Extraction of Information on Trees outside Forests Based on Very High Spatial Resolution Remote Sensing Images
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Collection and Processing
2.2.1. Remote Sensing Data
2.2.2. Field Survey Data
2.3. Method
2.3.1. Construction of U. pumila Feature Images
2.3.2. Construction of Multiscale Feature Space
2.3.3. Approximate location of the U. pumila targets detection
2.3.4. Location Identification of U. pumila Trees and Crown Detection
3. Results and Analysis
3.1. Parameter Analysis and Setting
3.1.1. Patch Size (Patch_min and Patch_max)
3.1.2. Sigma Value
3.2. Image Extraction Result
3.3. Comparison with the Image-Classification-Based Extraction Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- State Forestry Administration. A Bulletin of Status que of Desertification and Sandification in China; State Forestry Administration: Beijing, China, 2015.
- Schnell, S.; Kleinn, C.; Stahl, G. Monitoring trees outside forests: A review. Environ. Monit. Assess. 2015, 187, 600. [Google Scholar] [CrossRef] [PubMed]
- FAO. Global Forest Resources Assessment 2000; FAO: Rome, Italy, 2001. [Google Scholar]
- De Foresta, H.; Somarriba, E.; Temu, A.; Boulanger, D.; Feuilly, H.; Gauthier, M. Towards the Assessment of Trees outside Forests Forest; Resources Assessment Working Paper 183; FAO: Rome, Italy, 2013. [Google Scholar]
- Rossi, J.-P.; Garcia, J.; Roques, A.; Rousselet, J. Trees outside forests in agricultural landscapes: Spatial distribution and impact on habitat connectivity for forest organisms. Landsc. Ecol. 2016, 31, 243–254. [Google Scholar] [CrossRef]
- Xu, W.D. Sandy Forest Ecosystem of China; China Forestry Publishing House: Beijing, China, 1998. [Google Scholar]
- Li, G.; Jiang, G.; Gao, L.; Niu, S.; Liu, M.; Yu, S.; Peng, Y. Impacts of human disturbance on Elms-Motte-Veldt in hunshandak sandland. Acta Phytoecol. Sin. 2003, 27, 829–834. [Google Scholar]
- Liu, Z.; Li, H.L.; Dong, Z.; Li, G.T.; Wan, L.L.; Yue, Y.J. The Spatial Point Pattern of Ulmus pumila Population in Two Habitats in the Otindag Sandy Land. Sci. Silvae Sin. 2012, 48, 29–34. [Google Scholar] [CrossRef]
- Liu, Z. Population Structure and Distribution Pattern of Ulmus pumila L. Woodland under Fencing Enclosure in the Otindag Sand Land. Master’s Thesis, Shandong Agricultural University, Jinan, China, 2012. [Google Scholar]
- Park, G.E.; Lee, D.K.; Kim, K.W.; Batkhuu, N.-O.; Tsogtbaatar, J.; Zhu, J.-J.; Jin, Y.; Park, P.S.; Hyun, J.O.; Kim, H.S. Morphological Characteristics and Water-Use Efficiency of Siberian Elm Trees (Ulmus pumila L.) within Arid Regions of Northeast Asia. Forests 2016, 7, 280. [Google Scholar] [CrossRef]
- Leckie, D.G.; François, A.G.; Tinis, S.; Nelson, T.; Burnett, C.N.; Paradine, D. Automated tree recognition in old growth conifer stands with high resolution digital imagery. Remote Sens. Environ. 2005, 94, 311–326. [Google Scholar] [CrossRef]
- Hellesen, T.; Matikainen, L. An object-based approach for mapping shrub and tree cover on grassland habitats by use of LiDAR and CIR orthoimages. Remote Sens. 2013, 5, 558–583. [Google Scholar] [CrossRef]
- González-Roglich, M.; Swenson, J.J. Tree cover and carbon mapping of Argentine savannas: Scaling from field to region. Remote Sens. Environ. 2016, 172, 139–147. [Google Scholar] [CrossRef]
- Goldbergs, G.; Stefan, M.; Shaun, L.; Andrew, E. Efficiency of individual tree detection approaches based on light-weight and low-cost UAS imagery in Australian savannas. Remote Sens. 2018, 10, 161. [Google Scholar] [CrossRef]
- Zhao, L.; Wang, X.J.; Liu, G.H. Dynamics patterns and structure of major population in Ulmus pumila var. sabulosa sparse in Hunshandake sandland. J. Desert Res. 2009, 29, 508–513. [Google Scholar]
