Detecting mortality induced structural and functional changes in a piñon-juniper woodland using Landsat and RapidEye time series

Remote Sensing of Environment

Volumn 151, Issue , 2014, Pages 102-113

  Krofcheck, Dan J ^a   Eitel, Jan U H ^b   Vierling, Lee A ^b   Schulthess, Urs ^c   Hilton, Timothy M ^a   Dettweiler Robinson, Eva ^a   Pendleton, Rosemary ^d   Litvak, Marcy E ^a  

^a UNIVERSITY OF NEW MEXICO   (United States)

^b UNIVERSITY OF IDAHO   (United States)

^c McCall Outdoor Science School University of Idaho ^*  (Germany)

^d ROCKY MOUNTAIN RESEARCH STATION   (United States)

Author keywords

Ecosystem disturbance; Eddy covariance; High resolution; Red edge; Semi arid; Time series

Indexed keywords

CLIMATE CHANGE; DROUGHT; ECOSYSTEMS; SATELLITES; SOIL MOISTURE; TIME SERIES; VEGETATION;

ECOSYSTEM DISTURBANCE; EDDY-COVARIANCE; HIGH RESOLUTION; RED-EDGE; SEMI ARID;

REMOTE SENSING;

BIOME; CLIMATE EFFECT; FUNCTIONAL CHANGE; LANDSAT; MORTALITY; REGROWTH; REMOTE SENSING; SOIL MOISTURE; TIME SERIES; VEGETATION STRUCTURE; WOODLAND;

UNITED STATES;

CONIFEROPHYTA; JUNIPERUS; TILLANDSIA LINEATISPICA;

EID: 84906943728     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2013.11.009     Document Type: Article

References (60)

