<p> This study implemented LiDAR (Light Detection and Ranging) remote sensing technology and applied ITD (Individual Tree Detection) methods as an approach to estimate some essential tree variables, such as DBH (Diameter at Breast Height), height, volume, and biomass for Ford Forest Research Center in Upper Peninsula, Michigan. There were 34 deciduous (1 bigtooth aspen, 9 red oaks, 20 sugar maples, 2 white birches, and 2 yellow birches) and 17 coniferous (2 eastern hemlocks, 11 red pines, and 4 white pines) subject tree species. There were two different available LiDAR datasets from the same area that were collected in 2011 and 2017. Height measurements were done at 96% and 97% accuracy for hardwood and softwood tree species, respectively. </p><p> Several other tree variables derived from LiDAR point cloud were used to estimate DBH by using regression analysis for both 2017 and 2011 datasets. Estimation equations were tested on the other dataset. The best-fitted formula was 2017&rsquo;s, with 0.55 adjusted R&sup2; and less than 0.0001 p-values on 2017 LiDAR data while 0.42 adjusted R&sup2; and less than 0.0001 p-values on 2011&rsquo;s dataset. Some additional analysis that includes calculating PRMSE (Predicted Root Mean Square Error), BIAS (Mean Error), and MAD (Mean Absolute Difference) have been applied. The equation that was generated by using data from 2017 has &ndash;0.57 BIAS for Hardwood and 1.13 BIAS for softwood. That result indicates that the equation has &ndash;0.57 centimeters (cm) estimation error for hardwood and 1.13 cm for softwood on DBH estimations. </p><p>

Thesis (Ph.D.)--George Mason University, 2009. / Vita: p. 151. Thesis director: John J. Qu. Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Earth Systems and Geoinformation Sciences. Title from PDF t.p. (viewed Oct. 11, 2009). Includes bibliographical references (p. 136-150). Also issued in print.

Thesis (M.S.) in Forest Resources--University of Maine, 2008. / Includes vita. Includes bibliographical references (leaves 140-152).

Canada has 418 million hectares of forests, representing 10% of the forested land in the world [I]. In 1997, Canada signed the Kyoto Protocol and agreed to cut greenhouse gas emissions by six percent below the 1990 level between 2008 and 2012 [2]. This agreement was ratified in December 2002. It requires Canada to report Canada's sustainable forest resources, including information about forest carbon, afforestation, reforestation, and deforestation (ARD). To fulfill this commitment, effective and accurate measuring tools are needed. One of these tools is satellite remote sensing, a cost-effective way to examine large forested areas in Canada for timely forest information. Historically, the study of forest aboveground carbon was carried out with detailed forest inventory and field sampling from temporary and permanent sample plots, which severely limited the forest area that could be studied. For regional and global scales, it is necessary to use remote sensing for aboveground carbon and ARD mapping due to time and financial constraints. Therefore, the purpose of this research is to develop, implement, and evaluate a computing system that uses multitemporal Landsat satellite images [3] to estimate the Kyoto-Protocol-related forest parameters and create geo-referenced maps, showing the spatial distribution of these parameters in a Geographic Information System (GIs). The new computing system consists of a segment-based and supervised classification engine with feature selection functionality and a Kyoto-Protocol-products estimation unit. The inclusion of the feature selection reduces the large dimensionality of the feature space of multitemporal remote Landsat data sets. Thus, more images could be added into the data sets for analysis. The implementation of the segment-based classifiers provides more accurate forest cover classifications for estimating the Kyoto Protocol products than pixel-based classifiers. It is expected that this approach will be a new addition to the current existing methodologies for supporting Canada's reporting commitments on the sustainability of the forest resources in Canada. This approach can also be used by other countries to monitor Canada's compliance with international agreements.

Thesis (M.S.)--Rochester Institute of Technology, 1993. / Typescript. P. [81] is separate folded map, placed in a mylar sheet. Includes bibliographical references (leaves 86-89).

Thesis (M.S.) in Forestry--University of Maine, 2004. / Includes vita. Includes bibliographical references (leaves 74-80).

Thesis (Ph. D.)--University of Wisconsin--Madison, 1972. / Typescript. Vita. Description based on print version record. Includes bibliographical references (leaves 129-136).