- Liu, H. Study on Structural Characteristics of Forest Communities in the Eastern Margin of Elm in Hunshandake Sand. Master’s Thesis, Inner Mongolia Agricultural University, Hohhot, China, 2016. [Google Scholar]
- Wang, X. Study of Ecosystem Composition and Spatial Pattern and Its Response to Grazing Disturbance with Ulmus pumila Sparse Forest Grassland on the Otindag Sandy Land, China. Ph.D. Thesis, Beijing Forestry University, Beijing, China, 2016. [Google Scholar]
- Kandare, K.; Ørka, H.O.; Dalponte, M.; Næsset, E.; Gobakken, T. Individual tree crown approach for predicting site index in boreal forests using airborne laser scanning and hyperspectral data. Int. J. Appl. Earth Obs. 2017, 60, 72–82. [Google Scholar] [CrossRef]
- Wagner, F.H.; Ferreira, M.P.; Sanchez, A.; Hirye, M.C.M.; Zortea, M.; Gloor, E.; Phillips, O.L.; de Souza Filho, C.R.; Shimabukuro, Y.E.; Aragão, L.E.O.C. Individual tree crown delineation in a highly diverse tropical forest using very high resolution satellite images. ISPRS J. Photogramm. 2018, 145, 362–377. [Google Scholar] [CrossRef]
- Agarwal, S.; Vailshery, L.S.; Jaganmohan, M.; Nagendra, H. Mapping urban tree species using very high resolution satellite imagery: Comparing pixel-based and object-based approaches. ISPRS Int. J. Geo-Inf. 2013, 2, 220–236. [Google Scholar] [CrossRef]
- Karlson, M.; Reese, H.; Ostwald, M. Tree crown mapping in managed woodlands (parklands) of semi-arid West Africa using WorldView-2 imagery and geographic object based image analysis. Sensors 2014, 14, 22643–22669. [Google Scholar] [CrossRef] [PubMed]
- Gomes, M.F.; Maillard, P.; Deng, H. Individual tree crown detection in sub-meter satellite imagery using Marked Point Processes and a geometrical-optical model. Remote Sens. Environ. 2018, 211, 184–195. [Google Scholar] [CrossRef]
- Colgan, M.S.; Baldeck, C.A.; Féret, J.B.; Asner, G.P. Mapping savanna tree species at ecosystem scales using support vector machine classification and BRDF correction on airborne hyperspectral and LiDAR data. Remote Sens. 2012, 4, 3462–3480. [Google Scholar] [CrossRef]
- Strîmbu, V.F.; Strîmbu, B.M. A graph-based segmentation algorithm for tree crown extraction using airborne LiDAR data. ISPRS J. Photogramm. 2015, 104, 30–43. [Google Scholar] [CrossRef] [Green Version]
- Paris, C.; Kelbe, D.; van Aardt, J.; Bruzzone, L. A novel automatic method for the fusion of ALS and TLS LiDAR data for robust assessment of tree crown structure. IEEE Trans. Geosci. Remote Sens. 2017, 55, 3679–3693. [Google Scholar] [CrossRef]
- Wan Mohd Jaafar, W.S.; Woodhouse, I.H.; Silva, C.A.; Omar, H.; Abdul Maulud, K.N.; Hudak, A.T.; Llauberg, C.; Cardil, A.; Mohan, M. Improving Individual Tree Crown Delineation and Attributes Estimation of Tropical Forests Using Airborne LiDAR Data. Forests 2018, 9, 759. [Google Scholar] [CrossRef]
- Duan, F.; Wan, Y.; Deng, L. A novel approach for coarse-to-fine windthrown tree extraction based on unmanned aerial vehicle images. Remote Sens. 2017, 9, 306. [Google Scholar] [CrossRef]
- Mohan, M.; Silva, C.A.; Klauberg, C.; Jat, P.; Catts, G.; Cardil, A.; Hudak, A.T.; Dia, M. Individual tree detection from unmanned aerial vehicle (UAV) derived canopy height model in an open canopy mixed conifer forest. Forests 2017, 8, 340. [Google Scholar] [CrossRef]
- Koc-San, D.; Selim, S.; Aslan, N.; San, B.T. Automatic citrus tree extraction from UAV images and digital surface models using circular Hough transform. Comput. Electron. Agric. 2018, 150, 289–301. [Google Scholar] [CrossRef]
