22.5.4.1 Forest Extraction, Forest Analysis

Sayn-Wittgenstein, L.,
Patterns of spatial variation in forests and other natural populations,
PR(2), No. 4, December 1970, pp. 245-248.
WWW Version. 0309
BibRef

Li, X., and Strahler, A.H.,
Geometric-optical modeling of a conifer forest canopy,
GeoRS(23), No. 5, September 1985, pp. 705-720. BibRef 8509

Li, X., and Strahler, A.H.,
Geometric-optical bidirectional reflectance modeling of the discrete crown vegetation canopy: Effect of crown shape and mutual shadowing,
GeoRS(30), No. 3, March 1992, pp. 276-292. BibRef 9203

Holopainen, M., Wang, G.X.,
The Calibration of Digitized Aerial Photographs for Forest Stratification,
JRS(19), No. 4, March 10 1998, pp. 677-696. 9803
BibRef

Pekkarinen, A.,
A method for the segmentation of very high spatial resolution images of forested landscapes,
JRS(23), No. 14, July 2002, pp. 2817-2836. 0208
BibRef

Pekkarinen, A.[Anssi],
Image segment-based spectral features in the estimation of timber volume,
RSE(82), No. 2-3, October 2002, pp. 349-359.
HTML Version. 0210
BibRef

Varekamp, C., Hoekman, D.H.,
High-resolution InSAR image simulation for forest canopies,
GeoRS(40), No. 7, July 2002, pp. 1648-1655.
IEEE Top Reference. 0210
BibRef

Sheng, Y.W.[Yong-Wei], Gong, P.[Peng], Biging, G.S.[Gregory S.],
Model-Based Conifer Canopy Surface Reconstruction from Photographic Imagery: Overcoming the Occlusion, Foreshortening, and Edge Effects,
PhEngRS(69), No. 3, March 2003, pp. 249-258. The capability of the model-based surface reconstruction approach is extended from recovering the crown surface of a single tree to reconstructing the canopy surface of a tree stand, and is further developed to canopy surface reconstruction for complicated tree stands.
WWW Version. 0304
BibRef

Sheng, Y.W.[Yong-Wei], Gong, P.[Peng], Biging, G.S.[Gregory S.],
True Orthoimage Production for Forested Areas from Large-Scale Aerial Photographs,
PhEngRS(69), No. 3, March 2003, pp. 259-266. An effort for removing occlusion and correcting canopy relief displacement using a canopy surface model (CSM) in true orthoimage generation for forested areas is described.
WWW Version. 0304
BibRef

Pu, R.[Ruiliang], Gong, P.[Peng], Biging, G.S., Larrieu, M.R.,
Extraction of red edge optical parameters from Hyperion data for estimation of forest leaf area index,
GeoRS(41), No. 4, April 2003, pp. 916-921.
IEEE Abstract. IEEE Top Reference. 0307
BibRef

Sugumaran, R., Pavuluri, M.K., Zerr, D.,
The use of high-resolution imagery for identification of urban climax forest species using traditional and rule-based classification approach,
GeoRS(41), No. 9, September 2003, pp. 1933-1939.
IEEE Abstract. IEEE Top Reference. 0310
BibRef

Fang, H.L.[Hong-Liang], Liang, S.L.[Shun-Lin],
Retrieving leaf area index with a neural network method: simulation and validation,
GeoRS(41), No. 9, September 2003, pp. 2052-2062.
IEEE Abstract. IEEE Top Reference. 0310
BibRef

Vincini, M., Frazzi, E.,
Multitemporal evaluation of topographic normalization methods on Deciduous Forest TM Data,
GeoRS(41), No. 11, November 2003, pp. 2586-2590.
IEEE Abstract. IEEE Top Reference. 0311
BibRef

Franklin, S.E., Lavigne, M.B., Moskal, L.M., Wulder, M.A., and McCaffrey, T.M.,
Interpretation of partial harvest forest conditions in New Brunswick using Landsat TM enhanced wetness difference imagery (EWDI),
Can. J. Remote Sens.(27), 2001, pp. 118-128. BibRef 0100

Peddle, D.R., Franklin, S.E., Johnson, R.L., Lavigne, M.B., Wulder, M.A.,
Structural change detection in a disturbed conifer forest using a geometric optical reflectance model in multiple-forward mode,
GeoRS(41), No. 1, January 2003, pp. 163-166.
IEEE DOI Link
IEEE Top Reference. 0304
BibRef

