Mapping health levels of Robinia pseudoacacia forests in the Yellow River delta, China, using IKONOS and Landsat 8 OLI imagery

International Journal of Remote Sensing

Volumn 36, Issue 4, 2015, Pages 1114-1135

  Wang, Hong ^a,b   Pu, Ruiliang ^b   Zhu, Qi ^c   Ren, Liliang ^a   Zhang, Zhenzhen ^a  

^a HOHAI UNIVERSITY   (China)

^b UNIVERSITY OF SOUTH FLORIDA   (United States)

^c HOHAI UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

HEALTH; IMAGE CLASSIFICATION; IMAGE ENHANCEMENT; MAPPING; MAXIMUM LIKELIHOOD; REMOTE SENSING; SATELLITE IMAGERY;

ACCURACY OF CLASSIFICATIONS; CLASSIFICATION ACCURACY; COARSE-RESOLUTION IMAGERY; ECOLOGICAL AND ECONOMIC; HIGH RESOLUTION IMAGERY; MAXIMUM LIKELIHOOD CLASSIFICATIONS; OPERATIONAL LAND IMAGER; ROBINIA PSEUDO-ACACIA;

FORESTRY;

CANOPY ARCHITECTURE; FOREST HEALTH; GRASS; IKONOS; IMAGE CLASSIFICATION; INFECTIVITY; LANDSAT; MAPPING; MORTALITY; SATELLITE IMAGERY; SATELLITE SENSOR;

CLASSIFICATION; DISEASES; FORESTS; INFORMATION RETRIEVAL; ROBINIA; SENSORS;

CHINA; SHANDONG; YELLOW RIVER DELTA;

ROBINIA PSEUDOACACIA;

EID: 84923370170     PISSN: 01431161     EISSN: 13665901     Source Type: Journal    
DOI: 10.1080/01431161.2015.1007253     Document Type: Article

References (89)