- Abdollahnejad, A.; Panagiotidis, D.; Surový, P. Estimation and Extrapolation of Tree Parameters Using Spectral Correlation between UAV and Pléiades Data. Forests 2018, 9, 85. [Google Scholar] [CrossRef]
- Dongfang, X. Brief analysis of high-resolution satellite and its application in China. Satell. App. 2015, 3, 44–48. (In Chinese) [Google Scholar]
- Gibbes, C.; Adhikari, S.; Rostant, L.; Southworth, J.; Qiu, Y. Application of object based classification and high resolution satellite imagery for savanna ecosystem analysis. Remote Sens. 2010, 2, 2748–2772. [Google Scholar] [CrossRef]
- Karlson, M.; Ostwald, M.; Reese, H.; Bazié, H.R.; Tankoano, B. Assessing the potential of multi-seasonal WorldView-2 imagery for mapping West African agroforestry tree species. Int. J. Appl. Earth Obs. 2016, 50, 80–88. [Google Scholar] [CrossRef]
- Kang, J.; Wang, L.; Chen, F.; Niu, Z. Identifying tree crown areas in undulating eucalyptus plantations using JSEG multi-scale segmentation and unmanned aerial vehicle near-infrared imagery. Int. J. Remote Sens. 2017, 38, 2296–2312. [Google Scholar] [CrossRef]
- Moustakidis, S.; Mallinis, G.; Koutsias, N.; Theocharis, J.B.; Petridis, V. SVM-based fuzzy decision trees for classification of high spatial resolution remote sensing images. IEEE Trans. Geosci. Remote Sens. 2012, 50, 149–169. [Google Scholar] [CrossRef]
- Pham, L.T.; Brabyn, L.; Ashraf, S. Combining QuickBird, LiDAR, and GIS topography indices to identify a single native tree species in a complex landscape using an object-based classification approach. Int. J. Appl. Earth Obs. 2016, 50, 187–197. [Google Scholar] [CrossRef]
- Wu, B.; Yu, B.; Wu, Q.; Huang, Y.; Chen, Z.; Wu, J. Individual tree crown delineation using localized contour tree method and airborne LiDAR data in coniferous forests. Int. J. Appl. Earth Obs. 2016, 52, 82–94. [Google Scholar] [CrossRef]
- Malek, S.; Bazi, Y.; Alajlan, N.; AlHichri, H.; Melgani, F. Efficient framework for palm tree detection in UAV images. IEEE J.-STARS 2014, 7, 4692–4703. [Google Scholar] [CrossRef]
- Zhen, Z.; Quackenbush, L.; Zhang, L. Trends in automatic individual tree crown detection and delineation—Evolution of LiDAR data. Remote Sens. 2016, 8, 333. [Google Scholar] [CrossRef]
- Gougeon, F.A. A crown-following approach to the automatic delineation of individual tree crowns in high spatial resolution aerial images. Can. J. Remote Sens. 1995, 21, 274–284. [Google Scholar] [CrossRef]
- Katoh, M.; Gougeon, F.A.; Leckie, D.G. Application of high-resolution airborne data using individual tree crowns in Japanese conifer plantations. J. Forest Res.-Jpn. 2009, 14, 10–19. [Google Scholar] [CrossRef] [Green Version]
- Chen, Q.; Baldocchi, D.; Gong, P.; Kelly, M. Isolating individual trees in a savanna woodland using small footprint lidar data. Photogramm. Eng. Remote Sens. 2006, 72, 923–932. [Google Scholar] [CrossRef]
- Chang, A.; Eo, Y.; Kim, Y.; Kim, Y. Identification of individual tree crowns from LiDAR data using a circle fitting algorithm with local maxima and minima filtering. Remote Sens. Lett. 2013, 4, 29–37. [Google Scholar] [CrossRef]
- Panagiotidis, D.; Abdollahnejad, A.; Surový, P.; Chiteculo, V. Determining tree height and crown diameter from high-resolution UAV imagery. Int. J. Remote Sens. 2017, 38, 2392–2410. [Google Scholar] [CrossRef]
- Pollock, R. The Automatic Recognition of Individual Trees in Aerial Images of Forests Based on a Synthetic Tree Crown Image Model. Ph.D. Thesis, University of British Columbia, Vancouver, BC, Canada, 1996. [Google Scholar]
- Hung, C.; Bryson, M.; Sukkarieh, S. Multi-class predictive template for tree crown detection. ISPRS J. Photogramm. 2012, 68, 170–183. [Google Scholar] [CrossRef]
