Pre- and Post-Fire Comparison of Forest Areas in 3D
Abstract
A satellite processing platform for high resolution forest assessment (FORSAT) was developed. It generates the digital surface models (DSMs) of the forest canopy by advanced processing of the very-high resolution (VHR) optical satellite imagery and automatically matches the pre- and post-fire DSMs for 3D change detection. The FORSAT software system can perform the following tasks: pre-processing, point measurement, orientation, quasi-epipolar image generation, image matching, DSM extraction, orthoimage generation, photogrammetric restitution either in mono-plotting mode or in stereo models, 3D surface matching, co-registration, comparison and change detection. It can thoroughly calculate the planimetric and volumetric changes between the epochs. It supports most of the VHR optical imagery commonly used for civil applications. Capabilities of FORSAT have been tested in two real forest fire cases, where the burned areas are located in Cyprus and Austria. The geometric characteristics of burned forest areas have been identified both in 2D plane and 3D volume dimensions, using pre- and post-fire optical image data from different sensors. The test studies showed that FORSAT is an operational software capable of providing spatial (3D) and temporal (4D) information for monitoring of forest fire areas and sustainable forest management. Beyond the wildfires, it can be used for many other forest information needs.
References
- Abdollahi M, Islam T, Gupta A, Hassan QK (2018) An advanced forest fire danger forecasting system: integration of remote sensing and historical sources of ignition data. Remote Sens 10:923. https://doi.org/10.3390/rs10060923Google Scholar
- Ackermann F, Hahn M (1991) Image pyramids for digital photogrammetry. In: Ebner H, Fritsch D, Heipke C (eds) Digital photogrammetric systems. Wichmann, Karlsruhe, pp 43–58Google Scholar
- Addison P, Oommen T (2018) Utilizing satellite radar remote sensing for burn severity estimation. Int J Appl Earth Obs Geoinf 73:292–299Google Scholar
- Adelabu SA, Adepoju KA, Mofokeng OD (2018) Estimation of fire potential index in mountainous protected region using remote sensing. Geocarto International. https://doi.org/10.1080/10106049.2018.1499818
- Akca D, Gruen A (2005) Recent advances in least squares 3D surface matching. In: Gruen A, Kahmen H (eds) Proceedings of the optical 3-D measurement techniques VII, Vienna, Austria, 3–5 October 2005, vol. II, pp 197–206Google Scholar
- Akca D, Gruen A, Alkis Z, Demir N, Breuckmann B, Erduyan I, Nadir E (2006) 3D modeling of the Weary Herakles statue with a coded structured light system. Int Arch Photogramm Remote Sens Spat Inf Sci 36(5):14–19Google Scholar
- Akca D (2007) Least Squares 3D surface matching. Ph.D. thesis, Institute of Geodesy and Photogrammetry, ETH Zurich, Switzerland, Mitteilungen Nr. 92, p 78. https://doi.org/10.3929/ethz-a-005461765
- Akca D, Gruen A (2007) Generalized Least Squares multiple 3D surface matching. Int Archives Photogramm Remote Sens Spat Inf Sci 36(3/W52):1–7Google Scholar
- Akca D, Remondino F, Novàk D, Hanusch T, Schrotter G, Gruen A (2007) Performance evaluation of a coded structured light system for cultural heritage applications. Proc. of SPIE-IS&T Electronic Imaging, Videometrics IX, San Jose, California, January 29–30. SPIE 6491:64910V-1–12Google Scholar
- Akca D (2010) Co-registration of surfaces by 3D Least Squares matching. Photogramm Eng Remote Sens 76(3):307–318Google Scholar
- Akca D, Freeman M, Sargent I, Gruen A (2010) Quality assessment of 3D building data. Photogram Rec 25(132):339–355Google Scholar
