Mapping Annual Forest Cover in Sub-Humid and Semi-Arid Regions through Analysis of Landsat and PALSAR Imagery
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Advanced Land Observation Satellite (ALOS) Phased Array Type L-Band Synthetic Aperture Radar (PALSAR) Images and Pre-Processing
2.3. Landsat Images and Google Earth Engine
2.4. Algorithms for Mapping Forests through the Analyses of Landsat and PALSAR Images
2.4.1. PALSAR-Based Forest Mapping Algorithm
2.4.2. PALSAR/Landsat-Based Forest Mapping Algorithm
2.4.3. Implementation of the Forest Mapping Algorithms
2.5. Validation Samples for Accuracy Assessment of PALSAR/Landsat Forest Maps
2.6. Multiple Forest Datasets for Spatial and Areal Comparison in Oklahoma in Circa 2010
3. Results
3.1. The PALSAR/Landsat Forest Map in Oklahoma in 2010
3.2. Spatial and Areal Comparison Among Multiple Forest Datasets Circa 2010
3.3. Spatial and Areal Changes of Forests from 2007 to 2010
4. Discussion
4.1. Integration of PALSAR and Landsat Imagery for Forest Mapping
4.2. Reasons for the Differences between the PALSAR/Landsat Forest Map and Other Forest Data Products
4.3. Applications of the PALSAR/Landsat Forest Maps in the Forest Management in Oklahoma
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
- United Nations Convention to Combat Desertification. Redd+ and desertification. Available online: http://www.unccd.int/Lists/SiteDocumentLibrary/Publications/Factsheet%207%20redd.ENGweb.pdf (accessed on 16 June 2011).
- Sexton, J.O.; Noojipady, P.; Song, X.-P.; Feng, M.; Song, D.-X.; Kim, D.-H.; Anand, A.; Huang, C.; Channan, S.; Pimm, S.L.; et al. Conservation policy and the measurement of forests. Nat. Clim. Chang. 2015, 6, 192–196. [Google Scholar] [CrossRef]
- Qin, Y.; Xiao, X.; Dong, J.; Zhang, G.; Roy, P.S.; Joshi, P.K.; Gilani, H.; Murthy, M.S.; Jin, C.; Wang, J.; et al. Mapping forests in monsoon Asia with ALOS PALSAR 50-m mosaic images and MODIS imagery in 2010. Sci. Rep. 2016, 6, 20880. [Google Scholar] [CrossRef] [PubMed]
- Van der Werf, G.R.; Morton, D.C.; DeFries, R.S.; Olivier, J.G.J.; Kasibhatla, P.S.; Jackson, R.B.; Collatz, G.J.; Randerson, J.T. CO2 emissions from forest loss. Nat. Geosci. 2009, 2, 737–738. [Google Scholar] [CrossRef]
- Houghton, R.A.; Goetz, S.J. New satellites help quantify carbon sources and sinks. Eos Trans. Am. Geophys. Union 2008, 89, 417–418. [Google Scholar] [CrossRef]
- Hansen, M.C.; DeFries, R.S. Detecting long-term global forest change using continuous fields of tree-cover maps from 8-km advanced very high resolution radiometer (AVHRR) data for the years 1982–99. Ecosystems 2004, 7, 695–716. [Google Scholar] [CrossRef]
- Loveland, T.R.; Reed, B.C.; Brown, J.F.; Ohlen, D.O.; Zhu, Z.; Yang, L.; Merchant, J.W. Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data. Int. J. Remote Sens. 2000, 21, 1303–1330. [Google Scholar] [CrossRef]
- Hansen, M.C.; DeFries, R.S.; Townshend, J.R.G.; Carroll, M.; Dimiceli, C.; Sohlberg, R.A. Global percent tree cover at a spatial resolution of 500 meters: First results of the MODIS vegetation continuous fields algorithm. Earth Interact. 2003, 7, 1–15. [Google Scholar] [CrossRef]
