Linking the Conservation of Culture and Nature: A Case Study of Sacred Forests in Zimbabwe

This paper examines the role of traditional religious beliefs and traditional leaders in conserving remnant patches of a unique type of dry forest in the Zambezi Valley of northern Zimbabwe. We examined aerial photographs spanning more than three decades, interviewed and surveyed local residents, and met with communities to learn about the environmental history of the forests and the factors that have affected land use in the area. Our results show that forest loss is dramatically less in forests that are now considered sacred, or were in the past connected to sacred forests. This supports our hypothesis that traditional spiritual values have influenced human behavior affecting the forests, and have played a role in protecting them until now. We also found that rates of forest loss have been much higher in an area where traditional leaders are relatively disempowered within the post-independence political system compared to an area where traditional leaders have more power. These findings lead us to conclude that a strategy that links the conservation of culture and nature is likely to be more effective in conserving forests than a strategy that ignores traditional beliefs, values, and institutions

Effects of DTM Resolution On Slope Steepness And Soil Loss Prediction On Hillslope Profiles

Topographic attributes play a critical role in predicting erosion in models such as the Water Erosion Prediction Project (WEPP). The effects of four different high resolution hillslope profiles were studied using four different DTM resolutions: 1-m, 3-m, 5-m and 10-m. The WEPP model used a common scenario encountered in the forest environment and the selected hillslope profiles to calculate the average annual runoff, average annual soil loss and average annual sediment delivery. The DTM resolution affects the slope steepness as well as the erosion and sediment delivery predicted by WEPP. The slope steepness values generated from higher resolution DTMs were less than from lower resolution DTMs. The trends in predicted average annual soil loss as a function of DTM resolution showed the same pattern as for slope steepness

Effects of DTM Resolution On Slope Steepness And Soil Loss Prediction On Hillslope Profiles

Topographic attributes play a critical role in predicting erosion in models such as the Water Erosion Prediction Project (WEPP). The effects of four different high resolution hillslope profiles were studied using four different DTM resolutions: 1-m, 3-m, 5-m and 10-m. The WEPP model used a common scenario encountered in the forest environment and the selected hillslope profiles to calculate the average annual runoff, average annual soil loss and average annual sediment delivery. The DTM resolution affects the slope steepness as well as the erosion and sediment delivery predicted by WEPP. The slope steepness values generated from higher resolution DTMs were less than from lower resolution DTMs. The trends in predicted average annual soil loss as a function of DTM resolution showed the same pattern as for slope steepness

Geographic variability in lidar predictions of forest stand structure in the Pacific Northwest

Estimation of the amount of carbon stored in forests is a key challenge for understanding the global carbon cycle, one which remote sensing is expected to help address. However, carbon storage in moderate to high biomass forests is difficult to estimate with conventional optical or radar sensors. Lidar (light detection and ranging) instruments measure the vertical structure of forests and thus hold great promise for remotely sensing the quantity and spatial organization of forest biomass. In this study, we compare the relationships between lidar measured canopy structure and coincident field measurements of forest stand structure at five locations in the Pacific Northwest of the U.S.A. with contrasting composition. Coefficient of determination values (r2) ranged between 41% and 96%. Correlations for two important variables, LAI (81%) and above ground biomass (92%), were noteworthy, as was the fact that neither variable showed an asymptotic response. Of the 17 stand structure variables considered in this study, we were able to develop eight equations that were valid for all sites, including equations for two variables generally considered to be highly important (aboveground biomass and leaf area index). The other six equations that were valid for all sites were either related to height (which is most directly measured by lidar) or diameter at breast height (which should be closely related to height). Four additional equations (a total of 12) were applicable to all sites where either Douglas-fir (Pseudotsuga menziesii), western hemlock (Tsuga heterophylla) or Sitka spruce (Picea sitchensi) were dominant. Stand structure variables in sites dominated by true firs (Abies sp.) or ponderosa pine (Pinus ponderosa) had biases when predicted by these four additional equations. Productivity-related variables describing the edaphic, climatic and topographic environment of the sites where available for every regression, but only two of the 17 equations (maximum diameter at breast height, stem density) incorporated them. Given the wide range of these environmental conditions sampled, we conclude that the prediction of stand structure is largely independent of environmental conditions in this study area. Most studies of lidar remote sensing for predicting stand structure have depended on intensive data collections within a relatively small study area. This study indicates that the relationships between many stand structure indices and lidar measured canopy structure have generality at the regional scale. This finding, if replicated in other regions, would suggest that mapping of stand structure using lidar may be accomplished by distributing field sites extensively over a region, thus reducing the overall inventory effort required

