Forest Cover and Vegetation Degradation Detection in the Kavango Zambezi Transfrontier Conservation Area Using BFAST Monitor
Abstract
:1. Introduction
2. Methods
2.1. Study Area
2.2. Pre-Processing and Time Series Analysis
2.3. Sample-Based Reference Data Set
2.3.1. Sampling Design
2.3.2. Response Design
2.3.3. Analysis
2.4. Land Cover-Specific Degradation Detection
2.4.1. Degradation Estimation by Magnitude Threshold
2.4.2. Degradation Estimation by Random Forest Classification
2.4.3. Degradation Estimation by Combination of Magnitude Threshold and Random Forest Classification
3. Results
3.1. Magnitude Threshold-Based Degradation Determination
3.2. Random Forest-Based Degradation Determination
3.3. Combination of Magnitude Threshold and Random Forest Classification
4. Discussion
4.1. How Does Degradation Detection Performance Differ for Six Different Land Cover Classes in KAZA Using BFM?
4.2. How Does Threshold Based and Random Forest-Based BFM Post-Processing for Degradation Detection Compare?
4.3. How Are Method Variations Impacting Degradation Detection Accuracy and How to Combine Threshold-Based and Random Forest-Based BFM Post-Processing Methods Effectively?
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Naidoo, R.; Preez, P.D.; Stuart-Hill, G.; Beytell, P.; Taylor, R. Long-range migrations and dispersals of African buffalo (Syncerus caffer) in the Kavango–Zambezi Transfrontier Conservation area. Afr. J. Ecol. 2014, 52, 581–584. [Google Scholar] [CrossRef]
- Naidoo, R.; Fisher, B.; Manica, A.; Balmford, A. Estimating economic losses to tourism in Africa from the illegal killing of elephants. Nat. Commun. 2016, 7, 13379. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Metcalfe, S.; Kepe, T. ‘Your Elephant on Our Land’ The Struggle to Manage Wildlife Mobility on Zambian Communal Land in the Kavango-Zambezi Transfrontier Conservation Area. J. Environ. Dev. 2008, 17, 99–117. [Google Scholar] [CrossRef]
- Scholz, I.; Schmidt, L. Reducing Emissions from Deforestation and Forest Degradation in Developing Countries: Meeting the Main Challenges Ahead; Deutsches Institut für Entwicklungspolitik Bonn: Bonn, Germany, 2008. [Google Scholar]
- Herold, M.; Skutsch, M. Monitoring, reporting and verification for national REDD + programmes: Two proposals. Environ. Res. Lett. 2011, 6, 14002. [Google Scholar] [CrossRef]
- TÜV SÜD. Reducing Emissions from Deforestation and Carbon Enhancement from in Xe Pian National Protected Area, Lao PDR; Environmental Technology, Carbon Management Service Solitaire, I.T.I. Road, Aundh, Pune-411007, INDIA: Noida, India, 2018. [Google Scholar]
- Nellemann, C. Green Carbon, Black Trade: Illegal Logging, Tax Fraud and Laundering in the World’s Tropical Forests; United Nations Environment Programme, GRID-Arendal: Arendal, Norway, 2012. [Google Scholar]
- Petit, C.; Scudder, T.; Lambin, E. Quantifying processes of land-cover change by remote sensing: Resettlement and rapid land-cover changes in south-eastern Zambia. Int. J. Remote Sens. 2001, 22, 3435–3456. [Google Scholar] [CrossRef]
- Kamwi, J.M.; Kaetsch, C.; Graz, F.P.; Chirwa, P.; Manda, S. Trends in land use and land cover change in the protected and communal areas of the Zambezi Region, Namibia. Environ. Monit. Assess. 2017, 189, 242. [Google Scholar] [CrossRef] [PubMed]
- Wingate, V.R.; Phinn, S.R.; Kuhn, N.; Bloemertz, L.; Dhanjal-Adams, K.L. Mapping decadal land cover changes in the woodlands of north eastern Namibia from 1975 to 2014 using the Landsat satellite archived data. Remote Sens. 2016, 8, 681. [Google Scholar] [CrossRef]
- Mason, S.J. El Niño, climate change, and Southern African climate. Environmetrics 2001, 12, 327–345. [Google Scholar] [CrossRef]
- Roy, D.P.; Boschetti, L.; Justice, C.O.; Ju, J. The collection 5 MODIS burned area product—Global evaluation by comparison with the MODIS active fire product. Remote Sens. Environ. 2008, 112, 3690–3707. [Google Scholar] [CrossRef]