- Akca D (2012) 3D modeling of cultural heritage objects with a structured light system. Mediterr Archaeol Archaeom 12(1):139–152Google Scholar
- Akca D, Seybold HJ (2016) Monitoring of a laboratory-scale inland-delta formation using a structured-light system. Photogram Rec 31(154):121–142Google Scholar
- Akca D, Stylianidis E, Smagas K, Hofer M, Poli D, Gruen A, Martin VS, Altan O, Walli A, Jimeno E, Garcia A (2016) Volumetric forest change detection through VHR satellite imagery. Int Archives Photogramm Remote Sens Spat Inf Sci 41(B8):1213–1220Google Scholar
- Almeida-Filho R, Rosenqvist A, Shimabukuro YE, dos Santos JR (2005) Evaluation and perspectives of using multitemporal L-band SAR data to monitor deforestation in the Brazilian Amazonia. IEEE Geosci Remote Sens Lett 2(4):409–412Google Scholar
- Almeida-Filho R, Rosenqvist A, Shimabukuro YE, Silva-Gomez R (2007) Detection deforestation with multitemporal L-band SAR imagery: a case study in western Brazilian Amazonia. Int J Remote Sens 28(6):1383–1390Google Scholar
- Almeida-Filho R, Shimabukuro YE, Rosenqvist A, Sanchez GA (2009) Using dual-polarized ALOS PALSAR data for detecting new fronts of deforestation in the Brazilian Amazonia. Int J Remote Sens 30(14):3735–3743Google Scholar
- Altan O, Backhaus R, Boccardo P, van Manen N, Tonolo FG, Trinder J, Zlatanova S (2013). The value of geoinformation for disaster and risk management (VALID), Joint Board of Geospatial Information Society (JB GIS), Copenhagen, ISBN 97887-90907-88-4Google Scholar
- Alves DS (2002) Space-time dynamics of deforestation in Brazilian Amazonia. Int J Remote Sens 23(14):2903–2908Google Scholar
- Anderson LO, Shimabukuro YE, Defries RS, Morton D (2005) Assessment of deforestation in near real time over the Brazilian Amazon using multitemporal fraction images derived from Terra MODIS. IEEE Geosci Remote Sens Lett 2(3):315–318Google Scholar
- Baillarin F, Souza C, Gonzales G (2008) Use of Formosat-2 satellite imagery to detect near real time deforestation in Amazonia. IEEE International Geoscience & Remote Sensing Symposium (IGARSS’2008). https://doi.org/10.1109/IGARSS.2008.4779481
- Baltsavias E, Kocaman S, Akca D, Wolff K (2007) Geometric and radiometric investigations of Cartosat-1 Data. ISPRS Workshop on high resolution earth imaging for geospatial information, Hannover, Germany, 29 May–1 June 2007Google Scholar
- Bodart C, Eva H, Beuchle R et al (2011) Pre-processing of a sample of multi-scene and multi-date Landsat imagery used to monitor forest cover changes over the tropics. ISPRS J Photogramm Remote Sens 66:555–563Google Scholar
- Burnett JD, Wing MG (2018) A low-cost near-infrared digital camera for fire detection and monitoring. Int J Remote Sens 39(3):741–753Google Scholar
- Cabral AIR, Silva S, Silva PC, Vanneschi L, Vasconcelos MJ (2018) Burned are estimations derived from Landsat ETM+ and OLI data: comparing Genetic Programming with Maximum Likelihood and classification and regression trees. ISPRS J Photogramm Remote Sens 142:94–105Google Scholar
- Cailliez F (1992) Forest volume estimation and yield prediction. FAO For Paper 22(1):98Google Scholar
- Camaro W, Steffenino S, Vigna R (2013) Fire risk mapping and fire detection and monitoring. In: The value of Geoinformation for disaster and risk management (VALID), joint board of geospatial information society (JB GIS), Copenhagen, ISBN 97887-90907-88-4Google Scholar
- Colson D, Petropoulos GP, Ferentinos KP (2018) Exploring the potential of Sentinels-1 & 2 of the Copernicus Mission in support of rapid and cost-effective wildfire assessment. Int J Appl Earth Obs Geoinf 73:262–276Google Scholar
