Classification of Plot-Level Fire-Caused Tree Mortality in a Redwood Forest Using Digital Orthophotography and Lidar

Swanton Pacific Ranch is an approximately 1,300 ha working ranch and forest in northern Santa Cruz County, California, managed by California Polytechnic State University, San Luis Obispo (Cal Poly). On August 12, 2009, the Lockheed Fire burned 300 ha of forestland, 51% of the forested area on the property, with variable fire intensity and mortality. This study used existing inventory data from 47 permanent 0.08 ha (1/5 ac) plots to compare the accuracy of classifying mortality resulting from the fire using digital multispectral imagery and LiDAR. The percent mortality of trees at least 25.4 cm (10”) DBH was aggregated to three classes (0-25, 25-50, and 50-100%). Three separate Classification Analysis and Regression Tree (CART) models were created to classify plot mortality. The first used the best imagery predictor variable of those considered, the Normalized Difference Vegetation Index (NDVI) calculated from 2010 National Agricultural Imagery Program (NAIP) aerial imagery, with shadowed pixel values adjusted, and non-canopy pixels removed. The second used the same NDVI in combination with selected variables from post-fire LiDAR data collected in 2010. The third used the same NDVI in combination with selected variables from differenced LiDAR data calculated using post-fire LiDAR and pre-fire LiDAR collected in 2008. The imagery alone was 74% accurate; the imagery and post-fire LiDAR model was 85% accurate, while the imagery and differenced LiDAR model was 83% accurate. These findings indicate that remote sensing data can accurately estimate post-fire mortality, and that the addition of LiDAR data to imagery may yield only modest improvement

Initial floristic response to high severity wildfire in an old-growth coast redwood (Sequoia sempervirens (d. don) endl.) forest

Climate driven increases in fire frequency and severity are predicted for Mediterranean climatic zones, including the Pacific coast of California. A recent high severity wildfire that burned in the Santa Cruz Mountains affected a variety of vegetation types, including ancient coast redwood (Sequoia sempervirens (D. Don) Endl.) stands. The purpose of this study was to characterize the survival and initial recovery of vegetation approximately six months after the fire. We sampled thirty randomly selected points in an old-growth coast redwood forest to examine and compare survival, crown retention, and post fire regeneration of trees by species, and the recovery of associated understory plant species. Sequoia sempervirens exhibited the highest post-fire survival (95%), with lower survival rates for subcanopy hardwood associates including tanoak (Notholithocarpus densiflorus (Hook. & Arn.) Manos) (88%), coast live oak (Quercus agrifolia Nee.) (93%), Pacific wax myrtle (Myrica californica (Cham. & Schltdl.) Wilbur) (75%), Pacific madrone (Arbutus menziesii Pursh) (71%), and the lowest survival recorded for the canopy codominant Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) (15%). Canopy retention and post fire regeneration were also highest for S. sempervirens and lowest for P. menziesii, indicating that S. sempervirens had a competitive advantage over P. menziesii following high severity crown fire. Both canopy survival and regeneration were greater for larger height and diameter trees; and basal sprouting was positively associated with tree height and diameter for S. sempervirens and N. densiflorus. Observed recovery of understory species was modest but included the reemergence of coast redwood associated herbaceous species. The robust nature of survival and recovery of S. sempervirens following this extreme fire event suggest that the removal of scorched, and the seeding or planting of trees, following this type of fire is contraindicated. The decline of P. menziesii is of concern, however, and suggests that repeated high severity fires driven by climate change could eventually lead to vegetation type conversion

Influences of Forest Edges on the Growth and Health of Old-Growth Coast Redwood Forests

Coast redwood (Sequoia sempervirens) is the tallest species in the world, frequently attaining heights greater than 300 ft. The unique characteristics of the redwoods has led to the establishment of several preservation areas including national and state parks. However, abrupt forests edges created by previous logging and landcover changes has left the remaining stands exposed to elevated temperature, sunlight, and wind intensities, thereby making redwoods along the forest edge more susceptible to windthrow and drought stress. Despite the rarity of old-growth coast redwood forests and their ecological and cultural significance, very few studies have investigated how forests edges have impacted the productivity and health of these forests. In these studies, we combine dendrochronology with remote sensing methods to better understand the spatial and temporal patterns of redwood stress and how it has been impacted by habitat fragmentation. In the first study, we investigated how a previous road expansion has impacted the growth and drought stress of nearby redwoods. In the second study, we mapped declines in redwood crown health to better understand the relationship between crown health and environmental variables such as distance to forest edge, local tree density, and overall tree height. Our results indicated that previous road expansions caused growth declines in adjacent trees and caused elevated drought stress in the subsequent decades. Our results also indicated that taller trees were more susceptible to declines in crown health and crown dieback was found in higher concentrations where multiple roads and previously logged areas intersect old-growth stands

