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Relevant bibliographies by topics / Aerial photography in forestry

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Academic literature on the topic 'Aerial photography in forestry'

Author: Grafiati

Published: 4 June 2021

Last updated: 4 March 2023

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Journal articles on the topic "Aerial photography in forestry"

1

Khavar, Yulia, Yurii Hubar, Vira Sai, Oleksandra Hulko, and Liliya Vynarchyk. "GEODESY, CARTOGRAPHY AND AERIAL PHOTOGRAPHY." GEODESY, CARTOGRAPHY AND AERIAL PHOTOGRAPHY 96,2022, no. 96 (December 2022): 32–43. http://dx.doi.org/10.23939/istcgcap2022.96.032.

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The aims of our paper are to study the territory organization of the forest fund lands of the Skhidnytsya village council (Lviv region), performing their forest management with the development of a system of activities aimed at ensuring scientifically based multifunctional forestry management, protection and rational sustainable use. The possibility of performing cadastral works for the territorial organization of forestry lands using unmanned aerial vehicles (UAVs) is important for science and practice. The paper considers the main direction of sustainable development of forest areas with a recreational function, aimed at strengthening ecological, social and economic functions and protection of these forests, their rational use. Based on the division of forests into categories, their functional significance, the regime of forestry and forest use for the next revision period, the following economic units were formed: health and recreational forests with a special regime of use in the mountains. The distribution of the total area of health and recreational forests by functional zones, types of landscape, classes of aesthetic assessment, pedestrian accessibility, recreational assessment, resistance to recreational loads, stages of recreational digression, additional assessment is presented. The results obtained prove the practical significance of the use of UAVs for performing work on the organization of the territory (certain category of land), and the resulting cartographic materials fully comply with the instructive accuracy. The developed provisions of this study correspond to the basic principles of sustainable forest management, provide for a combination of economic, environmental and social aspects of forestry activities.

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Blinn, Charles R., Al Lyons, and Edward R. Buckner. "Color Aerial Photography for Assessing the Need for Fertilizers in Loblolly Pine Plantations." Southern Journal of Applied Forestry 12, no. 4 (November 1, 1988): 270–73. http://dx.doi.org/10.1093/sjaf/12.4.270.

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Abstract Color aerial photography was used to assess crown color classes in loblolly pine (Pinus taeda L.) plantations. Three distinct Munsell color classes were delineated on the resulting photographs. Foliar N levels and, to a lesser degree, foliar K levels were directly related to color. Significant relationships between color and site index and color and basal area were shown. Application of color aerial photography, combined with Munsell color coding, could expedite land classification and also make possible more efficient use of fertilizers. South J. Appl. For. 12(4):270-273.

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Gillis, Mark D., and Donald G. Leckie. "Forest inventory update in Canada." Forestry Chronicle 72, no. 2 (April 1, 1996): 138–56. http://dx.doi.org/10.5558/tfc72138-2.

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Modern forest management presents ever increasing demands for accurate and up-to-date forest inventory information. The process of inventory update is critical. Inventory update in Canada is examined including update for harvest, burns, insect and disease, silviculture, roads and other changes. The magnitude and requirements of the update task are documented. The procedures used are described and summarized by province in table form. Usage, advantages and disadvantages of current methods (e.g. conventional 9 × 9 aerial photography, supplemental aerial photography, satellite imagery, and aerial reconnaissance) are examined, new methods discussed and trends highlighted. Also outlined are issues related to the incorporation of silviculture and insect and disease information into inventories and the structure and responsibilities for update. Key words: forest inventory, inventory update, harvest, burns, insect and disease, blowdown, silviculture, aerial photography, satellite imagery, Global Positioning System, aerial reconnaissance, video

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Smith, James L., Shepard M. Zedaker, and Richard C. Heer. "Estimating Pine Density and Competition Condition in Young Pine Plantations Using 35mm Aerial Photography." Southern Journal of Applied Forestry 13, no. 3 (August 1, 1989): 107–12. http://dx.doi.org/10.1093/sjaf/13.3.107.