Nelson, T.[Trisalyn], Boots, B.[Barry], Wulder, M.[Mike], Feick, R.[Rob],
Predicting Forest Age Classes from High Spatial Resolution Remotely Sensed Imagery Using Voronoi Polygon Aggregation,
GeoInfo(8), No. 2, June 2004, pp. 143-155.
WWW Version. 0403
BibRef

Lipowezky, U.[Uri],
Groves decipherment from space photos using prototype matching,
PRL(25), No. 13, 1 October 2004, pp. 1479-1489.
WWW Version. 0410
BibRef

Santoro, M., Askne, J., Dammert, P.B.G.,
Tree height influence on ERS interferometric phase in boreal forest,
GeoRS(43), No. 2, February 2005, pp. 207-217.
IEEE Abstract. IEEE Top Reference. 0501
BibRef

Askne, J., Santoro, M.,
Multitemporal Repeat Pass SAR Interferometry of Boreal Forests,
GeoRS(43), No. 6, June 2005, pp. 1219-1228.
IEEE Abstract. IEEE Top Reference. 0506
BibRef
Earlier: Add A3, A4: Smith, G., Fransson, J.E.S., GeoRS(41), No. 7, July 2003, pp. 1540-1550.
IEEE Abstract. IEEE Top Reference. 0308
BibRef

Askne, J., Santoro, M.,
Automatic Model-Based Estimation of Boreal Forest Stem Volume From Repeat Pass C-band InSAR Coherence,
GeoRS(47), No. 2, February 2009, pp. 513-516.
IEEE DOI Link 0903
BibRef

Brewer, C.K.[C. Kenneth], Winne, J.C.[J. Chris], Redmond, R.L.[Roland L.], Opitz, D.W.[David W.], Mangrich, M.V.[Mark V.],
Classifying and Mapping Wildfire Severity: A Comparison of Methods,
PhEngRS(71), No. 11, November 2005, pp. 1311-1320.
WWW Version. 0602
A comparison of six remote sensing methods for classifying and mapping wildfire severity on forests and rangelands: artificial networks, principal component analysis, and normalized temporal image differencing. BibRef

Gislason, P.O.[Pall Oskar], Benediktsson, J.A.[Jon Atli], Sveinsson, J.R.[Johannes R.],
Random Forests for land cover classification,
PRL(27), No. 4, March 2006, pp. 294-300.
WWW Version. Random Forests; Classification; Decision trees; Multisource remote sensing data 0604
BibRef

Izzawati, Wallington, E.D., Woodhouse, I.H.,
Forest Height Retrieval From Commercial X-Band SAR Products,
GeoRS(44), No. 4, April 2006, pp. 863-870.
IEEE DOI Link 0604
BibRef

Cheng, L.[Li], Caelli, T.M., Sanchez-Azofeifa, A.[Arturo],
Component Optimization for Image Understanding: A Bayesian Approach,
PAMI(28), No. 5, May 2006, pp. 684-693.
IEEE DOI Link 0604
Integrate segmentation/annotation, 3D sensing (stereo) and 3D fitting within a Bayesian framework. Apply to forest inventory. See also Bayesian Stereo Matching. BibRef

Cheng, L.[Li], Caelli, T.M.[Terry M.],
Forestry Scene Geometry Estimation Via Statistical Learning,
LCV04(103).
IEEE DOI Link 0406
BibRef

Xu, F., Jin, Y.Q.,
Multiparameter Inversion of a Layer of Vegetation Canopy Over Rough Surface From the System Response Function Based on the Mueller Matrix Solution of Pulse Echoes,
GeoRS(44), No. 7, Part 2, July 2006, pp. 2003-2015.
IEEE DOI Link 0606
BibRef

Maselli, F., Chiesi, M.,
Evaluation of Statistical Methods to Estimate Forest Volume in a Mediterranean Region,
GeoRS(44), No. 8, August 2006, pp. 2239-2250.
IEEE DOI Link 0608
BibRef

Lucas, R.M., Lee, A.C., Williams, M.L.,
Enhanced Simulation of Radar Backscatter From Forests Using LiDAR and Optical Data,
GeoRS(44), No. 10, October 2006, pp. 2736-2754.
IEEE DOI Link 0609
BibRef

