Do red edge and texture attributes from high-resolution satellite data improve wood volume estimation in a semi-arid mountainous region?

Remote Sensing

Volumn 8, Issue 7, 2016, Pages

  Schumacher, Paul ^a   Mislimshoeva, Bunafsha ^a   Brenning, Alexander ^b   Zandler, Harald ^a   Brandt, Martin ^c   Samimi, Cyrus ^a   Koellner, Thomas ^a  

^a UNIVERSITY OF BAYREUTH   (Germany)

^b FRIEDRICH SCHILLER UNIVERSITY JENA   (Germany)

^c UNIVERSITY OF COPENHAGEN   (Denmark)

Author keywords

RapidEye; Red edge; Semi arid; Texture; Wood volume estimation; Woody biomass

Indexed keywords

BIOMASS; DECISION TREES; ECOSYSTEMS; FORESTRY; MEAN SQUARE ERROR; REMOTE SENSING; SATELLITES; TEXTURES; VEGETATION;

RAPIDEYE; RED EDGE; SEMI ARID; WOOD VOLUME; WOODY BIOMASS;

WOOD;

BIOMASS; TEXTURE; VOLUME; WOOD;

EID: 85011274936     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs8070540     Document Type: Article

References (96)

1. Lu, D. The potential and challenge of remote sensing-based biomass estimation. Int. J. Remote Sens. 2006, 27, 1297-1328.
2. Millennium Ecosystem Assessment. Ecosystems and Human Well-Being-Synthesis. Available online: http://www.millenniumassessment.org/documents/document.356.aspx.pdf (accessed on 15 February 2015).
3. Safriel, U.; Adeel, Z.; Niemeijer, D.; Puigdefabregas, J.; White, R.; Lal, R.; Winslow, M.; Ziedler, J.; Prince, S.; Archer, E.; et al. Dryland systems. In Ecosystems and Human Well-Being: Current State and Trends: Findings of the Condition Trends Work Group; Hassan, R., Scholes, R., Ash, N., Eds.; Island Press: Washington, DC, USA, 2005; Volume 1, pp. 623-662.
4. Ayanu, Y.Z.; Conrad, C.; Nauss, T.; Wegmann, M.; Koellner, T. Quantifying and mapping ecosystem services supplies and demands: A review of remote sensing applications. Environ. Sci. Technol. 2012, 46, 8529-8541.
5. Sarker, L.R.; Nichol, J.E. Improved forest biomass estimates using ALOS AVNIR-2 texture indices. Remote Sens. Environ. 2011, 115, 968-977.
6. Eisfelder, C.; Kuenzer, C.; Dech, S. Derivation of biomass information for semi-arid areas using remote-sensing data. Int. J. Remote Sens. 2012, 33, 2937-2984.
7. Avitabile, V.; Baccini, A.; Friedl, M.A.; Schmullius, C. Capabilities and limitations of Landsat and land cover data for aboveground woody biomass estimation of Uganda. Remote Sens. Environ. 2012, 117, 366-380.
8. Cutler, M.E.J.; Boyd, D.S.; Foody, G.M.; Vetrivel, A. Estimating tropical forest biomass with a combination of SAR image texture and Landsat TMdata: An assessment of predictions between regions. ISPRS J. Photogramm. Remote Sens. 2012, 70, 66-77.
9. Samimi, C.; Kraus, T. Biomass estimation using Landsat-TM and -ETM+. Towards a regional model for Southern Africa? GeoJ 2004, 59, 177-187.
10. Zandler, H.; Brenning, A.; Samimi, C. Quantifying dwarf shrub biomass in an arid environment: Comparing empirical methods in a high dimensional setting. Remote Sens. Environ. 2015, 158, 140-155.
11. Asner, G.P.; Wessman, C.A.; Bateson, C.A.; Privette, J.L. Impact of tissue, canopy, and landscape factors on the hyperspectral reflectance variability of arid ecosystems. Remote Sens. Environ. 2000, 74, 69-84.
12. Beeri, O.; Phillips, R.; Hendrickson, J.; Frank, A.B.; Kronberg, S. Estimating forage quantity and quality using aerial hyperspectral imagery for northern mixed-grass prairie. Remote Sens. Environ. 2007, 110, 216-225.