- Cucchiaro S, Cavalli M, Vericat D, Crema S, Llena M, Beinat A, Marchi L, Cazorzi F (2018) Monitoring topographic changes through 4D-structure-from-motion photogrammetry: Application to a debris-flow channel. Environ Earth Sci 77:632. https://doi.org/10.1007/s12665-018-7817-4Google Scholar
- Di Maio Mantovani AC, Setzer AW (1997) Deforestation detection in the Amazon with an AVHRR-based system. Int J Remote Sens 18(2):273–286Google Scholar
- Ebner H, Strunz G (1988) Combined point determination using digital terrain models as control information. Int Archives Photogramm Remote Sens 27(B11/3):578–587Google Scholar
- Edwards AC, Russell-Smith J, Maier SW (2018) A comparison and validation of satellite-derived fire severity mapping techniques in fire prone north Australian savannas: extreme fires and tree stem mortality. Remote Sens Environ 206:287–299Google Scholar
- Eva H, Carboni S et al (2010) Monitoring forest areas from continental to territorial levels using a sample of medium spatial resolution satellite imagery. ISPRS J Photogramm Remote Sens 65:191–197Google Scholar
- Fernandez-Garcia V, Santamarta M, Fernandez-Manso A, Quintano C, Marcos E, Calvo L (2018) Burn severity metrics in fire-prone pine ecosystems along a climatic gradient using Landsat imagery. Remote Sens Environ 206:205–217Google Scholar
- Filizzola C, Corrado R, Marchese F, Mazzeo G, Paciello R, Pergola N, Tramutoli V (2016) RST-FIRES, an exportable algorithm for early-fire detection and monitoring: description, implementation, and field validation in the case of the MSG-SEVIRI sensor. Remote Sens Environ 186:196–216Google Scholar
- Foody GM (2002) Status of land cover classification accuracy assessment. Remote Sens Environ 80:185–201Google Scholar
- Garcia-Lazaro JR, Moreno-Ruiz JA, Riano D, Arbelo M (2018) Estimation of burned area in the northeastern Siberian Boreal Forests from a long-term data record (LTDR) 1982-2015 time series. Remote Sens 10:940. https://doi.org/10.3390/rs10060940Google Scholar
- Giglio L, Descloitres J, Justice CO, Kaufman YJ (2003) An enhanced contextual fire detection algorithm for MODIS. Remote Sens Environ 87:273–282Google Scholar
- Giglio L, Schroeder W, Justice CO (2016) The collection 6 MODIS active fire detection algorithms and fire products. Remote Sens Environ 178:31–41Google Scholar
- Grodecki J, Dial G (2003) Block Adjustment of High-Resolution Satellite Images Described by Rational Polynomials. Photogramm Eng Remote Sens 69(1):59–68Google Scholar
- Gruen A, Poli D, Zhang L (2004) SPOT-5/HRS stereo images orientation and automated DSM generation. Int Archives Photogramm Remote Sens Spat Inf Sci 35(1):421–432Google Scholar
- Gruen A, Akca D (2005) Least squares 3D surface and curve matching. ISPRS J Photogramm Remote Sens 59(3):151–174Google Scholar
- GW website (2018) Insitu ScanEagle UAS helps suppress wildfires. https://www.geomatics-world.co.uk/content/news/insitu-scaneagle-uas-helps-suppress-wildfires. Accessed 09 Oct 2018
- Haboudane D, Bahri EM (2008) Deforestation detection and monitoring in cedar forests of the Moroccan Middle-Atlas Mountains. IEEE International Geoscience & Remote Sensing Symposium (IGARSS’2007). https://doi.org/10.1109/IGARSS.2007.4423809
- Heipke C, Mayer H, Wiedemann C, Jamet O (1997) Evaluation of automatic road extraction. Int Archives Photogramm Remote Sens 32(3–2W3):47–56Google Scholar
- Ichii K, Maruyama M, Yamaguchi Y (2003) Multi-temporal analysis of deforestation in Rondonia state in Brazil using Landsat MSS, ETM+ and NOAA AVHRR imagery and its relationship to changes in the local hydrological environment. Int J Remote Sens 24(22):4467–4479Google Scholar
