PDF(2576 KB)
Estimation LAI of Montane Evergreen Broad-Leaved Forest in Southwest Sichuan Using Different Spatial Prediction Models
ZHAO An-jiu, CHEN Kun, GUO Shi-gang
JOURNAL OF NATURAL RESOURCES ›› 2014, Vol. 29 ›› Issue (4) : 598-609.
PDF(2576 KB)
PDF(2576 KB)
Estimation LAI of Montane Evergreen Broad-Leaved Forest in Southwest Sichuan Using Different Spatial Prediction Models
Leaf Area Index (LAI) is a critical variable for forest management, and there are several dynamical models of forest management, based on the modeling of the interactions between the soil, the atmosphere and the vegetation. LAI is a critical input variable for these models. Currently, it is difficult to obtain accurate LAI estimations of high spatial resolution over large areas. Effective leaf area index (LAIe) of montane evergreen broad-leaved forest stands estimation was carried out in a region located in Southwest Sichuan, by means of different approaches including field inventory data, SPOT 5 imagery and spatial prediction models, LAIe was inventoried and assessed in a total of 83 sample field plots. And using remotely sensed data as auxiliary variables, LAIe spatial distribution, which is derived from Direct Radiometric Relationships (DRR), the geostatistical method Co-Kriging (CK) and Regression-Kriging (RK), respectively, were compared. Also, Inverse Distance Weighted (IDW), Global Polynomial Interpolation (GPI), Ordinary Kriging (OK), and Universal Kriging (UK) estimations were performed and tested. The results show that since forest landscape is not a continuous variable, the tested LAIe variables showed low spatial autocorrelation, which makes Kriging methods unsuitable to these purposes. But DRR, CK and RK methods produced lower statistical error values, and presented high spatial correlation existing between DRR and CK, RK methods. Despite the geostatistical method RK did not increase the accuracy of estimates developed by DRR, denser sampling schemes and different auxiliary variables should be explored, in order to test if the accuracy of predictions is improved.
geostatistics / effective leaf area index / montane evergreen broadleaved forest / SPOT 5 {{custom_keyword}} /
[1] Welles J M. Some indirect methods of estimating canopy structure [J]. Remote Sensing Reviews, 1990, 5(1): 31-43.
[2] Bonan G B. Importance of leaf area index and forest type when estimating photosynthesis in boreal forests [J]. Remote Sensing of Environment, 1993, 43: 303-314.
[3] Fang H L, Wei S S, Jiang C Y, et al. Theoretical uncertainty analysis of global MODIS, CYCLOPES, and GLOBCARBON LAI products using a triple collocation method [J]. Remote Sensing of Environment, 2012, 124: 610-621.
[4] Fang H L, Wei S S, Liang S L. Validation of MODIS and CYCLOPES LAI products using global field measurement data [J]. Remote Sensing of Environment, 2012, 119: 43-54.
[5] Shabanov N, Huang D, Yang W, et al. Analysis and optimization of the MODIS leaf area index algorithm retrievals over broadleaf forests [J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(8): 1855-1865.
[6] Kalacska M, Sanchez-Azofeifa G, Caelli T, et al. Estimating leaf area index from satellite imagery using Bayesian networks [J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(8): 1866-1873.
[7] Tian Q, Luo Z, Chen J M, et al. Retrieving leaf area index for coniferous forest in Xingguo County, China with Landsat ETM+ images [J]. Journal of Environmental Management, 2007, 85: 624-627.
[8] Pfeifer M, Gonsamo A, Disney M, et al. Leaf area index for biomes of the Eastern Arc Mountains: Landsat and SPOT observations along precipitation and altitude gradients [J]. Remote Sensing of Environment, 2012, 118: 103-115.
[9] Cohen W B, Maiersperger T K, Gower S T, et al. An improved strategy for regression of biophysical variables and Landsat ETM+data [J]. Remote Sensing of Environment, 2003, 84: 561-571.
[10] Friedl M A, Michaelsen J, Davis F W, et al. Estimating grassland biomass and leaf area index using ground and satellite data [J]. International Journal of Remote Sensing, 1994, 15: 1401-1420.