- Hansen, M.C.; Stehman, S.V.; Potapov, P.V.; Loveland, T.R.; Townshend, J.R.G.; DeFries, R.S.; Pittman, K.W.; Arunarwati, B.; Stolle, F.; Steininger, M.K.; et al. Humid tropical forest clearing from 2000 to 2005 quantified by using multitemporal and multiresolution remotely sensed data. Proc. Natl. Acad. Sci. USA 2008, 105, 9439–9444. [Google Scholar] [CrossRef] [PubMed]
- Friedl, M.A.; Sulla-Menashe, D.; Tan, B.; Schneider, A.; Ramankutty, N.; Sibley, A.; Huang, X.M. MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sens. Environ. 2010, 114, 168–182. [Google Scholar] [CrossRef]
- Hansen, M.C.; Potapov, P.V.; Moore, R.; Hancher, M.; Turubanova, S.A.; Tyukavina, A.; Thau, D.; Stehman, S.V.; Goetz, S.J.; Loveland, T.R.; et al. High-resolution global maps of 21st-century forest cover change. Science 2013, 342, 850–853. [Google Scholar] [CrossRef] [PubMed]
- Kim, D.H.; Sexton, J.O.; Noojipady, P.; Huang, C.Q.; Anand, A.; Channan, S.; Feng, M.; Townshend, J.R. Global, Landsat-based forest-cover change from 1990 to 2000. Remote Sens. Environ. 2014, 155, 178–193. [Google Scholar] [CrossRef]
- Townshend, J.R.; Masek, J.G.; Huang, C.Q.; Vermote, E.F.; Gao, F.; Channan, S.; Sexton, J.O.; Feng, M.; Narasimhan, R.; Kim, D.; et al. Global characterization and monitoring of forest cover using landsat data: Opportunities and challenges. Int. J. Digit. Earth 2012, 5, 373–397. [Google Scholar] [CrossRef]
- Homer, C.; Dewitz, J.; Yang, L.M.; Jin, S.; Danielson, P.; Xian, G.; Coulston, J.; Herold, N.; Wickham, J.; Megown, K. Completion of the 2011 national land cover database for the conterminous united states-representing a decade of land cover change information. Photogramm. Eng. Remote Sens. 2015, 81, 345–354. [Google Scholar]
- Jin, S.M.; Yang, L.M.; Danielson, P.; Homer, C.; Fry, J.; Xian, G. A comprehensive change detection method for updating the National Land Cover Database to circa 2011. Remote Sens. Environ. 2013, 132, 159–175. [Google Scholar] [CrossRef]
- Kovacs, J.M.; Lu, X.X.; Flores-Verdugo, F.; Zhang, C.; de Santiago, F.F.; Jiao, X. Applications of ALOS PALSAR for monitoring biophysical parameters of a degraded black mangrove (Avicennia germinans) forest. ISPRS J. Photogramm. Renmote Sens. 2013, 82, 102–111. [Google Scholar] [CrossRef]
- Ni, W.J.; Sun, G.Q.; Guo, Z.F.; Zhang, Z.Y.; He, Y.T.; Huang, W.L. Retrieval of forest biomass from ALOS PALSAR data using a lookup table method. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2013, 6, 875–886. [Google Scholar] [CrossRef]
- Lucas, R.M.; Cronin, N.; Lee, A.; Moghaddam, M.; Witte, C.; Tickle, P. Empirical relationships between AIRSAR backscatter and LIDAR-derived forest biomass, Queensland, Australia. Remote Sens. Environ. 2006, 100, 407–425. [Google Scholar] [CrossRef]
- Shimada, M. Long-term stability of L-band normalized radar cross section of Amazon rainforest using the JERS-1 SAR. Can. J. Remote Sens. 2005, 31, 132–137. [Google Scholar] [CrossRef]
- Sgrenzaroli, M.; De Grandi, G.F.; Eva, H.; Achard, F. Tropical forest cover monitoring: Estimates from the GRFM JERS-1 radar mosaics using wavelet zooming techniques and validation. Int. J. Remote Sens. 2002, 23, 1329–1355. [Google Scholar] [CrossRef]
- 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. IEEE Trans. Geosci. Remote Sens. 2000, 38, 2310–2321. [Google Scholar] [CrossRef]
- Saatchi, S.S.; Nelson, B.; Podest, E.; Holt, J. Mapping land cover types in the Amazon basin using 1 km JERS-1 mosaic. Int. J. Remote Sens. 2000, 21, 1201–1234. [Google Scholar] [CrossRef]
