Fire risk evaluation using multicriteria analysis—a case study

Forest fires are one of the major causes of ecological disturbance and environmental concerns in tropical deciduous forests of south India. In this study, we use fuzzy set theory integrated with decision-making algorithm in a Geographic Information Systems (GIS) framework to map forest fire risk. Fuzzy set theory implements classes or groupings of data with boundaries that are not sharply defined (i.e., fuzzy) and consists of a rule base, membership functions, and an inference procedure. We used satellite remote sensing datasets in conjunction with topographic, vegetation, climate, and socioeconomic datasets to infer the causative factors of fires. Spatial-level data on these biophysical and socioeconomic parameters have been aggregated at the district level and have been organized in a GIS framework. A participatory multicriteria decision-making approach involving Analytical Hierarchy Process has been designed to arrive at a decision matrix that identified the important causative factors of fires. These expert judgments were then integrated using spatial fuzzy decision-making algorithm to map the forest fire risk. Results from this study were quite useful in identifying potential “hotspots” of fire risk, where forest fire protection measures can be taken in advance. Further, this study also demonstrates the potential of multicriteria analysis integrated with GIS as an effective tool in assessing “where and when” forest fires will most likely occur.     

Development of Ecological Land Classification and mapping in support of forest management in northern Newfoundland,Canada

For the sustainable development of forest land, as recently prescribed by the Canadian Forest Strategy, a land classification project in northern Newfoundland was initiated to support the local forest management activities. The method adopted here is a modification of the Canadian Committee for Ecological Land Classification's (CCELC) system, and it applies various levels of mapping to uniform areas based on geomorphology, soils, vegetation, climate, water, and fauna.In this study, all CCELC levels were mapped; resulting maps were digitized and imported into a Geographic Informations System (GIS). The GIS data base contained the following maps: 1) digital terrain model, 2) bedrock geology, 3) surficial geology, 4) forest inventory, and 5) various levels of the ecological land classification, including Vegetation Types at the lowest level. In addition to the mapping, mensurational data were analyzed to provide stand and stock tables for each of the forest types, including growth curves that could be entered into specific forest growth modelling systems to predict wood supply scenarios based upon different management interventions.     

Evaluating the utility and seasonality of NDVI values for assessing post-disturbance recovery in a subalpine forest

Forest disturbances around the world have the potential to alter forest type and cover, with impacts on diversity, carbon storage, and landscape composition. These disturbances, especially fire, are common and often large, making ground investigation of forest recovery difficult. Remote sensing offers a means to monitor forest recovery in real time, over the entire landscape. Typically, recovery monitoring via remote sensing consists of measuring vegetation indices (e.g., NDVI) or index-derived metrics, with the assumption that recovery in NDVI (for example) is a meaningful measure of ecosystem recovery. This study tests that assumption using MODIS 16-day imagery from 2000 to 2010 in the area of the Colorado’s Routt National Forest Hinman burn (2002) and seedling density counts taken in the same area. Results indicate that NDVI is rarely correlated with forest recovery, and is dominated by annual and perennial forb cover, although topography complicates analysis. Utility of NDVI as a means to delineate areas of recovery or non-recovery are in doubt, as bootstrapped analysis indicates distinguishing power only slightly better than random. NDVI in revegetation analyses should carefully consider the ecology and seasonal patterns of the system in question.     

Forest dynamics modelling under natural fire cycles: A tool to define natural mosaic diversity for forest management

In natural boreal forests, disturbances such as fire and variation in surficial deposits create a mosaic of forest stands with different species composition and age. At the landscape level, this variety of stands can be considered as the natural mosaic diversity. In this paper, we describe a model that can be used to estimate the natural diversity level of landscapes. We sampled 624 stands for tree species composition and surficial deposits in eight stand-age classes corresponding to eight fire episodes in the region of Lake Duparquet, Abitibi, Québec at the southern fringe of the Boreal Forest. For six surficial deposit types, stand composition data were used to define equations for vegetation changes with time for a chronosequence of 230 years for four forest types. Using Van Wagner's (1978) model of age class distribution of stands, the proportion of each forest type for several lengths of fire cycle were defined. Finally, for real landscapes (ecological districts) of the ecological region of the Basses-Terres d'Amos, the proportion of forest types were weighted by the proportion of each surficial deposit type using ecological map information. Examples of the possible uses of the model for management purposes, such as biodiversity conservation and comparisons of different landscapes in terms of diversity and sensitivity to fire regime changes, are discussed.     