- Pekel, J.-F.; Cottam, A.; Gorelick, N.; Belward, A.S. High-resolution mapping of global surface water and its long-term changes. Nature 2016, 540, 418–422. [Google Scholar] [CrossRef] [PubMed]
- Avitabile, V.; Herold, M.; Heuvelink, G.B.; Lewis, S.L.; Phillips, O.L.; Asner, G.P.; Armston, J.; Ashton, P.S.; Banin, L.; Bayol, N.; et al. An integrated pan-tropical biomass map using multiple reference datasets. Glob. Chang. Biol. 2016, 22, 1406–1420. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Pricope, N.G.; Gaughan, A.E.; All, J.D.; Binford, M.W.; Rutina, L.P. Spatio-Temporal Analysis of Vegetation Dynamics in Relation to Shifting Inundation and Fire Regimes: Disentangling Environmental Variability from Land Management Decisions in a Southern African Transboundary Watershed. Land 2015, 4, 627–655. [Google Scholar] [CrossRef] [Green Version]
- Lambin, E.F.; Ehrlich, D. Land-cover changes in sub-Saharan Africa (1982–1991): Application of a change index based on remotely sensed surface temperature and vegetation indices at a continental scale. Remote Sens. Environ. 1997, 61, 181–200. [Google Scholar] [CrossRef]
- Brink, A.B.; Eva, H.D. Monitoring 25 years of land cover change dynamics in Africa: A sample based remote sensing approach. Appl. Geogr. 2009, 29, 501–512. [Google Scholar] [CrossRef] [Green Version]
- Mosugelo, D.K.; Moe, S.R.; Ringrose, S.; Nellemann, C. Vegetation changes during a 36-year period in northern Chobe National Park, Botswana. Afr. J. Ecol. 2002, 40, 232–240. [Google Scholar] [CrossRef]
- Olofsson, P.; Foody, G.M.; Herold, M.; Stehman, S.V.; Woodcock, C.E.; Wulder, M.A. Good practices for estimating area and assessing accuracy of land change. Remote Sens. Environ. 2014, 148, 42–57. [Google Scholar] [CrossRef] [Green Version]
- Woodcock, C.E.; Allen, R.G.; Anderson, M.; Belward, A. Free Access to Landsat Imagery. Science 2008, 320, 5879. [Google Scholar] [CrossRef] [PubMed]
- 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]
- Verbesselt, J.; Hyndman, R.; Zeileis, A.; Culvenor, D. Phenological change detection while accounting for abrupt and gradual trends in satellite image time series. Remote Sens. Environ. 2010, 114, 2970–2980. [Google Scholar] [CrossRef] [Green Version]
- Schultz, M.; Verbesselt, J.; Avitabile, V.; Souza, C.; Herold, M. Error Sources in Deforestation Detection Using BFAST Monitor on Landsat Time Series Across Three Tropical Sites. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2015, 9, 3667–3679. [Google Scholar] [CrossRef]
- Schultz, M.; Clevers, J.; Carter, S.; Verbesselt, J.; Avitabile, V.; Hien, V.Q. Performance of vegetation indices from Landsat time series in deforestation monitoring. Int. J. Appl. Earth Obs. Geoinf. 2016, 52, 318–327. [Google Scholar] [CrossRef]
- Pratihast, A.K.; DeVries, B.; Avitabile, V.; de Bruin, S.; Kooistra, L.; Tekle, M.; Herold, M. Combining satellite data and community-based observations for forest monitoring. Forests 2014, 5, 2464–2489. [Google Scholar] [CrossRef]
- DeVries, B.; Verbesselt, J.; Kooistra, L.; Herold, M. Robust monitoring of small-scale forest disturbances in a tropical montane forest using Landsat time series. Remote Sens. Environ. 2015, 161, 107–121. [Google Scholar] [CrossRef]
- Reiche, J.; Verbesselt, J.; Hoekman, D.H.; Herold, M. Fusing Landsat and SAR time series to detect deforestation in the tropics. Remote Sens. Environ. 2015, 156, 276–293. [Google Scholar] [CrossRef]
- Reiche, J.; Hamunyela, E.; Verbesselt, J.; Hoekman, D.; Herold, M. Improving near-real time deforestation monitoring in tropical dry forests by combining dense Sentinel-1 time series with Landsat and ALOS-2 PALSAR-2. Remote Sens. Environ. 2018, 204, 147–161. [Google Scholar] [CrossRef]
- Verbesselt, J.; Zeileis, A.; Herold, M. Near real-time disturbance detection using satellite image time series. Remote Sens. Environ. 2012, 123, 98–108. [Google Scholar] [CrossRef]
- Dutrieux, L.P.; Verbesselt, J.; Kooistra, L.; Herold, M. Monitoring forest cover loss using multiple data streams, a case study of a tropical dry forest in Bolivia. ISPRS J. Photogramm. Remote Sens. 2015, 107, 112–125. [Google Scholar] [CrossRef]
