Predicting relative species composition within mixed conifer forest pixels using zero-inflated models and Landsat imagery

Remote Sensing of Environment

Volumn 171, Issue , 2015, Pages 326-336

  Savage, Shannon L ^a   Lawrence, Rick L ^a   Squires, John R ^b  

^a MONTANA STATE UNIVERSITY   (United States)

^b ROCKY MOUNTAIN RESEARCH STATION   (United States)

Author keywords

Canopy cover mapping; Generalized linear model; Logistic regression; Operational Land Imager; RandomForest; Support vector machine; Thematic Mapper; Zero inflated data

Indexed keywords

FORESTRY; PIXELS; REGRESSION ANALYSIS; REMOTE SENSING; SUPPORT VECTOR MACHINES;

CANOPY COVER; GENERALIZED LINEAR MODEL; LOGISTIC REGRESSIONS; OPERATIONAL LAND IMAGER; RANDOMFOREST; THEMATIC MAPPERS; ZERO-INFLATED;

FORECASTING;

COMMUNITY COMPOSITION; CONFIDENCE INTERVAL; CONIFEROUS FOREST; FOREST CANOPY; LANDSAT THEMATIC MAPPER; MIXED FOREST; PIXEL; PREDICTION; REGRESSION ANALYSIS; REMOTE SENSING; SATELLITE IMAGERY; SUPPORT VECTOR MACHINE; VEGETATION MAPPING; VISUAL ANALYSIS;

MONTANA; UNITED STATES;

CONIFEROPHYTA;

EID: 84946430181     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2015.10.013     Document Type: Article

References (46)