1. Adams, H. D., C. H. Luce, D. D. Breshears, C. D. Allen, M. Weiler, V. C. Hale, A. M. S. Smith, and T. E. Huxman. 2012. “Ecohydrological Consequences of Drought- and Infestationtriggered Tree Die-Off: Insights and Hypotheses.” Ecohydrology 5 (2): 145–159. doi:10.1002/eco.233.
2. Agüera, F., F. J. Aguilar, and M. A. Aguilar. 2008. “Using Texture Analysis to Improve Per-Pixel Classification of Very High Resolution Images for Mapping Plastic Greenhouses.” ISPRS Journal of Photogrammetry and Remote Sensing 63 (6): 635–646. doi:10.1016/j.isprsjprs.2008.03.003.
3. Allen, C. D., A. K. Macalady, H. Chenchouni, D. Bachelet, N. McDowell, M. Vennetier, T. Kitzberger, A. Rigling, D. D. Breshears, E. H. Hogg, P. Gonzalez, R. Fensham, Z. Zhang, J. Castro, N. Demidova, J.-H. Lim, G. Allard, S. W. Running, A. Semerci, and N. Cobb. 2010. “A Global Overview of Drought and Heat-Induced Tree Mortality Reveals Emerging Climate Change Risks for Forests.” Forest Ecology and Management 259 (4): 660–684. doi:10.1016/j.foreco.2009.09.001.
4. Anderegg, W. R. L., J. Kane, and L. D. L. Anderegg. 2012. “Consequences of Widespread Tree Mortality Triggered by Drought and Temperature Stress.” Nature Climate Change 3 (1): 30–36. doi:10.1038/NCLIMATE1635.
5. Bentz, B. J., and D. Endreson 2003. “Evaluating Satellite Imagery for Estimating Mountain Pine Beetle-Caused Lodgepole Pine Mortality: Current Status.” Paper presented at Mountain Pine Beetle Symposium: Challenges and Solutions, Kelowna, British Columbia, October 30–31.
6. Bonneau, L. R., K. S. Shields, and D. L. Civco. 1999. “Using Satellite Images to Classify and Analyze the Health of Hemlock Forests Infested by the Hemlock Woolly Adelgid.” Biological Invasions 1 (2/3): 255–267. doi:10.1023/A:1010021629127.
7. Brouwers, N., G. Matusick, K. Ruthrof, T. Lyons, and G. Hardy. 2013. “Landscape-Scale Assessment of Tree Crown Dieback following Extreme Drought and Heat in a Mediterranean Eucalypt Forest Ecosystem.” Landscape Ecology 28 (1): 69–80. doi:10.1007/s10980-012-9815-3.
8. Carter, G. A. 1994. “Ratios of Leaf Reflectances in Narrow Wavebands as Indicators of Plant Stress.” International Journal of Remote Sensing 15 (3): 697–703. doi:10.1080/01431169408954109.
9. Chrysopolitou, V., A. Apostolakis, D. Avtzis, N. Avtzis, S. Diamandis, D. Kemitzoglou, D. Papadimos, C. Perlerou, V. Tsiaoussi, and S. Dafis. 2013. “Studies on Forest Health and Vegetation Changes in Greece under the Effects of Climate Changes.” Biodiversity and Conservation 22 (5): 1133–1150. doi:10.1007/s10531-013-0451-2.
10. Ciesla, W. M. 2000. Remote Sensing in Forest Health Protection. FHTET report No 00-03. USDA Forest Service. 266 pp. http://www.fs.fed.us/foresthealth/technology/pub_titles.shtml#R.
11. Clark, D. B., C. S. Castro, L. D. A. Alvarado, and J. M. Read. 2004. “Quantifying Mortality of Tropical Rain Forest Trees Using High-Spatial-Resolution Satellite Data.” Ecology Letters 7 (1): 52–59. doi:10.1046/j.1461-0248.2003.00547.x.
12. Cobb, R., V. T. Eviner, and D. M. Rizzo. 2013. “Mortality and Community Changes Drive Sudden Oak Death Impacts on Litterfall and Soil Nitrogen Cycling.” New Phytologist 200 (2): 422–431. doi:10.1111/nph.12370.
13. Congalton, R. G., and R. A. Mead. 1983. “A Quantitative Method to Test for Consistency and Correctness in Photointerpretation.” Photogrammetric Engineering and Remote Sensing 49 (1): 69–74.
14. Coops, N. C., M. Johnson, M. A. Wulder, and J. C. White. 2006. “Assessment of Quickbird High Spatial Resolution Imagery to Detect Red Attack Damage Due to Mountain Pine Beetle Infestation.” Remote Sensing of Environment 103 (1): 67–80. doi:10.1016/j.rse.2006.03.012.
