Small fires, frequent clouds, rugged terrain and no training data: a methodology to reconstruct fire history in complex landscapes
Davide Fornacca
A B C D , Guopeng Ren A C D and Wen Xiao A C D E F
+ Author Affiliations
- Author Affiliations
A Institute of Eastern-Himalaya Biodiversity Research, Dali University, Hongsheng Road 2, Dali 671003, China.
B EnviroSPACE Lab, Institute for Environmental Sciences, University of Geneva, 66 Boulevard Carl Vogt, Geneva 1205, Switzerland.
C Collaborative Innovation Center for Biodiversity and Conservation in the Three Parallel Rivers Region of China, Dali 671003, China.
D Er’hai Catchment Sustainable Development Laboratory of the Yunnan Education Department, Dali 671003, China.
E Provincial Innovation Team of Biodiversity Conservation and Utility of the Three Parallel Rivers Region, Dali 671003, China.
F Corresponding author. Email: xiaow@eastern-himalaya.cn
International Journal of Wildland Fire - https://doi.org/10.1071/WF20072
Submitted: 15 May 2020 Accepted: 15 October 2020 Published online: 5 November 2020
Abstract
An automated burned area extraction routine that attempts to overcome the particular difficulties of remote sensing applications in complex landscapes is presented and tested in the mountainous region of northwest Yunnan, China. In particular, the lack of burned samples to use for training and testing, the rugged relief, the small size of fires and the constant presence of clouds during the rainy season heavily affecting the number of usable scenes within a year are addressed. The algorithm flows through five phases: creation of standardised difference vegetation indices time series; automatic extraction of multiclass training areas using adaptive z-score thresholds; Random Forests classification; Seeded Region Growing; and spatiotemporal clustering to form polygons representing fire events. A final database spanning the period 1987–2018 was created. Accuracy assessment of location and number of fire polygons using a stratified random sampling design showed satisfactory results with reduced omission and commission errors compared with global fire products in the same region (20 and 22% respectively). Mapping accuracy of single burned areas showed higher omission (27%) but reduced commission (13%) errors. This methodology takes a step forward towards the inclusion of regions characterised by small fires that are often poorly represented in impact assessments at the global scale.
Keywords: adaptive thresholds, fire events, fire history reconstruction, Landsat, mountainous area, remote sensing, time series normalisation, training data.
References
Adams R, Bischof L (1994) Seeded Region Growing. IEEE Transactions on Pattern Analysis and Machine Intelligence 16, 641–647.| Seeded Region Growing.Crossref | GoogleScholarGoogle Scholar |
Baig MHA, Zhang L, Shuai T, Tong Q (2014) Derivation of a tasselled cap transformation based on Landsat 8 at-satellite reflectance. Remote Sensing Letters 5, 423–431
Bastarrika A, Chuvieco E, Martín MP (2011) Mapping burned areas from Landsat TM/ETM+ data with a two-phase algorithm: balancing omission and commission errors. Remote Sensing of Environment 115, 1003–1012.
| Mapping burned areas from Landsat TM/ETM+ data with a two-phase algorithm: balancing omission and commission errors.Crossref | GoogleScholarGoogle Scholar |
Bastarrika A, Alvarado M, Artano K, Martinez M, Mesanza A, Torre L, Ramo R, Chuvieco E (2014) BAMS: a tool for supervised burned area mapping using Landsat data. Remote Sensing 6, 12360–12380.
| BAMS: a tool for supervised burned area mapping using Landsat data.Crossref | GoogleScholarGoogle Scholar |
Bechtel B, Ringeler A, Böhner J (2008) Segmentation for Object Extraction of Trees Using MATLAB and SAGA. Hamburger Beiträge zur Physischen Geographie und Landschaftsökologie 19, 1–12. Available at http://downloads.sourceforge.net/saga-gis/hbpl19_01.pdf [verified 28 October 2020]
Belgiu M, Drăgu L (2016) Random forest in remote sensing: a review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing 114, 24–31.
| Random forest in remote sensing: a review of applications and future directions.Crossref | GoogleScholarGoogle Scholar |
Boschetti L, Stehman SV, Roy DP (2016) A stratified random sampling design in space and time for regional to global scale burned area product validation. Remote Sensing of Environment 186, 465–478.
