scholarly journals Mapping caribou habitat north of the 51st parallel in Québec using Landsat imagery

Rangifer ◽  
2003 ◽  
Vol 23 (5) ◽  
pp. 235
Author(s):  
Stéphanie Chalifoux ◽  
Isabelle Saucier ◽  
G. Jean Doucet ◽  
Pierre Lamothe
Keyword(s):  
Landsat Imagery ◽  
Principal Component ◽  
Computer Assisted ◽  
Vegetation Types ◽  
Visual Interpretation ◽  
Winter Diet ◽  
Map Updating ◽  
Lichen Woodland ◽  
Colour Composite ◽  
George River

A methodology using Landsat Thematic Mapper (TM) images and vegetation typology, based on lichens as the principal component of caribou winter diet, was developed to map caribou habitat over a large and diversified area of Northern Québec. This approach includes field validation by aerial surveys (helicopter), classification of vegetation types, image enhancement, visual interpretation and computer assisted mapping. Measurements from more than 1500 field sites collected over six field campaigns from 1989 to 1996 represented the data analysed in this study. As the study progressed, 14 vegetation classes were defined and retained for analyses. Vegetation classes denoting important caribou habitat included six classes of upland lichen communities (Lichen, Lichen-Shrub, Shrub-Lichen, Lichen-Graminoid-Shrub, Lichen-Woodland, Lichen-Shrub-Woodland). Two classes (Burnt-over area, Regenerating burnt-over area) are related to forest fire, and as they develop towards lichen communities, will become important for caribou. The last six classes are retained to depict remaining vegetation cover types. A total of 37 Landsat TM scenes were geocoded and enhanced using two methods: the Taylor method and the false colour composite method (bands combination and stretching). Visual inter¬pretation was chosen as the most efficient and reliable method to map vegetation types related to caribou habitat. The 43 maps produced at the scale of 1:250 000 and the synthesis map (1:2 000 000) provide a regional perspective of caribou habitat over 1200 000 km2 covering the entire range of the George river herd. The numerical nature of the data allows rapid spatial analysis and map updating.

scholarly journals Signature capture of red soil patches and their acidity-A case study of Banka district, Bihar, India

2016 ◽  
Vol 8 (2) ◽  
pp. 874-878
Author(s):  
Binod K. Vimal ◽  
Rajkishore Kumar ◽  
C. D. Choudhary ◽  
Sunil Kumar ◽  
Rakesh Kumar ◽  
...  
Keyword(s):  
Spectral Band ◽  
Satellite Image ◽  
Geographical Area ◽  
Principal Component ◽  
Red Soil ◽  
Visual Interpretation ◽  
Spectral Bands ◽  
Total Geographical Area ◽  
Colour Composite ◽  
Soil Patch

Colour in soils as well as other object is the visual perceptual property which is perceived by human eye. They are governed by spectrum of light corresponding to wavelength or reflected energy of the material. Developed model for soil acidity is based on visual interpretation, principal component and spectral enhancement techniques by using of the satellite image (IRS LISS III, 2014). In this context, red soil patch is much sensitive in red spectral band comparison to green and blue spectral bands and perceived as red tone by human eyes but same soil patch appears green in false colour composite (FCC) image of NIR (0.70-0.80μm), Red (0.60-0.70 μm) and Green (0.50-0.60μm) bands. The maximum coverage of red soil patches having low pH < 6.5 (1:2.5) was recognized in 44.07 per cent of the total geographical area (3019.56 sq.km) under Banka district. Maximum red soil patches having their acidity were recognised in Katoria (18.56%), Chanan (15.15%), Bounsi (10.44%) and Banka (9.92%) blocks. Overall results indicated that variation of tone in different bands helps for the separation of red soil patches.

The forest resource of Labrador: Landsat Thematic Mapper imagery provides a unique and current perspective

1993 ◽  
Vol 69 (6) ◽  
pp. 667-671 ◽  
Author(s):  
John A. Drieman
Keyword(s):  
Forest Cover ◽  
Thematic Mapper ◽  
Landsat Thematic Mapper ◽  
Forest Resource ◽  
Visual Interpretation ◽  
Digital Format ◽  
Current Perspective ◽  
Colour Composite ◽  
Verification Process ◽  
Forest Cover Types