15. Coops, N. C., R. H. Waring, M. A. Wulder, and J. C. White. 2009. “Prediction and Assessment of Bark Beetle-Induced Mortality of Lodgepole Pine Using Estimates of Stand Vigor Derived from Remotely Sensed Data.” Remote Sensing of Environment 113 (5): 1058–1066. doi:10.1016/j.rse.2009.01.013.
16. de Beurs, K. M., and P. A. Townsend. 2008. “Estimating the Effect of Gypsy Moth Defoliation Using MODIS.” Remote Sensing of Environment 112 (10): 3983–3990. doi:10.1016/j.rse.2008.07.008.
17. Dye, M., O. Mutanga, and R. Ismail. 2012. “Combining Spectral and Textural Remote Sensing Variables Using Random Forests: Predicting the Age of Pinus Patula Forests in KwaZulu-Natal, South Africa.” Journal of Spatial Science 57 (2): 193–211. doi:10.1080/14498596.2012.733620.
18. ESRI. 2006. ArcGIS Version 9.1. Redland, CA: ERSI.
19. Fan, Y., P. J. Weisberg, and R. S. Nowak. 2014. “Spatio-Temporal Analysis of Remotely-Sensed Forest Mortality Associated with Road De-Icing Salts.” Science of the Total Environment 472: 929–938. doi:10.1016/j.scitotenv.2013.11.103.
20. FAO (Food and Agriculture Organization of the United Nations). 2005. Global Forest Resource Assessment 2005: Progress toward Sustainable Forest Management. FAO Forestry Paper No 147. Rome: Food and Agriculture Organization of the United Nations.
21. Franklin, S. E., M. A. Wulder, and G. R. Gerylo. 2001. “Texture Analysis of IKONOS Panchromatic Data for Douglas-Fir Forest Age Class Separability in British Columbia.” International Journal of Remote Sensing 22 (13): 2627–2632. doi:10.1080/01431160120769.
22. Franklin, S. E., M. A. Wulder, R. S. Skakun, and A. L. Carroll. 2003. “Mountain Pine Beetle Red-Attack Forest Damage Classification Using Stratified Landsat TM Data in British Columbia, Canada.” Photogrammetric Engineering and Remote Sensing 69 (3): 283–288. doi:10.14358/PERS.69.3.283.
23. Gilabert, M. A., J. González-Piqueras, F. J. Garcı́a-Haro, and J. Meliá. 2002. “A Generalized Soil-Adjusted Vegetation Index.” Remote Sensing of Environment 82 (2–3): 303–310. doi:10.1016/S0034-4257(02)00048-2.
24. Goodwin, N., N. C. Coops, and C. Stone. 2005. “Assessing Plantation Canopy Condition from Airborne Imagery Using Spectral Mixture Analysis and Fractional Abundances.” International Journal of Applied Earth Observation and Geoinformation 7 (1): 11–28. doi:10.1016/j.jag.2004.10.003.
25. Gray, J., and C. Song. 2012. “Mapping Leaf Area Index Using Spatial, Spectral, and Temporal Information from Multiple Sensors.” Remote Sensing of Environment 119: 173–183. doi:10.1016/j.rse.2011.12.016.
26. Hardisky, M. A., V. Klemas, and R. M. Smart. 1983. “The Influence of Soil Salinity, Growth Form, and Leaf Moisture on the Spectral Reflectances of Spartina Alterniflora Canopies.” Photogrammetric Engineering and Remote Sensing 49: 77–83.
27. Haywood, A., and C. Stone. 2011. “Mapping Eucalypt Forest Susceptible to Dieback Associated with Bell Miners (Manorina Melanophys) Using Laser Scanning, SPOT 5 and Ancillary Topographical Data.” Ecological Modelling 222 (5): 1174–1184. doi:10.1016/j.ecolmodel.2010.12.012.
28. Hunt, E. R., and B. N. Rock. 1989. “Detection of Changes in Leaf Water Content Using Near and Middle-Infrared Reflectances.” Remote Sensing of Environment 30: 43–54.
29. Hurtt, G., X. Xiao, M. Keller, M. Palace, G. P. Asner, R. Braswell, E. S. Brondizio, M. Cardoso, C. J. R. Carvalho, M. G. Fearon, L. Guild, S. Hagen, S. Hetrick, B. Moore, C. Nobre, J. M. Read, T. Sa, A. Schloss, G. Vourlitis, and A. J. Wickel. 2003. “IKONOS Imagery for the Large Scale Biosphere–Atmosphere Experiment in Amazonia (LBA).” Remote Sensing of Environment 88 (1–2): 111–127. doi:10.1016/j.rse.2003.04.004.