Fine-resolution mapping of wildfire fuel types for Canada: Fuzzy logic modeling for an Alberta pilot area

The Canadian Forest Fire Danger Rating System (CFFDRS) is used daily across Canada for evaluating forest fire danger. Fuel-type information is one of the inputs required by the models used in the CFFDRS. In this project, three fuel-type maps with a 25 m resolution were produced for a pilot study area located in Alberta using land cover only; land cover and biomass; and, land cover, biomass and leaf area index data derived from satellite imagery. The relationships between inputs and fuel types were determined mainly by the opinions of forest fire scientists and incorporated into a computer program using fuzzy set methodology. Not all the CFFDRS fuel types could be distinguished using these inputs; three of the coniferous types had to be grouped into one common fuel type. Overall accuracy was between 74 and 83% based on ground-truth comparisons. The most accurate map resulted from land cover and biomass data. Detailed accuracy assessment indicated that the overall accuracy increased up to 86% if ambiguous fuel type identification was considered. No combination of inputs was able to define a fuel type with absolute certainty, which is a reflection of differing expert opinions and the small number of inputs used to produce the maps.     

Weed mapping in cotton using ground-based sensors and GIS

Site-specific weed management presupposes the careful monitoring and mapping of weed infestation areas. Cut-edge sensor technologies coupled with geographical information systems (GIS) provide the means for reliable decision-making concerning weed management even in sub-field level. In present research, two different spectral sensing systems were engaged in order to digitally map weed patches as grown in four different cotton fields in Central Greece. The systems used were a set of two Crop Circle multispectral sensors ACS-430 and a digital camera Nikon D300S. The spaces between cotton rows were scanned and photographed with the two systems accordingly. Raw recorded data were stored and analyzed in GIS environment producing spatially interpolated maps of red-edge normalized difference vegetation index (NDVI) and weed cover percentage values. Both mapping approaches were satisfactorily related to weed distribution as occurred in the fields; however, the photographic method tended to underestimate weed populations. Correlation of red-edge NDVI and weed cover values, at the points where photographs were taken, as revealed by Pearson’s correlation coefficient was high (r?>?0.83) and statistically significant at the 0.01 level. A first-degree linear equation adequately modeled (R²?>?0.7) the between value pair relations, strengthening the validity of the two methodologies in spatially monitoring weed patches. The methodologies and the technologies used in the study can be used for yearly mapping weed flora in cotton cultivation and potentially constitute a means of rationalizing herbicide application in terms of doses and spatio-temporal decision-making.     

Review of monitoring and assessing ground vegetation biodiversity in national forest inventories

Ground vegetation (GV) is an important component from which many forest biodiversity indicators can be estimated. To formulate policies at European level, taking into account biodiversity, European National Forest Inventories (NFIs) are one of the most important sources of forest information. However, for monitoring GV, there are several definitions, data collection methods, and different possible indicators. Even though it must be considered that natural conditions in different countries form very different understory types, each one has its own cost-efficient monitoring design, and they can hardly be compared. Therefore, the development of general guidelines is a particularly complex issue. This paper is a review of data collection methods and consequently a selection of the best available methods for the set of indicators with an emphasis on GV sampling methodologies in NFIs. As a final result, recommendations on GV definitions and classifications, sampling methodologies, and indicators are formulated for NFIs. Different sampling areas are recommended for each life form (shrubs, herbs, etc.). Inventory cycles and sampling seasons (depending on the phonological stages) should be specially considered and evaluated in the results. The proposed indicators are based on composition at different levels of sampling intensity for each life form and on coverage measurements.     