- Jakovac, C.C.; Dutrieux, L.P.; Siti, L.; Peña-Claros, M.; Bongers, F. Spatial and temporal dynamics of shifting cultivation in the middle-Amazonas river: Expansion and intensification. PLoS ONE 2017, 12, e0181092. [Google Scholar] [CrossRef] [PubMed]
- Dutrieux, L.P.; Jakovac, C.C.; Latifah, S.H.; Kooistra, L. Reconstructing land use history from Landsat time-series: Case study of a swidden agriculture system in Brazil. Int. J. Appl. Earth Obs. Geoinf. 2016, 47, 112–124. [Google Scholar] [CrossRef]
- DeVries, B.; Decuyper, M.; Verbesselt, J.; Zeileis, A.; Herold, M.; Joseph, S. Tracking disturbance-regrowth dynamics in tropical forests using structural change detection and Landsat time series. Remote Sens. Environ. 2015, 169, 320–334. [Google Scholar] [CrossRef]
- Murillo-Sandoval, P.J.; van den Hoek, J.; Hilker, T. Leveraging Multi-Sensor Time Series Datasets to Map Short-and Long-Term Tropical Forest Disturbances in the Colombian Andes. Remote Sens. 2017, 9, 179. [Google Scholar] [CrossRef]
- Maynard, J.J.; Karl, J.W.; Browning, D.M. Effect of spatial image support in detecting long-term vegetation change from satellite time-series. Landsc. Ecol. 2016, 31, 2045–2062. [Google Scholar] [CrossRef]
- Watts, L.M.; Laffan, S.W. Effectiveness of the BFAST algorithm for detecting vegetation response patterns in a semi-arid region. Remote Sens. Environ. 2014, 154, 234–245. [Google Scholar] [CrossRef]
- Breiman, L. Random forests. Mach. Learn. 2001, 45, 5–32. [Google Scholar] [CrossRef]
- DeVries, B.; Pratihast, A.K.; Verbesselt, J.; Kooistra, L.; Herold, M. Characterizing forest change using community-based monitoring data and Landsat time series. PLoS ONE 2016, 11, e0147121. [Google Scholar] [CrossRef] [PubMed]
- Urbazaev, M.; Thiel, C.; Migliavacca, M.; Reichstein, M.; Rodriguez-Veiga, P.; Schmullius, C. Improved Multi-Sensor Satellite-Based Aboveground Biomass Estimation by Selecting Temporally Stable Forest Inventory Plots Using NDVI Time Series. Forests 2016, 7, 169. [Google Scholar] [CrossRef]
- Card, D.H. Using known map category marginal frequencies to improve estimates of thematic map accuracy. Am. Soc. Photogramm. 1982, 48, 431–439. [Google Scholar]
- Congalton, R. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens. Environ. 1991, 37, 35–46. [Google Scholar] [CrossRef]
- Liu, J.; Heiskanen, J.; Maeda, E.E.; Pellikka, P.K. Burned area detection based on Landsat time series in savannas of southern Burkina Faso. Int. J. Appl. Earth Obs. Geoinf. 2018, 64, 210–220. [Google Scholar] [CrossRef]
- Pratihast, A.K.; DeVries, B.; Avitabile, V.; de Bruin, S.; Herold, M.; Bergsma, A. Design and implementation of an interactive web-based near real-time forest monitoring system. PLoS ONE 2016, 11, e0150935. [Google Scholar] [CrossRef] [PubMed]
- Gao, B.-C. NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens. Environ. 1996, 58, 257–266. [Google Scholar] [CrossRef]
- Huete, A.R. A soil-adjusted vegetation index (SAVI). Remote Sens. Environ. 1988, 25, 295–309. [Google Scholar] [CrossRef]
- Miura, T.; Huete, A.R.; van Leeuwen, W.J.D.; Didan, K. Vegetation detection through smoke-filled AVIRIS images: An assessment using MODIS band passes. J. Geophys. Res. Atmos. 1998, 103, 32001–32011. [Google Scholar] [CrossRef] [Green Version]
- Hayes, D.J.; Cohen, W.B.; Sader, S.A.; Irwin, D.E. Estimating proportional change in forest cover as a continuous variable from multi-year MODIS data. Remote Sens. Environ. 2008, 112, 735–749. [Google Scholar] [CrossRef] [Green Version]
- Thompson, M. Land-Cover Classification for Peace Parks, KAZA Extension for Namibia, Botswana, Zimbabwe and Zambia—Data Users Report and Meta Data; GeoTerraImage: Pretoria, South Africa, 2014. [Google Scholar]
- Masek, J.; Vermote, E.F.; Saleous, N.; Wolfe, R.; Hall, F.G.; Huemmrich, F.; Gao, F.; Kutler, J.; Lim, T.K. LEDAPS Landsat Calibration, Reflectance, Atmospheric Correction Preprocessing Code; NASA: Oak Ridge, TN, USA, 2012.