The need for a current, regional perspective of the forest of Labrador was identified. Mapping of forest cover types, peat-lands, recent burns and clearcut disturbances was accomplished through visual interpretation of 1:1,000,000 scale Landsat Thematic mapper colour composite transparencies and the transfer of interpreted polygons to a geographic information system. The mapping and verification process is described in this paper. The end product, a forest resource map, provides the most up-to-date and detailed information on Labrador's forest cover types and disturbances available on a single map. The digital format of the map facilities area summaries, viewing and printing.

scholarly journals Facial Reconstruction as a Regression Problem

2011 ◽  
pp. 68-87
Author(s):  
Maxime Berar ◽  
Françoise Tilotta ◽  
Joan A. Glaunès ◽  
Yves Rozenholc ◽  
Michel Desvignes ◽  
...  
Keyword(s):  
Soft Tissue ◽  
Principal Component ◽  
Latent Root ◽  
Facial Reconstruction ◽  
Computer Assisted ◽  
Regression Methods ◽  
Tissue Surface ◽  
Tissue Surfaces ◽  
Statistical Shape ◽  
The Individual

This chapter presents a computer-assisted method for facial reconstruction. This method provides an estimation of the facial outlook associated with unidentified skeletal remains. Current computer-assisted methods using a statistical framework rely on a common set of points extracted form the bone and soft-tissue surfaces. Facial reconstruction then attempts to predict the position of the soft-tissue surface points knowing the positions of the bone surface points. This chapter proposes to use linear latent variable regression methods for the prediction (such as Principal Component Regression or Latent Root Root Regression) and to compare the results obtained to those given by the use of statistical shape models. In conjunction, the influence of the number of skull landmarks used was evaluated. Anatomical skull landmarks are completed iteratively by points located upon geodesics linking the anatomical landmarks. They enable artificial augmentation of the number of skull points. Facial landmarks are obtained using a mesh-matching algorithm between a common reference mesh and the individual soft-tissue surface meshes. The proposed method is validated in terms of accuracy, based on a leave-one-out cross-validation test applied on a homogeneous database. Accuracy measures are obtained by computing the distance between the reconstruction and the ground truth. Finally, these results are discussed in regard to current computer-assisted facial reconstruction techniques, including deformation based techniques.

scholarly journals A Comprehensive Feature Analysis of the Fetal Heart Rate Signal for the Intelligent Assessment of Fetal State

2018 ◽  
Vol 7 (8) ◽  
pp. 223 ◽  
Author(s):  
Zhidong Zhao ◽  
Yang Zhang ◽  
Yanjun Deng
Keyword(s):  
Heart Rate ◽  
Fetal Heart Rate ◽  
Fetal Heart ◽  
Principal Component ◽  
Machine Learning Algorithms ◽  
Medical Decision ◽  
Support Vector ◽  
Visual Interpretation ◽  
Adaptive Boosting ◽  
Original Dataset

Continuous monitoring of the fetal heart rate (FHR) signal has been widely used to allow obstetricians to obtain detailed physiological information about newborns. However, visual interpretation of FHR traces causes inter-observer and intra-observer variability. Therefore, this study proposed a novel computerized analysis software of the FHR signal (CAS-FHR), aimed at providing medical decision support. First, to the best of our knowledge, the software extracted the most comprehensive features (47) from different domains, including morphological, time, and frequency and nonlinear domains. Then, for the intelligent assessment of fetal state, three representative machine learning algorithms (decision tree (DT), support vector machine (SVM), and adaptive boosting (AdaBoost)) were chosen to execute the classification stage. To improve the performance, feature selection/dimensionality reduction methods (statistical test (ST), area under the curve (AUC), and principal component analysis (PCA)) were designed to determine informative features. Finally, the experimental results showed that AdaBoost had stronger classification ability, and the performance of the selected feature set using ST was better than that of the original dataset with accuracies of 92% and 89%, sensitivities of 92% and 89%, specificities of 90% and 88%, and F-measures of 95% and 92%, respectively. In summary, the results proved the effectiveness of our proposed approach involving the comprehensive analysis of the FHR signal for the intelligent prediction of fetal asphyxia accurately in clinical practice.

scholarly journals Spatiotemporal Characterization of Mangrove Phenology and Disturbance Response: The Bangladesh Sundarban

2019 ◽  
Vol 11 (17) ◽  
pp. 2063 ◽  
Author(s):  
Christopher Small ◽  
Daniel Sousa
Keyword(s):  
Time Series ◽  
Mangrove Forest ◽  
Landsat Imagery ◽  
Methodological Approach ◽  
Principal Component ◽  
Spatiotemporal Analysis ◽  
Low Rank ◽  
Eof Analysis ◽  
Vegetation Fraction ◽  
Disturbance Response