Forest site-quality estimation using Forest Ecosystem Classification in Northwestern Ontario

Site index for jack pine, black spruce and trembling aspen was found to be poorly related to soil types described in the Northwestern Ontario Forest Ecosystem Classification (NWO FEC). Statistical analyses showed that average site indices for most soil types and groupings of soil types were not significantly different from each other.Site index varies greatly within presently defined NWO FEC soil types because certain soil and topographic features that are closely related to site index vary greatly within soil types or are not well described by the NWO FEC soil types. These critical soil features have been identified by soil-site studies that show features most closely related to site index usually are surface soil features found within the effective rooting zone of forest trees. These critical features include depth to bedrock, depth to root restricting soil layers, and coarse fragment content and texture of surface soil horizons.Site-quality research in Northwestern Ontario is closely integrated with the NWO FEC program, thus future NWO FEC soil classifications probably will use results from our soil-site research as a basis for soil type revisions. The result will be future soil types that are more closely related to forest site quality and thus to the capability of forest land to produce tree growth.     

Carbon storage and emissions offset potential in an African dry forest, the Arabuko-Sokoke Forest, Kenya

Concerns about rapid tropical deforestation, and its contribution to rising atmospheric concentrations of greenhouse gases, increase the importance of monitoring terrestrial carbon storage in changing landscapes. Emerging markets for carbon emission offsets may offer developing nations needed incentives for reforestation, rehabilitation, and avoided deforestation. However, relatively little empirical data exists regarding carbon storage in African tropical forests, particularly for those in arid or semi-arid regions. Kenya's 416 km(2) Arabuko-Sokoke Forest (ASF) is the largest remaining fragment of East African coastal dry forest and is considered a global biodiversity hotspot (Myers et al. 2000), but has been significantly altered by past commercial logging and ongoing extraction. Forest carbon storage for ASF was estimated using allometric equations for tree biomass, destructive techniques for litter and herbaceous vegetation biomass, and spectroscopy for soils. Satellite imagery was used to assess land cover changes from 1992 to 2004. Forest and thicket types (Cynometra webberi dominated, Brachystegia spiciformis dominated, and mixed species forest) had carbon densities ranging from 58 to 94 Mg C/ha. The ASF area supported a 2.8-3.0 Tg C carbon stock. Although total forested area in ASF did not change over the analyzed time period, ongoing disturbances, quantified by the basal area of cut tree stumps per sample plot, correlated with decreased carbon densities. Madunguni Forest, an adjoining forest patch, lost 86% of its forest cover and at least 76% of its terrestrial carbon stock in the time period. Improved management of wood harvesting in ASF and rehabilitation of Madunguni Forest could substantially increase terrestrial carbon sequestration in the region.     

Monitoring Sustainable Forest Management in Different Jurisdictions

The concept of sustainable forest management (SFM) requires forest resource managers to monitor and collect information pertaining to their environmental, economic and social impact. There are increasing expectations from a variety of publics (government, customers, and other stakeholders) that forests be demonstrably well-managed, creating incentives for forest managers to design credible systems for assessing their management performance. It is against this background that local, national and international approaches to regulating forest practices have been evolving. This article reviews the different dimensions of governance as they relate to monitoring and information reporting in the forest sector. Specifically, it discusses the changing role of sovereignty, the effects of globalization and the emergence of civil society stakeholders in forestry-related decision-making. Concepts such as sovereignty and globalization have important implications for monitoring forest practices and for defining SFM. Whether SFM standard creation and enforcement involves a sovereign, shared-sovereignty or civil society approach will affect the level and nature of SFM monitoring. As a result, we need to better consider the concept of monitoring appropriate to the scale and intensity of operations, how monitoring and information reporting standards differ between jurisdictions, and what this means for independently verifying SFM at an inter-jurisdictional level.     