Swanton Pacific Ranch: Student Research Bibliography

Swanton Pacific Ranch (SPR) is a 3,200-acre ranch in Santa Cruz County, California, outside the town of Davenport. The ranch is an educational and research facility owned by the Cal Poly Corporation and managed by the California Polytechnic State University (Cal Poly) College of Agriculture, Food and Environmental Sciences. SPR is a learning laboratory that employs Cal Poly’s Learn By Doing philosophy. Many students have completed research projects at SPR but no complete list of student projects exists. This bibliography includes Cal Poly authored student research and those co-authored with Cal Poly faculty and staff. Documents include senior projects, master’s theses, class projects, reports, and more. Though this bibliography is the most comprehensive to date, it is not exhaustive. The purpose of this document is to provide researchers with citations for difficult to locate gray literature. A number of resources were used to collect these citations, including DigitalCommons@CalPoly, Cal Poly’s library catalog, physical documents located at Swanton Pacific Ranch, and citation lists provided by Cal Poly staff and faculty. The citations include as much detail as available and the information was not edited or updated. The following document types are included: a “Senior Project” is a course or sequence that many departments require for a student to earn a bachelor\u27s degree; a “Master’s Thesis“ is the product of a systematic study of a significant problem; a “Class Project” corresponds with a specific class the author took during the author\u27s time at the university; and a “Case Study”, “Special Problem”, or “Report” involves detailed research on a specific subject. Please note that names of departments and classes have changed over time, and there are also name variations for some locations (ex. “Scott” Creek, “Scotts” Creek, “Scott’s” Creek). Missing citation information is noted by the following abbreviations UN (unknown document type), ND (no department listed), and DU (date unknown)

Large-Area, High Spatial Resolution Land Cover Mapping Using Random Forests, GEOBIA, and NAIP Orthophotography: Findings and Recommendations

Despite the need for quality land cover information, large-area, high spatial resolution land cover mapping has proven to be a difficult task for a variety of reasons including large data volumes, complexity of developing training and validation datasets, data availability, and heterogeneity in data and landscape conditions. We investigate the use of geographic object-based image analysis (GEOBIA), random forest (RF) machine learning, and National Agriculture Imagery Program (NAIP) orthophotography for mapping general land cover across the entire state of West Virginia, USA, an area of roughly 62,000 km2. We obtained an overall accuracy of 96.7% and a Kappa statistic of 0.886 using a combination of NAIP orthophotography and ancillary data. Despite the high overall classification accuracy, some classes were difficult to differentiate, as highlight by the low user’s and producer’s accuracies for the barren, impervious, and mixed developed classes. In contrast, forest, low vegetation, and water were generally mapped with accuracy. The inclusion of ancillary data and first- and second-order textural measures generally improved classification accuracy whereas band indices and object geometric measures were less valuable. Including super-object attributes improved the classification slightly; however, this increased the computational time and complexity. From the findings of this research and previous studies, recommendations are provided for mapping large spatial extents

Biomass forest modelling using UAV LiDAR data under fire effect

Mestrado em Engenharia Florestal e dos Recursos Naturais / Instituto Superior de Agronomia. Universidade de LisboaThe main goal of the study is to analyse the possibility of quantifying the loss of biomass in burned forest stands using Light Detection and Ranging (LiDAR) data. Since wildfires are not uncommon in Mediterranean areas, it is useful to quantify the magnitude of fire damage in forests. With the use of remote sensing, it is possible to plan post-fire recovery management and to quantify the losses of biomass and carbon stock. Mata Nacional de Leiria (MNL) was chosen, because, after the fire in October 2017, it showed areas with low and medium-high fire severity. MNL is divided in several rectangular management units (MU). To achieve our objective, it was necessary to find a MU with burned and unburned areas. In this selection process, we used Sentinel-2 images. The fire severity was estimated by deriving a spectral index related with the effects of fire and to compute the temporal difference (pre- minus post-fire) of this index, the delta normalized burn ratio (DNBR). Forest inventory was carried out in four plots installed in the selected MU. Allometric equations were used to estimate values of stand aboveground biomass. These values were used to fit a relationship with data extracted from LiDAR cloud metrics. The LiDAR data were acquired with a VLP-16 Velodyne LiDAR PUCK™ mounted on an Unmanned Aerial Vehicles (UAV) at an altitude of 60 m above the ground. The point clouds were then processed with the FUSION software until a cloud metrics was generated and then regression models were used to fit equations related to LiDAR-derived parameters. Two biomass equations were fit, one with the whole tree metrics having a R² = 0,95 and a second one only considering the tree crown metrics presenting a R² = 0,93. The state of the forest (unburned/burned) was significant on the final equationN/

Ash Tree Identification Based on the Integration of Hyperspectral Imagery and High-density Lidar Data