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Abstract A prediction system was developed that estimated pine density and hardwood competition levels in young plantations using simple measurements made on 35mm aerial photographs. The precision of the prediction system was found to be moderate to good. The use of these photo-based modelsin a decision-making situation was examined. Ground-based decisions regarding replanting, spraying for competition control, or no treatment were compared to similar decisions reached strictly from the aerial photographic measurements. Approximately 80% of all decisions agreed, and 90% of theno-treatment decisions agreed. While aerial photographs do not totally eliminate the need for field work, it is clear that photographic information can often produce reliable decisions with reduced field efforts. South. J. Appl. For. 13(3):107-112.

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Miller, R. G. "FORESTRY AND AERIAL PHOTOGRAPHS." Photogrammetric Record 4, no. 22 (August 26, 2006): 276–82. http://dx.doi.org/10.1111/j.1477-9730.1963.tb00357.x.

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Kamioka, Hisaaki, and Nobuyuki Abe. "Estimating Stand Structure Using Digitalized Aerial Photography." Journal of Forest Research 4, no. 2 (May 1999): 75–78. http://dx.doi.org/10.1007/bf02762229.

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Fensham, R. J., and R. J. Fairfax. "Assessing woody vegetation cover change in north-west Australian savanna using aerial photography." International Journal of Wildland Fire 12, no. 4 (2003): 359. http://dx.doi.org/10.1071/wf03022.

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Models to calibrate tree and shrub cover assessed from aerial photography with field measurements were developed for a range of vegetation types in north-western Australia. The models verify previous studies indicating that woody cover can be successfully determined from aerial photography. The calibration models were applied to estimates of woody vegetation cover determined for 279 randomly located sample areas in the Ord–Victoria Rivers region using aerial photography from 1948 to 1950 and 1988 to 1997. Overstorey cover increased from a regional average of 11.5% to 13.5% and understorey cover increased from 1.3% to 2.0%. Downs, Limestone Hills and Alluvia land-types showed the most substantial increases in overstorey cover while overstorey cover in the Limestone plains land-type decreased. Relatively open structured vegetation is most susceptible to thickening. Rainfall records reveal an extreme multi-year rainfall deficit in the study area in the 1930s and relatively wet times in the 1970s and 1980s. Interpretation of a limited set of aerial photographs taken between 1964 and 1972 suggests that most of the increases in cover have occurred since this time. The study highlights the possibility that the average trend of vegetation thickening represents recovery during the relatively wet times after the 1970s. There was no relationship between structural change and a grazing intensity surrogate (distance of sample points to stock watering-points). However, the causes of structural change are undoubtedly multi-factored and the relative contributions of climate, fire and grazing vary for different landscapes and tree species.

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Fairfax, R. J., and R. J. Fensham. "Corrigendum to: Assessing woody vegetation cover change in north-west Australian savanna using aerial photography." International Journal of Wildland Fire 13, no. 1 (2004): 131. http://dx.doi.org/10.1071/wf03022_co.

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Models to calibrate tree and shrub cover assessed from aerial photography with field measurements were developed for a range of vegetation types in north-western Australia. The models verify previous studies indicating that woody cover can be successfully determined from aerial photography. The calibration models were applied to estimates of woody vegetation cover determined for 279 randomly located sample areas in the Ord–Victoria Rivers region using aerial photography from 1948 to 1950 and 1988 to 1997. Overstorey cover increased from a regional average of 11.5% to 13.5% and understorey cover increased from 1.3% to 2.0%. Downs, Limestone Hills and Alluvia land-types showed the most substantial increases in overstorey cover while overstorey cover in the Limestone plains land-type decreased. Relatively open structured vegetation is most susceptible to thickening. Rainfall records reveal an extreme multi-year rainfall deficit in the study area in the 1930s and relatively wet times in the 1970s and 1980s. Interpretation of a limited set of aerial photographs taken between 1964 and 1972 suggests that most of the increases in cover have occurred since this time. The study highlights the possibility that the average trend of vegetation thickening represents recovery during the relatively wet times after the 1970s. There was no relationship between structural change and a grazing intensity surrogate (distance of sample points to stock watering-points). However, the causes of structural change are undoubtedly multi-factored and the relative contributions of climate, fire and grazing vary for different landscapes and tree species.

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Spencer, Ray D. "Small format aerial photography: methods and achievements in Australian forestry." Australian Forestry 61, no. 4 (January 1998): 267–74. http://dx.doi.org/10.1080/00049158.1998.10674751.