- Isoguchi O, Shimada M, Uryu Y (2009) A preliminary study on deforestation monitoring in Sumatra island by PALSAR. IEEE International Geoscience & Remote Sensing Symposium (IGARSS’2009). https://doi.org/10.1109/IGARSS.2009.5417928
- Justice CO, Townshend JRG, Vermote EF, Masuoka E, Wolfe RE, Saleous N, Roy DP Morisette JT (2002a) An overview of MODIS Land data processing and product status. Remote Sensing of Environment 83:3–15Google Scholar
- Justice CO, Giglio L, Korontzi S, Owens J, Morisette JT, Roy D, Descloitres J, Alleaume S, Petitcolin F, Kaufman Y (2002b) The MODIS fire products. Remote Sens Environ 83:244–262Google Scholar
- Koch B (2010) Status and future of laser scanning, synthetic aperture radar and hyperspectral remote sensing data for forest biomass assessment. ISPRS J Photogramm Remote Sens 65:581–590Google Scholar
- Krasovskii A, Khabarov N, Pirker J, Kraxner F, Yowargana P, Schepaschenko D, Obersteiner M (2018) Forests 9:437. https://doi.org/10.3390/f9070437Google Scholar
- Koltunov A, Ustin SL, Quayle B, Schwind B, Ambrosia VG, Li W (2016) The development and first validation of the GOES Early Fire Detection (GOES-EFD) algorithm. Remote Sens Environ 184:436–453Google Scholar
- Lee H (2008) Mapping deforestation and age of evergreen trees by applying a binary coding method to time-series Landsat November images. IEEE Trans Geosci Remote Sens 46(11):3926–3936Google Scholar
- Li X, Zhang H, Yang G, Ding Y, Zhao J (2018) Post-fire vegetation succession and surface energy fluxes derived from remote sensing. Remote Sens 10:1000. https://doi.org/10.3390/rs10071000Google Scholar
- Lin Z, Chen F, Niu Z, Li B, Yu B, Jia H, Zhang M (2018) An active fire detection algorithm based on multi-temporal FengYun-3C VIRR data. Remote Sens Environ 211:376–387Google Scholar
- Mancini LD, Elia M, Barbati A, Salvati L, Corona P, Lafortezza R, Sanesi G (2018) Are wildfires knocking on the built-up areas door? Forests 9:234. https://doi.org/10.3390/f9050234Google Scholar
- Mayr MJ, Vanselow KA, Samimi C (2018) Fire regimes at the arid fringe: A 16-year remote sensing perspective (2000–2016) on the controls of fire activity in Namibia from spatial predictive models. Ecol Ind 91:324–337Google Scholar
- McCarley TR, Kolden CA, Vaillant NM, Hudak AT, Smith AMS, Wing BM, Kellogg BS, Kreitler J (2017) Multi-temporal LiDAR and Landsat quantification of fire-induced changes to forest structure. Remote Sens Environ 191:419–432Google Scholar
- McKeown DM, Bulwinkle T, Cochran S, Harvey W, McGlone C, Shufelt JA (2000) Performance evaluation for automatic feature extraction. Int Archives Photogramm Remote Sens 33(B2):379–394Google Scholar
- Meng R, Wu J, Schwager KL, Zhao F, Dennison PE, Cook BD, Brewster K, Green TM, Serbin SP (2017) Using high spatial resolution satellite imagery to map forest burn severity across spatial scales in a Pine Barrens ecosystem. Remote Sens Environ 191:95–109Google Scholar
- Millington AC, Velez-Liendo XM, Bradley AV (2003) Scale dependence in multitemporal mapping of forest fragmentation in Bolivia: implications for explaining temporal trends in landscape ecology and applications to biodiversity conservation. ISPRS J Photogramm Remote Sens 57:289–299Google Scholar
- Mitchell HL, Chadwick RG (1999) Digital photogrammetric concepts applied to surface deformation studies. Geomatica 53(4):405–414Google Scholar
- Mondal P, Southworth J (2010) Protection vs. commercial management: spatial and temporal analysis of land cover changes in the tropical forests of Central India. For Ecol Manage 259:1009–1017Google Scholar