Trias-Sanz, R.[Roger],
Texture Orientation and Period Estimator for Discriminating Between Forests, Orchards, Vineyards, and Tilled Fields,
GeoRS(44), No. 10, October 2006, pp. 2755-2760.
IEEE DOI Link 0609
BibRef

Trias-Sanz, R.[Roger], Boldo, D.[Didier],
A High-Reliability, High-Resolution Method for Land Cover Classification Into Forest and Non-forest,
SCIA05(831-840).
Springer DOI Link 0506
BibRef

Hollaus, M., Wagner, W., Eberhöfer, C., Karel, W.,
Accuracy of large-scale canopy heights derived from LiDAR data under operational constraints in a complex alpine environment,
PandRS(60), No. 5, August 2006, pp. 323-338.
WWW Version. 0610
Alpine forest; LiDAR; Canopy height; DTM; Forest inventory BibRef

Simard, M., Saatchi, S.S., de Grandi, G.,
The Use of Decision Tree and Multiscale Texture for Classification of JERS-1 SAR Data over Tropical Forest,
GeoRS(38), No. 5, September 2000, pp. 2310-2321.
IEEE Top Reference. 0010
BibRef

Saatchi, S., Halligan, K.Q., Despain, D.G., Crabtree, R.L.,
Estimation of Forest Fuel Load From Radar Remote Sensing,
GeoRS(45), No. 6, June 2007, pp. 1726-1740.
IEEE DOI Link 0706
BibRef

Huang, S.L.[Sheng-Li], Hager, S.A.[Stacey A.], Halligan, K.Q.[Kerry Q.], Fairweather, I.S.[Ian S.], Swanson, A.K.[Alan K.], Crabtree, R.L.[Robert L.],
A Comparison of Individual Tree and Forest Plot Height Derived from LiDAR and InSAR,
PhEngRS(75), No. 2, February 2009, pp. 159-168.
WWW Version. 0902
A comparison of three meter resolution lidar and Ku-band InSAR for deriving heights of isolated, individual trees and forest plots. BibRef

Simard, M.[Marc], Zhang, K.Q.[Ke-Qi], Rivera-Monroy, V.H.[Victor H.], Ross, M.S.[Michael S.], Ruiz, P.L.[Pablo L.], Castañeda-Moya, E.[Edward], Twilley, R.R.[Robert R.], Rodriguez, E.[Ernesto],
Mapping Height and Biomass of Mangrove Forests in the Everglades National Park with SRTM Elevation Data,
PhEngRS(72), No. 3, March 2006, pp. 299-312.
WWW Version. 0610
Production of a landscape scale map of mean tree height using SRTM data, and deriving height and biomass relationships based on field data. BibRef

Chubey, M.S.[Michael S.], Franklin, S.E.[Steven E.], Wulder, M.A.[Michael A.],
Object-based Analysis of Ikonos-2 Imagery for Extraction of Forest Inventory Parameters,
PhEngRS(72), No. 4, April 2006, pp. 383-394.
WWW Version. 0610
A new approach for extracting forest inventory parameters from high spatial resolution satellite imagery based on analysis of image objects. BibRef

Musy, R.[Rebecca], Wynne, R.H.[Randolph H.], Blinn, C.E.[Christine E.], Scrivani, J.A.[John A.], McRoberts, R.[Ronald],
Automated Forest Area Estimation Using Iterative Guided Spectral Class Rejection,
PhEngRS(72), No. 8, August 2006, pp. 949-960.
WWW Version. 0610
USDA Forest Service Inventory and Analysis (FIA) forest area estimates were successfully derived from Landsat EMT+ images classified using an automated hybrid classifier. BibRef

Phillips, R.D., Blinn, C.E., Watson, L.T., Wynne, R.H.,
An Adaptive Noise-Filtering Algorithm for AVIRIS Data With Implications for Classification Accuracy,
GeoRS(47), No. 9, September 2009, pp. 3168-3179.
IEEE DOI Link 0909
BibRef

Sun, C.M.[Chang-Ming], Jones, R.[Ronald], Talbot, H.[Hugues], Wu, X.L.[Xiao-Liang], Cheong, K.[Kevin], Beare, R.[Richard], Buckley, M.[Michael], Berman, M.[Mark],
Measuring the distance of vegetation from powerlines using stereo vision,
PandRS(60), No. 4, June 2006, pp. 269-283.
WWW Version.
PDF Version. 0610
Stereo matching; powerline inspection; power pole segmentation; vegetation clearance; 3D vegetation surface BibRef