1. Adams H., Macalady A., Breshears D.D., Allen C.D., Stephenson N.L., Saleska S.R., Huxman T.E., McDowell N. Climate- Induced Tree Mortality: Earth System Consequences. Eos, Transactions 2010, 91:2009-2011.
2. Allen C.D., Macalady A.K., Chenchouni H., Bachelet D., Mcdowell N., Vennetier M., et al. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. Forest Ecology and Management 2010, 660-684.
3. Anderson-Teixeira K.J., Delong J.P., Fox A.M., Brese D.A., Litvak M.E. Differential responses of production and respiration to temperature and moisture drive the carbon balance across a climatic gradient in New Mexico. Global Change Biology 2011, 17:410-424.
4. Asner G.P., Heidebrecht K.B. Spectral unmixing of vegetation, soil and dry carbon cover in arid regions: Comparing multispectral and hyperspectral observations. International Journal of Remote Sensing 2002, 23:3939-3958.
5. Breshears D.D., Barnes F.J. Interrelationships between plant functional types and soil moisture heterogeneity for semiarid landscapes within the grassland/forest continuum: A unified conceptual model. Landscape Ecology 1998, 14:465-478.
6. Breshears D.D., Cobb N.S., Rich P.M., Price K.P., Allen C.D., Balice R.G., et al. Regional vegetation die-off in response to global-change-type drought. Proceedings of the National Academy of Sciences of the United States of America 2005, 102:15144-15148.
7. Breshears D., Rich P. Overstory-imposed heterogeneity in solar radiation and soil moisture in a semiarid woodland. Ecological Applications 1997, 4:1201-1215.
8. Chavez P.S. An improved dark-object subtraction techniquefor atmospheric scattering correction of multispectral data. Remote Sensing of Environment 1988, 24:459-479.
9. Canty M., Nielsen A. Automatic radiometric normalization of multitemporal satellite imagery with the iteratively re-weighted MAD transformation. Remote Sensing of Environment 2008, 112:1025-1036.
10. Chen X., Vierling L.A., 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(1):14-26.
11. Choat B., Jansen S., Brodribb T.J., Cochard H., Delzon S., Bhaskar R., Bucci S.J., Feild T.S., Gleason S.M., Hacke U.G., Jacobsen A.L., Lens F., Maherali H., Martínez-Vilalta J., Mayr S., Mencuccini M., Mitchell P.J., Nardini A., Pittermann J., Pratt R.B., Sperry J.S., Westoby M., Wright I.J., Zanne A.E. Global convergence in the vulnerability of forests to drought. Nature 2012, 491:752-755.
12. Clifford M., Rocca M., Delph R. Drought induced tree mortality and ensuing Bark beetle outbreaks in Southwestern pinyon-juniper woodlands. USDA Forest Service Proceedings 2008, 39-51.
13. Coops N.C., Johnson M., Wulder M.A., White J.C. Assessment of Quickbird high spatial resolution imagery to detect red attack damage due to mountain pine beetle infestation. Remote Sensing of Environment 2006, 103:67-80.
14. Collett L.J., Goulevitch B.M., Danaher T.J. SLATSRadiometric Correction: A Semi-automated, Multi-stage Processfor the Standardisation of Temporal and Spatial Radiometric Differences. Queensland Department of Natural Resources 1997, URL:http://www.nrm.qld.gov.au/slats/pdf/arspc9_rad_corrections1.pdf.
15. Detto M., Montaldo N., Albertson J.D., Mancini M., Katul G. Soil moisture and vegetation controls on evapotranspiration in a heterogeneous Mediterranean ecosystem on Sardinia, Italy. Water Resources Research 2006, 42:1-16.
16. Edburg S.L., Hicke J.A., Brooks P.D., Pendall E.G., Ewers B.E., Norton U., et al. Cascading impacts of bark beetle-caused tree mortality on coupled biogeophysical and biogeochemical processes. Frontiers in Ecology and the Environment 2012, 10:416-424.
17. Eitel J.U.H., Long D.S., Gessler P.E., Hunt E.R., Brown D.J. Sensitivity of ground-based remote sensing estimates of wheat chlorophyll content to variation in soil reflectance. Soil Science Society of America Journal 2009, 73:1715.
18. Eitel J.U.H., Vierling L.A., Litvak M.E., Long D.S., Schulthess U., Ager A.A., et al. Broadband, red-edge information from satellites improves early stress detection in a New Mexico conifer woodland. Remote Sensing of Environment 2011, 115:3640-3646.
19. Elmore A.J., Mustard J.F., Manning S.J., Lobell D.B. Quantifying Vegetation Change in Semiarid Environments : Precision and Accuracy of Spectral Mixture Analysis and the Normalized Difference Vegetation Index 2000, 102:87-102.
20. Flanagan L., Wever L., Carlson P. Seasonal and interannual variation in carbon dioxide exchange and carbon balance in a northern temperate grassland. Global Change Biology 2002, 8:599-615.
21. Fox A.J., Hilton T., Krofcheck D.J., Sinsabaugh R.L., Pendleton R., Dettweiler-Robinson E., et al. Ecosystem experimental manipulation and model-data fusion to quantify carbon cycle dynamics to piñon pine mortality. Global Change Biology 2013.
22. Gamon J.A., Penuelas J., Field C.B. A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sensing of Environment 1992, 41:35-44.
23. Gamon J., Serrano L., Surfus J. The photochemical reflectance index: An optical indicator of photosynthetic radiation use efficiency across species, functional types, and nutrient levels. Oecologia 1997, 112:492-501.
24. Gitelson A., Merzlyak M. Signature analysis of leaf reflectance spectra: Algorithm development for remote sensing of chlorophyll. Journal of Plant Physiology 1996, 148:494-500.
25. Gitelson A., Vina A., Ciganda V., Rundquist D.C. Remote estimation of canopy chlorophyll content in crops. Geophysical Research Letters 2005, 32:4-7.
26. Hsieh C., Katul G., Chi T. An approximate analytical model for footprint estimation of scalar fluxes in thermally stratified atmospheric flows. Advances in Water Resources 2000, 23:765-772.
27. Huang C., Asner G., Barger N. Modeling regional variation in net primary production of pinyon-juniper ecosystems. Ecological Modelling 2012, 227:82-92.
28. Huang C., Asner G.P., Barger N.N., Neff J.C., Floyd M.L. Regional aboveground live carbon losses due to drought-induced tree dieback in piñon-juniper ecosystems. Remote Sensing of Environment 2010, 114:1471-1479.