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Spencer, R. D. "FILM TRIALS OF AERIAL PHOTOGRAPHY FOR FORESTRY IN VICTORIA, AUSTRALIA." Photogrammetric Record 9, no. 51 (August 26, 2006): 391–403. http://dx.doi.org/10.1111/j.1477-9730.1978.tb00431.x.

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Dissertations / Theses on the topic "Aerial photography in forestry"

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Holmström, Hampus. "Data acquisition for forestry planning by remote sensing based sample plot imputation /." Umeå : Swedish Univ. of Agricultural Sciences (Sveriges lantbruksuniv.), 2001. http://epsilon.slu.se/avh/2001/91-576-6086-7.pdf.

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Metzler, Jacob W. "Use of Multi-temporal IKONOS and LANDSAT ETM+ Satellite Imagery to Determine Forest Stand Conditions in Northern Maine." Fogler Library, University of Maine, 2004. http://www.library.umaine.edu/theses/pdf/MetzlerJW2004.pdf.

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Bleier, Mary F. "Use of prior distributions from aerial photographs in forest inventory." Thesis, Virginia Tech, 1986. http://hdl.handle.net/10919/41543.

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Bayesian estimates of gross cubic- foot volume per acre were computed for four stand types (plantation pine, natural pine. hardwood. and mixed wood stands) using aerial photo volume tables as the prior information source. Aerial photographs provided a reliable source of information even though most photographs were nearly five years old. For a given level of precision within a particular stand, Bayesian methods reduced the required field sample size up to 50% using all or half of the prior information available. Those priors which utilized a regression or a regression/topographic correction in the estimation of photo heights required less field information for the given precision level than those priors which used uncorrected or topographic corrected photo heights. In order to obtain meaningful gains in sample size reduction corrections to the estimated photo heights should be made. Although the uncorrected prior produced generally less biased estimates. the reduction in sample size was not as large as that observed using other prior types. Greater gains were attributed to the better accuracy of the prior distribution. Although Bayesian methods are biased, it appeared that these methods tempered severely biased prior distributions. In the hardwood stand for example, the average bias present in the photo volume data amounted to -140%. After combining the prior with the field sample, the greatest average bias was -50%. Bayesian methods performed better than the traditional estimation methods in terms of precision. In a one to one comparison. the Bayes standard error was consistently less than its non-Bayes counterpart. The one exception to this trend was the regression prior from the hardwood stand. The poor performance of the prior was due to the weak height regression correction equation. Modal priors utilized were not subject to the extreme input values for prior distribution development as their conservative empirical prior counterparts were. Less overall variation was observed 1n the estimated values. Under the conditions for mode selection set forth in this project, modal priors provided another good source of prior information.
Master of Science

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Pacurari, Doru I. "Evaluation of the use of remotely sensed images to speciate mixed Appalachian forests." Morgantown, W. Va. : [West Virginia University Libraries], 2000. http://etd.wvu.edu/templates/showETD.cfm?recnum=1550.

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Thesis (M.S.)--West Virginia University, 2000.
Title from document title page. Document formatted into pages; contains x, 128 p. : ill. (some col.), maps (some col.) Vita. Includes abstract. Includes bibliographical references (p. 116-121).

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Korpela, Ilkka. "Individual tree measurements by means of digital aerial photogrammetry." Helsinki : Finnish Forest Research Institute, Finnish Society of Forest Science, 2004. http://catalog.hathitrust.org/api/volumes/oclc/55872310.html.

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Holt, Ryan Samuel. "Three enabling technologies for vision-based, forest-fire perimeter surveillance using multiple unmanned aerial systems /." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd1894.pdf.

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Zagalikis, Georgios D. "Estimation of forest stand parameters using digital orthorectified aerial photographs." Thesis, University of Aberdeen, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.274879.