- Mora B, Wulder MA, White JC, Hobart G (2013) Modeling stand height, volume, and biomass from very high spatial resolution satellite imagery and samples of airborne LiDAR. Remote Sens 5:2308–2326Google Scholar
- Navarro G, Caballero I, Silva G, Parra PC, Vazquez A, Caldeira R (2017) Evaluation of forest fire on Madeira Island using Sentinel-2A MSI imagery. Int J Appl Earth Observ Geoinf 58:97–106Google Scholar
- Nyongesa KW, Vacik H (2018) Fire management in Mount Kenya: a case study of Gathiuru forest station. Forests 9:481. https://doi.org/10.3390/f9080481Google Scholar
- Pahari K, Murai S (1999) Modelling for prediction of global deforestation based on the growth of human population. ISPRS J Photogramm Remote Sens 54:317–324Google Scholar
- Pasquarella VJ, Holden CE, Kaufman L, Woodcock CE (2016) From imagery to ecology: leveraging time series of all available Landsat observations to map and monitor ecosystem state & dynamics. Remote Sens Ecol Conserv 2(3):152–170. https://doi.org/10.1002/rse2.24Google Scholar
- Poli D (2005) Modelling of Spaceborne Linear Array Sensors. Ph.D. thesis, Institute of Geodesy and Photogrammetry, ETH Zurich, Switzerland, Mitteilungen Nr. 85, p 217Google Scholar
- Poli D (2007) A Rigorous Model for Spaceborne Linear Array Sensors. Photogramm Eng Remote Sens 73(2):187–196Google Scholar
- Ramo R, Garcia M, Rodriguez D, Chuvieco E (2018) A data mining approach for global burning area mapping. Int J Appl Earth Observ Geoinf 73:39–51Google Scholar
- Remondino F (2011) Heritage recording and 3D modelling with photogrammetry and 3D scanning. Remote Sensing 3:1104–1138Google Scholar
- Rosenholm D, Torlegard K (1988) Three-dimensional absolute orientation of stereo models using digital elevation models. Photogramm Eng Remote Sens 54(10):1385–1389Google Scholar
- Rutzinger M, Rottensteiner F, Pfeifer N (2009) A comparison of evaluation techniques for building extraction from airborne laser scanning. IEEE J Sel Topics Appl Earth Observ Remote Sens 2(1):11–20Google Scholar
- Ryu JH, Han KS, Hong S, Park NW, Lee YW, Cho J (2018) Satellite-based evaluation of the post-fire recovery process from the worst forest case in South Korea. Remote Sens 10:918. https://doi.org/10.3390/rs10060918Google Scholar
- Santos JR, Mura JC, Paradella WP, Dutra LV, Goncalves FG (2008) Mapping recent deforestation in the Brazilian Amazon using simulated L-band MAPSAR images. Int J Remote Sens 29(16):4879–4884Google Scholar
- Schanz D, Huhn F, Schroeder A (2018) Large-scale volumetric flow measurement of a thermal plume using Lagrangian Particle Tracking (Shake-The-Box). In: Raffel M et al (eds) Particle Image Velocimetry, Springer, 606–610. https://doi.org/10.1007/978-3-319-68852-7_18Google Scholar
- Schroeder W, Oliva P, Giglio L, Csiszar IA (2014) The new VIIRS 375 m active fire detection data product: algorithm description and initial assessment. Remote Sens Environ 143:85–96Google Scholar
- Sefercik UG, Alkan M, Buyuksalih G, Jacobsen K (2013) Generation and validation of high-resolution DEMs from Worldview-2 stereo data. Photogramm Rec 28(144):362–374Google Scholar
- Shufelt JA (1999) Performance evaluation and analysis of monocular building extraction from aerial imagery. IEEE Trans Pattern Anal Mach Intell 21(4):311–326Google Scholar
- Silva Junior CHL, Aragao LEOC, Fonseca MG, Almeida CT, Vedovato LB, Anderson LO (2018) Deforestation-induced fragmentation increases forest fire occurrence in Central Brazilian Amazonia. Forests 9:305. https://doi.org/10.3390/f9060305Google Scholar
- Solberg S, Astrup R, Weydahl DJ (2013) Detection of forest clear-cuts with Shuttle Radar Topography Mission (SRTM) and Tandem-X InSAR data. Remote Sensing 5:5449–5462Google Scholar