This work presents a spatiotemporal analysis of the phenology and disturbance response in the Sundarban mangrove forest on the Ganges-Brahmaputra Delta in Bangladesh. The methodological approach is based on an Empirical Orthogonal Function (EOF) analysis of the new Harmonized Landsat Sentinel-2 (HLS) BRDF and atmospherically corrected reflectance time series, preceded by a Robust Principal Component Analysis (RPCA) separation of Low Rank and Sparse components of the image time series. Low Rank components are spatially and temporally pervasive while Sparse components are transient and localized. The RPCA clearly separates subtle spatial variations in the annual cycle of monsoon-modulated greening and senescence of the mangrove forest from the spatiotemporally complex agricultural phenology surrounding the Sundarban. A 3 endmember temporal mixture model maps spatially coherent differences in the 2018 greening-senescence cycle of the mangrove which are both concordant and discordant with existing species composition maps. The discordant patterns suggest a phenological response to environmental factors like surface hydrology. On decadal time scales, a standard EOF analysis of vegetation fraction maps from annual post-monsoon Landsat imagery is sufficient to isolate locations of shoreline advance and retreat related to changes in sedimentation and erosion, as well as cyclone-induced defoliation and recovery.

scholarly journals Deep Learning Based Wildfire Event Object Detection from 4K Aerial Images Acquired by UAS

AI ◽  
2020 ◽  
Vol 1 (2) ◽  
pp. 166-179 ◽  
Author(s):  
Ziyang Tang ◽  
Xiang Liu ◽  
Hanlin Chen ◽  
Joseph Hupy ◽  
Baijian Yang
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Object Detection ◽  
Aerial Images ◽  
Unmanned Aerial Systems ◽  
Computer Assisted ◽  
Visual Interpretation ◽  
Two Phase ◽  
Aerial Systems ◽  
Spot Fires

Unmanned Aerial Systems, hereafter referred to as UAS, are of great use in hazard events such as wildfire due to their ability to provide high-resolution video imagery over areas deemed too dangerous for manned aircraft and ground crews. This aerial perspective allows for identification of ground-based hazards such as spot fires and fire lines, and to communicate this information with fire fighting crews. Current technology relies on visual interpretation of UAS imagery, with little to no computer-assisted automatic detection. With the help of big labeled data and the significant increase of computing power, deep learning has seen great successes on object detection with fixed patterns, such as people and vehicles. However, little has been done for objects, such as spot fires, with amorphous and irregular shapes. Additional challenges arise when data are collected via UAS as high-resolution aerial images or videos; an ample solution must provide reasonable accuracy with low delays. In this paper, we examined 4K ( 3840 × 2160 ) videos collected by UAS from a controlled burn and created a set of labeled video sets to be shared for public use. We introduce a coarse-to-fine framework to auto-detect wildfires that are sparse, small, and irregularly-shaped. The coarse detector adaptively selects the sub-regions that are likely to contain the objects of interest while the fine detector passes only the details of the sub-regions, rather than the entire 4K region, for further scrutiny. The proposed two-phase learning therefore greatly reduced time overhead and is capable of maintaining high accuracy. Compared against the real-time one-stage object backbone of YoloV3, the proposed methods improved the mean average precision(mAP) from 0 . 29 to 0 . 67 , with an average inference speed of 7.44 frames per second. Limitations and future work are discussed with regard to the design and the experiment results.

Greening dynamics and shrubland extent from remote sensing data using NDVI and NARI Indices: case study of the George River basin (Nunavik, Canada).

2020 ◽  
Author(s):  
Dr. Jean-Pierre Dedieu ◽  
Johann Housset ◽  
Arthur Bayle ◽  
Esther Lévesque ◽  
José Gérin-Lajoie
Keyword(s):  
Remote Sensing ◽  
River Basin ◽  
Vegetation Change ◽  
Vegetation Index ◽  
Spectral Band ◽  
Human Populations ◽  
Vegetation Types ◽  
Local Study ◽  
Basin Scale ◽  
George River