Although remotely sensed data have been employed to assess various environmental problems, relatively few previous studies have focused on the impacts of mining. In Indonesia, mining activities have increasingly become one of major drivers of land cover change. The majority of remote sensing research projects on mining environments have exploited optical data which are frequently complicated by tmospheric disturbance, especially in tropical territories. Active remote sensors such as Synthetic Aperture Radar (SAR) are invaluable in this case. Monitoring by Independent SAR data has been limited due to single polarisation. Dual-polarised data have been employed considerably, although for some forestry applications the data were found insufficient to retrieve basic information. This Masters thesis is devoted to assess fully polarimetric SAR data for environmental monitoring of the tailings deposition zone of the PT Freeport Indonesia Grasberg mine in Papua, Indonesia. The main data were two granules of the AIRSAR datasets acquired during the PACRIM-II campaign. To support the interpretation and analysis, a scene of Landsat ETM February 2001) was used, juxtaposed with classified aerial photographs and a series of SPOT VEGETATION images. Both backscattering information and complex coherence matrices, as common representations of polarimetric data, were studied. Primary applications of this research were on degraded forest and environmental rehabilitation. Most parts of Indonesian forests have experienced abrupt changes as an impact of clear-cut deforestation. Gradual changes such as those due to fire or flooded tailings, however, are least studied. It was shown that the Cloude-Pottier polarimetric decomposition provided a convenient way to interpret various stages of forest disturbance. The result suggested that the Entropy parameter of the Cloude-Pottier decomposition could be used as a disturbance indicator. Using the fully polarimetric dataset combined with Support Vector Machine learning, the outcomes were generally acceptable. It was possible to improve classification accuracy by incorporating decomposition parameters, although it seemed insignificant. Land rehabilitation on tailings deposits has been a central concern of the government and the mining operator. Indigenous plant pioneers such as reeds (Phragmites) can naturally grow on dry tailings where soil structure is fairly well developed. To assist such efforts, a part of this research involved identification of dry tailings. On the first assessment, interpretation of surface scatterers was aided by polarimetric signatures. Apparently, longer wavelengths such as L- and P-band were overpenetrated; hence, growing reeds on dry tailings were less detectable. In this case, the use of C-band data was found fairly robust. Employing Mahalanobis statistics, the combination of HH and VV performed well on classification, having similar accuracy with quad polarimetric data. Extension on previous results was made through the Freeman-Durden decomposition. Interpretation using a three-component image of odd, even bounce and volume scattering showed that dry and wet tailings could be well distinguished. The application was benefited from unique responses of dielectric materials in the tailings deposit on SAR signals; hence it is possible to discriminate tailings with different moisture levels. However, further assessment of tailings moisture was not possible due to security reasons and access limitations at the study site. Fully polarimetric data were also employed to support rehabilitation of stressed mangrove forest on the southern coast. In this case, the Cloude-Pottier decomposition was employed along with textural parameters. Inclusion of textural properties was found invaluable for the classification using various statistical trees, and more important than decomposition parameters. It was concluded that incorporating polarimetric decompositions and textural parameters into coherence matrix leads to profound accuracy.

Several alien plants are invading subtropical forest ecosystems through canopy gaps, resulting in the loss of native species biodiversity. The loss of native species in such habitats may result in reduced ecosystem functioning. The control and eradication of these invaders requires accurate mapping of the levels of invasion in canopy gaps. Our study tested (i) the utility of WorldView-2 imagery to map forest canopy gaps, and (ii) an integration of WorldView-2 data with environmental data to model the probability of occurrence of invasive Chromolaena odorata (triffid weed) in Dukuduku forest canopy gaps of KwaZulu- Natal, South Africa. Both pixel-based classification and object-based classification were explored for the delineation of forest canopy gaps. The overall classification accuracies increased by ± 12% from a spectrally resampled 4 band image similar to Landsat (74.64%) to an 8 band WorldView-2 imagery (86.90%). This indicates that the new bands of WorldView such as the red edge band can improve on the capability of common red, blue, green and near-infrared bands in delineating forest canopy gaps. The maximum likelihood classifier (MLC) in pixel-based classification yielded the overall classification accuracy of 86.90% on an 8 band WorldView-2 image, while the modified plant senescence reflectance index (mPSRI) in object-based classification yielded 93.69%. The McNemar’s test indicated that there was a statistical difference between the MLC and the mPSRI. The mPSRI is a vegetation index that incorporates the use of the red edge band, which solves a saturation problem common in sensors such as Landsat and SPOT. An integrated model (with both WorldView-2 data and environmental data) used to predict the occurrence of Chromolaena odorata in forest gaps yielded a deviance of about 42% (D2 = 0.42), compared to the model derived from environmental data only (D2 = 0.12) and WorldView-2 data only (D2 = 0.20). A D2 of 0.42 means that a model can explain about 42% of the variability of the presence/absence of Chromolaena odorata in forest gaps. The Distance to Stream and Aspect were the significant environmental variables (ρ < 0.05) which were positively correlated with presence/absence of Chromolaena in forest gaps. WorldView-2 bands such as the coastal band (λ425 nm) yellow band (λ605 nm) and the nearinfrared- 1 (λ833 nm) are positively and significantly related to the presence/absence of invasive species (ρ < 0.05). On the other hand, a significant negative correlation (ρ < 0.05) of near-infrared-2 band (λ950 nm) and the red edge normalized difference vegetation index (NDVI725) suggests that the probability of occurrence of invasive Chromolaena increases forest gaps with low vegetation density. This study highlights the importance of WorldView- 2 imagery and its application in subtropical indigenous coastal forest monitoring. / Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2013.

Three-dimensional canopy relectance models provide a physical-structural basis to satellite image analysis, representing a potentially more robust, objective and accurate approach for obtaining forest cover type and structural information with minimal ground truth data. The Geometric Optical Mutual Shadowing (GOMS) canopy relectance model was run in multiple-forward-mode (MFM) using digital multispectral IKONOS satellite imagery to estimate tree height and stand volume over 100m2 homogeneous forest plots in mountainous terrain, Kananaskis, Alberta. Height was computed within 2.7m for trembling aspen and 1.8m fr lodgepole pine, with basal area estimated within 0.05m2. Stand volume, estimated as the product of mean tree height and basal area, had an absolute mean difference from field measurements of 0.85m3/100m2 and 0.61m3/100m2 for aspen and pine, respectively. / xiii, 143 leaves : ill. (some col.) ; 29 cm.