Classification multi-spectrale et apport de la bande TM7, dans la distinction des dépôts meubles de l'île d'Anticosti, Québec

1985 ◽  
Vol 22 (8) ◽  
pp. 1139-1148 ◽  
Author(s):  
Sylvain Perras ◽  
Ferdinand Bonn ◽  
Hugh Gwyn ◽  
Jean-Marie Dubois
Keyword(s):  
Vegetation Index ◽  
Forest Cover ◽  
Near Infrared ◽  
Spectral Simulation ◽  
Alluvial Deposits ◽  
Strong Reflection ◽  
Infrared Wavelengths ◽  
Anticosti Island ◽  
Surficial Deposits

The differentiation between various surficial deposits and bedrock on Anticosti Island is difficult because of the dense and homogeneous forest cover and because of the subdued topography. Remote sensing allows us to solve this problem by making use of the physical characteristics of Quaternary deposits and the weathered bedrock, which influence internal drainage and the availability of soil moisture to the vegetation. A spectral simulation of LANDSAT-4 was made using an airborne Daedalus 1260, 11-channel scanner. Several supervised classifications of the digital images were made using test sites studied in the field. Using the raw data from Thematic Mapper bands TM2, TM3, TM4, and TM7, the geologic environments and the ecodynamic units could be distinguished with 70% accuracy. However, the integration of bands TM2 and TM4 with the vegetation index (VI) = [(TM4 – TM3)/(TM4 + TM3)] and the algorithme (A) = [(TM7 − VI)/(TM7 + VI)] resulted in a classification accuracy of 80%. Band TM7 (2,08–2,35 μm) distinguishes itself from the other bands by having a strong reflection over bare bedrock and an absorption by water, which allow the characterization of modern alluvial deposits. The characteristics of TM7 can also be distinguished from those of the near-infrared wavelengths of TM4, which are absorbed by forest vegetation.

scholarly journals Automated detection of individual juniper tree location and forest cover changes using Google Earth Engine

2021 ◽  
Vol 64 (1) ◽  
pp. 61-72
Author(s):  
Sudeera Wickramarathna ◽  
Jamon Van Den Hoek ◽  
Bogdan Strimbu
Keyword(s):  
Random Forest ◽  
Vegetation Index ◽  
Forest Cover ◽  
Near Infrared ◽  
Google Earth ◽  
Random Forest Algorithm ◽  
Individual Tree ◽  
Western Juniper ◽  
Effective Manner ◽  
Google Earth Engine

Tree detection is the first step in the appraisal of a forest, especially when the focus is monitoring the growth of tree canopy. The acquisition of annual very high-resolution aerial images by the National Agriculture Imagery Program (NAIP) and their accessibility through Google Earth Engine (GEE) supports the delineation of tree canopies and change over time in a cost and time-effective manner. The objectives of this study are to develop an automated method to detect the crowns of individual western Juniper (Juniperus occidentalis) trees and to assess the change of forest cover from multispectral 1-meter resolution NAIP images collected from 2009 to 2016 in Oregon, USA. The Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Ratio Vegetation Index (RVI) were calculated from the NAIP images, in addition to the red-green-blue-near infrared bands. To identify the most suitable approach for individual tree crown identification, we created two training datasets: one considering yearly images separately and one merging all images, irrespective of the year. We segmented individual tree crowns using a random forest algorithm implemented in GEE and seven rasters, namely the reflectance of four spectral bands as recorded by the NAIP images (i.e., the red-green-blue-near infrared) and three calculated indices (i.e., NDVI, NDWI, and RVI). We compared the estimated location of the trees, computed as the centroid of the crown, with the visually identified treetops, which were considered as validation locations. We found that tree location errors were smaller when years were analyzed individually than by merging the years. Measurements of completeness (74%), correctness (94%), and mean accuracy detection (82 %) show promising performance of the random forest algorithm in crown delineation, considering that only four original input bands were used for crown segmentation. The change in the calculated crown area for western juniper follows a sinusoidal curve, with a decrease from 2011 to 2012 and an increase from 2012 to 2014. The proposed approach has the potential to estimate individual tree locations and forest cover area dynamics at broad spatial scales using regularly collected airborne imagery with easy-to-implement methods.

scholarly journals Enriching Low-Density Terrain Maps from Satellite with Autonomous Robots Data

2021 ◽  
Vol 6 (1) ◽  
pp. 66
Author(s):  
Edmundo Guerra ◽  
Antoni Grau ◽  
Yolanda Bolea ◽  
Rodrigo Munguia
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Autonomous Robots ◽  
High Density ◽  
Low Density ◽  
Soil Features ◽  
Density Maps ◽  
Learning Techniques ◽  
Radar Information