13. Qi, J.; Wallace, O.; Lansing, E. Biophysical attributes estimation from satellite images in arid regions. In Proceedings of the 2002 IEEE International Geoscience and Remote Sensing Symposium, Toronto, ON, Canada, 24-28 June 2002.
14. Svoray, T.; Shoshany, M. Herbaceous biomass retrieval in habitats of complex composition: A model merging sar images with unmixed landsat tm data. IEEE Trans. Geosci. Remote Sens. 2003, 41, 1592-1601.
15. 2 Advanced Very High Resolution Radiometer (AVHRR) NDVI in Kruger National Park, South Africa. Int. J. Remote Sens. 2006, 27, 951-973.
16. Brandt, M.; Verger, A.; Diouf, A.A.; Baret, F.; Samimi, C. Local vegetation trends in the sahel of Mali and Senegal using long time series FAPAR satellite products and field measurement (1982-2010). Remote Sens. 2014, 6, 2408-2434.
17. Diouf, A.; Lambin, E.F. Monitoring land-cover changes in semi-arid regions: Remote sensing data and field observations in the Ferlo, Senegal. J. Arid Environ. 2001, 48, 129-148.
18. Holm, A.M.; Cridland, S.W.; Roderick, M.L. The use of time-integrated NOAA NDVI data and rainfall to assess landscape degradation in the arid shrubland of Western Australia. Remote Sens. Environ. 2003, 85, 145-158.
19. Kawamura, K.; Akiyama, T.; Yokota, H.; Tsutsumi, M.; Yasuda, T.; Watanabe, O.; Wang, S. Quantifying grazing intensities using geographic information systems and satellite remote sensing in the Xilingol steppe region, Inner Mongolia, China. Agric. Ecosyst. Environ. 2005, 107, 83-93.
20. Tucker, C.J.; Vanpraet, C.L.; Sharman, M.J.; Van Ittersum, G. Satellite remote sensing of total herbaceous biomass production in the senegalese sahel: 1980-1984. Remote Sens. Environ. 1985, 17, 233-249.
21. Xu, B.; Yang, X.C.; Tao,W.G.; Miao, J.M.; Yang, Z.; Liu, H.Q.; Jin, Y.X.; Zhu, X.H.; Qin, Z.H.; Lv, H.Y.; et al. MODIS-based remote-sensing monitoring of the spatiotemporal patterns of China's grassland vegetation growth. Int. J. Remote Sens. 2013, 34, 3867-3878.
22. Aranha, J.T.; Viana, H.F.; Rodrigues, R. Vegetation classification and quantification by satellite image processing. A case study in North Portugal. In Proceedings of the 2008 International Conference and Exhibition on Bioenergy: Challenges and Opportunities, Guimarães, Portugal, 6-9 April 2008.
23. Kraus, T.; Samimi, C. Biomass estimation for land use management and fire management using Landsat-TM and -ETM+. Erdkunde 2002, 56, 130-143.
24. Samimi, C. DasWeidepotential im Gutu Distrikt (Zimbabwe): Möglichkeiten und Grenzen der Modellierung unter Verwendung von Landsat TM-5. Available online: http://www.klimatologie.uni-bayreuth.de/pdf/ publications/Das-Weidepotential-im-Gutu-Distrikt.pdf (accessed on 10 November 2015).
25. Wylie, B.K.; Meyer, D.J.; Tieszen, L.L.; Mannel, S. Satellite mapping of surface biophysical parameters at the biome scale over the North American grasslands A case study. Remote Sens. Environ. 2002, 79, 266-278.
26. Eitel, J.U.H.; Vierling, L.A.; Litvak, M.E.; Long, D.S.; Schulthess, U.; Ager, A.A.; Krofcheck, D.J.; Stoscheck, L. Broadband, red-edge information from satellites improves early stress detection in a New Mexico conifer woodland. Remote Sens. Environ. 2011, 115, 3640-3646.
27. Zandler, H.; Brenning, A.; Samimi, C. Potential of space-borne hyperspectral data for biomass quantification in an arid environment: Advantages and limitations. Remote Sens. 2015, 7, 4565-4580.