Monitoring and management of ash trees has become particularly important in recent years due to the heightened risk of attack from the invasive pest, the emerald ash borer (EAB). However, distinguishing ash from other deciduous trees can be challenging. Both hyperspectral imagery and Light detection and ranging (LiDAR) data are two valuable data sources that are often used for tree species classification. Hyperspectral imagery measures detailed spectral reflectance related to the biochemical properties of vegetation, while LiDAR data measures the three-dimensional structure of tree crowns related to morphological characteristics. Thus, the accuracy of vegetation classification may be improved by combining both techniques. Therefore, the objective of this research is to integrate hyperspectral imagery and LiDAR data for improving ash tree identification. Specifically, the research aims include: 1) using LiDAR data for individual tree crowns segmentation; 2) using hyperspectral imagery for extraction of relative pure crown spectra; 3) fusing hyperspectral and LiDAR data for ash tree identification. It is expected that the classification accuracy of ash trees will be significantly improved with the integration of hyperspectral and LiDAR techniques. Analysis results suggest that, first, 3D crown structures of individual trees can be reconstructed using a set of generalized geometric models which optimally matched LiDAR-derived raster image, and crown widths can be further estimated using tree height and shape-related parameters as independent variables and ground measurement of crown widths as dependent variables. Second, with constrained linear spectral mixture analysis method, the fractions of all materials within a pixel can be extracted, and relative pure crown-scale spectra can be further calculated using illuminated-leaf fraction as weighting factors for tree species classification. Third, both crown shape index (SI) and coefficient of variation (CV) can be extracted from LiDAR data as invariant variables in tree’s life cycle, and improve ash tree identification by integrating with pixel-weighted crown spectra. Therefore, three major contributions of this research have been made in the field of tree species classification:1) the automatic estimation of individual tree crown width from LiDAR data by combining a generalized geometric model and a regression model, 2) the computation of relative pure crown-scale spectral reflectance using a pixel-weighting algorithm for tree species classification, 3) the fusion of shape-related structural features and pixel-weighted crown-scale spectral features for improving of ash tree identification

The use of remote sensing for characterizing forests in wildlife habitat modeling

Spatially explicit maps of habitat relationships have proven to be valuable tools for conservation and management applications including evaluating how and which species may be impacted by large scale climate change, ongoing fragmentation of habitat, and local land-use practices. Studies have turned to remote sensing datasets as a way to characterize vegetation for the examination of habitat selection and for mapping realized relationships across the landscape. Although the use of remote sensing in wildlife studies has increased in recent years, the use of these datasets is still limited and some data sources and methods are yet to be explored. The overall goal of this dissertation was to look at the state of the wildlife ecology discipline in the use of geospatial data for habitat mapping, and to advance this area through the fusion of remote sensing tools for the mapping of previously difficult to characterize forest metrics for inclusion in avian cavity-nester habitat models. Chapter 2 reviewed over 60 years of selected wildlife literature to examine the wildlife ecology disciple through historic trends and recent advances in the use of remote sensing for habitat characterization focusing on aspects of scale and the use of available technology. We discuss commonly used remote sensing data sources, point out recent advances in the use of geospatial data for characterizing forest wildlife habitat (the use of lidar data and the creation of spatially explicit habitat prediction maps), and provide future suggestions for increased utilization of available datasets (secondary lidar metrics and time series Landsat data). In chapters 3 and 4 we explored the use of remote sensing for characterizing forest components previously difficult to map across landscapes at scales relevant to local wildlife habitat selection. Chapter 3 found promise in the fusion of lidar structure and Landsat time series disturbance products in the modeling and mapping of post-fire snag and shrub distributions at fine scales and at size/cover thresholds relevant for habitat mapping applications for many wildlife species. The study was conducted within the 2003 B&B Fire Complex in central Oregon. Using 164 field calibration plots and remote sensing predictors, we modeled the presence/absence of snag classes (dbh ≥40cm, ≥50cm, and ≥75cm) and woody shrub cover resulting in 10m output predictive grid maps. Remote sensing predictors included various lidar structure and topography variables and Landsat time series products representing the pre-fire forest, disturbance magnitude, and current forest conditions. We were able to model and map all habitat metrics with acceptable predictive performance and low-moderate errors. The utility of these snag and shrub metrics for representing important nesting habitat features for a cavity-nesting species of conservation concern, the Lewis's Woodpecker (Melanerpes lewis), was demonstrated in Chapter 4. We were able to model nesting habitat with good accuracies according to multiple performance measures and then map realized relationships for this species of conservation concern in an identified source habitat type, providing a potential resource for local scale conservation and management efforts and adding to the regional knowledge of habitat selection for the Lewis's Woodpecker. To our knowledge, these chapters represent first attempts to fuse lidar and time series Landsat disturbance metrics in a post-fire landscape and for the mapping of snag and shrub distributions at scales relevant to avian cavity nesting habitat

Earth Resources: A continuing bibliography with indexes, issue 36

This bibliography lists 576 reports, articles, and other documents introduced into the NASA Scientific and Technical Information System between October 1 and December 31, 1982. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

Earth resources: A continuing bibliography with indexes (issue 60)

This bibliography lists 485 reports, articles, and other documents introduced into the NASA scientific and technical information system between October 1 and December 31, 1988. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, oceanography and marine resources, hydrology and water management, data processing and distribution systems, and instrumentation and sensors