Vaiphasa, C.[Chaichoke], Skidmore, A.K.[Andrew K.], de Boer, W.F.[Willem F.],
A post-classifier for mangrove mapping using ecological data,
PandRS(61), No. 1, October 2006, pp. 1-10.
WWW Version. 0610
expert system; multispectral; remote sensing; vegetation BibRef

Henning, J.G.[Jason G.], Radtke, P.J.[Philip J.],
Ground-based Laser Imaging for Assessing Three Dimensional Forest Canopy Structure,
PhEngRS(72), No. 12, December 2006, pp. 1349-1358.
WWW Version. 0704
Spatial distributions of plant area, leaf area, tree positions, canopy height, and terrain elevation were generated for a deciduous forest in an exploratory application of high-resolution ground-based laser imaging. BibRef

Henning, J.G.[Jason G.], Radtke, P.J.[Philip J.],
Multiview range-image registration for forested scenes using explicitly-matched tie points estimated from natural surfaces,
PandRS(63), No. 1, January 2008, pp. 68-83.
WWW Version. 0711
Alignment; Terrestrial laser scanning; Canopy structure; Point cloud; Ground-based lidar; Stem map; Stem profile; Digital terrain model BibRef

Chasmer, L.[Laura], Hopkinson, C.[Chris], Smith, B.[Brent], Treitz, P.[Paul],
Examining the Influence of Changing Laser Pulse Repetition Frequencies on Conifer Forest Canopy Returns,
PhEngRS(72), No. 12, December 2006, pp. 1359-1368.
WWW Version. 0704
The characteristics associated with differing laser pulse emission frequencies are found to vary the penetration of pulses within conifer forest canopies. BibRef

Hinsley, S.A.[Shelley A.], Hill, R.A.[Ross A.], Bellamy, P.E., Balzter, H.[Heiko],
The Application of Lidar in Woodland Bird Ecology: Climate, Canopy Structure, and Habitat Quality,
PhEngRS(72), No. 12, December 2006, pp. 1399-1406.
WWW Version. 0704
Measuring woodland vegetation structure and the relationship of climate in determining habitat quality for breeding birds. BibRef

Rowland, C.S.[Clare S.], Balzter, H.[Heiko],
Data Fusion for Reconstruction of a DTM, Under a Woodland Canopy, From Airborne L-band InSAR,
GeoRS(45), No. 5, May 2007, pp. 1154-1163.
IEEE DOI Link 0704
BibRef

Norjamäki, I., Tokola, T.,
Comparison of Atmospheric Correction Methods in Mapping Timber Volume with Multitemporal Landsat Images in Kainuu, Finland,
PhEngRS(73), No. 2, February 2007, pp. 155-164.
WWW Version. 0704
The estimation of forest characteristics from an atmospherically corrected Landsat EMT+ mosaic. BibRef

Evans, J.S.[Jeffrey S.], Hudak, A.T.[Andrew T.],
A Multiscale Curvature Algorithm for Classifying Discrete Return LiDAR in Forested Environments,
GeoRS(45), No. 4, April 2007, pp. 1029-1038.
IEEE DOI Link 0704
BibRef

Verhoef, W., Jia, L., Xiao, Q., Su, Z.,
Unified Optical-Thermal Four-Stream Radiative Transfer Theory for Homogeneous Vegetation Canopies,
GeoRS(45), No. 6, June 2007, pp. 1808-1822.
IEEE DOI Link 0706
BibRef

Wang, Z., Boesch, R.,
Color- and Texture-Based Image Segmentation for Improved Forest Delineation,
GeoRS(45), No. 10, October 2007, pp. 3055-3062.
IEEE DOI Link 0711
BibRef

Solberg, S., Naesset, E.,
Mapping Defoliation with Lidar,
Laser07(379).
PDF Version. 0709
BibRef

Tottrup, C.[Christian],
Forest and Land Cover Mapping in a Tropical Highland Region,
PhEngRS(73), No. 9, September 2007, pp. 1057-1066.
WWW Version. 0709
Tropical forest and land-cover classes within a topographically complex area are mapped from a terrain corrected SPOT HRVIR image and using linear mixture modeling in combination with a decision tree classifier. BibRef