&lt;p&gt;Arctic greening trends are well documented at various scales (Fraser et al., 2011; Tremblay et al., 2012; Bjorkman et al., 2018). In this context, Remote Sensing offers a unique tool for estimating the high latitude vegetation evolution in the relatively long-term, i.e. the Landsat archive since the 80&amp;#8217;s. Spectral indices derived from visible and infra-red wavelengths provide relations that can be used to quantify vegetation dynamics, we will combine the well-used Normalized Difference Vegetation Index (NDVI) and the recent Normalized Anthocyanins Reflectance Index (Bayle et al., 2019), using red-edge spectral band (690 to 710 &amp;#181;m) from Sentinel-2, to better quantify vegetation change over 30 years.&lt;/p&gt;&lt;p&gt;The application area is located in Nunavik, northern Qu&amp;#233;bec (Canada), and concerns the George River catchment (565 km length, 41&amp;#160;700&amp;#160;km&amp;#178;). This large river basin covers vegetation from boreal forest (South) to arctic tundra (North). Local study sites stem from the Kangiqsualujjuaq village (Ungava Bay) to 300 km south, along the main river and its tributaries.&lt;/p&gt;&lt;p&gt;NDVI: surface reflectance Landsat bands were gathered for three years 1985, 2000 and 2015 (respectively Landsat missions 5, 7 and 8). For each period of interest, the best August cloud-free scenes were chosen and merged to create a cloud free mosaic covering the study area. NDVI bands were calculated and compared after cloud and water masking. NDVI trends were compared between the main vegetation types following the newly released &amp;#8220;Ecological mapping of the vegetation of northern Quebec&amp;#8221; (MRNFP, 2018). Centroid of polygons within the main vegetation types of the map were used to classify the NDVI results and assess changes per type. Results of NDVI time evolution revealed a clear greening trend at the river basin scale. Although greening was observed across the whole latitudinal gradient, the relative NDVI increase was stronger on the northern half of the study area, mostly covered with tundra and subarctic vegetation. Both shrublands and sparsely vegetated zones dominated by rocks had the greatest relative NDVI increase. This is likely caused by improved growth of established prostrate vegetation over the past 30 years in response to increasing temperatures trend.&lt;/p&gt;&lt;p&gt;NARI: greening trends in the Eastern Canadian Arctic have been partly attributed to increases in shrub cover (Myers-smith et al., 2011) and specifically to Betula glandulosa (e.g. Tremblay et al., 2012). Such land cover changes alter species competition (Shevtosa et al., 1997) and soil thermal regime (Domine et al., 2015; Paradis et al., 2016). Transformations in biotic and abiotic conditions reduce the fruit productivity of low stature shrubs of the Ericaceae family (Lussier 2017), which in turn is expected to impact animal (Prescott and Richard 2013) and human populations (L&amp;#233;vesque et al., 2013; Boulanger-Lapointe et al., 2019). An innovative method has been developed in the French Alps to detect the late-fall reddening of shrub leaves and map shrublands (Bayle et al., 2019). Quantifying NARI dynamics related to NDVI dynamics could allow to gain a better understanding of species composition change related to current landscape transformation.&lt;/p&gt;

COMPUTER‐ASSISTED ANALYSIS TECHNIQUES FOR REMOTE SENSING DATA INTERPRETATION

Geophysics ◽  
1977 ◽  
Vol 42 (3) ◽  
pp. 468-481 ◽  
Author(s):  
Paul E. Anuta
Keyword(s):  
Gamma Ray ◽  
Remote Sensing Data ◽  
Data Interpretation ◽  
Magnetic Data ◽  
Computer Assisted ◽  
Data Sets ◽  
Visual Interpretation ◽  
Computer Based ◽  
Interpretation Process ◽  
Assisted Analysis

The development of airborne and satellite multispectral scanning radiometers has created widespread interest in the application of such sensors to mapping of earth resources. The energy sensed in each band can be used as a parameter in a computer‐based, multidimensional‐pattern‐recognition process to aid in the interpretation of the nature of elements in the scene. Images from each band can also be interpreted visually. Visual interpretation of 5 or 10 multispectral images simultaneously becomes impractical, especially as the area studied increases; hence, great emphasis has been placed on machine (computer‐assisted) techniques in the interpretation process. A number of other data sets have recently been studied and integrated by digital registration with the multispectral reflectance and radiance phenomena. Topographic data, which have been registered with four‐band Landsat multispectral scanner (MSS) data, are being studied to determine relationships between spectral and topographic variables. Geophysical variables. including gamma‐ray and magnetic data, have also been registered and studied using the multivariate analysis approach.

Sign in / Sign up

Export Citation Format

Share Document