28. Clevers, J.G.P.W.; De Jong, S.M.; Epema, G.F.; Van der Meer, F.D.; Bakker, W.H.; Skidmore, A.K.; Scholte, K.H. Derivation of the red edge index using the MERIS standard. Int. J. Remote Sens. 2002, 23, 3169-3184.
29. Brantley, S.T.; Zinnert, J.C.; Young, D.R. Application of hyperspectral vegetation indices to detect variations in high leaf area index temperate shrub thicket canopies. Remote Sens. Environ. 2011, 115, 514-523.
30. Vanselow, K.; Samimi, C. Predictive mapping of dwarf shrub vegetation in an arid high mountain ecosystem using remote sensing and random forests. Remote Sens. 2014, 6, 6709-6726.
31. Attarchi, S.; Gloaguen, R. Improving the estimation of above ground biomass using dual polarimetric PALSAR and ETM+ data in the Hyrcanian mountain forest (Iran). Remote Sens. 2014, 6, 3693-3715.
32. Eckert, S. Improved forest biomass and carbon estimations using texture measures from WorldView-2 satellite data. Remote Sens. 2012, 4, 810-829.
33. Kelsey, K.; Neff, J. Estimates of aboveground biomass from texture analysis of Landsat imagery. Remote Sens. 2014, 6, 6407-6422.
34. Lu, D. Aboveground biomass estimation using Landsat TM data in the Brazilian Amazon. Int. J. Remote Sens. 2005, 26, 2509-2525.
35. Luckman, A.J.; Frery, A.C.; Yanasse, C.C.F.; Groom, G.B. Texture in airborne SAR imagery of tropical forest and its relationship to forest regeneration stage. Int. J. Remote Sens. 1997, 18, 1333-1349.
36. Castillo-Santiago, M.á.; Ghilardi, A.; Oyama, K.; Hernández-Stefanoni, J.L.; Torres, I.; Flamenco-Sandoval, A.; Fernández, A.; Mas, J.-F. Estimating the spatial distribution of woody biomass suitable for charcoal making from remote sensing and geostatistics in central Mexico. Energy Sustain. Dev. 2013, 17, 177-188.
37. Forzieri, G. Satellite retrieval of woody biomass for energetic reuse of riparian vegetation. Biomass Bioenergy 2012, 36, 432-438.
38. Wiedemann, C.; Salzmann, S.; Mirshakarov, I.; Volkmer, H. Thermal insulation in high mountainous regions. Mt. Res. Dev. 2012, 32, 294-303.
39. Mislimshoeva, B.; Hable, R.; Fezakov, M.; Samimi, C.; Abdulnazarov, A.; Koellner, T. Factors influencing households' firewood consumption in the Western Pamirs, Tajikistan. Mt. Res. Dev. 2014, 34, 147-156.
40. Breu, T.; Maselli, D.; Hurni, H. Knowledge for sustainable development in the Tajik Pamir Mountains. Mt. Res. Dev. 2005, 25, 139-146.
41. Hoeck, T.; Droux, R.; Breu, T.; Hurni, H.; Maselli, D. Rural energy consumption and land degradation in a post-Soviet setting-An example from the west Pamir mountains in Tajikistan. Energy Sustain. Dev. 2007, 11, 48-57.
42. Breckle, S.-W.; Wucherer, W. Vegetation of the Pamir (Tajikistan): Land use and desertification problems. In Land Use Change and Mountain Biodiversity; Spehn, E., Liberman, M., Körner, C., Eds.; CRC Press: Boca Raton, FL, USA, 2006; pp. 225-237.
43. Schumacher, P. Quantification ofWoody Biomass in theWestern Pamirs-Assessing Firewood Availability in a Semi-Arid High Mountainous Region Using High Resolution Satellite Data and Field Measurements. Master's Thesis, University of Bayreuth, Bayreuth, Germany, 2014.
44. METI, NASA. ASTER Global Digital Elevation Model V002. Sioux Falls, SD. 2009. Available online: https://lpdaac.usgs.gov/dataset_discovery/aster/aster_products_table/astgtm (accessed on 7 August 2015).