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Ground based forest inventory surveys can provide highly accurate measurements of tree and stand characteristics, but are time-consuming and costly, and therefore typically limited to number of sample plots. Estimating tree and stand characteristics from digitised aerial photographs can provide measurements from the whole stand, but is less accurate. The goal of this study was to evaluate the application of scanned, stereo aerial photography and digital photogrammetry in combination with tree crown delineation techniques to measure tree and stand characteristics in two sites in Scotland, Rosarie and Leanachan forests. Existing medium-resolution scanned images of true colour aerial photographs (1:10,000) were used to derive Digital Elevation Models (DEMs) of the forest canopy and digital orthophotographs. Tree crown delineation techniques were used on the derived digital orthophotographs and tree crown measurements including crown area and coordinates of each crown were derived. The DEMs in combination with Digital Terrain Models (DTMs) derived from digital contour maps, were used for the estimation of tree and stand heights. Equations derived from regression analysis of individual tree measurements on the ground, and the orthophotographs from Rosarie forest, were used for the estimation of tree and stand characteristics of both sites. For Rosarie forest the estimations of stand top height, basal area, stand volume stand biomass and stand density (~23.7%) were similar with the ground measured stand characteristics (±10%), where as for Leanachan forest the estimations were less accurate due to the non-optimum illumination conditions during the acquisition of the aerial photographs. The level of accuracy achieved in this study is adequate for measuring tree and stand characteristics, if the acquisition conditions of aerial photographs are optimal. Higher level of accuracy may be possible, but requires more accurate DTMs, possibly derived using active airborne remote sensing sensors.

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Wiles, Steven Jay. "Evaluation of photographic properties for area estimation." Thesis, Virginia Tech, 1988. http://hdl.handle.net/10919/44695.

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From the known image positional errors on aerial photographs, this thesis computes and evaluates acreage estimation errors. Four hypothetical tracts were used in simulating aerial photographs with 104 different camera orientation combinations. Flying heights of 4000 and 6000 feet, focal lengths of 24 and 50 millimeters with and without lens distortion, and tilts of 0, 3, 6, and 12 degrees were simulated. The 416 photographs were all simulated with the camera exposure station centered above the midpoint of the respective tract's bounding rectangle. The topographic relief of the tracts ranged from 19 feet in the Coastal Plain to 105 feet in the Piedmont.

It was found that lens focal length did not have an independent effect on the acreage estimates. Relief error, the lowest, averaged -0.080%. In comparison, small errors in calculating scale were shown to be larger than relief errors. Tilt was recommended to be limited to six degrees, averaging +1.6% error at six degrees tilt. Because of its positive exponential nature when the tracts are centered, tilt can induce large biases. including tilts from zero to six degrees,the average was 0.634%. Lens distortion error averaged -0.686%. Overall, the average acreage error was 0.363% for simulations up to and including six degrees of tilt with and without lens distortion. This result is for centered tracts, and it was felt many of the errors were compensating given this situation. In conclusion, the photographic images can estimate areas to $1%, however, additional errors are imparted during actual measurement of the photographs.
Master of Science

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Magnvall, Andreas, and Alexander Henne. "Real-time Aerial Photograph Alignment using Feature Matching." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176658.

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With increased mobile hardware capabilities, improved UAVs and modern algorithms, accurate maps can be created in real-time by capturing overlapping photographs of the ground. A method for mapping that can be used is to position photos by relying purely on the GPS position and altitude. However, GPS inaccuracies will be visible in the created map. In this paper, we will instead present a method for aligning the photos correctly with the help of feature matching. Feature matching is a well-known method which analyses two photos to find similar parts. If an overlap exists, feature matching can be used to find and localise those parts, which can be used for positioning one image over the other at the overlap. When repeating the process, a whole map can be created. For this purpose, we have also evaluated a selection of feature detection and matching algorithms. The algorithm found to be the best was SIFT with FLANN, which was then used in a prototype for creating a complete map of a forest. Feature matching is in many cases superior to GPS positioning, although it cannot be fully depended on as failed or incorrect matching is a common occurrence.

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Heer, Richard C. "Estimation of seedling density and evaluation of woody competition in young loblolly pine plantations using 35mm color aerial photography." Thesis, Virginia Polytechnic Institute and State University, 1986. http://hdl.handle.net/10919/94464.