- USGS. Landsat Processing Details; USGS: Sioux Falls, SD, USA, 2016.
- Roy, D.P.; Borak, J.S.; Devadiga, S.; Wolfe, R.E.; Zheng, M.; Descloitres, J. The MODIS land product quality assessment approach. Remote Sens. Environ. 2002, 83, 62–76. [Google Scholar] [CrossRef]
- Zhu, Z.; Woodcock, C.E. Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sens. Environ. 2012, 118, 83–94. [Google Scholar] [CrossRef]
- Foody, G.M. Sample size determination for image classification accuracy assessment and comparison. Int. J. Remote Sens. 2009, 30, 5273–5291. [Google Scholar] [CrossRef]
- Cohen, W.B.; Yang, Z.; Kennedy, R. Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync—Tools for calibration and validation. Remote Sens. Environ. 2010, 114, 2911–2924. [Google Scholar] [CrossRef]
- Strahler, A.H.; Boschetti, L.; Foody, G.M.; Friedl, M.A.; Hansen, M.C.; Herold, M.; Morisette, J.T.; Stehman, S.V.; Woodcock, C.E. Global Land Cover Validation: Recommendations for Evaluation and Accuracy Assessment of Global Land Cover Maps; JRC European Commission: Brussels, Belgium, 2006. [Google Scholar]
- Congalton, R.G.; Green, K. Assessing the Accuraccy of Remotely Sensed Data Principles and Practices; CRC Press: New York, NY, USA, 2009. [Google Scholar]
- Akaike, H. Information Theory and an Extension of the Maximum Likelihood Principle. In The Second International Symposium on Information Theory; Akademiai Kiado: Budapest, Hungary, 1973; pp. 267–281. [Google Scholar]
- Souza, C.M., Jr.; Roberts, D.A.; Cochrane, M.A. Combining spectral and spatial information to map canopy damage from selective logging and forest fires. Remote Sens. Environ. 2005, 98, 329–343. [Google Scholar] [CrossRef]
Class Name | Description |
---|---|
no data | Absence of data |
CW—closed canopy woodland | predominantly tree and herbaceous strata, where tree cover may be greater than 70% |
CF—closed canopy forest | multiple vegetation strata likely, where tree cover is likely to be greater than 70% |
CB—closed canopy bushland, thicket | closed–medium canopy short bushland and/or thicket areas multiple vegetation strata likely, where shrub and bush cover is likely to be greater than 70% |
OW—open canopy woodland | Open canopy woodland and/or bushland, where the tree and bush cover is likely to be between 40–70% |
OB—open canopy bushland, sparse | Sparse canopy woodland and/or bushland, where the tree and bush cover is likely to be less than 40% |
OG—open grassland | Grass dominated areas with little or no tree, shrub or bush cover (tree and bush cover is likely to be less than 5–10% and/or where the herbaceous cover is likely to be less than 40%) |
OL—Other land | Other land than the above |
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Schultz, M.; Shapiro, A.; Clevers, J.G.P.W.; Beech, C.; Herold, M. Forest Cover and Vegetation Degradation Detection in the Kavango Zambezi Transfrontier Conservation Area Using BFAST Monitor. Remote Sens. 2018, 10, 1850. https://doi.org/10.3390/rs10111850
Schultz M, Shapiro A, Clevers JGPW, Beech C, Herold M. Forest Cover and Vegetation Degradation Detection in the Kavango Zambezi Transfrontier Conservation Area Using BFAST Monitor. Remote Sensing. 2018; 10(11):1850. https://doi.org/10.3390/rs10111850
Chicago/Turabian StyleSchultz, Michael, Aurélie Shapiro, Jan G. P. W. Clevers, Craig Beech, and Martin Herold. 2018. "Forest Cover and Vegetation Degradation Detection in the Kavango Zambezi Transfrontier Conservation Area Using BFAST Monitor" Remote Sensing 10, no. 11: 1850. https://doi.org/10.3390/rs10111850