[go: up one dir, main page]

Kiadtikornthaweeyot et al., 2018 - Google Patents

Automatic detection of forest fire burnt scar from Landsat-8 image of northern part of Thailand

Kiadtikornthaweeyot et al., 2018

Document ID
15038449607140727935
Author
Kiadtikornthaweeyot W
Sukawattanavijit C
Rungsipanich A
Publication year
Publication venue
2018 15th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)

External Links

Snippet

Earth observation satellite imaging is considered as an excellent tool to extract the Earth's surface information. Forest fire burnt scars are a spectacular problem for land surface analysis. Fire burnt scar mapping is very important to be able to identify these areas. The …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
    • G06K9/0063—Recognising patterns in remote scenes, e.g. aerial images, vegetation versus urban areas
    • G06K9/00657—Recognising patterns in remote scenes, e.g. aerial images, vegetation versus urban areas of vegetation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10032—Satellite or aerial image; Remote sensing
    • G06T2207/10036—Multispectral image; Hyperspectral image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30181—Earth observation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10024—Color image

Similar Documents

Publication Publication Date Title
Ahady et al. Classification comparison of Landsat-8 and Sentinel-2 data in Google Earth Engine, study case of the city of Kabul
Thorp et al. Effect of image spatial and spectral characteristics on mapping semi-arid rangeland vegetation using multiple endmember spectral mixture analysis (MESMA)
Mucher et al. Land cover characterization and change detection for environmental monitoring of pan-Europe
Xie et al. Remote sensing imagery in vegetation mapping: a review
Knoth et al. Unmanned aerial vehicles as innovative remote sensing platforms for high‐resolution infrared imagery to support restoration monitoring in cut‐over bogs
Xu et al. Automatic cloud removal for Landsat 8 OLI images using cirrus band
Deshayes et al. The contribution of remote sensing to the assessment of drought effects in forest ecosystems
Huang et al. The importance of measurement errors for deriving accurate reference leaf area index maps for validation of moderate-resolution satellite LAI products
Wallace et al. Using orthoimages generated from oblique terrestrial photography to estimate and monitor vegetation cover
Shimabukuro et al. NOAA-AVHRR data processing for the mapping of vegetation cover
Kiadtikornthaweeyot et al. Automatic detection of forest fire burnt scar from Landsat-8 image of northern part of Thailand
Coops et al. Procedures for predicting habitat and structural attributes in eucalypt forests using high spatial resolution remotely sensed imagery
Martínez-Sánchez et al. UAV and satellite imagery applied to alien species mapping in NW Spain
Kavzoglu Simulating Landsat ETM+ imagery using DAIS 7915 hyperspectral scanner data
Yailymova et al. A multimodal dataset for forest damage detection and machine learning
Darvishsefat et al. Application of hyperspectral data for forest stand mapping
McGovern et al. Remotely sensed satellite imagery as an information source for industrial peatlands management
Brede et al. The Speulderbos fiducial reference site for continuous monitoring of forest biophysical variables
Costantino et al. Using images generated by sentinel-2 satellite optical sensor for burned area mapping
Kadzuwa et al. Effect of Leaf Phenology, Topography and Wind Speed on Forest Canopy Height and Above Ground Biomass Estimation Using Optical UAV Data in Malawi’s Miombo Woodlands
Danaher et al. A regression model approach for mapping woody foliage projective cover using Landsat imagery in Queensland, Australia
Jarman et al. Remote sensing and vegetation mapping in South Africa
Brivio et al. Exploiting spatial and temporal information for extracting burned areas from time series of SPOT-VGT data
Lund et al. Scanned, zapped, timed, and digitized: advanced technologies for measuring and monitoring vegetation diversity
Mummoorthy et al. Satellite image processing biomass estimation