30. Hyyppä, J., H. Hyyppä, M. Inkinen, M. Engdahl, S. Linko, and Y.-H. Zhu. 2000. “Accuracy Comparison of Various Remote Sensing Data Sources in the Retrieval of Forest Stand Attributes.” Forest Ecology and Management 128 (1–2): 109–120. doi:10.1016/S0378-1127(99)00278-9.
31. Ingram, J. C., T. P. Dawson, and R. J. Whittaker. 2005. “Mapping Tropical Forest Structure in Southeastern Madagascar Using Remote Sensing and Artificial Neuralnet Works.” Remote Sensing of Environment 94: 491–507. doi:10.1016/j.rse.2004.12.001.
32. Irons, J. R., J. L. Dwyer, and J. A. Barsi. 2012. “The Next Landsat Satellite: The Landsat Data Continuity Mission.” Remote Sensing of Environment 122 (S1): 11–21. doi:10.1016/j.rse.2011.08.026.
33. Ismail, R., O. Mutanga, and U. Bob. 2007. “Forest Health and Vitality: The Detection and Monitoring of Pinus Patula Trees Infected by Sirexnoctilio Using Digital Multispectral Imagery.” Southern Hemisphere Forestry Journal 69 (1): 39–47. doi:10.2989/SHFJ.2007.69.1.5.167.
34. ITT. 2006. ENVI Version 4.3. Boulder, CO: ITT Industries.
35. Jensen, J. R. 2005. Introductory Digital Image Processing: A Remote Sensing Perspective. Upper Saddle River, NJ: Prentice Hall.
36. Jiang, X. Y., S. A. Rauscher, T. D. Ringler, D. M. Lawrence, A. P. Williams, C. D. Allen, A. L. Steiner, D. M. Cai, and N. G. McDowell. 2013. “Projected Future Changes in Vegetation in Western North America in the Twenty-First Century.” Journal of Climate 26 (11): 3671–3687. doi:10.1175/JCLI-D-12-00430.1.
37. Jordan, C. F. 1969. “Derivation of Leaf Area Index from Quality of Light on the Forest Floor.” Ecology 50: 663–666.
38. Joria, P. E., and S. C. Ahearn. 1991. “A Comparison of the SPOT and Landsat Thematic Mapper Satellite Systems for Detecting Gypsy Moth Defoliation in Michigan.” Photogrammetric Engineering and Remote Sensing 57 (12): 1605–1612.
39. Joshi, P. K., A. Ghosh, A. Chakraborty, R. Sharma, and A. Joshi. 2013. “Landsat Again–Continuing Remote Sensing, Monitoring, Mapping.” Current Science 105 (6): 761–763.
40. Kayitakire, F., C. Hamel, and P. Defourny. 2006. “Retrieving Forest Structure Variables Based on Image Texture Analysis and IKONOS-2 Imagery.” Remote Sensing of Environment 102 (3–4): 390–401. doi:10.1016/j.rse.2006.02.022.
41. Krofcheck, D. J., J. U. H. Eitel, L. A. Vierling, U. Schulthess, T. M. Hilton, E. Dettweiler- Robinson, R. Pendleton, and M. E. Litvak 2013. “Detecting Mortality Induced Structural and Functional Changes in a Piñon-Juniper Woodland Using Landsat and Rapideye Time Series.” Remote Sensing of Environment. doi:10.1016/j.rse.2013.11.009.
42. Leckie, D. G., C. Jay, F. A. Gougeon, R. N. Sturrock, and D. Paradine. 2004. “Detection and Assessment of Trees with Phellinus Weirii (Laminated Root Rot) Using High Resolution Multi-Spectral Imagery.” International Journal of Remote Sensing 25 (4): 793–818. doi:10.1080/0143116031000139926.
43. Lee, S. H., and H. K. Cho. 2006. “Detection of the Pine Trees Damaged by Pine Wilt Disease Using High Spatial Remote Sensing Data.” Paper presented at Proceedings of the ISPRS Commission VII Symposium ‘Remote Sensing: From Pixels to Processes’, Enschede, the Netherlands, May 8–11.
44. Lévesque, J., and D. J. King. 2003. “Spatial Analysis of Radiometric Fractions from High-Resolution Multispectral Imagery for Modelling Individual Tree Crown and Forest Canopy Structure and Health.” Remote Sensing of Environment 84 (4): 589–602. doi:10.1016/S0034-4257(02)00182-7.