Plourde, L.C.[Lucie C.], Ollinger, S.V.[Scott V.], Smith, M.L.[Marie-Louise], Martin, M.E.[Mary E.],
Estimating Species Abundance in a Northern Temperate Forest Using Spectral Mixture Analysis,
PhEngRS(73), No. 7, July 2007, pp. 829-840.
WWW Version. 0709
Spectral mixture analysis is used to classify sugar maple and American beech abundance in a heterogeneous forest in the northeastern U.S. BibRef

Potere, D.[David], Woodcock, C.[Curtis], Schneider, A.[Annemarie], Ozdogan, M.[Mutlu], Baccini, A.[Alessandro],
Patterns in Forest Clearing Along the Appalachian Trail Corridor,
PhEngRS(73), No. 7, July 2007, pp. 783-792.
WWW Version. 0709
The GeoCover Landsat dataset was used to estimate that 75,000 hectares of forest were cleared on a corridor 3,500 km long. BibRef

Nelson, M.[Mark], Moisen, G.[Gretchen], Finco, M.[Mark], Brewer, K.[Ken],
Forest Inventory and Analysis in the United States: Remote Sensing and Geospatial Activities (Adobe PDF 202Kb),
PhEngRS(73), No. 7, July 2007, pp. 729-735.
WWW Version. 0709
BibRef

Walker, J.S.[Jason S.], Briggs, J.M.[John M.],
An Object-oriented Approach to Urban Forest Mapping in Phoenix,
PhEngRS(73), No. 5, May 2007, pp. 577-584.
WWW Version. 0709
A object-oriented approach technique for regular monitoring of structural vegetation detection using high-resolution, color imagery. BibRef

Garestier, F., Dubois-Fernandez, P.C., Papathanassiou, K.P.,
Pine Forest Height Inversion Using Single-Pass X-Band PolInSAR Data,
GeoRS(46), No. 1, January 2008, pp. 59-68.
IEEE DOI Link 0712
BibRef

Garestier, F., Dubois-Fernandez, P.C., Champion, I.,
Forest Height Inversion Using High-Resolution P-Band Pol-InSAR Data,
GeoRS(46), No. 11, November 2008, pp. 3544-3559.
IEEE DOI Link 0812
BibRef

Mallinis, G.[Georgios], Koutsias, N.[Nikos], Tsakiri-Strati, M.[Maria], Karteris, M.[Michael],
Object-based classification using Quickbird imagery for delineating forest vegetation polygons in a Mediterranean test site,
PandRS(63), No. 2, March 2008, pp. 237-250.
WWW Version. 0803
Forest classification; Texture; Quickbird; Object-based; Multi-scale BibRef

Dalponte, M., Bruzzone, L.[Lorenzo], Gianelle, D.,
Fusion of Hyperspectral and LIDAR Remote Sensing Data for Classification of Complex Forest Areas,
GeoRS(46), No. 5, May 2008, pp. 1416-1427.
IEEE DOI Link 0804
BibRef

van Aardt, J.A.N.[Jan A.N.], Wynne, R.H.[Randolph H.], Scrivani, J.A.[John A.],
Lidar-based Mapping of Forest Volume and Biomass by Taxonomic Group Using Structurally Homogenous Segments,
PhEngRS(74), No. 8, August 2008, pp. 1033-1044.
WWW Version. 0804
An evaluation of an object-oriented approach to deciduous and coniferous forest classification, as well as volume and biomass estimation, using small-footprint lidar height and intensity distributions, and highlights of the potential of perobject lidar data analysis for stand-level forest inventories. BibRef

Henry, M.C.[Mary C.],
Comparison of Single- and Multi-date Landsat Data for Mapping Wildfire Scars in Ocala National Forest, Florida,
PhEngRS(74), No. 7, July 2008, pp. 881-892.
WWW Version. 0804
Datasets classified using a traditional maximum likelihood classification method and a non-parametric classification and regression tree technique. BibRef

Haapanen, R.[Reija], Tuominen, S.[Sakari],
Data Combination and Feature Selection for Multisource Forest Inventory,
PhEngRS(74), No. 7, July 2008, pp. 869-880.
WWW Version. 0804
Feature selection and weighting among satellite image features and aerial photograph spectral and textural features were used to boost the accuracy when estimating forest variables. BibRef

