Martinez et al., 2021 - Google Patents
Multi-sensor approach to leaf area index estimation using statistical machine learning models: A case on mangrove forestsMartinez et al., 2021
View PDF- Document ID
- 8344054820496422964
- Author
- Martinez K
- Burgos D
- Blanco A
- Salmo III S
- Publication year
- Publication venue
- ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
External Links
Snippet
Leaf Area Index (LAI) is a quantity that characterizes canopy foliage content. As leaf surfaces are the primary sites of energy, mass exchange, and fundamental production of terrestrial ecosystem, many important processes are directly proportional to LAI. With this, LAI can be …
- 240000002044 Rhizophora apiculata 0 title abstract description 21
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
-
- 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
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Maimaitijiang et al. | Vegetation index weighted canopy volume model (CVMVI) for soybean biomass estimation from unmanned aerial system-based RGB imagery | |
Phinzi et al. | The assessment of water-borne erosion at catchment level using GIS-based RUSLE and remote sensing: A review | |
Lu et al. | A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems | |
Shao et al. | Stacked sparse autoencoder modeling using the synergy of airborne LiDAR and satellite optical and SAR data to map forest above-ground biomass | |
Lu et al. | Land cover change detection by integrating object-based data blending model of Landsat and MODIS | |
Thapa et al. | Potential of high-resolution ALOS–PALSAR mosaic texture for aboveground forest carbon tracking in tropical region | |
Shimada et al. | New global forest/non-forest maps from ALOS PALSAR data (2007–2010) | |
Morresi et al. | Mapping burn severity in the western Italian Alps through phenologically coherent reflectance composites derived from Sentinel-2 imagery | |
Luo et al. | Retrieving aboveground biomass of wetland Phragmites australis (common reed) using a combination of airborne discrete-return LiDAR and hyperspectral data | |
Noordermeer et al. | Predicting and mapping site index in operational forest inventories using bitemporal airborne laser scanner data | |
Meyer et al. | Forest degradation and biomass loss along the Chocó region of Colombia | |
Safari et al. | Integration of synthetic aperture radar and multispectral data for aboveground biomass retrieval in Zagros oak forests, Iran: An attempt on Sentinel imagery | |
Na et al. | Assessing breeding habitat suitability for the endangered red-crowned crane (Grus japonensis) based on multi-source remote sensing data | |
Zhao et al. | Optimizing ground photons for canopy height extraction from ICESat-2 data in mountainous dense forests | |
Sharma et al. | Assessing the potentials of multi-temporal sentinel-1 SAR data for paddy yield forecasting using artificial neural network | |
Martinez et al. | Multi-sensor approach to leaf area index estimation using statistical machine learning models: A case on mangrove forests | |
Tesfamichael et al. | Retrieval of narrow-range LAI of at multiple lidar point densities: Application on Eucalyptus grandis plantation | |
Uehara et al. | Time-series metrics applied to land use and land cover mapping with focus on landslide detection | |
Kukkonen et al. | Volumes by tree species can be predicted using photogrammetric UAS data, Sentinel-2 images and prior field measurements | |
Ozkan et al. | Predicting forest stand attributes using the integration of airborne laser scanning and Worldview-3 data in a mixed forest in Turkey | |
Waser et al. | Towards automated forest mapping | |
Yu et al. | Mapping global mangrove canopy height by integrating Ice, Cloud, and Land Elevation Satellite-2 photon-counting LiDAR data with multi-source images | |
Rezayan et al. | Estimating biophysical parameters of Persian oak coppice trees using UltraCam-D airborne imagery in Zagros semi-arid woodlands | |
Jin et al. | UAV-RGB-image-based aboveground biomass equation for planted forest in semi-arid Inner Mongolia, China | |
Dong et al. | Forest aboveground biomass estimation using GEDI and earth observation data through attention-based deep learning |