45. Lichtenthaler, H. K., O. Wenzel, C. Buschmann, and A. Gitelson. 1998. “Plant Stress Detection by Reflectance and Fluorescence.” In Annals of the New York Academy of Sciences, edited by P. Csermely, vol. 851, 271–285. New York: New York Academy of Sciences.
46. Ma, Y., D. Wang, and J. Liu. 2013. “Relationships between Typical Vegetations, Soil Salinity, and Groundwater Depth in The yellow River Delta of China.” Yingyong Shengtai Xuebao 24 (9): 2423–2430. http://yystxb.periodicals.net.cn/default.html.
47. Maness, H., P. J. Kushner, and I. Fung. 2012. “Summertime Climate Response to Mountain Pine Beetle Disturbance in British Columbia.” Nature Geoscience 6 (1): 65–70. doi:10.1038/NGEO1642.
48. McConnell, T. J., E. W. Johnson, and B. Burns 2000. A Guide to Conducting Aerial Sketchmapping Surveys. FHTET 00-001. Fort Collins, CO: USDA Forest Service, Forest Health Technology Enterprise Team.
49. Meddens, A. J. H., J. A. Hicke, L. A. Vierling, and A. T. Hudak. 2013. “Evaluating Methods to Detect Bark Beetle-Caused Tree Mortality Using Single-Date and Multi-Date Landsat Imagery.” Remote Sensing of Environment 132: 49–58. doi:10.1016/j.rse.2013.01.002.
50. Morales, R. M., T. Idol, and J. B. Friday. 2011. “Assessment of Acacia Koa Forest Health across Environmental Gradients in Hawaii Using Fine Resolution Remote Sensing and GIS.” Sensors 11 (6): 5677–5694. doi:10.3390/s110605677.
51. Negrón-Juárez, R. I., J. Q. Chambers, G. Guimaraes, H. Zeng, C. F. M. Raupp, D. M. Marra, G. H. P. M. Ribeiro, S. S. Saatchi, B. W. Nelson, and N. Higuchi. 2010. “Widespread Amazon Forest Tree Mortality from a Single Cross Basin Squall Line Event.” Geosphysical Research Letters 37: L16701. doi:10.1029/2010GL043733.
52. Padwick, C., M. Deskevich, F. Pacifici, and S. Smallwood. 2010. “Worldview 2 Pan-Sharpening.” ASPRS Annual Conference, San Diego, CA, April 26–30.
53. Palik, B. J., M. E. Ostry, R. C. Venette, and E. Abdela. 2011. “Fraxinus Nigra (Black Ash) Dieback in Minnesota: Regional Variation and Potential Contributing Factors.” Forest Ecology and Management 261 (1): 128–135. doi:10.1016/j.foreco.2010.09.041.
54. Peng, C. H., Z. H. Ma, X. D. Lei, Q. Zhu, H. Chen, W. F. Wang, S. R. Liu, W. Z. Li, X. Q. Fang, and X. L. Zhou. 2011. “A Drought-Induced Pervasive Increase in Tree Mortality across Canada’s Boreal Forests.” Nature Climate Change 1 (9): 467–471. doi:10.1038/NCLIMATE1293.
55. Poona, N. K., and R. Ismail. 2013. “Discriminating the Occurrence of Pitch Canker Fungus in Pinus Radiate Trees Using Quickbird Imagery and Artificial Neural Networks.” Southern Forests: A Journal of Forest Science 75 (1): 29–40. doi:10.2989/20702620.2012.748255.
56. Pouliot, D. A., D. J. King, F. W. Bell, and D. G. Pitt. 2002. “Automated Tree Crown Detection and Delineation in High-Resolution Digital Camera Imagery of Coniferous Forest Regeneration.” Remote Sensing of Environment 82 (2–3): 322–334. doi:10.1016/S0034-4257(02)00050-0.
57. Pu, R., and S. Landry. 2012. “A Comparative Analysis of High Spatial Resolution IKONOS and Worldview-2 Imagery for Mapping Urban Tree Species.” Remote Sensing of Environment 124: 516–533. doi:10.1016/j.rse.2012.06.011.
58. Radeloff, V. C., D. J. Mladenoff, and M. S. Boyce. 1999. “Detecting Jack Pine Budworm Defoliation Using Spectral Mixture Analysis: Separating Effects from Determinants.” Remote Sensing of Environment 69 (2): 156–169. doi:10.1016/S0034-4257(99)00008-5.
59. Ranson, K. J., G. Sun, V. I. Kharuk, and K. Kovacs. 2004. “Assessing Tundra–Taiga Boundary with Multi-Sensor Satellite Data.” Remote Sensing of Environment 93 (3): 283–295. doi:10.1016/j.rse.2004.06.019.