- Shimada, M.; Itoh, T.; Motooka, T.; Watanabe, M.; Shiraishi, T.; Thapa, R.; Lucas, R. New global forest/non-forest maps from ALOS PALSAR data (2007–2010). Remote Sens. Environ. 2014, 155, 13–31. [Google Scholar] [CrossRef]
- Motohka, T.; Shimada, M.; Uryu, Y.; Setiabudi, B. Using time series PALSAR gamma nought mosaics for automatic detection of tropical deforestation: A test study in Riau, Indonesia. Remote Sens. Environ. 2014, 155, 79–88. [Google Scholar] [CrossRef]
- Pantze, A.; Santoro, M.; Fransson, J.E.S. Change detection of boreal forest using bi-temporal ALOS PALSAR backscatter data. Remote Sens. Environ. 2014, 155, 120–128. [Google Scholar] [CrossRef]
- Dong, J.W.; Xiao, X.M.; Sheldon, S.; Biradar, C.; Duong, N.D.; Hazarika, M. A comparison of forest cover maps in mainland southeast Asia from multiple sources: PALSAR, MERIS, MODIS and FRA. Remote Sens. Environ. 2012, 127, 60–73. [Google Scholar] [CrossRef]
- Ranson, K.J.; Sun, G.Q. An evaluation of AIRSAR and SIR-C/X-SAR images for mapping northern forest attributes in Maine, USA. Remote Sens. Environ. 1997, 59, 203–222. [Google Scholar] [CrossRef]
- Asner, G.P. Cloud cover in Landsat observations of the Brazilian Amazon. Int. J. Remote. Sens. 2001, 22, 3855–3862. [Google Scholar] [CrossRef]
- Gamon, J.A.; Field, C.B.; Goulden, M.L.; Griffin, K.L.; Hartley, A.E.; Joel, G.; Penuelas, J.; Valentini, R. Relationships between NDVI, canopy structure, and photosynthesis in 3 Californian vegetation types. Ecol. Appl. 1995, 5, 28–41. [Google Scholar] [CrossRef]
- Yan, E.P.; Wang, G.X.; Lin, H.; Xia, C.Z.; Sun, H. Phenology-based classification of vegetation cover types in Northeast China using MODIS NDVI and EVI time series. Int. J. Remote Sens. 2015, 36, 489–512. [Google Scholar] [CrossRef]
- Ranson, K.J.; Kovacs, K.; Sun, G.; Kharuk, V.I. Disturbance recognition in the boreal forest using radar and Landsat-7. Can. J. Remote Sens. 2003, 29, 271–285. [Google Scholar] [CrossRef]
- Khazendar, A.; Rignot, E.; Larour, E. Larsen B Ice Shelf rheology preceding its disintegration inferred by a control method. Geophys. Res. Lett. 2007, 34, 1–6. [Google Scholar] [CrossRef]
- Ban, Y.F. Synergy of multitemporal ERS-1 SAR and Landsat TM data for classification of agricultural crops. Can. J. Remote Sens. 2003, 29, 518–526. [Google Scholar] [CrossRef]
- Haack, B.N.; Solomon, E.K.; Bechdol, M.A.; Herold, N.D. Radar and optical data comparison/integration for urban delineation: A case study. Photogramm. Eng. Remote Sens. 2002, 68, 1289–1296. [Google Scholar]
- Reiche, J.; Verbesselt, J.; Hoekman, D.; Herold, M. Fusing Landsat and SAR time series to detect deforestation in the tropics. Remote Sens. Environ. 2015, 156, 276–293. [Google Scholar] [CrossRef]
- Lehmann, E.A.; Caccetta, P.; Lowell, K.; Mitchell, A.; Zhou, Z.S.; Held, A.; Milne, T.; Tapley, I. SAR and optical remote sensing: Assessment of complementarity and interoperability in the context of a large-scale operational forest monitoring system. Remote Sens. Environ. 2015, 156, 335–348. [Google Scholar] [CrossRef]
- Reiche, J.; Lucas, R.; Mitchell, A.L.; Verbesselt, J.; Hoekman, D.H.; Haarpaintner, J.; Kellndorfer, J.M.; Rosenqvist, A.; Lehmann, E.A.; Woodcock, C.E.; et al. Combining satellite data for better tropical forest monitoring. Nat. Clim. Chang. 2016, 6, 120–122. [Google Scholar] [CrossRef]