45. Birk, S. Development of the FGIS (Forest Geographical Information System); Interim Mission Report for Deutsche Gesellschaft für Technische Zusammenarbeit (GTZ); Deutsche Gesellschaft für Technische Zusammenarbeit (GTZ) GmbH: Eschborn, Germany, 2009; p. 13.
46. Mueller-Dombois, D.; Ellenberg, H. Aims and Methods of Vegetation Ecology; JohnWiley & Sons: New York, NY, USA, 1974; p. 287.
47. Coulloudon, B.; Eshelman, K.; Gianola, J.; Habich, N.; Hughes, L.; Johnson, C.; Pellant, M.; Podborny, P.; Rasmussen, A.; Robles, B.; et al. Sampling Vegetation Attributes; BLM Technical Reference 4400-4; Bureau of Land Management's National Applied Resource Sciences Center: Denver, CO, USA, 1999; p. 171.
48. Akhmadov, K. Forest and Forest Products Country Profile: Tajikistan; Geneva Timber and Forest Discussion Paper 46; United Nations Economic Commission for Europe, Food and Agriculture Organizations of the United Nations: Rome, Italy, 2008; p. 48.
49. Kirchhoff, J.F.; Fabian, A. Forestry Sector Analysis of the Republic of Tajikistan; Deutsche Gesellschaft für Technische Zusammenarbeit (GTZ): Eschborn, Germany, 2010; p. 56.
50. Akhmadov, K. Country statement Tajikistan-Forest resources assessment for sustainable forest management. In Proceedings of the Capacity Building in Sharing Forest and Market Information Workshop, Krtiny, Czech Republic, 24-28 October 2005.
51. Akhmadov, K.M.; Breckle, S.-W.; Breckle, U. Effects of grazing on biodiversity, productivity, and soil erosion of alpine pastures in Tajik mountains. In Land Use Change and Mountain Biodiversity; Spehn, E., Liberman, M., Körner, C., Eds.; CRC Press: Boca Raton, FL, USA, 2006; pp. 241-250.
52. Cannell, M.G.R. Woody biomass of forest stands. For. Ecol. Manag. 1984, 8, 299-312.
53. Gray, H.R. The Form and Taper of Forest-Tree Stems; Institute Paper No. 32; Imperial Forestry Institute, University of Oxford: Oxford, UK, 1956; p. 84.
54. Hoyer, G.E. Tree Form Quotients as Variables in Volume Estimation; U.S. Department of Agriculture, Forest Service, Pacific Northwest Forest and Range Experiment Station: Portland, OR, USA, 1985; p. 16.
55. Van Laar, A.; Akca, A. Forest Mensuration, 1st ed.; Springer: Houten, The Netherlands, 2007; p. 389.
56. Spiekermann, R.; Brandt, M.; Samimi, C. Woody vegetation and land cover changes in the Sahel of Mali (1967-2011). Int. J. Appl. Earth Observ. Geoinf. 2015, 34, 113-121.
57. Zandler, H. Assessment of Woody Biomass and Solar Energy Resources with Remote Sensing and GIS Techniques-A Regional Study in the High Mountains of the Eastern Pamirs (Tajikistan). Ph.D. Thesis, University of Bayreuth, Bayreuth, Germany, September 2015.
58. Tucker, C.J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 1979, 8, 127-150.
59. Verrelst, J.; Koetz, B.; Kneubühler, M.; Schaepman, M.; Verrelst, J.; Schaepman, M. Directional Sensitivity Analysis of Vegetation Indices from Multi-Angular CHRIS/PROBA Data. Available online: http://www. isprs.org/proceedings/XXXVI/part7/PDF/227.pdf (accessed on 11 March 2015).
60. Gilabert, M.A.; Gonzales-Piqueras, F.J.; Garcia-Haro, F.; Melia, J. A generalized soil-adjusted vegetation index. Remote Sens. Environ. 2002, 82, 303-310.
61. Haralick, R.M.; Shanmugam, K.; Dinstein, I. Textural features for image classification. IEEE Trans. Syst. Man Cybern. 1973, 3, 610-621.