| A stratified random sampling design in space and time for regional to global scale burned area product validation.Crossref | GoogleScholarGoogle Scholar | 30416212PubMed |
Bowman DMJS, Balch JK, Artaxo P, Bond WJ, Cochrane MA, D’Antonio CM, Defries RS, Johnston FH, Keeley JE, Krawchuk MA, Kull CA, Mack M, Moritz MA, Pyne S, Roos CI, Scott AC, Sodhi NS, Swetnam TW (2011) The human dimension of fire regimes on Earth. Journal of Biogeography 38, 2223–2236.
| The human dimension of fire regimes on Earth.Crossref | GoogleScholarGoogle Scholar |
Breiman L (2001) Random Forests. Machine Learning 45, 5–32.
| Random Forests.Crossref | GoogleScholarGoogle Scholar |
Bruzzone L, Fernandez Prieto D (2000) Automatic analysis of the difference image for unsupervised change detection. IEEE Transactions on Geoscience and Remote Sensing 38, 1171–1182.
| Automatic analysis of the difference image for unsupervised change detection.Crossref | GoogleScholarGoogle Scholar |
CEPF (2002) Ecosystem profile: mountains of south-west China hotspot. Available at https://www.cepf.net/our-work/biodiversity-hotspots/mountains-southwest-china. [verified 19 October 2020]
Chuvieco E, Yue C, Heil A, Mouillot F, Alonso-Canas I, Padilla M, Pereira JMC, Oom D, Tansey K (2016) A new global burned area product for climate assessment of fire impacts. Global Ecology and Biogeography 25, 619–629.
| A new global burned area product for climate assessment of fire impacts.Crossref | GoogleScholarGoogle Scholar |
Chuvieco E, Mouillot F, van der Werf GR, San Miguel J, Tanasse M, Koutsias N, García M, Yebra M, Padilla M, Gitas I, Heil A, Hawbaker TJ, Giglio L (2019) Historical background and current developments for mapping burned area from satellite Earth observation. Remote Sensing of Environment 225, 45–64.
| Historical background and current developments for mapping burned area from satellite Earth observation.Crossref | GoogleScholarGoogle Scholar |
Cohen J (1960) A coefficient of agreement for nominal scales. Educational and Psychological Measurement 20, 37–46.
| A coefficient of agreement for nominal scales.Crossref | GoogleScholarGoogle Scholar | http://epm.sagepub.com
Collins L, Newell G, Mellor A (2018) The utility of Random Forests for wildfire severity mapping. Remote Sensing of Environment 216, 374–384.
| The utility of Random Forests for wildfire severity mapping.Crossref | GoogleScholarGoogle Scholar |
Crist EP (1985) A TM Tasseled Cap equivalent transformation for reflectance factor data. Remote Sensing of Environment 17, 301–306
Crist EP, Cicone RC (1984) A physically-based transformation of Thematic Mapper data - The TM Tasseled Cap. IEEE Transactions on Geoscience and Remote Sensing GE-22, 256–263.
D’Addabbo A, Satalino G, Pasquariello G, Blonda P (2004) Three different unsupervised methods for change detection: an application. In ‘IGARSS 2004. 2004 IEEE international geoscience and remote sensing symposium’, Anchorage, AK, USA. pp. 1980–1983. (IEEE: Anchorage, AK, USA)
de Carvalho Júnior OA, Guimarães RF, Silva CR, Gomes RAT (2015) Standardized time series and interannual phenological deviation: new techniques for burned-area detection using long-term MODIS-NBR dataset. Remote Sensing 7, 6950–6985.
| Standardized time series and interannual phenological deviation: new techniques for burned-area detection using long-term MODIS-NBR dataset.Crossref | GoogleScholarGoogle Scholar |
Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26, 297–302.