- Diamond, D.D.; Elliott, L.F. Oklahoma Ecological Systems Mapping Interpretive Booklet: Methods, Short Type Descriptions, and Summary Results; Oklahoma Department of Wildlife Conservation: Norman, OK, USA, 2015. [Google Scholar]
- Shimada, M.; Isoguchi, O.; Tadono, T.; Isono, K. PALSAR radiometric and geometric calibration. IEEE Trans. Geosci. Remote Sens. 2009, 47, 3915–3932. [Google Scholar] [CrossRef]
- Food and Agriculture Organization of the United Nations. Global Forest Resource Assessment (FRA) 2010; FAO: Rome, Italy, 2012. [Google Scholar]
- Qin, Y.; Xiao, X.; Dong, J.; Zhang, G.; Shimada, M.; Liu, J.; Li, C.; Kou, W.; Moore, B., III. Forest cover maps of China in 2010 from multiple approaches and data sources: PALSAR, Landsat, MODIS, FRA, and NFI. ISPRS J. Photogramm. Remote Sens. 2015, 109, 1–16. [Google Scholar] [CrossRef]
- Jiao, T.; Liu, R.G.; Liu, Y.; Pisek, J.; Chen, J.M. Mapping global seasonal forest background reflectivity with multi-angle imaging spectroradiometer data. J. Geophys. Res. Biogeosci. 2014, 119, 1063–1077. [Google Scholar] [CrossRef]
- Turner, D.P.; Cohen, W.B.; Kennedy, R.E.; Fassnacht, K.S.; Briggs, J.M. Relationships between leaf area index and Landsat TM spectral vegetation indices across three temperate zone sites. Remote Sens. Environ. 1999, 70, 52–68. [Google Scholar] [CrossRef]
- Li, X.; Gong, P.; Liang, L. A 30-year (1984–2013) record of annual urban dynamics of Beijing city derived from Landsat data. Remote Sens. Environ. 2015, 166, 78–90. [Google Scholar] [CrossRef]
- Johnson, E.; Geissler, G.; Murray, D. The Oklahoma Forest Resource Assessment, 2010; Oklahoma Forestry Services, Oklahoma Department of PublicationAgriculture, Food, and Forestry: Oklahoma City, OK, USA, 2010.
- Asner, G.P.; Knapp, D.E.; Broadbent, E.N.; Oliveira, P.J.C.; Keller, M.; Silva, J.N. Selective logging in the Brazilian Amazon. Science 2005, 310, 480–482. [Google Scholar] [CrossRef] [PubMed]
- Gonzalez, P.; Asner, G.P.; Battles, J.J.; Lefsky, M.A.; Waring, K.M.; Palace, M. Forest carbon densities and uncertainties from Lidar, quickbird, and field measurements in California. Remote Sens. Environ. 2010, 114, 1561–1575. [Google Scholar] [CrossRef]
- Gong, P.; Wang, J.; Yu, L.; Zhao, Y.C.; Zhao, Y.Y.; Liang, L.; Niu, Z.G.; Huang, X.M.; Fu, H.H.; Liu, S.; et al. Finer resolution observation and monitoring of global land cover: First mapping results with Landsat TM and ETM+ data. Int. J. Remote Sens. 2013, 34, 2607–2654. [Google Scholar] [CrossRef]
- Eldridge, D.J.; Bowker, M.A.; Maestre, F.T.; Roger, E.; Reynolds, J.F.; Whitford, W.G. Impacts of shrub encroachment on ecosystem structure and functioning: Towards a global synthesis. Ecol. Lett. 2011, 14, 709–722. [Google Scholar] [CrossRef] [PubMed]
- Ratajczak, Z.; Nippert, J.B.; Collins, S.L. Woody encroachment decreases diversity across North American grasslands and savannas. Ecology 2012, 93, 697–703. [Google Scholar] [CrossRef] [PubMed]
- Barger, N.N.; Archer, S.R.; Campbell, J.L.; Huang, C.Y.; Morton, J.A.; Knapp, A.K. Woody plant proliferation in North American drylands: A synthesis of impacts on ecosystem carbon balance. J. Geophys. Res. Biogeosci. 2011, 116, 165–176. [Google Scholar] [CrossRef]