Greenhouse gas emissions from vegetation fires in Southern Africa

Methane (CH₄), carbon monoxide (CO), nitrogen oxides (NO_x), volatile organic carbon, and aerosols emitted as a result of the deliberate or accidental burning of natural vegetation constitute a large component of the greenhouse gas emissions of many African countries, but the data needed for calculating these emissions by the IPCC methodology is sparse and subject to estimation errors. An improved procedure for estimating emissions from fires in southern Africa has been developed. The proposed procedure involves reclassifying existing vegetation maps into one of eleven broad, functional vegetation classes. Fuel loads are calculated within each 0.5 × 0.5° cell based on empirical relationships to climate data for each class. The fractional area of each class that burns is estimated by using daily low-resolution satellite fire detection, which is calibrated against a subsample of pre- and post-fire high-resolution satellite images. The emission factors that relate the quantity of gas released to the mass of fuel burned are based on recent field campaigns in Africa and are related to combustion efficiency, which is in turn related to the fuel mix. The emissions are summed over the 1989 fire season for Africa south of the equator. The estimated emissions from vegetation burning in the subcontinent are 0.5 Tg CH₄, 14.9 Tg CO, 1.05 Tg NO_x, and 1.08 Tg of particles smaller than 2.5µm. The 324 Tg CO₂ emitted is expected to be reabsorbed in subsequent years. These estimates are smaller than previous estimates.     

Developing a National Indicator of Soil Quality on U.S Forestlands: Methods and Initial Results

The Montreal Process was formed in 1994 to develop an internationally agreed upon set of criteria and indicators for the conservation and sustainable management of temperate and boreal forests. In response to this initiative, the Forest Inventory and Analysis (FIA) and Forest Health Monitoring (FHM) programs of the United States Department of Agriculture Forest Service have implemented soil measurements as part of a national monitoring program to address specific questions related to the conservation of soil and water resources. Integration of soil assessments into the national FIA program provides for systematic monitoring of soil properties across all forested regions of the U.S. using standardized collection, laboratory, and statistical procedures that are compatible with existing forest inventory data. The resulting information will provide quantitative benchmarks for regional, national, and international reporting on sustainable forest management and enhance our understanding of management effects on soil quality. This paper presents an overview of the FIA soil monitoring program, outlines the field and laboratory protocols as currently implemented, and provides examples of how these data may be used to assess indicators of sustainable management as defined by the Montreal Process.     

An Ecosystem Approach to Combat Desertification on the Colorado Plateau

Desertification of shrub and grassland into pinyon-juniper woodland is occurring over much of the Colorado Plateau in the southwestern United States. As trees invade, they out-compete shrubs and grasses, increasing erosion rates and reducing infiltration of moisture into the soil. This has caused habitat problems for wildlife, and reduced forage for livestock. These impacts also affect the human communities that rely on ranching and tourism related to hunting. Past land use and management practices including heavy livestock grazing, fire suppression and introduction of exotic annual plants are believed to have led to current conditions. The Montrose office of the Bureau of Land Management has implemented an ecosystem-based program to reverse the desertification process on public land. The program is centered on detailed landscape objectives describing the desired vegetation mosaic on 360 000 ha of public land. The objectives outline proportions of plant seral stages and arrays of patch sizes for each planning unit. These objectives are based on priority management issues and the need to replicate a natural vegetation mosaic. Where the existing mosaic does not meet objectives, mechanical vegetation treatments and prescribed fire are used to create early and mid-seral patches on the ground. This restored vegetation pattern and type should be sustained over time through a natural fire regime and improved livestock management. Because many uncertainties exist, an adaptive management process is being used that allows mosaic objectives to be changed or processes modified where monitoring or scientific research indicate a need.     

An alternative approach for delineating eco-sensitive zones around a wildlife sanctuary applying geospatial techniques

The dynamics, degradation, and conservation of forest ecosystems are matters of prime concerns worldwide at the present. Proper planning and management of a forest area are essentially needed to protect it from the grasp of burgeoning pressure of urban-industrial sprawl. Establishment of eco-sensitive zones (ESZs), which act as buffer areas around the core forests, is one of the key approaches towards achieving this goal. This paper deals with the applicability of geospatial techniques to identify the ESZ around an Indian wildlife sanctuary following the different rules and acts prescribed by the Government of India. Gumti Wildlife Sanctuary, located in the northeastern state of Tripura in India, has been selected here as a case study. Collected pieces of information on the distribution of biodiversity and human population in the area were also used to make the approach more holistic. As inferred from this study, remote sensing and geographical information systems were found to be easily implementable and time as well as cost-effective tools for this purpose with a distinct advantage of spatial as well as temporal accuracy in identifying the existing land use and land cover patterns in pilot assessments. However, the results indicated that only appropriate hybridization of field-based information on the biodiversity and ecological aspects of the forest as well as patterns of human interferences with the remote sensing and GIS-based data could make this approach more relevant in actual implementations.     