1. Ahmed O.S., Franklin S.E., Wulder M.A. Integration of LiDAR and Landsat data to estimate forest canopy cover in coastal British Columbia. Photogrammetric Engineering & Remote Sensing 2014, 80:953-961.
2. Barry S.C., Welsh A.H. Generalized additive modelling and zero inflated count data. Ecological Modelling 2002, 157:179-188.
3. Breiman L. Random forests. Machine Learning 2001, 45:4-32.
4. Brown S., Barber J. The region 1 existing vegetation mapping program (VMap) flathead national forest overview; version 12. Region one vegetation classification, mapping, inventory and analysis report 12-34 2012, USDA Forest Service, Missoula, MT.
5. Carreiras J.M.B., Pereira J.M.C., Pereira J.S. Estimation of tree canopy cover in evergreen oak woodlands using remote sensing. Forest Ecology and Management 2006, 223:45-53.
6. Chai T., Draxler R.R. Root mean square error (RMSE) or mean absolute error (MAE)? -Arguments against avoiding RMSE in the literature. Geoscientific Model Development 2014, 7:1247-1250.
7. Chen X., Vierling L., Rowell E., DeFelice T. Using LiDAR and effective LAI data to evaluate IKONOS and Landsat 7 ETM+ vegetation cover estimates in a ponderosa pine forest. Remote Sensing of Environment 2004, 91:14-26.
8. Coulston J.W., Jacobs D.M., King C.R., Elmore I.C. The influence of multi-season imagery on models of canopy cover: A case study. Photogrammetric Engineering & Remote Sensing 2013, 79:469-477.
9. Cunningham R.B., Lindenmayer D.B. Modeling count data of rare species: Some statistical issues. Ecology 2005, 86:1135-1142.
10. Falkowski M.J., Gessler P.E., Morgan P., Hudak A.T., Smith A.M.S. Characterizing and mapping forest fire fuels using ASTER imagery and gradient modeling. Forest Ecology and Management 2005, 217:129-146.
11. Fassnacht F.E., Hartig F., Latifi H., Berger C., Hernandez J., Corvalan P., Koch B. Importance of sample size, data type and prediction method for remote sensing-based estimations of aboveground forest biomass. Remote Sensing of Environment 2014, 154:102-114.
12. Fiala A.C.S., Garman S.L., Gray A.N. Comparison of five canopy cover estimation techniques in the western Oregon cascades. Forest Ecology and Management 2006, 232:188-197.
13. Fielding A.H., Bell J.F. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation 1997, 24:38-49.
14. Fuller A.K., Harrison D.J. Movement paths reveal scale-dependent habitat decisions by Canada lynx. Journal of Mammalogy 2010, 91:1269-1279.
15. Hansen M.C., DeFries R.S., Townshend J.R.G., Carroll M., Dimiceli C., Sohlberg R.A. Global percent tree cover at spatial resolution of 500meters: First results of the MODIS vegetation continuous fields algorithm. Earth Interactions 2003, 7:1-15.
16. Hicke J.H., Logan J. Mapping whitebark pine mortality caused by a mountain pine beetle outbreak with high spatial resolution satellite imagery. International Journal of Remote Sensing 2009, 30:4427-4441.
17. Ho H.C., Knudby A., Sirovyak P., Xu Y., Hodul M., Henderson S.B. Mapping maximum urban air temperature on hot summer days. Remote Sensing of Environment 2014, 154:38-45.
18. Hodges K.E. Ecology of snowshoe hares in southern boreal and montane forests. Ecology and conservation of lynx in the United States 2000, 163-206. University Press of Colorado, Boulder, CO, (480 pp.). L.F. Ruggiero, K.B. Aubry, S.W. Buskirk, G.M. Koehler, C.J. Krebs, K.S. McKelvey, J.R. Squires (Eds.).
19. Holling C.S. Cross-scale morphology, geometry and dynamics of ecosystems. Ecological Monographs 1992, 62:447-502.
20. Homer C., Huang C., Yang L., Wylie B., Coan M. Development of a 2001 national land-cover database for the United States. Photogrammetric Engineering & Remote Sensing 2004, 70:829-840.
21. Jennings S.B., Brown N.D., Sheil D. Assessing forest canopies and understory illumination: Canopy closure, canopy cover and other measures. Forestry 1999, 72:59-73.
22. Jewett J., Lawrence R., Marshall L., Gessler P., Powell S., Savage S. Spatiotemporal relationships between climate and whitebark pine mortality in the Greater Yellowstone Ecosystem. Forest Science 2011, 57:320-335.
23. Johansen K., Phinn S. Mapping structural parameters and species composition of riparian vegetation using IKONOS and Landsat ETM+ data in Australian tropical savannahs. Photogrammetric Engineering & Remote Sensing 2006, 72:71-80.
24. Koy K., McShea W.J., Leimgruber P., Haack B.N., Aung M. Percentage canopy cover - Using Landsat imagery to delineate habitat for Myanmar's endangered Eld's deer (Cervus eldi). Animal Conservation 2005, 8:289-296.
25. Kuchler M., Ecker K., Feldmeyer-Christe E., Graf U., Waser L.T. Predictive models of mire habitats: Bias in detection of change. WETLANDS: Monitoring, modelling and management 2007, 91-101. Taylor and Francis Group, London.
26. Lawrence R.L., Wood S., Sheley R. Mapping invasive plants using hyperspectral imagery and Breiman Cutler classifications (randomForest). Remote Sensing of Environment 2006, 100:356-362.
27. Lee A.C., Lucas R.M. A LiDAR-derived canopy density model for tree stem and crown mapping in Australian forests. Remote Sensing of Environment 2007, 111:493-518.
28. Liaw A., Wiener M. Classification and regression by randomForest. R News 2002, 2/3:18-22.
29. Morsdorf F., Kotz B., Meier E., Itten K.I., Allgower B. Estimation of LAI and fractional cover from small footprint airborne laser scanning data based on gap fraction. Remote Sensing of Environment 2006, 104:50-61.
30. Mountrakis G., Im J., Ogole C. Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing 2011, 66:247-259.
31. Ozdemir I. Linear transformation to minimize the effects of variability in understory to estimate percent tree canopy cover using RapidEye data. GIScience & Remote Sensing 2014, 51:288-300.
32. Pal M. Random forest classifier for remote sensing classification. International Journal of Remote Sensing 2005, 26:217-222.
33. Peterson G., Allen C.R., Holling C.S. Ecological resilience, biodiversity, and scale. Ecosystems 1998, 1:6-18.
34. Pierce K.B., Ohmann J.L., Wimberly M.C., Gregory M.J., Fried J.S. Mapping wildland fuels and forest structure for land management: A comparison of nearest neighbor imputation and other methods. Canadian Journal of Forest Research 2009, 39:1901-1916.
35. Prasad A.M., Iverson L.R., Liaw A. New classification and regression tree techniques: Bagging and random forests for ecological prediction. Ecosystems 2006, 9:181-199.
36. Pu R., Xu B., Gong P. Oakwood crown closure estimation by unmixing Landsat TM data. International Journal of Remote Sensing 2003, 24:4433-4445.
37. Singh A. Review article: Digital change detection techniques using remotely-sensed data. International Journal of Remote Sensing 1989, 10:989-1003.
38. Smart S.M., Scott W.A. Bias in Ellenberg indicator values: Problems with detection of the effect of vegetation type. Journal of Vegetation Science 2004, 15:843-846.
39. Smith A.M.S., Falkowski M.J., Hudak A.T., Evans J.S., Robinson A.P., Steele C.M. A cross-comparison of field, spectral, and lidar estimates of forest canopy cover. Canadian Journal of Remote Sensing 2009, 35:447-459.
40. Somers E.C., Zhao W., Lewis E.E., Wang L., Wing J.J., Sundaram B., Kaplan M.J. Type I interferons are associated with subclinical markers of cardiovascular disease in a cohort of systemic lupus erythematosus patients. PloS One 2012, 7.
41. Squires J.R., DeCesare N.J., Kolbe J.A., Ruggiero L.F. Seasonal resource selection of Canada lynx in managed forests of the Northern Rocky Mountains. Journal of Wildlife Management 2010, 74:1648-1660.
42. StatSoft, Inc. Electronic statistics textbook 2013, StatSoft, Tulsa, OK, (WEB: http://www.statsoft.com/textbook/, last accessed 15 April 2014).
43. Tottrup C., Rasmussen M.S., Eklundh L., Jonsson P. Mapping the fractional forest cover across the highlands of Southeast Asia using MODIS data and regression tree modelling. International Journal of Remote Sensing 2007, 28:23-46.
44. White P.S. Pattern, process, and natural disturbance in vegetation. Botanical Review 1979, 45:229-299.
45. Yuan D., Elvidge C.D. Comparison of relative radiometric normalization techniques. ISPRS Journal of Photogrammetry and Remote Sensing 1996, 51:117-126.
46. Zhao K., Popescu S., Meng X., Pang Y., Agca M. Characterizing forest canopy structure with LiDAR composite metrics and machine learning. Remote Sensing of Environment 2011, 115:1978-1996.