Wing et al., 2015 - Google Patents
Individual snag detection using neighborhood attribute filtered airborne lidar dataWing et al., 2015
View PDF- Document ID
- 12236984591168189453
- Author
- Wing B
- Ritchie M
- Boston K
- Cohen W
- Olsen M
- Publication year
- Publication venue
- Remote Sensing of Environment
External Links
Snippet
The ability to estimate and monitor standing dead trees (snags) has been difficult due to their irregular and sparse distribution, often requiring intensive sampling methods to obtain statistically significant estimates. This study presents a new method for estimating and …
- 238000001514 detection method 0 title abstract description 84
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
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
- G06T17/05—Geographic models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
- G06T11/20—Drawing from basic elements, e.g. lines or circles
- G06T11/206—Drawing of charts or graphs
-
- 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
- G06T2207/10—Image acquisition modality
- G06T2207/10032—Satellite or aerial image; Remote sensing
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Wing et al. | Individual snag detection using neighborhood attribute filtered airborne lidar data | |
Sylvain et al. | Mapping dead forest cover using a deep convolutional neural network and digital aerial photography | |
Ma et al. | Comparison of canopy cover estimations from airborne LiDAR, aerial imagery, and satellite imagery | |
Morsdorf et al. | Discrimination of vegetation strata in a multi-layered Mediterranean forest ecosystem using height and intensity information derived from airborne laser scanning | |
Latifi et al. | Forest inventories by LiDAR data: A comparison of single tree segmentation and metric-based methods for inventories of a heterogeneous temperate forest | |
Trier et al. | Automatic detection of mound structures in airborne laser scanning data | |
Seidel et al. | Assessing different components of three-dimensional forest structure with single-scan terrestrial laser scanning: A case study | |
Wing et al. | Prediction of understory vegetation cover with airborne lidar in an interior ponderosa pine forest | |
Côté et al. | A fine-scale architectural model of trees to enhance LiDAR-derived measurements of forest canopy structure | |
Zald et al. | Influence of lidar, Landsat imagery, disturbance history, plot location accuracy, and plot size on accuracy of imputation maps of forest composition and structure | |
Matasci et al. | Mapping tree canopies in urban environments using airborne laser scanning (ALS): a Vancouver case study | |
Brovkina et al. | Mapping forest aboveground biomass using airborne hyperspectral and LiDAR data in the mountainous conditions of Central Europe | |
Joyce et al. | Detection of coarse woody debris using airborne light detection and ranging (LiDAR) | |
Ørka et al. | Subalpine zone delineation using LiDAR and Landsat imagery | |
Han et al. | Extraction of multilayer vegetation coverage using airborne LiDAR discrete points with intensity information in urban areas: A case study in Nanjing City, China | |
Mbaabu et al. | Quantification of carbon stock to understand two different forest management regimes in Kayar Khola watershed, Chitwan, Nepal | |
Coomes et al. | Airborne laser scanning of natural forests in New Zealand reveals the influences of wind on forest carbon | |
White et al. | Evaluating the capacity of single photon lidar for terrain characterization under a range of forest conditions | |
Ahmed et al. | Integration of lidar and landsat data to estimate forest canopy cover in coastal British Columbia | |
Ahmad et al. | Natural resource mapping using landsat and lidar towards identifying digital elevation, digital surface and canopy height models | |
Bohlin et al. | Quantifying post-fire fallen trees using multi-temporal lidar | |
Morsdorf et al. | The laegeren site: An augmented forest laboratory: combining 3-D reconstruction and radiative transfer models for trait-based assessment of functional diversity | |
Du et al. | Characterizing spatiotemporal variations of forest canopy gaps using aerial laser scanning data | |
Yépez-Rincón et al. | Assessing vertical structure of an endemic forest in succession using terrestrial laser scanning (TLS). Case study: Guadalupe Island | |
Kumar et al. | Characterization and classification of vegetation canopy structure and distribution within the Great Smoky Mountains National Park using LiDAR |