Mapping forest composition from the Canadian National Forest Inventory and land cover classification maps

Canada's National Forest Inventory (CanFI) provides coarse-grained, aggregated information on a large number of forest attributes. Though reasonably well suited for summary reporting on national forest resources, the coarse spatial nature of this data limits its usefulness in modeling applications that require information on forest composition at finer spatial resolutions. An alternative source of information is the land cover classification produced by the Canadian Forest Service as part of its Earth Observation for Sustainable Development of Forests (EOSD) initiative. This product, which is derived from Landsat satellite imagery, provides relatively high resolution coverage, but only very general information on forest composition (such as conifer, mixedwood, and deciduous). Here we link the CanFI and EOSD products using a spatial randomization technique to distribute the forest composition information in CanFI to the forest cover classes in EOSD. The resultant geospatial coverages provide randomized predictions of forest composition, which incorporate the fine-scale spatial detail of the EOSD product and agree in general terms with the species composition summaries from the original CanFI estimates. We describe the approach and provide illustrative results for selected major commercial tree species in Canada.     

Spatial Distribution of Forest Fires and Controlling Factors in Andhra Pradesh, India Using Spot Satellite Datasets

Fires are one of the major causes of forest disturbance and destruction in several dry deciduous forests of southern India. In this study, we use remote sensing data sets in conjunction with topographic, vegetation, climate and socioeconomic factors for determining the potential causes of forest fires in Andhra Pradesh, India. Spatial patterns in fire characteristics were analyzed using SPOT satellite remote sensing datasets. We then used nineteen different metrics in concurrence with fire count datasets in a robust statistical framework to arrive at a predictive model that best explained the variation in fire counts across diverse geographical and climatic gradients. Results suggested that, of all the states in India, fires in Andhra Pradesh constituted nearly 13.53% of total fires. District wise estimates of fire counts for Andhra Pradesh suggested that, Adilabad, Cuddapah, Kurnool, Prakasham and Mehbubnagar had relatively highest number of fires compared to others. Results from statistical analysis suggested that of the nineteen parameters, population density, demand of metabolic energy (DME), compound topographic index, slope, aspect, average temperature of the warmest quarter (ATWQ) along with literacy rate explained 61.1% of total variation in fire datasets. Among these, DME and literacy rate were found to be negative predictors of forest fires. In overall, this study represents the first statewide effort that evaluated the causative factors of fire at district level using biophysical and socioeconomic datasets. Results from this study identify important biophysical and socioeconomic factors for assessing ‘forest fire danger’ in the study area. Our results also identify potential ‘hotspots’ of fire risk, where fire protection measures can be taken in advance. Further this study also demonstrate the usefulness of best-subset regression approach integrated with GIS, as an effective method to assess ‘where and when’ forest fires will most likely occur.     

Quantifying the Effects of Forest Management Strategies on the Production of Forest Values: Timber,Carbon, Oxygen,Water, and Soil

Forest management practices alter forest structure quantified with ecosystem characteristics and values. In this paper, we utilized a forest management simulation model to assess the effects of three forest management strategies focusing on timber production, carbon sequestration, oxygen production, soil erosion, and water production of a forest management unit in Turkey. A forest simulation model “ETÇAPSimülasyon” was developed and used to project forest ecosystem development over 100 years under three forest management policies of timber-oriented forest management (TFM), multipurpose forest management (MFM), and no intervention (NI). The results showed that TFM strategy produced more timber and its net present value than MFM and NI strategies did. The amount of carbon sequestration and oxygen production potential was also found to be the highest with TFM strategy than with the MFM and NI strategies. Compared with the other strategies, however, NI strategy produced the highest amount of water production and soil losses over the planning horizon. The effects of a forest management strategy depend mainly on the initial forest structure, the rate of development and the level of forest management interventions. Therefore, forest dynamics under various management strategies should be explained before the final management decision. Understanding long-term effects of any management strategies on forest structure will provide the basis for better reaching the management objectives.