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The potential for using large scale, small format aerial photography to obtain seedling density and woody competition information was investigated. Factors affecting photo interpretation of seedlings were examined and equations to predict seedling density and woody competition levels were developed and evaluated. Two scales of imagery, 1:4000 and 1:6000 were considered to compare their relative merits for these purposes. Greater age of the seedlings and their inherent development generally served to improve photo interpretation. The amount of woody competition present in the plots tended to hinder seedling identification at the 1:4000 scale, while enhancing it when 1:6000 scale data was used. Seedling density estimation and evaluation of competition through Free-To-Grow classification predictions yielded results comparable to ground surveys. Estimation of total groundline basal area in all woody competition, and classification of the plots by the amount of hard-to-control competition they contained, were less successful. These results may have been due in part to the partial leaf fall that occurred prior to obtaining the imagery. Many of the results found in this study favored the larger scale (1:4000) imagery, and its use for the procedures described is recommended.
M.S.

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Books on the topic "Aerial photography in forestry"

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D, Kiser James, ed. Aerial photography and image interpretation. 2nd ed. Hoboken, NJ: John Wiley, 2003.

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U.S. Fish and Wildlife Service, Long Term Resource Monitoring Program (Environmental Management Program), and Environmental Management Technical Center (U.S.), eds. Vegetation workshop, aerial photography interpretation. Onalaska, WI: U.S. Fish and Wildlife Service, Environmental Management Technical Center, 1990.

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Kiser, James D. (James Donald), ed. Aerial photography and image interpretation. 3rd ed. Hoboken: Wiley, 2012.

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Setzer, Theodore S. Verification of aerial photo stand volume tables for southeast Alaska. Portland, Or: U.S. Dept. of Agriculture, Forest Service, Pacific Northwest Research Station, 1988.

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Setzer, Theodore S. Verification of aerial photo stand volume tables for southeast Alaska. [Portland, Or.]: U.S. Dept. of Agriculture, Forest Service, Pacific Northwest Research Station, 1988.

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Stellingwerf, Donald A. Applications of aerial photographs and other remote sensing imagery in forestry (tropical regions). Enschede, Netherlands: International Institute for Aerospace Survey and Earth Sciences, 1986.

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Hershey, Rachel Riemann. Aerial photo guide to New England forest cover types. Radnor, PA: U.S. Dept. of Agriculture, Forest Service, Northeastern Forest Experiment Station, 1995.

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Korpela, Ilkka. Individual tree measurements by means of digital aerial photogrammetry. Helsinki: Finnish Society of Forest Science ; Finnish Forest Research Institute, 2004.

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Arnup, R. W. Predicting the abundance of advance growth in black spruce forests in Northeastern Ontario: An aerial photograph interpretive key. Sault Ste. Marie, Ont: Great Lakes Forestry Centre, 1996.

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Centre, Great Lakes Forestry, and Dendron Resource Surveys Ltd, eds. Enhancing Ontario's forest resource inventory using large-scale sampling photographs. Sault Ste Marie, Ont: Great Lakes Forestry Centre, 1997.

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Book chapters on the topic "Aerial photography in forestry"

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Hall, Ronald J. "The Roles of Aerial Photographs in Forestry Remote Sensing Image Analysis." In Remote Sensing of Forest Environments, 47–75. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0306-4_3.

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Sedykh, V. N. "Using Aerial Photography and Satellite Imagery to Monitor Forest Cover in Western Siberia." In Boreal Forests and Global Change, 499–507. Dordrecht: Springer Netherlands, 1995. http://dx.doi.org/10.1007/978-94-017-0942-2_47.

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Kentsch, Sarah, Savvas Karatsiolis, Andreas Kamilaris, Luca Tomhave, and Maximo Larry Lopez Caceres. "Identification of Tree Species in Japanese Forests Based on Aerial Photography and Deep Learning." In Progress in IS, 255–70. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61969-5_18.

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Guthrie, Richard. "Aerial Photography." In Selective Neck Dissection for Oral Cancer, 1–6. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-12127-7_7-1.

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Guthrie, Richard. "Aerial Photography." In Selective Neck Dissection for Oral Cancer, 1–6. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-12127-7_7-2.

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Hangay, George, Severiano F. Gayubo, Marjorie A. Hoy, Marta Goula, Allen Sanborn, Wendell L. Morrill, Gerd GÄde, et al. "Aerial Photography." In Encyclopedia of Entomology, 53. Dordrecht: Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-6359-6_84.