[11] Turner D P, Cohen W B, Kennedy R E, et al. Relationships between leaf area index and Landsat TM spectral vegetation indices across three temperate zone sites [J]. Remote Sensing of Environment, 1999, 70: 52-68.
[12] Chen J M, Cihlar J. Retrieving leaf area index of boreal conifer forests using Landsat TM images [J]. Remote Sensing of Environment, 1996, 55: 153-162.
[13] Fassnacht K S, Gower S T, MacKenzie M D, et al. Estimating the leaf area index of north central Wisconsin forests using the Landsat Thematic Mapper [J]. Remote Sensing of Environment, 1997, 61: 229-245.
[14] Isaaks E H, Srivastava R M. An Introduction to Applied Geostatistics [M]. New York: Oxford University Press, 1989: 542.
[15] Goovaerts P. Geostatistics for Natural Resources Evaluation [M]. New York: Oxford University Press, 1997.
[16] Burrough P A, McDonnell R A. Principles of Geographical Information Systems [M]. New York: Oxford University Press, 1998: 333.
[17] Lloyd C D. Local Models for Spatial Analysis [M]. Boca Raton: CRC Press, 2007: 244.
[18] Hengl T. A Practical Guide to Geostatistical Mapping [M]. Second Edition. Amsterdam: University of Amsterdam, 2009.
[19] Kint V, van Meirvenne M, Nachtergale L, et al. Spatial methods for quantifying forest stand structure development: A comparison between nearest-neighbor indices and variogram analysis [J]. Forest Science, 2003, 49: 36-49.
[20] Dungan J. Spatial prediction of vegetation quantities using ground and image data [J]. International Journal of Remote Sensing, 1998, 19: 267-285.
[21] Nanos N, Calama R, Montero G, et al. Geostatistical prediction of height/diameter models [J]. Forest Ecology and Management, 2004, 195: 221-235.
[22] Berterretche M, Hudak A T, Cohen W B, et al. Comparison of regression and geostatistical methods for mapping Leaf Area Index (LAI) with Landsat ETM+ data over a boreal forest [J]. Remote Sensing of Environment, 2005, 96: 49-61.
[23] Sales M H, Souza C M, Kyriakidis P C, et al. Improving spatial distribution estimation of forest biomass with geostatistics: A case study for Rondnia, Brazil [J]. Ecological Modelling, 2007, 205: 221-230.
[24] Meng Q, Cieszewski C, Madden M. Large area forest inventory using Landsat ETM+: A geostatistical approach [J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2009, 64: 27-36.
[25] Palmer D J, Hck B K, Kimberley M O, et al. Comparison of spatial prediction techniques for developing Pinus radiata productivity surfaces across New Zealand [J]. Forest Ecology and Management, 2009, 258: 2046-2055.
[26] Pierce K B, Ohmann J L, Wimberly M C, et al. Mapping wildland fuels and forest structure for land management: a comparison of nearest neighbor imputation and other methods [J]. Canadian Journal of Forest Research, 2009, 39: 901-1916.
[27] Akhavan R, Kia-Daliri H. Spatial variability and estimation of tree attributes in a plantation forest in the Caspian region of Iran using geostatistical analysis Caspian [J]. Journal of Environmental Sciences, 2010, 8:163-172.
[28] 胡庭兴, 等. 西南山地森林生态系统研究[M]. 北京: 科学出版社, 2011: 3-10.
[29] 赵安玖, 胡庭兴, 陈小红. 西南山地阔叶混交林群落空间结构的多尺度特征[J]. 生物多样性, 2009, 17(1): 43-50.
[30] Chen J M. Optically-based methods for measuring seasonal variation of leaf area index in boreal conifer stands [J]. Agricultural and Forest Meteorology, 1996, 80: 135-163.
[31] Jonckheere I, Fleck S, Nackaerts K, et al. Review of methods for in situ leaf area index determination. Part I. Theories, sensors and hemispherical photography [J]. Agricultural and Forest Meteorology, 2004,121(1/2): 19-35.