60. Redmond, M., and N. Barger. 2013. “Tree Regeneration Following Drought- and Insect-Induced Mortality in Piñon-Juniper Woodlands.” New Phytologist 200 (2): 402–412. doi:10.1111/nph.12366.
61. Reis, S., and K. Taşdemir. 2011. “Identification of Hazelnut Fields Using Spectral and Gabor Textural Features.” ISPRS Journal of Photogrammetry and Remote Sensing 66 (5): 652–661. doi:10.1016/j.isprsjprs.2011.04.006.
62. Rich, R. L., L. Frelich, P. B. Reich, and M. E. Bauer. 2010. “Detecting Wind Disturbance Severity and Canopy Heterogeneity in Boreal Forest by Coupling High-Spatial Resolution Satellite Imagery and Field Data.” Remote Sensing of Environment 114 (2): 299–308. doi:10.1016/j.rse.2009.09.005.
63. Riley, J. R. 1989. “Remote Sensing in Entomology.” Annual Review of Entomology 34: 247–271. doi:10.1146/annurev.en.34.010189.001335.
64. Royle, D. D., and R. G. Lathrop. 1997. “Monitoring Hemlock Forest Health in New Jersey Using Landsat TM Data and Change Detection Techniques.” Forest Science 43 (3): 327−335.
65. Salvador, R., and X. Pons. 1998. “On the Reliability of Landsat TM for Estimating Forest Variables by Regression Techniques: A Methodological Analysis.” IEEE Transactions on Geoscience and Remote Sensing 36 (6): 1888–1897. doi:10.1109/36.729360.
66. Schomaker, M. E., S. J. Zarnoch, W. A. Bechtold, D. J. Latelle, W. G. Burkman, and S. M. Cox. 2007. Crown Condition Classification: A Guide to Data Collection and Analysis. Fort Collins, CO: USDA Forest Service, Southern Research Station. http://efetac4.sref.info/products/publications/ Crown_condition_classification.pdf
67. Sims, N. C., C. Stone, N. C. Coops, and P. Ryan. 2007. “Assessing the Health of Pinus Radiata Plantations Using Remote Sensing Data and Decision Tree Analysis.” New Zealand Journal of Forestry Science 37 (1): 57–80.
68. Skakun, R. S., M. A. Wulder, and S. E. Franklin. 2003. “Sensitivity of the Thematic Mapper Enhanced Wetness Difference Index to Detect Mountain Pine Beetle Red-Attack Damage.” Remote Sensing of Environment 86 (4): 433–443. doi:10.1016/S0034-4257(03)00112-3.
69. Stone, C., and N. C. Coops. 2004. “Assessment and Monitoring of Damage from Insects in Australian Eucalypt Forests and Commercial Plantations.” Australian Journal of Entomology 43: 283–292. doi:10.1111/j.1326-6756.2004.00432.x.
70. St-Onge, B. A., and F. Cavayas. 1997. “Automated Forest Structure Mapping from High Resolution Imagery Based on Directional Semivariogram Estimates.” Remote Sensing of Environment 61 (1): 82–95. doi:10.1016/S0034-4257(96)00242-8.
71. Story, M., and R. G. Congalton. 1986. “Accuracy Assessment - A User’s Perspective.” Photogrammetric Engineering and Remote Sensing 52 (3): 397−399.
72. Townsend, P. A., A. Singh, J. R. Foster, N. J. Rehberg, C. C. Kingdon, K. N. Eshleman, and S. W. Seagle. 2012. “A General Landsat Model to Predict Canopy Defoliation in Broadleaf Deciduous Forests.” Remote Sensing of Environment 119: 255–265. doi:10.1016/j.rse.2011.12.023.
73. Trial, J. H., and M. E. Devine. 1994. Forest Health Monitoring Evaluation: Brown Ash (Fraxinus Nigra) in Maine. A Survey of Occurrence and Health. Technical Report No. 33. Augusta, ME: Maine Forest Service, Insect and Disease Management Division.
74. Trotter, C. M., J. R. Dymond, and C. J. Goulding. 1997. “Estimation of Timber Volume in a Coniferous Plantation Forest Using Landsat TM.” International Journal of Remote Sensing 18 (10): 2209–2223. doi:10.1080/014311697217846.
