegetation structure and composition may eventually collapse. Until now relatively few reported cases of species extinctions can be directly attributed to climate change. However, climate change in combination with habitat destruction, degradation, and fragmentation may lead to new waves of species extinctions in the near future as species are set on the move but are unable to reach cooler refuges due to altered, obstructing landscapes. To mitigate the future risks of extinctions as well as climate change, major efforts should be undertaken to protect intact large areas of forests and restore wildlife corridors. Carbon sequestration may be seen as just one of many other environmental services of forest biodiversity that deserve economic valuation as alternatives to conversion to often unsustainable agricultural uses.

Sustainable Land Use and Watershed Management in Response to Climate Change Impacts

With the changes in climatic, biophysical, socio-cultural, economic, and technological components, paradigm shifts in natural resources management are unavoidably and have to be adapted/modified to harmonize with the global changes and the local communities’ needs. This chapter focuses on sustainable land use and watershed management in response to climate change impacts. The first part covers some definitions and background on sustainable land use, watershed management approach, and sustainable watershed management. The second part describes the use of the Geographic Information System (GIS) and Spatial Decision Support System (SDSS) model focusing on the framework for a planning and decision making, computer-based system for supporting spatial decisions. The mathematical programming has been reviewed focusing on optimization algorithms that include optimization modeling and simulation modeling for decision making. Finally, the example of methodology development for sustainable land use and watershed management in response to climate change in Dong Nai watershed, Vietnam is presented.

Linkage between Biodiversity, Land Use Informatics and Climate Change

Biodiversity is the variety and variability among living organisms and ecological complexes in which they occur, and it can be divided into three levels – gene, species and ecosystems. Biodiversity is an essential component of human development and security in terms of proving ecosystem services, but also it is important for its own right to exist in the globe. Failure to conserve and use biological diversity in a sustainable manner would result in degrading environments, new and more rampant illnesses, deepening poverty and a continued pattern of inequitable and untenable growth. This chapter provides a coherent presentation of the essential concepts, key terminology, historical background of biodiversity, and drivers to biodiversity loss, especially land use/land cover and climate change. A number of land use change models and a general circulation model for prediction of future climate change and its effects on individuals, populations, species, and ecosystems are briefly described. The chapter also introduces the structure of the book including summaries of each chapter.

Embedding Biodiversity Modelling in the Policy Process

Biodiversity modeling for supporting policy processes is a relatively new field. Models can help policy makers to get a quick assessment of biodiversity and provide them with answers to some of their key questions on biodiversity. Models also allow them to evaluate the effects of proposed environmental policies on biodiversity and whether the policies are likely to meet their environmental targets and thus allow policies to be revised accordingly to meet the targets. In order to use modeling as a standard tool to support policy makers, it should be embedded in a policy process. The Strategic Environmental Assessment (SEA) is such a process that is well suited to include biodiversity modeling. Besides, it is forward-looking, has proper scale and timing components, and it needs an integrated approach to link social consequences on land use change and impacts on biodiversity. The modeled impacts on biodiversity can be used in SEA to guide the decision process. The results of the GLOBIO3 application at national level in Vietnam were considered useful for policymakers; however, the tools are not yet properly embedded in a policy context requiring number of conditions to be met to deliver appropriate information to the policy makers.

The Influence of Changing Conservation Paradigms on Identifying Priority Protected Area Locations

Conservation planning for climate change adaptation is only one in a long sequence of conservation paradigms. To identify priority locations for protected areas it must compete with three other contemporary paradigms: conservation of ecosystem services, optimizing conservation of ecosystem services and poverty alleviation, and reducing carbon emissions from deforestation and forest degradation. This chapter shows how conservation paradigms evolved, discusses the merits of different approaches to modelling potential impacts of climate change on biodiversity, and describes the hybrid BIOCLIMA model and its application to Amazonia. It then discusses conservation planning applications of the three other contemporary paradigms, illustrated by examples from Amazonia and Kenya. It finds that rapid paradigm evolution is not a handicap if earlier paradigms can be nested within later ones. But more sophisticated planning tools are needed to identify optimal locations of protected areas when climate is changing, and to use protection to mitigate climate change. These should encompass the complex interactions between biodiversity, hydrological services, carbon cycling services, climate change, and human systems.

Monitoring Biodiversity Using Remote Sensing and Field Surveys

This chapter concludes that, in combination with additional environmental data sets, it is now possible to model quantitatively the spatial extent of widespread habitats and landscapes on the basis of land cover information derived from satellite imagery. Although it is now possible to model the spatial extent of widespread European habitats, these patterns cannot be directly translated into area estimates. The retrieval of accurate land cover information is not only crucial for the spatial modelling of European landscapes and habitats, but also for their monitoring. Operational remote sensing enables land cover characterization at various scales but the classification accuracies are still insufficient at continental and global scales for monitoring purposes. Instead, the use of continuous thematic fraction layers, as derived from linear unmixing, provides a good basis for monitoring land cover changes of Europe’s complex landscapes. However, gradual and small changes in habitats and their quality are not easily detected from space by satellite imagery, and therefore, additional information from field surveys is needed. Protocols for rapid field surveying of habitats have been developed that can provide a European baseline based on a sampling design across European landscapes. The information from the field samples (e.g. square kilometres) can be used for the validation and calibration of the obtained distribution maps of European habitats. The field surveying method is amongst others based on the estimation of the main plant life forms, which are highly correlated with vegetation structure. The latter has been shown to have a good relationship with satellite imagery. Field surveys are always limited to relatively small areas in Europe, and therefore, the spatial modelling of habitats and landscapes with the help of remotely sensed information remains important for providing a synoptic overview.