| Measures of the amount of ecologic association between species.Crossref | GoogleScholarGoogle Scholar |
Fornacca D, Ren G, Xiao W (2017) Performance of three MODIS fire products (MCD45A1, MCD64A1, MCD14ML), and ESA Fire_CCI in a mountainous area of northwest Yunnan, China, characterized by frequent small fires. Remote Sensing 9, 1131
| Performance of three MODIS fire products (MCD45A1, MCD64A1, MCD14ML), and ESA Fire_CCI in a mountainous area of northwest Yunnan, China, characterized by frequent small fires.Crossref | GoogleScholarGoogle Scholar |
Fornacca D, Ren G, Xiao W (2018) Evaluating the best spectral indices for the detection of burn scars at several post-fire dates in a mountainous region of northwest Yunnan, China. Remote Sensing 10, 1196
| Evaluating the best spectral indices for the detection of burn scars at several post-fire dates in a mountainous region of northwest Yunnan, China.Crossref | GoogleScholarGoogle Scholar |
Fung T, Ledrew E (1988) The determination of optimal threshold levels for change detection using various accuracy indices. Photogrammetric Engineering and Remote Sensing 54, 1449–1454. https://www.asprs.org/wp-content/uploads/pers/1988journal/oct/1988_oct_1449-1454.pdf
Gitas IZ, Devereux BJ (2006) The role of topographic correction in mapping recently burned Mediterranean forest areas from LANDSAT TM images. International Journal of Remote Sensing 27, 41–54.
| The role of topographic correction in mapping recently burned Mediterranean forest areas from LANDSAT TM images.Crossref | GoogleScholarGoogle Scholar |
Gómez C, White JC, Wulder MA (2016) Optical remotely sensed time series data for land cover classification: a review. ISPRS Journal of Photogrammetry and Remote Sensing 116, 55–72.
| Optical remotely sensed time series data for land cover classification: a review.Crossref | GoogleScholarGoogle Scholar |
Grafarend EW (2006) ‘Linear and non-linear models: fixed effects, random effects, and mixed models.’ (Walter de Gruyter: Berlin, Germany)
Hawbaker TJ, Vanderhoof MK, Beal YJG, Takacs JD, Schmidt GL, Falgout JT, Williams B, Fairaux NM, Caldwell MK, Picotte JJ, Howard SM, Stitt S, Dwyer JL (2017) Mapping burned areas using dense time series of Landsat data. Remote Sensing of Environment 198, 504–522.
| Mapping burned areas using dense time series of Landsat data.Crossref | GoogleScholarGoogle Scholar |
He G, Zhang Z, Jiao W, Long T, Peng Y, Wang G, Yin R, Wang W, Zhang X, Liu H, Cheng B, Xiang B (2018) Generation of ready to use (RTU) products over China based on Landsat series data. Big Earth Data 2, 56–64.
| Generation of ready to use (RTU) products over China based on Landsat series data.Crossref | GoogleScholarGoogle Scholar |
Hermosilla T, Wulder MA, White JC, Coops NC, Hobart GW (2015) An integrated Landsat time series protocol for change detection and generation of annual gap-free surface reflectance composites. Remote Sensing of Environment 158, 220–234.
| An integrated Landsat time series protocol for change detection and generation of annual gap-free surface reflectance composites.Crossref | GoogleScholarGoogle Scholar |
Huang C, Goward SN, Masek JG, Thomas N, Zhu Z, Vogelmann JE (2010) An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks. Remote Sensing of Environment 114, 183–198.
| An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks.Crossref | GoogleScholarGoogle Scholar |
Jackson RD, Huete AR (1991) Interpreting vegetation indices. Preventive Veterinary Medicine 11, 185–200.
| Interpreting vegetation indices.Crossref | GoogleScholarGoogle Scholar |
Justice CO, Giglio L, Korontzi S, Owens J, Morisette JT, Roy DP, Descloitres J, Alleaume S, Petitcolin F, Kaufman Y (2002) The MODIS fire products. Remote Sensing of Environment 83, 244–262.
| The MODIS fire products.Crossref | GoogleScholarGoogle Scholar |
Kennedy RE, Yang Z, Cohen WB (2010) Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr – Temporal segmentation algorithms. Remote Sensing of Environment 114, 2897–2910.
| Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr – Temporal segmentation algorithms.Crossref | GoogleScholarGoogle Scholar |
Key CH (2006) Ecological and sampling constraints on defining landscape fire severity. Fire Ecology 2, 34–59.
| Ecological and sampling constraints on defining landscape fire severity.Crossref | GoogleScholarGoogle Scholar |
Key CH, Benson NC (2006) Landscape assessment: ground measure of severity, the Composite Burn Index; and remote sensing of severity, the Normalized Burn Ratio. USDA Forest Service, Rocky Mountain Research Station, Report RMRS-GTR-164-CD: LA 1–51. (Ogden, UT, USA). Available at https://pubs.er.usgs.gov/publication/2002085 [verified 28 October 2020].