62. Dube, T.; Mutanga, O. Investigating the robustness of the new Landsat-8 Operational Land Imager derived texture metrics in estimating plantation forest aboveground biomass in resource constrained areas. ISPRS J. Photogramm. Remote Sens. 2015, 108, 12-32.
63. Gitelson, A.; Vina, A.; Ciganda, V.; Rundquist, D.C.; Arkebauer, T.J. Remote estimation of canopy chlorophyll content in crops. Geophys. Res. Lett. 2005, 32, 1-4.
64. Gitelson, A.A.; Merzlyak, M.N. Remote estimation of chlorophyll content in higher plant leaves. Int. J. Remote Sens. 1997, 18, 2691-2697.
65. Loris, V.; Damiano, G. Mapping the green herbage ratio of grasslands using both aerial and satellite-derived spectral reflectance. Agric. Ecosyst. Environ. 2006, 115, 141-149.
66. Wang, F.; Huang, J.; Tang, Y.;Wang, X. New vegetation index and its application in estimating leaf area index of rice. Rice Sci. 2007, 14, 195-203.
67. Merzlyak, M.N.; Gitelson, A.A.; Chivkunova, O.B.; Solovchenko, A.E.; Pogosyan, S.I. Application of reflectance spectroscopy for analysis of higher plant pigments. Russ. J. Plant Physiol. 2003, 50, 704-710.
68. Barnes, E.; Clarke, T.; Richards, S.; Colaizzi, P.; Haberland, J.; Kostrzewski, P.; Choi, C.; Riley, E.; Thompson, T.; Lascano, R.; et al. Coincident detection of crop water stress, nitrogen status and canopy density using ground-based multispectral data. In Proceedings of the Fifth International Conference on Precision Agriculture, Bloomington, MN, USA, 16-19 July 2000.
69. Gitelson, A.; Merzlyak, M.N. Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation. J. Plant Physiol. 1994, 143, 286-292.
70. Kauth, R.J.; Thomas, G.S. The Tasselled Cap-A Graphic Description of the Spectral-Temporal Development of Agricultural Crops as Seen by Landsat. Available online: http://citeseerx.ist.psu.edu/viewdoc/ download?doi=10.1.1.461.6381&rep=rep1&type=pdf (accessed on 14 March 2015).
71. Bannari, A.;Morin, D.; Bonn, F.; Huete, A.R. A review of vegetation indices. Remote Sens. Rev. 1995, 13, 95-120.
72. Huete, A.R.; Liu, H.Q.; Batchily, K.; Leeuwen,W.V. A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sens. Rev. 1997, 59, 440-451.
73. Huete, A.R. A Soil-Adjusted Vegetation Index (SAVI). Remote Sens. Environ. 1988, 25, 295-309.
74. Gitelson, A.A.; Chivkunova, O.B.; Merzlyak, M.N. Nondestructive estimation of anthocyanins and chlorophylls in anthocyanic leaves. Am. J. Bot. 2009, 96, 1861-1868.
75. James, G.; Witten, D.; Hastie, T.; Tibshirani, R. An Introduction to Statistical Learning with Applications in R, 1st ed.; Springer: New York, NY, USA, 2013; p. 436.
76. Hastie, T.; Tibshirani, R.; Friedman, J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed.; Springer: New York, NY, USA, 2009; p. 750.
77. Pal, M. Random forest classifier for remote sensing classification. Int. J. Remote Sens. 2005, 26, 217-222.
78. Brenning, A. Spatial cross-validation and bootstrap for the assessment of prediction rules in remote sensing: The R package sperrorest. In Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany, 22-27 July 2012; pp. 5372-5375.
79. Baskerville, G.L. Use of logarithmic regression in the estimation of plant biomass. Can. J. For. Res. 1972, 2, 49-53.
80. Strobl, C.; Boulesteix, A.-L.; Zeileis, A.; Hothorn, T. Bias in random forest variable importance measures: Illustrations, sources and a solution. BMC Bioinform. 2007, 8.