Evaluating Aster Satellite Imagery And Gradient Modeling For Mapping And Characterizing Wildland Fire Fuels

Land managers need cost-effective methods for mapping and characterizing fire fuels quickly and accurately. The advent of sensors with increased spatial resolution may improve the accuracy and reduce the cost of fuels mapping. The objective of this research is to evaluate the accuracy and utility of imagery from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite and gradient modeling for mapping fuel layers for fire behavior modeling within FARSITE. An empirical model, based upon field data and spectral information from an ASTER image, was employed to test the efficacy of ASTER for mapping and characterizing canopy closure and crown bulk density. Surface fuel models (NFFL 1-13) were mapped using a classification tree based upon three gradient layers; potential vegetation type, cover type, and structural stage

The Relationship of Field Burn Severity Measures To Satellite-derived Burned Area Reflectance Classification (Barc) Maps

Preliminary results are presented from ongoing research on spatial variability of fire effects on soils and vegetation from the Black Mountain Two and Cooney Ridge wildfires, which burned in western Montana during the 2003 fire season. Extensive field fractional cover data were sampled to assess the efficacy of quantitative satellite image-derived indicators of burn severity. The objective of this study was to compare the field burn severity measures to the digital numbers used to produce Burned Area Reflectance Classification (BARC) maps. Canopy density was the field variable most highly correlated to BARC data derived from either SPOT Multispectral (XS) or Landsat Thematic Mapper (TM) imagery. Among the other field variables, old litter depth and duff depth correlated better with the satellite data than did old litter cover. Ash cover correlated most poorly. Old litter cover correlated better with the satellite data than did exposed mineral soil or rock cover, but combining the mineral soil and rock cover fractions into a single inorganic cover fraction improved the correlation to a comparable level. Most field variables, with the notable exception of ash, tended to vary more at low and moderate severity sites than at high severity sites. Semivariograms of the field variables revealed spatial autocorrelation across the spatial scales sampled (2 – 130 m), which the 20 m or 30 m resolution satellite imagery only weakly detected. Future analyses will be broadened to quantify burn severity characteristics in other forest types and to consider erosion processes, such as soil water infiltration following fire

The Relationship of Field Burn Severity Measures To Satellite-derived Burned Area Reflectance Classification (Barc) Maps

Preliminary results are presented from ongoing research on spatial variability of fire effects on soils and vegetation from the Black Mountain Two and Cooney Ridge wildfires, which burned in western Montana during the 2003 fire season. Extensive field fractional cover data were sampled to assess the efficacy of quantitative satellite image-derived indicators of burn severity. The objective of this study was to compare the field burn severity measures to the digital numbers used to produce Burned Area Reflectance Classification (BARC) maps. Canopy density was the field variable most highly correlated to BARC data derived from either SPOT Multispectral (XS) or Landsat Thematic Mapper (TM) imagery. Among the other field variables, old litter depth and duff depth correlated better with the satellite data than did old litter cover. Ash cover correlated most poorly. Old litter cover correlated better with the satellite data than did exposed mineral soil or rock cover, but combining the mineral soil and rock cover fractions into a single inorganic cover fraction improved the correlation to a comparable level. Most field variables, with the notable exception of ash, tended to vary more at low and moderate severity sites than at high severity sites. Semivariograms of the field variables revealed spatial autocorrelation across the spatial scales sampled (2 – 130 m), which the 20 m or 30 m resolution satellite imagery only weakly detected. Future analyses will be broadened to quantify burn severity characteristics in other forest types and to consider erosion processes, such as soil water infiltration following fire