Koutsias N, Karteris M (2000) Burned area mapping using logistic regression modeling of a single post-fire Landsat-5 Thematic Mapper image. International Journal of Remote Sensing 21, 673–687.
| Burned area mapping using logistic regression modeling of a single post-fire Landsat-5 Thematic Mapper image.Crossref | GoogleScholarGoogle Scholar |
Krebs P, Pezzatti GB, Mazzoleni S, Talbot LM, Conedera M (2010) Fire regime: history and definition of a key concept in disturbance ecology. Theory in Biosciences 129, 53–69.
| Fire regime: history and definition of a key concept in disturbance ecology.Crossref | GoogleScholarGoogle Scholar | 20502984PubMed |
Leys C, Ley C, Klein O, Bernard P, Licata L (2013) Detecting outliers: do not use standard deviation around the mean, use absolute deviation around the median. Journal of Experimental Social Psychology 49, 764–766.
| Detecting outliers: do not use standard deviation around the mean, use absolute deviation around the median.Crossref | GoogleScholarGoogle Scholar |
Li P, Jiang L, Feng Z (2013) Cross-comparison of vegetation indices derived from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) sensors. Remote Sensing 6, 310–329.
| Cross-comparison of vegetation indices derived from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) sensors.Crossref | GoogleScholarGoogle Scholar |
Long T, Zhang Z, He G, Jiao W, Tang C (2019) 30-m-Resolution global annual burned area mapping based on Landsat images and Google Earth Engine. Remote Sensing 11, 489
| 30-m-Resolution global annual burned area mapping based on Landsat images and Google Earth Engine.Crossref | GoogleScholarGoogle Scholar |
López García MJ, Caselles V (1991) Mapping burns and natural reforestation using thematic mapper data. Geocarto International 6, 31–37.
| Mapping burns and natural reforestation using thematic mapper data.Crossref | GoogleScholarGoogle Scholar |
Lu D, Mausel P, Brondizio E, Moran EF (2004) Change detection techniques. International Journal of Remote Sensing 25, 2365–2407.
| Change detection techniques.Crossref | GoogleScholarGoogle Scholar |
Ma Z, Liu J, Yang S (2013) Climate change in south-west China during 1961–2010: impacts and adaptation. Advances in Climate Change Research 4, 223–229.
| Climate change in south-west China during 1961–2010: impacts and adaptation.Crossref | GoogleScholarGoogle Scholar |
Mancino G, Ferrara A, Padula A, Nolè A (2020) Cross-comparison between Landsat 8 (OLI) and Landsat 7 (ETM+) derived vegetation indices in a Mediterranean environment. Remote Sensing 12, 291
| Cross-comparison between Landsat 8 (OLI) and Landsat 7 (ETM+) derived vegetation indices in a Mediterranean environment.Crossref | GoogleScholarGoogle Scholar |
Masek JG, Vermote EF, Saleous N, Wolfe R, Hall FG, Huemmrich F, Gao F, Kutler J, Lim TK (2013) LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2. ORNL DAAC. (Oak Ridge, TN, USA). Available at https://doi.org/10.3334/ORNLDAAC/1146 [verified 19 October 2020]
Ming Q, Shi Z (2007) New discussion on dry valley formation in the Three Parallel Rivers region. Journal of Desert Research 27, 99–104.