Xie, Z.X.[Zhi-Xiao], Roberts, C.[Charles], Johnson, B.[Brian],
Object-based target search using remotely sensed data: A case study in detecting invasive exotic Australian Pine in south Florida,
PandRS(63), No. 6, November 2008, pp. 647-660.
WWW Version. 0811
Geographic image retrieval; Object based; Regression tree; Similarity threshold; Invasive exotic species BibRef

Lee, H.,
Mapping Deforestation and Age of Evergreen Trees by Applying a Binary Coding Method to Time-Series Landsat November Images,
GeoRS(46), No. 11, November 2008, pp. 3926-3936.
IEEE DOI Link 0812
BibRef

Lippitt, C.D.[Christopher D.], Rogan, J.[John], Li, Z.[Zhe], Eastman, J.R.[J. Ronald], Jones, T.G.[Trevor G.],
Mapping Selective Logging in Mixed Deciduous Forest: A Comparison of Machine Learning Algorithms,
PhEngRS(74), No. 10, October 2008, pp. 1201-1212.
WWW Version. 0804
A back-propagation multilayer perceptron, self-organizing map, fuzzy ARTMAP, and gini and entropy univariate decision trees compared in terms of their ability to cope with small, unrepresentative, and variable training sets. BibRef

Peuhkurinen, J.[Jussi], Maltamo, M.[Matti], Vesa, L.[Lauri], Packalén, P.[Petteri],
Estimation of Forest Stand Characteristics Using Spectral Histograms Derived from an Ikonos Satellite Image,
PhEngRS(74), No. 11, November 2008, pp. 1335-1342.
WWW Version. 0804
The potential of Ikonos satellite images for estimating forest stand characteristics studied in boreal conditions. BibRef

Hecht, R., Meinel, G., Buchroithner, M.F.,
Estimation of Urban Green Volume Based on Single-Pulse LiDAR Data,
GeoRS(46), No. 11, November 2008, pp. 3832-3840.
IEEE DOI Link 0812
BibRef

Kushida, K.[Keiji], Yoshino, K.[Kunihiko], Nagano, T.[Toshihide], Ishida, T.[Tomoyasu],
Automated 3D Forest Surface Model Extraction from Balloon Stereo Photographs,
PhEngRS(75), No. 1, January 2009, pp. 25-37.
WWW Version. 0902
An automated forest digital surface model (DSM) extraction method from balloon stereo photographs upgraded through the evaluations of the image matching accuracy and forest surface height estimation of a tropical peat swamp forest in Narathiwat, Thailand BibRef

de Grandi, G.D., Lucas, R.M., Kropacek, J.,
Analysis by Wavelet Frames of Spatial Statistics in SAR Data for Characterizing Structural Properties of Forests,
GeoRS(47), No. 2, February 2009, pp. 494-507.
IEEE DOI Link 0903
BibRef

Yang, C.H.[Cheng-Hai], Everitt, J.H.[James H.], Fletcher, R.S.[Reginald S.], Jensen, R.R.[Ryan R.], Mausel, P.W.[Paul W.],
Evaluating AISA+ Hyperspectral Imagery for Mapping Black Mangrove along the South Texas Gulf Coast,
PhEngRS(75), No. 4, April 2009, pp. 425-436.
WWW Version. 0903
Airborne hyperspectral imagery combined with image transformation and classification techniques can be a useful tool for monitoring and mapping black mangrove distributions in coastal environments. BibRef

Wang, C., Menenti, M., Stoll, M.P., Feola, A., Belluco, E., Marani, M.,
Separation of Ground and Low Vegetation Signatures in LiDAR Measurements of Salt-Marsh Environments,
GeoRS(47), No. 7, July 2009, pp. 2014-2023.
IEEE DOI Link 0906
BibRef

Disney, M.I., Lewis, P.E., Bouvet, M., Prieto-Blanco, A., Hancock, S.,
Quantifying Surface Reflectivity for Spaceborne Lidar via Two Independent Methods,
GeoRS(47), No. 9, September 2009, pp. 3262-3271.
IEEE DOI Link 0909
BibRef

Hancock, S.[Steven], Disney, M.I.[Mathias I.], Lewis, P.E.[Philip E.], Muller, J.P.[Jan-Peter],
Exploring the Measurement Of Forests With Full Waveform LIDAR through Monte-Carlo Ray Tracing,
ISPRS08(B1: 235 ff).
PDF Version. 0807
BibRef