81. Brenning, A. Spatial Error Estimation and Variable Importance: R Package "Sperrorest". Available online: https://cran.r-project.org/web/packages/ (accessed on 16 June 2015).
82. Friedman, A.J.; Hastie, T.; Simon, N.; Tibshirani, R. Lasso and Elastic-Net Regularized Generalized Linear Models: R Package "Glmnet". Available online: https://cran.r-project.org/web/packages/ (accessed on 16 June 2015).
83. Liaw, A.; Wiener, M. Breiman and Cutler's Random Forests for Classification and Regression: R Package "Randomforest". Available online: https://cran.r-project.org/web/packages/ (accessed on 16 June 2015).
84. Brenning; Alexander SAGA Geoprocessing and Terrain Analysis in R: R Package "RSAGA". Available online: https://cran.r-project.org/web/packages/ (accessed on 16 June 2015).
85. Ali, M.; Montzka, C.; Stadler, A.; Menz, G.; Thonfeld, F.; Vereecken, H. Estimation and validation of RapidEye-based time-series of leaf area index for winter wheat in the Rur Catchment (Germany). Remote Sens. 2015, 7, 2808-2831.
86. Kross, A.; McNairn, H.; Lapen, D.; Sunohara, M.; Champagne, C. Assessment of RapidEye vegetation indices for estimation of leaf area index and biomass in corn and soybean crops. Int. J. Appl. Earth Observ. Geoinf. 2015, 34, 235-248.
87. Li, X.; Gao, Z.; Bai, L.; Huang, Y. Potential of high resolution RapidEye data for sparse vegetation fraction mapping in arid regions. In Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany, 22-27 July 2012; pp. 420-423.
88. Ramoelo, A.; Skidmore, A.K.; Cho, M.A.; Schlerf, M.; Mathieu, R.; Heitkönig, I.M.A. Regional estimation of savanna grass nitrogen using the red-edge band of the spaceborne RapidEye sensor. Int. J. Appl. Earth Observ. Geoinf. 2012, 19, 151-162.
89. Ren, H.; Zhou, G.; Zhang, X. Estimation of green aboveground biomass of desert steppe in Inner Mongolia based on red-edge reflectance curve area method. Biosyst. Eng. 2011, 109, 385-395.
90. Cho, M.A.; Skidmore, A.K. Hyperspectral predictors for monitoring biomass production in Mediterranean mountain grasslands: Majella National Park, Italy. Int. J. Remote Sens. 2009, 30, 499-515.
91. Fuchs, H.; Magdon, P.; Kleinn, C.; Flessa, H. Estimating aboveground carbon in a catchment of the Siberian forest tundra: Combining satellite imagery and field inventory. Remote Sens. Environ. 2009, 113, 518-531.
92. Powell, S.L.; Cohen,W.B.; Healey, S.P.; Kennedy, R.E.;Moisen, G.G.; Pierce, K.B.; Ohmann, J.L. Quantification of live aboveground forest biomass dynamics with Landsat time-series and field inventory data: A comparison of empirical modeling approaches. Remote Sens. Environ. 2010, 114, 1053-1068.
93. Shoshany, M.; Svoray, T. Multidate adaptive unmixing and its application to analysis of ecosystem transitions along a climatic gradient. Remote Sens. Environ. 2002, 82, 5-20.
94. Immitzer, M.; Vuolo, F.; Atzberger, C. First experience with Sentinel-2 data for crop and tree species classifications in central Europe. Remote Sens. 2016, 8.
95. Radoux, J.; Chomé, G.; Jacques, D.C.; Waldner, F.; Bellemans, N.; Matton, N.; Lamarche, C.; d'Andrimont, R.; Defourny, P. Sentinel-2's potential for sub-pixel landscape feature detection. Remote Sens. 2016, 8.
96. Lazaridis, D.C.; Verbesselt, J.; Robinson, A.P. Penalized regression techniques for prediction: A case study for predicting tree mortality using remotely sensed vegetation indices. Can. J. For. Res. 2011, 41, 24-34.