Mitri GH, Gitas IZ (2004) A semi-automated object-oriented model for burned area mapping in the Mediterranean region using Landsat-TM imagery. International Journal of Wildland Fire 13, 367–376.
| A semi-automated object-oriented model for burned area mapping in the Mediterranean region using Landsat-TM imagery.Crossref | GoogleScholarGoogle Scholar |
Mitsopoulos I, Chrysafi I, Bountis D, Mallinis G (2019) Assessment of factors driving high fire severity potential and classification in a Mediterranean pine ecosystem. Journal of Environmental Management 235, 266–275.
| Assessment of factors driving high fire severity potential and classification in a Mediterranean pine ecosystem.Crossref | GoogleScholarGoogle Scholar | 30685582PubMed |
Mouillot F, Schultz MG, Yue C, Cadule P, Tansey K, Ciais P, Chuvieco E (2014) Ten years of global burned area products from spaceborne remote sensing – a review: analysis of user needs and recommendations for future developments. International Journal of Applied Earth Observation and Geoinformation 26, 64–79.
| Ten years of global burned area products from spaceborne remote sensing – a review: analysis of user needs and recommendations for future developments.Crossref | GoogleScholarGoogle Scholar |
Oguro Y, Suga Y, Takeuchi S, Ogawa H, Tsuchiya K (2003) Monitoring of a rice field using Landsat-5 TM and Landsat-7 ETM+ data. Advance in Space Research 32, 2223–2228.
| Monitoring of a rice field using Landsat-5 TM and Landsat-7 ETM+ data.Crossref | GoogleScholarGoogle Scholar |
Padilla M, Stehman SV, Chuvieco E (2014) Validation of the 2008 MODIS-MCD45 global burned area product using stratified random sampling. Remote Sensing of Environment 144, 187–196.
| Validation of the 2008 MODIS-MCD45 global burned area product using stratified random sampling.Crossref | GoogleScholarGoogle Scholar |
Padilla M, Stehman SV, Ramo R, Corti D, Hantson S, Oliva P, Alonso-Canas I, Bradley AV, Tansey K, Mota B, Pereira JMC, Chuvieco E (2015) Comparing the accuracies of remote sensing global burned area products using stratified random sampling and estimation. Remote Sensing of Environment 160, 114–121.
| Comparing the accuracies of remote sensing global burned area products using stratified random sampling and estimation.Crossref | GoogleScholarGoogle Scholar |
Padilla M, Olofsson P, Stehman SV, Tansey K, Chuvieco E (2017) Stratification and sample allocation for reference burned area data. Remote Sensing of Environment 203, 240–255.
| Stratification and sample allocation for reference burned area data.Crossref | GoogleScholarGoogle Scholar |
Roteta E, Bastarrika A, Padilla M, Storm T, Chuvieco E (2019) Development of a Sentinel-2 burned area algorithm: generation of a small fire database for sub-Saharan Africa. Remote Sensing of Environment 222, 1–17.
| Development of a Sentinel-2 burned area algorithm: generation of a small fire database for sub-Saharan Africa.Crossref | GoogleScholarGoogle Scholar |
Roy DP, Boschetti L (2009) Southern Africa validation of the MODIS, L3JRC, and GlobCarbon burned-area products. IEEE Transactions on Geoscience and Remote Sensing 47, 1032–1044.
| Southern Africa validation of the MODIS, L3JRC, and GlobCarbon burned-area products.Crossref | GoogleScholarGoogle Scholar |
Roy DP, Kovalskyy V, Zhang HK, Vermote EF, Yan L, Kumar SS, Egorov A (2016) Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity. Remote Sensing of Environment 185, 57–70.
| Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity.Crossref | GoogleScholarGoogle Scholar | 32020954PubMed |
Sahoo PK, Soltani S, Wong AKC (1988) A survey of thresholding techniques. Computer Vision Graphics and Image Processing 41, 233–260.
| A survey of thresholding techniques.Crossref | GoogleScholarGoogle Scholar |
Schneiderbauer S, Zebisch M, Steurer C (2007) Applied remote sensing in mountain regions: a workshop organized by EURAC in the core of the Alps. Mountain Research and Development 27, 286–287.
| Applied remote sensing in mountain regions: a workshop organized by EURAC in the core of the Alps.Crossref | GoogleScholarGoogle Scholar |
Sola I, González-Audícana M, Álvarez-Mozos J (2016) Multicriteria evaluation of topographic correction methods. Remote Sensing of Environment 184, 247–262.