- Leckie, D.G.; Walsworth, N.; Gougeon, F. Recognition and possible remediation of automated tree delineations with multiple isolations per tree (split cases) on high-resolution imagery. CAN J. Remote Sens. 2016, 42, 656–679. [Google Scholar] [CrossRef]
- Whiteside, T.G.; Boggs, G.S.; & Maier, S.W. Extraction of tree crowns from high resolution imagery over Eucalypt dominant tropical savannas. Photogramm. Eng. Remote Sens. 2011, 77, 813–824. [Google Scholar] [CrossRef]
- Whiteside, T.G.; Boggs, G.S.; Maier, S.W. Comparing object-based and pixel-based classifications for mapping savannas. Int. J. Appl. Earth Obs. 2011, 13, 884–893. [Google Scholar] [CrossRef]
- Yin, W.; Yang, J.; Yamamoto, H.; Li, C. Object-based larch tree-crown delineation using high-resolution satellite imagery. Int. J. Remote Sens. 2015, 36, 822–844. [Google Scholar] [CrossRef]
- Hirschmugl, M.; Ofner, M.; Raggam, J.; Schardt, M. Single tree detection in very high resolution remote sensing data. Remote Sens. Environ. 2007, 110, 533–544. [Google Scholar] [CrossRef]
- Yao, W.; Krzystek, P.; Heurich, M. Tree species classification and estimation of stem volume and DBH based on single tree extraction by exploiting airborne full-waveform LiDAR data. Remote Sens. 2012, 123, 368–380. [Google Scholar] [CrossRef]
- Bottai, L.; Arcidiaco, L.; Chiesi, M.; Maselli, F. Application of a single-tree identification algorithm to LiDAR data for the simulation of stem volume current annual increment. J. Appl. Remote Sens. 2013, 7, 073699. [Google Scholar] [CrossRef]
- Wu, Z.; Wu, J.; Liu, J.; He, B.; Lei, T.; Wang, Q. Increasing terrestrial vegetation activity of ecological restoration program in the Beijing–Tianjin Sand Source Region of China. Ecol. Eng. 2013, 52, 37–50. [Google Scholar] [CrossRef]
- Li, X.; Wang, H.; Wang, J.; Gao, Z. Land degradation dynamic in the first decade of twenty-first century in the Beijing–Tianjin dust and sandstorm source region. Environ. Earth Sci. 2015, 74, 4317–4325. [Google Scholar] [CrossRef]
- Zhao, L.; Li, Q.; Huang, H. Study of automated extraction method for plains rural resident based on high resolution remote sensing image. Remote Sens. Technol. Appl. 2016, 31, 784–792. [Google Scholar] [CrossRef]
- Unsalan, C.; Boyer, K.L. Linearized vegetation indices based on a formal statistical framework. IEEE Trans. Geosci. Remote Sens. 2004, 42, 1575–1585. [Google Scholar] [CrossRef]
- Lindeberg, T. Feature detection with automatic scale selection. Int. J. Comput. Vis. 1998, 30, 79–116. [Google Scholar] [CrossRef]
- Kong, H.; Sarma, S.E.; Tang, F. Generalizing Laplacian of Gaussian filters for vanishing-point detection. IEEE Trans. Intell. Transp. 2013, 14, 408–418. [Google Scholar] [CrossRef]
Methods | Producer’s Accuracy (%) | User Accuracy (%) |
---|---|---|
Maximum Likelihood | 74.91 | 33.52 |
Mahalanobis Distance | 63.61 | 37.39 |
Neural Net | 58.7 | 66.79 |
Support Vector Machine | 73.2 | 72.18 |
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Sun, B.; Gao, Z.; Zhao, L.; Wang, H.; Gao, W.; Zhang, Y. Extraction of Information on Trees outside Forests Based on Very High Spatial Resolution Remote Sensing Images. Forests 2019, 10, 835. https://doi.org/10.3390/f10100835
Sun B, Gao Z, Zhao L, Wang H, Gao W, Zhang Y. Extraction of Information on Trees outside Forests Based on Very High Spatial Resolution Remote Sensing Images. Forests. 2019; 10(10):835. https://doi.org/10.3390/f10100835
Chicago/Turabian StyleSun, Bin, Zhihai Gao, Longcai Zhao, Hongyan Wang, Wentao Gao, and Yuanyuan Zhang. 2019. "Extraction of Information on Trees outside Forests Based on Very High Spatial Resolution Remote Sensing Images" Forests 10, no. 10: 835. https://doi.org/10.3390/f10100835