Characterizing forest succession with lidar data: An evaluation for the Inland Northwest, USA

Quantifying forest structure is important for sustainable forest management, as it relates to a wide variety of ecosystem processes and services. Lidar data have proven particularly useful for measuring or estimating a suite of forest structural attributes such as canopy height, basal area, and LAI. However, the potential of this technology to characterize forest succession remains largely untested. The objective of this study was to evaluate the use of lidar data for characterizing forest successional stages across a structurally diverse, mixed-species forest in Northern Idaho. We used a variety of lidar-derived metrics in conjunction with an algorithmic modeling procedure (Random Forests) to classify six stages of three-dimensional forest development and achieved an overall accuracy \u3e95%. The algorithmic model presented herein developed ecologically meaningful classifications based upon lidar metrics quantifying mean vegetation height and canopy cover, among others. This study highlights the utility of lidar data for accurately classifying forest succession in complex, mixed coniferous forests; but further research should be conducted to classify forest successional stages across different forests types. The techniques presented herein can be easily applied to other areas. Furthermore, the final classification map represents a significant advancement for forest succession modeling and wildlife habitat assessment

Evaluation of the MODIS LAI product using independent lidar-derived LAI: A case study in mixed conifer forest

This study presents an alternative assessment of the MODIS LAI product for a 58,000 ha evergreen needleleaf forest located in the western Rocky Mountain range in northern Idaho by using lidar data to model (R2=0.86, RMSE=0.76) and map LAI at higher resolution across a large number of MODIS pixels in their entirety. Moderate resolution (30 m) lidar-based LAI estimates were aggregated to the resolution of the 1-km MODIS LAI product and compared to temporally-coincident MODIS retrievals. Differences in the MODIS and lidar-derived values of LAI were grouped and analyzed by several different factors, including MODIS retrieval algorithm, sun/sensor geometry, and sub-pixel heterogeneity in both vegetation and terrain characteristics. Of particular interest is the disparity in the results when MODIS LAI was analyzed according to algorithm retrieval class. We observed relatively good agreement between lidar-derived and MODIS LAI values for pixels retrieved with the main RT algorithm without saturation for LAI LAI≤4. Moreover, for the entire range of LAI values, considerable overestimation of LAI (relative to lidar-derived LAI) occurred when either the main RT with saturation or back-up algorithm retrievals were used to populate the composite product regardless of sub-pixel vegetation structural complexity or sun/sensor geometry. These results are significant because algorithm retrievals based on the main radiative transfer algorithm with or without saturation are characterized as suitable for validation and subsequent ecosystem modeling, yet the magnitude of difference appears to be specific to retrieval quality class and vegetation structural characteristics

Characterizing forest succession with lidar data: An evaluation for the Inland Northwest, USA

Quantifying forest structure is important for sustainable forest management, as it relates to a wide variety of ecosystem processes and services. Lidar data have proven particularly useful for measuring or estimating a suite of forest structural attributes such as canopy height, basal area, and LAI. However, the potential of this technology to characterize forest succession remains largely untested. The objective of this study was to evaluate the use of lidar data for characterizing forest successional stages across a structurally diverse, mixed-species forest in Northern Idaho. We used a variety of lidar-derived metrics in conjunction with an algorithmic modeling procedure (Random Forests) to classify six stages of three-dimensional forest development and achieved an overall accuracy \u3e95%. The algorithmic model presented herein developed ecologically meaningful classifications based upon lidar metrics quantifying mean vegetation height and canopy cover, among others. This study highlights the utility of lidar data for accurately classifying forest succession in complex, mixed coniferous forests; but further research should be conducted to classify forest successional stages across different forests types. The techniques presented herein can be easily applied to other areas. Furthermore, the final classification map represents a significant advancement for forest succession modeling and wildlife habitat assessment