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Mancini, Keith, and John Sidoriak. "Aerial Photography." In Fundamentals of Forensic Photography, 129–51. New York : Routledge, 2017. | Series: Applications in scientific photography: Routledge, 2017. http://dx.doi.org/10.4324/9781315693125-7.

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Guthrie, Richard. "Aerial Photography." In Encyclopedia of Earth Sciences Series, 8–13. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73568-9_7.

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Schmidt, Dietmar, and Friedrich Kühn. "Aerial Photography." In Environmental Geology, 23–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74671-3_3.

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Aldred, Oscar. "The Aerial Imagination." In Archaeology and Photography, 193–208. London; New York: Bloomsbury Visual Arts, 2019. |: Routledge, 2020. http://dx.doi.org/10.4324/9781003103325-11.

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Conference papers on the topic "Aerial photography in forestry"

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Hyyppae, Juha M., Hannu Hyyppae, Mikko Inkinen, Mathias Schardt, and Michaela Ziegler. "Forest inventory based on laser scanning and aerial photography." In AeroSense 2000, edited by Gary W. Kamerman, Upendra N. Singh, Christian Werner, and Vasyl V. Molebny. SPIE, 2000. http://dx.doi.org/10.1117/12.397783.

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Petrova, M. S., K. T. Protasov, and S. N. Velisevich. "Recognition and evaluation of cedar forests from aerial photography data." In SPIE Proceedings, edited by Gelii A. Zherebtsov and Gennadii G. Matvienko. SPIE, 2006. http://dx.doi.org/10.1117/12.675878.

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KARLOVSKA, Amanda, Inga GRĪNFELDE, Ina ALSIŅA, Gints PRIEDĪTIS, and Daina ROZE. "PLANT REFLECTED SPECTRA DEPENDING ON BIOLOGICAL CHARACTERISTICS AND GROWTH CONDITIONS." In Rural Development 2015. Aleksandras Stulginskis University, 2015. http://dx.doi.org/10.15544/rd.2015.045.

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Sustainable and economically based forestry needs modern inventory and monitoring techniques. One of the most common technologies for identification of forest tree species and monitoring of forest growth conditions is the hyperspectral remote sensing. This technology gives an opportunity to economize human resources and time for data collecting and processing. The spectral behaviour of plant leaves depends on number of factors, including environmental background. The aim of this study was to assess the tree reflectance spectra in relation to the growth conditions to take into account potential differences for increasing precision of species identification in Latvian forests and for estimating of forest growth conditions. Remote sensing data were obtained using a specialized aircraft (Pilatus PC-6), which is equipped with a high-performance airborne VNIR pushbroom hyperspectral system (AisaEAGLE). The study area was flown at 1000 m altitude. Data was recorded in the 400–970 nm spectral range, spectral resolution was 3.3 nm, ground resolution 0.5 m. Data processing consisted of manually selecting trees with a recognizable tree crowns in the airborne images. Tree centres were adjusted by putting them in the accurate position according to the situation in aerial photography. All trees with a diameter at breast height DBH of more than 5 cm were measured and for each tree coordinates, its species, height, DBH, crown width and length were recorded. Differentially corrected Global Positioning System measurements were used to determine the position of each plot centre. Data from different hyperspectral bands were compared using ANOVA at confidence level 95 %. Four species: Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) H. Karst), silver birch (Betula pendula Roth), and European aspen (Populus tremula L.) – were examined in distinct forest site types. The spectral response of studied species was 1) different between species and 2) different between site types within each species, correlating with soil fertility gradient and soil moisture gradient. Differences between species occurred most in the intensity of reflected electromagnetic radiation rather than distinctive locations of maximums or minimums in spectrum curve, and near infrared (NIR) region of spectrum showed more differences between species than visible light zone. Most informative wavebands for distinguishing differences between site types were 805 nm and 644 nm.

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Angga Permana, Widastama, and Fahmy Rinanda Saputri. "Analysis of Aerial Photography with Unmanned Aerial Vehicle (UAV) Using eCognition Developer to Estimate the Number of Trees in the Forest Area." In 2022 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology (ICARES). IEEE, 2022. http://dx.doi.org/10.1109/icares56907.2022.9993522.

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Naimeng, Cang, Yu Wanjun, and Wu Xiaoyu. "Smoke detection for early forest fire in aerial photography based on GMM background and wavelet energy." In 2021 IEEE International Conference on Power Electronics, Computer Applications (ICPECA). IEEE, 2021. http://dx.doi.org/10.1109/icpeca51329.2021.9362647.