75. Tucker, C. J. 1979. “Red and Photographic Infrared Linear Combinations for Monitoring Vegetation.” Remote Sensing of Environment 8: 127–150.
76. Wang, C. Z., Z. Q. Lu, and T. L. Haithcoat. 2007. “Using Landsat Images to Detect Oak Decline in the Mark Twain National Forest, Ozark Highlands.” Forest Ecology and Management 240 (1–3): 70–78. doi:10.1016/j.foreco.2006.12.007.
77. Wang, H., J. Gao, R. Pu, L. Ren, Y. Kong, H. Li, and L. Li. 2014. “Natural and Anthropogenic Influences on a Red-Crowned Crane Habitat in the Yellow River Delta Natural Reserve, 1992–2008.” Environmental Monitoring and Assessment 186: 4013–4028. doi:10.1007/s10661-014-3676-y.
78. Ward, K., M. Ostry, R. Venette, B. Palik, M. Hansen, and M. Hatfield. 2009. “Assessment of Black Ash (Fraxinus Nigra) Decline in Minnesota.” Paper presented at the Proceedings of the eighth annual forest inventory and analysis symposium, Monterey, CA, October16–19, 2006.
79. White, J. C., M. A. Wulder, D. Brooks, R. Reich, and R. D. Wheate. 2005. “Detection of Red Attack Stage Mountain Pine Beetle Infestation with High Spatial Resolution Satellite Imagery.” Remote Sensing of Environment 96 (3–4): 340–351. doi:10.1016/j.rse.2005.03.007.
80. Wolter, P. T., and P. A. Townsend. 2011. “Multi-Sensor Data Fusion for Estimating Forest Species Composition and Abundance in Northern Minnesota.” Remote Sensing of Environment 115 (2): 671–691. doi:10.1016/j.rse.2010.10.010.
81. Wolter, P. T., P. A. Townsend, B. R. Sturtevant, and C. C. Kingdon. 2008. “Remote Sensing of the Distribution and Abundance of Host Species for Spruce Budworm in Northern Minnesota and Ontario.” Remote Sensing of Environment 112 (10): 3971–3982. doi:10.1016/j.rse.2008.07.005.
82. Woodall, C. W., R. S. Morin, J. R. Steinman, and C. H. Perry. 2010. “Comparing Evaluations of Forest Health Based on Aerial Surveys and Field Inventories: Oak Forests in the Northern United States.” Ecological Indicators 10 (3): 713–718. doi:10.1016/j.ecolind.2009.11.012.
83. Wulder, M. A., J. C. White, B. Bentz, M. F. Alvarez, and N. C. Coops. 2006. “Estimating the Probability of Mountain Pine Beetle Red-Attack Damage.” Remote Sensing of Environment 101 (2): 150–166. doi:10.1016/j.rse.2005.12.010.
84. Xu, X. G., H. H. Guo, X. L. Chen, H. P. Lin, and Q. L. Du. 2002. “A Multi-Scale Study on Land Use and Land Cover Quality Change: The Case of the Yellow River Delta in China.” GeoJournal 56 (3): 177–183. doi:10.1023/A:1025175409094.
85. Yuan, X., D. King, and J. Vlcek. 1991. “Sugar Maple Decline Assessment Based on Spectral and Textural Analysis of Multispectral Aerial Videography.” Remote Sensing of Environment 37 (1): 47–54. doi:10.1016/0034-4257(91)90049-C.
86. Yue, A. Z., C. Zhang, J. Yang, W. Su, W. Yun, and D. Zhu. 2013. “Texture Extraction for Object-Oriented Classification of High Spatial Resolution Remotely Sensed Images Using a Semivariogram.” International Journal of Remote Sensing 34 (11): 3736–3759. doi:10.1080/01431161.2012.759298.
87. Zarnoc, S. J., W. A. Bechtold, and K. W. Stolte. 2004. “Using Crown Condition Variables as Indicators of Forest Health.” Canadian Journal of Forest Research 34 (5): 1057–1070. doi:10.1139/X03-277.
88. Zeng, S., T. Zhang, Y. Gao, B. Li, C.-M. Fang, S. L. Flory, and B. Zhao. 2012. “Road Effects on Vegetation Composition in a Saline Environment.” Journal of Plant Ecology 5 (2): 206–218. doi:10.1093/jpe/rtr014.
89. Zhang, T., S. Zeng, Y. Gao, Z. Ouyang, B. Li, C. Fang, and B. Zhao. 2011. “Using Hyperspectral Vegetation Indices as a Proxy to Monitor Soil Salinity.” Ecological Indicators 11 (6): 1552–1562. doi:10.1016/j.ecolind.2011.03.025.