29. Huang C., Asner G., Martin R., Barger N.N., Neef J.C. Multiscale analysis of tree cover and aboveground carbon stocks in pinyon-juniper woodlands. Ecological Applications 2009, 19:668-681.
30. Huete A.R., Jackson R.D., Post D.F. Spectral response of a plant canopy with different soil backgrounds. Remote Sensing of Environment. 1985, 17:37-53.
31. Huete A.R. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment 1988, 25:53-70.
32. Huete A.R. Soil influences in remotely sensed vegetation-canopy spectra. Theory and Applications of Optical Remote Sensing 1988, 107-141. G. Asrar (Ed.).
33. Huxman T.E., Snyder K.A., Tissue D., Leffler A.J., Ogle K., Pockman W.T., et al. Precipitation pulses and carbon fluxes in semiarid and arid ecosystems. Oecologia 2004, 141:254-268.
34. IPCC Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change 2004, Cambridge University Press, Cambridge, UK and New York.
35. Jenkins J.C., Chojnacky D.C., Heath L.S., Birdsey R.A. National-scale biomass estimators for United States tree species. Forest Science 2003, 49:12-35.
36. Kennedy R., Cohen W., Schroeder T. Trajectory-based change detection for automated characterization of forest disturbance dynamics. Remote Sensing of Environment 2007, 110:370-386.
37. Kennedy R.E., Yang Z., Cohen W.B. Remote Sensing of Environment Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr: Temporal segmentation algorithms. Remote Sensing of Environment 2010, 114:2897-2910.
38. Larson K.M., Small E.E., Gutmann E.D., Bilich A.L., Braun J.J., Zavorotny V.U. Use of GPS receivers as a soil moisture network for water cycle studies. Geophysical Research Letters 2008, 35:24.
39. Lasslop G., Reichstein M., Papale D., Richardson A.D., Arneth A., Barr A., et al. Separation of net ecosystem exchange into assimilation and respiration using a light response curve approach: Critical issues and global evaluation. Global Change Biology 2010, 16:187-208.
40. Li X., Gao Z., Bai L., Huang Y. Potential of high resolution RapidEye data for sparse vegetation fraction mapping in arid regions. 2012 IEEE International 2012, 420-423.
41. Liu S., Bond-Lamberty B., Hicke J. Simulating the impacts of disturbances on forest carbon cycling in North America: Processes, data, models, and challenges. Journal of Geophysical Research 2011, 116:1-22.
42. Massman W. A simple method for estimating frequency response corrections for eddy covariance systems. Agricultural and Forest Meteorology 2000, 104:185-198.
43. Muldavin E., Moore D., Collins S. Aboveground net primary production dynamics in a northern Chihuahuan Desert ecosystem. Oecologia 2008, 155:123-132.
44. Nielsen A., Conradsen K., Simpson J. Multivariate alteration detection (MAD) and MAF postprocessing in multispectral, bitemporal image data: New approaches to change detection studies. Remote Sensing of Environment 1998, 64:1-19.
45. Qi J., Chehbouni A., Huete A. A modified soil adjusted vegetation index. Remote Sensing of Environment 1994, 48:119-126.
46. R Core Team R: A language and environment for statistical computing Vienna, Austria. http://www.R-project.org.
47. Rich P.M., Breshears D.D., White A.B. Phenology of mixed woody-herbaceous ecosystems following extreme events: Net and differential responses. Ecological Society of America 2008, 89:342-352.
48. Royer P.D., Cobb N.S., Clifford M.J., Huang C.-Y., Breshears D.D., Adams H.D., et al. Extreme climatic event-triggered overstorey vegetation loss increases understorey solar input regionally: Primary and secondary ecological implications. Journal of Ecology 2011, 99:714-723.
49. Schwalm C.R., Williams C.A., Schaefer K., Baldocchi D., Black T.A., Goldstein A.H., et al. Reduction in carbon uptake during turn of the century drought in western North America. Nature Geoscience 2012, 5:551-556.
50. Shaw J., Steed B., DeBlander L. Forest Inventory and Analysis (FIA) annual inventory answers the question: What is happening to pinyon-juniper woodlands?. Journal of Forestry 2005, 103:280-285.
51. Spruce J., Sader S., Ryan R., Smoot J. Assessment of MODIS NDVI time series data products for detecting forest defoliation by gypsy moth outbreaks. Remote Sensing of Environment 2010, 115:427-437.
52. Storey T.O. Estimating sagebrush cover in a portion of the Pinedale Anticline Project 2004, University of Wyoming, Department of Geography.
53. Tong A., He Y. Comparative analysis of SPOT, Landsat, MODIS, and AVHRR normalized difference vegetation index data on the estimation of leaf area index in a mixed grassland ecosystem. Journal of Applied Remote Sensing 2013, 7:073599.
54. Tucker C. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment 1979, 8:127-150.
55. Wang Q., Adiku S., Tenhunen J., Granier A. On the relationship of NDVI with leaf area index in a deciduous forest site. Remote Sensing of Environment 2005, 94:244-255.
56. Webb E., Pearman G.I., Leuning R. Correction of flux measurements for density effects due to heat and water vapour transfer. Quarterly Journal of the Royal Meteorological Society 1980, 106:85-100.
57. Williams A., Allen C., Macalady A. Temperature as a potent driver of regional forest drought stress and tree mortality. Nature Climate 2012, 3:8-13.
58. Yang J., Weisberg P.J., Bristow N.A. Landsat remote sensing approaches for monitoring long-term tree cover dynamics in semi-arid woodlands: Comparison of vegetation indices and spectral mixture analysis. Remote Sensing of Environment 2012, 119:62-71.
59. Williams A., Allen C., Millar C.I., Swetnam T.W., Michaelsen J., Still C.J., et al. Forest responses to increasing aridity and warmth in the southwestern United States. Proceedings of the National Academy of Sciences 2010, 107:21289-21294.
60. Wulder M.A., Dymond C.C., White J.C., Leckie D.G., Carroll A.L. Surveying mountain pine beetle damage of forests: A review of remote sensing opportunities. Forest Ecology and Management 2006, 221:27-41.