[32] Viana H, Aranha J, Lopes D, et al. Estimation of crown biomass of Pinus pinaster stands and shrubland aboveground biomass using forest inventory data, remotely sensed imagery and spatial prediction models [J]. Ecological Modelling, 2012, 226: 22-35.
[33] Demarez V, Duthoit S, Baret F, et al. Estimation of leaf area and clumping indexes of crops with hemispherical photographs[J]. Agricultural and Forest Meteorology, 2008, 128(4): 644-655.
[34] Leblanc S G, Chen J M. A practical scheme for correcting multiple scattering effects on optical LAI measurements[J]. Agricultural and Forest Meteorology, 2001, 110: 125-139.
[35] Montes F, Pita P, Rubio A, et al. Leaf area index estimation in mountain even-aged Pinus silvestris L. stands from hemispherical photographs [J]. Agricultural and Forest Meteorology, 2007, 145: 215-228
[36] Cressie N A C. Statistics for Spatial Data [M]. New York: John Wiley & Sons, 1993: 416.
[37] Burrough P A, McDonnell R A. Principles of Geographical Information Systems [M]. New York: Oxford University Press, 1998: 333.
[38] Kevin J, Jay M V H. Using ArcGIS Geostatistical Analyst [M]. ESRI Press, 2001.
[39] Webster R, Oliver M A. Geostatistics for Environmental Scientists [M]. Second Edition. John Wiley & Sons Ltd., 2007: 332.
[40] Wackernagel H. Multivariate Geostatistics [M]. Springer, 2003.
[41] Davis B. Uses and abuses of cross-validation in geostatistics [J]. Mathematical Geology, 1987, 19: 241-248.
[42] Gunnarsson F, Holm S, Holmgren P, et al. On the potential of Kriging for forest management planning [J]. Scandinavian Journal of Forest Research, 1998, 13:237-245.
[43] de Jong S M, Pebesma E J, van der Meer F D. Spatial variability, mapping methods, image analysis and pixels[M]// de Jong S M, van der Meer F D. Remote Sensing Image Analysis: Including the Spatial Domain. Vol. 5. Springer, Netherlands, 2006: 17-35.
[44] Pettorelli N, Vik J O, Mysterud A, et al. Using the satellite-derived NDVI to assess ecological responses to environmental change [J]. Trends in Ecology & Evolution, 2005, 20(9): 503-510.
[45] Glenn E P, Huete A R, Nagler P L, et al. Relationship between remotely-sensed vegetation indices, canopy attributes and plant physiological processes: What vegetation indices can and cannot tell us about the landscape [J]. Sensors, 2008, 8: 2136-2160.
[46] Zarate-Valdez J L, Whiting M L, Lampinen B D, et al. Prediction of leaf area index in almonds by vegetation indexes[J]. Computers and Electronics in Agriculture, 2012, 85: 24-32.
[47] Aspinall R J, Marcus W A, Boardman J W. Considerations in collecting, processing, and analyzing high spatial resolution hyperspectral data for environmental investigations [J]. Journal of Geographical Systems, 2002, 4: 15-29.
[48] Carmen H, Leónia N, Domingos L, et al. Data fusion for high spatial resolution LAI estimation [J]. Information Fusion, 2012, http://dx.doi.org/10.1016/j.inffus.2012.04.001.
[49] 陈健, 倪绍祥, 李静静, 等. 植被叶面积指数遥感反演的尺度效应及空间变异性[J]. 生态学报, 2006, 26(5): 1502-1508.
[50] Gilbert B, Lowell K. Forest attributes and spatial autocorrelation and interpolation: Effects of alternative sampling schemata in the boreal forest [J]. Landscape and Urban Planning, 1997, 37: 235-244.
[51] Freeman E A, Moisen G G. Evaluating kriging as a tool to improve moderate resolution maps of forest biomass [J]. Environmental Monitoring and Assessment, 2007, 128: 395-410.
PDF(2576 KB)
/
| 〈 |
|
〉 |