Johansen, K.[Kasper], Phinn, S.[Stuart], Witte, C.[Christian], Philip, S.[Seonaid], Newton, L.[Lisa],
Mapping Banana Plantations from Object-oriented Classification of SPOT-5 Imagery,
PhEngRS(75), No. 9, September 2009, pp. 1069-1082.
WWW Version. 0910
The extent of banana plantations was mapped using panchromatic and multispectral SPOT-5 imagery and object-oriented segmentation and classification in Definiens Professional 5. BibRef

Garestier, F., Dubois-Fernandez, P.C., Guyon, D., Le Toan, T.,
Forest Biophysical Parameter Estimation Using L- and P-Band Polarimetric SAR Data,
GeoRS(47), No. 10, October 2009, pp. 3379-3388.
IEEE DOI Link 0910
BibRef

Cuevas, G.[Gabriela], Benítez, J.[Jorge], Vega-Guzmán, Á.[Álvaro], Coria-Tapia, V.[Valdemar],
An Accuracy Index with Positional and Thematic Fuzzy Bounds for Land-use / Land-cover Maps,
PhEngRS(75), No. 7, July 2009, pp. 789-806.
WWW Version. 0910
A framework for assessing taxonomically detailed landcover/land-use maps at regional scale is proposed and illustrated on the Mexican National Forest Inventory map of a subtropical densely forested area. BibRef

Kim, M.H.[Min-Ho], Madden, M.[Marguerite], Warner, T.A.[Timothy A.],
Forest Type Mapping using Object-specific Texture Measures from Multispectral Ikonos Imagery: Segmentation Quality and Image Classification Issues,
PhEngRS(75), No. 7, July 2009, pp. 819-830.
WWW Version. 0910
The effect of scale and associated segmentation quality on classification results of forest types in a National Park, U.S. was investigated with spectral and spatial information of multispectral Ikonos imagery. BibRef

Tang, F.F., Liu, J.N., Zhang, X.H., Ruan, Z.M.,
Derivation of Digital Terrain Model in Forested Area with Airborne LIDAR Data,
ISPRS08(B3b: 343 ff).
PDF Version. 0807
BibRef

Lo, C.Y., Chen, L.C.,
Canopy Extraction Using Airborne Laser Scanning Data in Forestry Areas,
ISPRS08(B3b: 367 ff).
PDF Version. 0807
BibRef

Pál, I.[István],
Measurements of Forest Inventory Parameters on Terrestrial Laser Scanning Data Using Digital Geometry and Topology,
ISPRS08(B3b: 373 ff).
PDF Version. 0807
BibRef

Bao, Y.F.[Yun-Fei], Li, G.P.[Guo-Ping], Cao, C.X.[Chun-Xiang], Li, X.W.[Xiao-Wen], Zhang, H.[Hao], He, Q.S.[Qi-Sheng], Bai, L.Y.[Lin-Yan], Chang, C.Y.[Chao-Yi],
Classification of LIDAR Point Cloud and Generation of DTM from LIDAR Height and Intensity Data In Forested Area,
ISPRS08(B3b: 313 ff).
PDF Version. 0807
BibRef

Zhang, Q., Mercer, J.B., Cloude, S.R.,
Forest Height Estimation from INDREX-II L-Band Polarimetric InSAR Data,
ISPRS08(B1: 343 ff).
PDF Version. 0807
BibRef

Dong, L.X.[Li-Xin], Wu, B.F.[Bing-Fang],
A Comparison of Estimating Forest Canopy Height Integrating Multi-sensor data Synergy: A Case Study in Mountain Area of Three Gorges,
ISPRS08(B1: 379 ff).
PDF Version. 0807
BibRef

Lalonde, J., Vandapel, N., and Hebert, M.,
Automatic Three-Dimensional Point Cloud Processing for Forest Inventory,
CMU-RI-TR-06-21, July, 2006.
WWW Version. BibRef 0607

Gross, H., Jutzi, B., Thoennessen, U.,
Segmentation of Tree Regions Using Data of a Full-Waveform Laser,
PIA07(57).
PDF Version. 0711
BibRef

Straatsma, M., Middelkoop, H.,
Extracting Structural Characteristics of Dormant Herbaceous Floodplain Vegetation from Airborne Laser Scanner Data,
Laser07(395).
PDF Version. 0709
BibRef

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Chapter on Cartography, Aerial Images, Remote Sensing, Buildings, Roads, Terrain, ATR continues in
SRI General Cartography Systems .