- Soto-Berelov M, Jones SD, Clarke E, Reddy S, Gupta V, Felipe MLC (2018) Assessing two large area burnt area products across Australian Southern Forests. Int J Remote Sens 39(3):879–905Google Scholar
- Souza CM, Siqueira JV, Sales MH et al (2013) Ten-year Landsat classification of deforestation and forest degradation in the Brazilian Amazon. Remote Sens 5:5493–5513Google Scholar
- Sun P, Zhang Y (2018) A probabilistic method predicting forest fire occurrence combining firebrands and the weather-fuel complex in the northern part of the Daxinganling region. China For 9:428. https://doi.org/10.3390/f9070428Google Scholar
- Svancara LK, Scott JM, Loveland TR, Pidgorna AB (2009) Assessing the landscape context and conversion risk of protected areas using satellite data products. Remote Sens Environ 113:1357–1369Google Scholar
- Tao CV, Hu Y (2001) A Comprehensive Study of the Rational Function Model for Photogrammetric Processing. Photogramm Eng Remote Sens 66(12):1477–1485Google Scholar
- Tian L, Wang J, Zhou H, Wang J (2018) Automatic detection of forest fire disturbance based on dynamic modelling from MODIS time-series observations. Int J Remote Sens 39(12):3801–3815Google Scholar
- Toschi I, Remondino F, Kellenberger T, Streilein A (2017) A survey of geomatics solutions for the rapid mapping of natural hazards. Photogramm Eng Remote Sens 83(12):843–859Google Scholar
- Toschi I, Allocca M, Remondino F (2018) Geomatics mapping of natural hazards: overview and experiences. Int Archives Photogramm Remote Sens Spat Inf Sci 42(3/W4):505–512Google Scholar
- Tucker CJ, Townshend JRG (2000) Strategies for monitoring tropical deforestation using satellite data. Int J Remote Sens 21(6):1461–1471Google Scholar
- Vega SGD, de las Heras J, Moya D (2018) Post-fire regeneration and diversity response to burn severity in pinus halepensis Mill. forests. Forests 9:299. https://doi.org/10.3390/f9060299Google Scholar
- Wallis R (1976) An approach to the space variant restoration and enhancement of images. In: Proc of Symposium on Current Mathematical Problems in Image Science, Monterey, CAGoogle Scholar
- Wheeler D, Guzder-Williams B, Petersen R, Thau D (2018) Rapid MODIS-based detection of tree cover loss. Int J Appl Earth Obs Geoinf 69:78–87Google Scholar
- Xu C, Manley B, Morgenroth J (2018) Evaluation of modelling approaches in predicting forest volume and stand age for small-scale plantations forests in New Zealand with RapidEye and LiDAR. Int J Appl Earth Observ Geoinf 73:386–396Google Scholar
- Yu B, Chen F, Li B, Wang L, Wu M (2017) Fire risk prediction using remote sensed products: a case of Cambodia. Photogrammetric Engineering and Remote Sensing 83(1):19–25Google Scholar
- Zhang L, Gruen A (2004) Automatic DSM generation from linear array imagery data. Int Archives Photogramm Remote Sens Spat Inf Sci 35(B3):128–133Google Scholar
- Zhang L (2005) Automatic Digital Surface Model (DSM) Generation from Linear array Images. Ph.D. thesis, Institute of Geodesy and Photogrammetry, ETH Zurich, Switzerland, Mitteilungen Nr.88, p 219. ISBN 3-906467-55-4Google Scholar
- Zhang L, Gruen A (2006) Multi-image matching for DSM generation from IKONOS imagery. ISPRS J Photogramm Remote Sens 60:195–211Google Scholar
- Zhang L, Kocaman S, Akca D, Kornus W, Baltsavias E (2006) Test and performance evaluation of DMC images and new methods for their processing. In: Proceedings ISPRS commission I symposium, Paris, 3–6 Jul 2006Google Scholar
- Zhang Y, Song C, Band LE, Sun G, Li J (2017) Reanalysis of global terrestrial vegetation trends from MODIS products: browning or greening? Remote Sens Environ 191:145–155Google Scholar