| Multicriteria evaluation of topographic correction methods.Crossref | GoogleScholarGoogle Scholar |
Sørensen T (1948) A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on Danish commons. Biologiske skrifter, Vol. V, p. 42. (Det Kongelige Danske Videnskabernes Selskab: Copenhagen, Denmark) Available at http://www.royalacademy.dk/Publications/High/295_S%C3%B8rensen,%20Thorvald.pdf [verified 19 October 2020]
Spearman C (1904) The proof and measurement of association between two things. The American Journal of Psychology 15, 72–101.
| The proof and measurement of association between two things.Crossref | GoogleScholarGoogle Scholar |
Steven MD, Malthus TJ, Baret F, Xu H, Chopping MJ (2003) Intercalibration of vegetation indices from different sensor systems. Remote Sensing of Environment 88, 412–422.
| Intercalibration of vegetation indices from different sensor systems.Crossref | GoogleScholarGoogle Scholar |
Stroppiana D, Bordogna G, Carrara P, Boschetti M, Boschetti L, Brivio PA (2012) A method for extracting burned areas from Landsat TM/ETM+ images by soft aggregation of multiple spectral indices and a region growing algorithm. ISPRS Journal of Photogrammetry and Remote Sensing 69, 88–102.
| A method for extracting burned areas from Landsat TM/ETM+ images by soft aggregation of multiple spectral indices and a region growing algorithm.Crossref | GoogleScholarGoogle Scholar |
Tan B, Masek JG, Wolfe R, Gao F, Huang C, Vermote EF, Sexton JO, Ederer G (2013) Improved forest change detection with terrain illumination corrected Landsat images. Remote Sensing of Environment 136, 469–483.
| Improved forest change detection with terrain illumination corrected Landsat images.Crossref | GoogleScholarGoogle Scholar |
Vanderhoof MK, Fairaux NM, Beal YJG, Hawbaker TJ (2017) Validation of the USGS Landsat Burned Area Essential Climate Variable (BAECV) across the conterminous United States. Remote Sensing of Environment 198, 393–406.
| Validation of the USGS Landsat Burned Area Essential Climate Variable (BAECV) across the conterminous United States.Crossref | GoogleScholarGoogle Scholar |
Vázquez-Jiménez R, Romero-Calcerrada R, Novillo CJ, Ramos-Bernal RN, Arrogante-Funes P (2017) Applying the chi-square transformation and automatic secant thresholding to Landsat imagery as unsupervised change detection methods. Journal of Applied Remote Sensing 11, 016016
| Applying the chi-square transformation and automatic secant thresholding to Landsat imagery as unsupervised change detection methods.Crossref | GoogleScholarGoogle Scholar |
Vázquez-Jiménez R, Ramos-Bernal RN, Romero-Calcerrada R, Arrogante-Funes P, Tizapa SS, Novillo CJ (2018) Thresholding algorithm optimization for change detection to satellite imagery. In ‘Color. Image process.’ (Ed. C Travieso-Gonzalez) pp. 163–182. (IntechOpen: London, UK)
Verbyla DL, Kasischke ES, Hoy EE (2008) Seasonal and topographic effects on estimating fire severity from Landsat TM/ETM+ data. International Journal of Wildland Fire 17, 527–534.
| Seasonal and topographic effects on estimating fire severity from Landsat TM/ETM+ data.Crossref | GoogleScholarGoogle Scholar |
Vermote E, Justice C, Claverie M, Franch B (2016) Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sensing of Environment 185, 46–56.
| Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product.Crossref | GoogleScholarGoogle Scholar | 32020955PubMed |
Vetrita Y, Cochrane MA (2019) Annual burned area from Landsat, Mawas, central Kalimantan, Indonesia, 1997–2015. ORNL DAAC. (Oak Ridge, TN, USA). Available at https://doi.org/10.3334/ORNLDAAC/1708. [verified 19 October 2020
Wang C, He Z, Zhu W (2001) Outline of vegetation in Dulongjiang river watershed, Yunnan. Shengtaixue Zazhi 20, 26–33.