- Huxman, T.E.; Wilcox, B.P.; Breshears, D.D.; Scott, R.L.; Snyder, K.A.; Small, E.E.; Hultine, K.; Pockman, W.T.; Jackson, R.B. Ecohydrological implications of woody plant encroachment. Ecology 2005, 86, 308–319. [Google Scholar] [CrossRef]
- Anadon, J.D.; Sala, O.E.; Turner, B.L.; Bennett, E.M. Effect of woody-plant encroachment on livestock production in North and South America. Proc. Natl. Acad. Sci. USA 2014, 111, 12948–12953. [Google Scholar] [CrossRef] [PubMed]
Forest Cover Datasets (Extent) | Forest Cover Types | Spatial Resolution (Meters) | Algorithms | Data Sources | Major References |
---|---|---|---|---|---|
JAXA F/NF (Global) | Woody vegetation coverage over 10% determined by high spatial resolution images in Google Earth | 25 | Rule-based | PALSAR FBD Polarization mode data in main growing season | [23] |
NLCD2011 (National) | Areas dominated by trees generally greater than 5 m tall, and greater than 20% of total vegetation cover. | 30 | Decision tree | Landsat images in circa 2011 | [14,15] |
OKESM (State) | >25% total tree canopy (>4 m tall) | 10 | Decision tree | Landsat images and aerial imagery from National Agriculture Imagery Program (NAIP) | [38] |
OKFRA2010 (State) | Areas dominated by trees and shrubs greater than 20% of total vegetation cover. | 30 | Decision tree | Landsat images in circa 2001 | [45] |
PALSAR/Landsat (State) | Woody vegetation coverage over 10% determined by high spatial resolution images in Google Earth | 30 | Rule-based | PALSAR FBD Polarization mode data in main growing season and Landsat NDVImax | This study |
Class | Ground Reference (Pixels) | Total Classified Pixels | User Accuracy (%) | Commission Error (%) | ||
---|---|---|---|---|---|---|
Forest | Non-Forest | |||||
Classification | Forest | 1133 | 80 | 1213 | 93.4 | 6.6 |
Non-forest | 363 | 2173 | 2536 | 85.7 | 14.3 | |
Total ground truth pixels | 1496 | 2253 | 3749 | |||
Producer accuracy (%) | 75.74 | 96.5 | Overall accuracy = 88.2% | |||
Omission error (%) | 24.26 | 3.5 | Kappa coefficient = 0.75 |
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Qin, Y.; Xiao, X.; Wang, J.; Dong, J.; Ewing, K.; Hoagland, B.; Hough, D.J.; Fagin, T.D.; Zou, Z.; Geissler, G.L.; et al. Mapping Annual Forest Cover in Sub-Humid and Semi-Arid Regions through Analysis of Landsat and PALSAR Imagery. Remote Sens. 2016, 8, 933. https://doi.org/10.3390/rs8110933
Qin Y, Xiao X, Wang J, Dong J, Ewing K, Hoagland B, Hough DJ, Fagin TD, Zou Z, Geissler GL, et al. Mapping Annual Forest Cover in Sub-Humid and Semi-Arid Regions through Analysis of Landsat and PALSAR Imagery. Remote Sensing. 2016; 8(11):933. https://doi.org/10.3390/rs8110933
Chicago/Turabian StyleQin, Yuanwei, Xiangming Xiao, Jie Wang, Jinwei Dong, Kayti Ewing, Bruce Hoagland, Daniel J. Hough, Todd D. Fagin, Zhenhua Zou, George L. Geissler, and et al. 2016. "Mapping Annual Forest Cover in Sub-Humid and Semi-Arid Regions through Analysis of Landsat and PALSAR Imagery" Remote Sensing 8, no. 11: 933. https://doi.org/10.3390/rs8110933
APA StyleQin, Y., Xiao, X., Wang, J., Dong, J., Ewing, K., Hoagland, B., Hough, D. J., Fagin, T. D., Zou, Z., Geissler, G. L., Xian, G. Z., & Loveland, T. R. (2016). Mapping Annual Forest Cover in Sub-Humid and Semi-Arid Regions through Analysis of Landsat and PALSAR Imagery. Remote Sensing, 8(11), 933. https://doi.org/10.3390/rs8110933