Satellite imagery and remote sensoring have been used for some years in agriculture to create terrain maps for different soil features (humidity, vegetation index, etc.). Multichannel information provides lots of data, but with a big drawback: the low density of information per surface unit; that is, the multi-channeled pixels correspond to a large surface, and a fine characterization of the targeted areas is not possible. In this research, the authors propose the enrichment of such data by the use of autonomous robots that explore and sense the same targeted area of the satellite but yielding a finer detail of terrain, complementing and fusing both information sources. The sensory elements of the autonomous robots are in the visual spectrum as well as in the near-infrared spectrum, together with Lidar and radar information. This enrichment will provide a high-density map of the soil to the final user to improve crops, irrigation, seeding and other agricultural processes. The methodology to fuse data and create high-density maps will be deep learning techniques. The system will be validated in real fields with the use of real sensors to measure the data given by satellites and robots’ sensors.

scholarly journals Characterization of Sierpinski carpet optical antenna at visible and near-infrared wavelengths

2014 ◽  
Vol 16 (9) ◽  
pp. 093024 ◽  
Author(s):  
Ting Lee Chen ◽  
Dirk Jan Dikken ◽  
Jord C Prangsma ◽  
Frans Segerink ◽  
Jennifer L Herek
Keyword(s):  
Near Infrared ◽  
Optical Antenna ◽  
Sierpinski Carpet ◽  
Infrared Wavelengths ◽  
Sierpiński Carpet

scholarly journals A multi-biome study of tree cover detection using the Forest Cover Index

2021 ◽  
Author(s):  
Sarah Becker ◽  
Megan Maloney ◽  
Andrew Griffin
Keyword(s):  
Vegetation Index ◽  
Forest Cover ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Tree Cover ◽  
Future Research ◽  
Vegetative Cover ◽  
Ancillary Data ◽  
Land Covers ◽  
Sentinel 2

Tree cover maps derived from satellite and aerial imagery directly support civil and military operations. However, distinguishing tree cover from other vegetative land covers is an analytical challenge. While the commonly used Normalized Difference Vegetation Index (NDVI) can identify vegetative cover, it does not consistently distinguish between tree and low-stature vegetation. The Forest Cover Index (FCI) algorithm was developed to take the multiplicative product of the red and near infrared bands and apply a threshold to separate tree cover from non-tree cover in multispectral imagery (MSI). Previous testing focused on one study site using 2-m resolution commercial MSI from WorldView-2 and 30-m resolution imagery from Landsat-7. New testing in this work used 3-m imagery from PlanetScope and 10-m imagery from Sentinel-2 in imagery in sites across 12 biomes in South and Central America and North Korea. Overall accuracy ranged between 23% and 97% for Sentinel-2 imagery and between 51% and 98% for PlanetScope imagery. Future research will focus on automating the identification of the threshold that separates tree from other land covers, exploring use of the output for machine learning applications, and incorporating ancillary data such as digital surface models and existing tree cover maps.

scholarly journals Caracterización espectral de Quillaja saponaria (Mol.)

2016 ◽  
pp. 65
Author(s):  
T. Acuña ◽  
C. Mattar ◽  
H. J. Hernández
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Spectral Response ◽  
Health State ◽  
High Concentration ◽  
Health States ◽  
Plant Vigour ◽  
Quillaja Saponaria ◽  
Spectral Signatures

<p align="justify">This paper presents a spectral reflectance characterization of the specie Quillaja saponaria (Mol.), endemic tree of Chile and valued by society due to its provision of several ecosystem services that gives to society and also for its high concentration of saponins in cortex widely used in the pharmacological industry. For spectral characterization a foliar spectral signatures protocol was designed which included standardized instrumental and environmental parameters. The spectral response of different individuals was measured to evaluate the spectral behaviour and degree of variability within species in the visible and near infrared ranges (VNIR; 400-990 nm) with two hyperspectral sensors (ASD HH and camera PDF-65-V10E). The resulting spectral signatures obtained with ASD HH showed a variation less than 5% of reflectance in VNIR and lesser than that in the transition zone from red to near infrared (red-edge; 680-730 nm). Additionally, two distinctive spectral features were detected for the specie, the first is related to a fast increase of reflectance in bands 450-480 nm and the second, to a marked decrease in the 920-970 nm range associated with water absorption features. At branch level, these distinctive features are maintained but with a smaller magnitude of reflectance, which could indicate that they are useful characteristic spectral patterns that can eventually be used for monitoring the physical health state of the specie using remote sensing. On the other hand, we used a PDF-65 camera for study the plant vigour from different health states (healthy, ill, died) with spectral vegetation index. The Plant Senescence Reflectance Index detected stress on leaves, and Triangular Vegetation Index allows for a gradually characterization of every state. This work provides the first spectral reference for one of the most important sclerophyll species of Chile.</p>

scholarly journals Experimental characterization of dielectric-loaded plasmonic waveguide-racetrack resonators at near-infrared wavelengths

2012 ◽  
Vol 107 (2) ◽  
pp. 401-407 ◽  
Author(s):  
C. Garcia ◽  
V. Coello ◽  
Z. Han ◽  
I. P. Radko ◽  
S. I. Bozhevolnyi
Keyword(s):  
Near Infrared ◽  
Plasmonic Waveguide ◽  
Experimental Characterization ◽  
Infrared Wavelengths
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