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de Vogels, M. F. A., S. M. de Jong, G. Sterk, and E. A. Addink. "A semi-automatic cropland mapping approach using GEOBIA and random forests on black-and-white aerial photography." In GEOBIA 2016 : Solutions and Synergies. University of Twente Faculty of Geo-Information and Earth Observation (ITC), 2016. http://dx.doi.org/10.3990/2.462.

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Lateb, Mustapha, Chariton Kalaitzidis, Maria Tompoulidou, and Ioannis Gitas. "Development of an object-based classification model for mapping mountainous forest cover at high elevation using aerial photography." In Fourth International Conference on Remote Sensing and Geoinformation of the Environment, edited by Kyriacos Themistocleous, Diofantos G. Hadjimitsis, Silas Michaelides, and Giorgos Papadavid. SPIE, 2016. http://dx.doi.org/10.1117/12.2240738.

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Johnston, Katherine, Betsy Waddington, Mark Leir, and Corey Kenny. "Re-Introducing the Benefits of Terrain Mapping for Pipeline Routing and Design." In 2016 11th International Pipeline Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/ipc2016-64285.

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Terrain mapping is the process of the interpretation of aerial photographs, LiDAR and satellite imagery plus field based ground truthing to delineate and characterize terrain polygons with similar surficial materials, landforms and geological processes [1]. For new pipeline projects, detailed terrain mapping is usually completed at a map scale of 1:20,000 corresponding to ground accuracy, at best, of 20 m. Although typically used to support the forestry industry in planning and developing forestry operations in British Columbia, Canada [2], and despite the rapid advancements of remote sensing technology, the art and science of terrain mapping continues to be an essential. albeit somewhat forgotten, tool for new and existing pipeline projects in a variety of terrain settings. For new pipeline projects, a quality terrain mapping product has been be used to characterize ground conditions and support the estimation of design inputs for numerous aspects of pipeline routing and design [3,4]. It is the backbone of most terrain and geohazard related tasks on a pipeline project and it is useful through many stages of a project’s development [5]. At routing and feasibility stages of a project, terrain mapping can be used to efficiently identify geohazards to avoid and to allow comparison of the terrain between different corridor options. Later on at the early design stages, terrain mapping can be used to develop and maintain a geohazard inventory to support geohazard risk assessment and design through geohazards that could not be avoided [6], delineate areas of shallow groundwater where buoyancy control and construction dewatering maybe required, help estimate soil spring parameters to support pipe stress analysis, delineate areas of shallow bedrock to support construction cost estimates and planning [8], and to identify sources of sands and gravels that maybe used for pipeline construction. This paper is intended to re-introduce the ongoing benefits of terrain mapping for new pipeline projects and describe how terrain mapping can cost-effectively support a pipeline project through its lifecycle of feasibility, design, and construction. Examples of the benefits of terrain mapping for routing and design of two proposed transmission pipelines in northern BC are presented. This work will be of interest to project managers, engineers, scientists and regulators involved with routing, design, and construction of new pipelines projects.

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Barbezat, Vincent, Philippe Kreiss, Armin Sulzmann, and Jacques Jacot. "Automated recognition of forest patterns using aerial photographs." In Photonics East '96, edited by George E. Meyer and James A. DeShazer. SPIE, 1996. http://dx.doi.org/10.1117/12.262870.

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Ferreira, Michel, Hugo Conceição, Ricardo Fernandes, and Ozan K. Tonguz. "Stereoscopic aerial photography." In the sixth ACM international workshop. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1614269.1614279.

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Reports on the topic "Aerial photography in forestry"

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Lannom, Keith B., David L. Evans, and Zhiliang Zhu. Comparison of AVHRR classification and aerial photography interpretation for estimation of forest area. New Orleans, LA: U.S. Department of Agriculture, Forest Service, Southern Forest Experiment Station, 1995. http://dx.doi.org/10.2737/so-rp-292.

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2

Taverna, Kristin. Vegetation classification and mapping of land additions at Richmond National Battlefield Park, Virginia: Addendum to technical report NPS/NER/NRTR 2008/128. National Park Service, September 2022. http://dx.doi.org/10.36967/2294278.