Weiss DJ, Walsh SJ (2009) Remote sensing of mountain environments. Geography Compass 3, 1–21.
| Remote sensing of mountain environments.Crossref | GoogleScholarGoogle Scholar |
Willson A (2006) Forest conversion and land use change in rural northwest Yunnan, China. Mountain Research and Development 26, 227–236.
| Forest conversion and land use change in rural northwest Yunnan, China.Crossref | GoogleScholarGoogle Scholar |
Wilson EH, Sader SA (2002) Detection of forest harvest type using multiple dates of Landsat TM imagery. Remote Sensing of Environment 80, 385–396.
| Detection of forest harvest type using multiple dates of Landsat TM imagery.Crossref | GoogleScholarGoogle Scholar |
Winkler D (2000) Patterns of forest distribution and the impact of fire and pastoralism in the forest region of the Tibetan Plateau. In ‘Environmental Change in High Asia’. (Eds G Miehe, Y Zhang) pp. 201–227. (Marburger Geographische Schriften: Marburg)
Woźniak E, Aleksandrowicz S (2019) Self-adjusting thresholding for burnt area detection based on optical images. Remote Sensing 11, 2669
| Self-adjusting thresholding for burnt area detection based on optical images.Crossref | GoogleScholarGoogle Scholar |
Wu D, Li S, Yi S, Wang W (2009)
[Forest Fire Management Research Report]. In ‘
’ [Study on the conservation of Bio-cultural diversity and the sustainable and coordinated development of economy and society in northwest Yunnan] (Eds S Duan, Y He) pp. 282–295. (Yunnan Publishing Group, Yunnan Science and Technology Press: Kunming, China) Wulder MA, Masek JG, Cohen WB, Loveland TR, Woodcock CE (2012) Opening the archive: how free data has enabled the science and monitoring promise of Landsat. Remote Sensing of Environment 122, 2–10.
| Opening the archive: how free data has enabled the science and monitoring promise of Landsat.Crossref | GoogleScholarGoogle Scholar |
Wulder MA, Loveland TR, Roy DP, Crawford CJ, Masek JG, Woodcock CE, Allen RG, Anderson MC, Belward AS, Cohen WB, Dwyer J, Erb A, Gao F, Griffiths P, Helder D, Hermosilla T, Hipple JD, Hostert P, Hughes MJ, Huntington J, Johnson DM, Kennedy R, Kilic A, Li Z, Lymburner L, McCorkel J, Pahlevan N, Scambos TA, Schaaf C, Schott JR, Sheng Y, Storey J, Vermote E, Vogelmann J, White JC, Wynne RH, Zhu Z (2019) Current status of Landsat program, science, and applications. Remote Sensing of Environment 225, 127–147.
| Current status of Landsat program, science, and applications.Crossref | GoogleScholarGoogle Scholar |
Yan X, Ohara T, Akimoto H (2006) Bottom–up estimate of biomass burning in mainland China. Atmospheric Environment 40, 5262–5273.
| Bottom–up estimate of biomass burning in mainland China.Crossref | GoogleScholarGoogle Scholar |
Yang Y (2014) Discussion on existing problems and countermeasures of rural fire control – take Yunnan province as an example. Procedia Engineering 71, 519–522.
| Discussion on existing problems and countermeasures of rural fire control – take Yunnan province as an example.Crossref | GoogleScholarGoogle Scholar |
Zhao F, Shu L, Tian X, Wang M (2009) Change trends of forest fire danger in Yunnan province in 1957–2007. Shengtaixue Zazhi 28, 2333–2338.
Zhu Z (2017) Change detection using Landsat time series: a review of frequencies, preprocessing, algorithms, and applications. ISPRS Journal of Photogrammetry and Remote Sensing 130, 370–384.
| Change detection using Landsat time series: a review of frequencies, preprocessing, algorithms, and applications.Crossref | GoogleScholarGoogle Scholar |
Zhu Z, Wulder MA, Roy DP, Woodcock CE, Hansen MC, Radeloff VC, Healey SP, Schaaf C, Hostert P, Strobl P, Pekel JF, Lymburner L, Pahlevan N, Scambos TA (2019) Benefits of the free and open Landsat data policy. Remote Sensing of Environment 224, 382–385.
| Benefits of the free and open Landsat data policy.Crossref | GoogleScholarGoogle Scholar |