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In 2008 and 2015, the Virginia Department of Conservation and Recreation, Division of Natural Heritage produced vegetation maps for Richmond National Battlefield Park, following the protocols of the United States Geological Survey (USGS) – National Park Service (NPS) Vegetation Mapping Program. The original 2008 report was part of a regional project to map and classify the vegetation in seven national parks in Virginia. The 2015 report was an addendum to the original report and mapped the vegetation in newly acquired parcels. Since 2015, the park has acquired an additional 820 acres of land within 12 individual parcels, including the 650 acre North Anna unit. This report is an addendum to the 2008 and 2015 reports and documents the mapping of vegetation and other land-use classes for the 12 new land parcels at Richmond National Battlefield Park, with an updated vegetation map for the entire park. The updated map and associated data provide information on the sensitivity and ecological integrity of habitats and can help prioritize areas for protection. The vegetation map of the new land parcels includes eighteen map classes, representing 14 associations from the United States National Vegetation Classification, one nonstandard, park-specific class, and three Anderson Level II land-use categories. The vegetation classification and map classes are consistent with the original 2008 report. Vegetation-map classes for the new land parcels were identified through field reconnaissance, data collection, and aerial photo interpretation. Aerial photography from 2017 served as the base map for mapping the 12 new parcels, and field sampling was conducted in the summer of 2020. Three new map classes for the Park were encountered and described during the study, all within the North Anna park unit. These map classes are Coastal Plain / Outer Piedmont Basic Mesic Forest, Northern Coastal Plain / Piedmont Oak – Beech / Heath Forest, and Southern Piedmont / Inner Coastal Plain Floodplain Terrace Forest. The examples of Coastal Plain / Outer Piedmont Basic Mesic Forest and Southern Piedmont / Inner Coastal Plain Floodplain Terrace Forest at North Anna meet the criteria of size, condition, and landscape context to be considered a Natural Heritage exemplary natural community occurrence and should be targeted for protection and management as needed. New local and global descriptions for the three map classes are included as part of this report. Refinements were made to the vegetation field key to include the new map classes. The updated field key is part of this report. An updated table listing the number of polygons and total hectares for each of the 28 vegetation- map classes over the entire park is also included in the report. A GIS coverage containing a vegetation map for the entire park with updated Federal Geographic Data Committee (FGDC) compliant metadata was completed for this project. The attribute table field names are the same as the 2008 and 2015 products, with the exception of an additional field indicating the year each polygon was last edited.

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DeRaps, M. R., and N. E. M. Kinsman. Spatially referenced oblique aerial photography of the Golovin shoreline, July 2012. Alaska Division of Geological & Geophysical Surveys, October 2012. http://dx.doi.org/10.14509/24465.

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DeRaps, M. R., and N. E. M. Kinsman. Spatially referenced oblique aerial photography of the Eastern Norton Sound shoreline, July 2011. Alaska Division of Geological & Geophysical Surveys, February 2012. http://dx.doi.org/10.14509/23143.

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Christel, L. M. Using historical aerial photography and softcopy photogrammetry for waste unit mapping in L Lake. Office of Scientific and Technical Information (OSTI), October 1997. http://dx.doi.org/10.2172/658133.

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Provencher, L., and J. M. Dubois. Interpretation guide of natural geographic features from ETM+ Landsat imagery and aerial photography: dune. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2005. http://dx.doi.org/10.4095/314945.

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Provencher, L., and J. M. Dubois. Interpretation guide of natural geographic features from ETM+ Landsat imagery and aerial photography: esker. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2005. http://dx.doi.org/10.4095/314947.

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Provencher, L., and J. M. Dubois. Interpretation guide of natural geographic features from ETM+ Landsat imagery and aerial photography: moraine. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2005. http://dx.doi.org/10.4095/314951.

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Provencher, L., and J. M. Dubois. Interpretation guide of natural geographic features from ETM+ Landsat imagery and aerial photography: pingo. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2005. http://dx.doi.org/10.4095/314961.

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Provencher, L., and J. M. Dubois. Interpretation guide of natural geographic features from ETM+ Landsat imagery and aerial photography: reef. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2005. http://dx.doi.org/10.4095/314963.

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