scholarly journals Mapping and Monitoring of Biomass and Grazing in Pasture with an Unmanned Aerial System

2019 ◽  
Vol 11 (5) ◽  
pp. 473 ◽  
Author(s):  
Adrien Michez ◽  
Philippe Lejeune ◽  
Sébastien Bauwens ◽  
Andriamandroso Herinaina ◽  
Yannick Blaise ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Spatial Information ◽  
High Spatial Resolution ◽  
3D Structure ◽  
Model Fitting ◽  
Unmanned Aerial Systems ◽  
Data Types ◽  
Sward Height ◽  
Biomass Monitoring ◽  
Grass Growth

The tools available to farmers to manage grazed pastures and adjust forage demand to grass growth are generally rather static. Unmanned aerial systems (UASs) are interesting versatile tools that can provide relevant 3D information, such as sward height (3D structure), or even describe the physical condition of pastures through the use of spectral information. This study aimed to evaluate the potential of UAS to characterize a pasture’s sward height and above-ground biomass at a very fine spatial scale. The pasture height provided by UAS products showed good agreement (R2 = 0.62) with a reference terrestrial light detection and ranging (LiDAR) dataset. We tested the ability of UAS imagery to model pasture biomass based on three different combinations: UAS sward height, UAS sward multispectral reflectance/vegetation indices, and a combination of both UAS data types. The mixed approach combining the UAS sward height and spectral data performed the best (adj. R2 = 0.49). This approach reached a quality comparable to that of more conventional non-destructive on-field pasture biomass monitoring tools. As all of the UAS variables used in the model fitting process were extracted from spatial information (raster data), a high spatial resolution map of pasture biomass was derived based on the best fitted model. A sward height differences map was also derived from UAS-based sward height maps before and after grazing. Our results demonstrate the potential of UAS imagery as a tool for precision grazing study applications. The UAS approach to height and biomass monitoring was revealed to be a potential alternative to the widely used but time-consuming field approaches. While reaching a similar level of accuracy to the conventional field sampling approach, the UAS approach provides wall-to-wall pasture characterization through very high spatial resolution maps, opening up a new area of research for precision grazing.

scholarly journals Hyperspectral Pansharpening Based on Homomorphic Filtering and Weighted Tensor Matrix

2019 ◽  
Vol 11 (9) ◽  
pp. 1005
Author(s):  
Jiahui Qu ◽  
Yunsong Li ◽  
Qian Du ◽  
Wenqian Dong ◽  
Bobo Xi
Keyword(s):  
Spatial Resolution ◽  
Spatial Information ◽  
Mean Squared Error ◽  
High Spatial Resolution ◽  
Experimental Results ◽  
Spectral Information ◽  
Morphological Operation ◽  
Effective Technique ◽  
Squared Error ◽  
Homomorphic Filtering

Hyperspectral pansharpening is an effective technique to obtain a high spatial resolution hyperspectral (HS) image. In this paper, a new hyperspectral pansharpening algorithm based on homomorphic filtering and weighted tensor matrix (HFWT) is proposed. In the proposed HFWT method, open-closing morphological operation is utilized to remove the noise of the HS image, and homomorphic filtering is introduced to extract the spatial details of each band in the denoised HS image. More importantly, a weighted root mean squared error-based method is proposed to obtain the total spatial information of the HS image, and an optimized weighted tensor matrix based strategy is presented to integrate spatial information of the HS image with spatial information of the panchromatic (PAN) image. With the appropriate integrated spatial details injection, the fused HS image is generated by constructing the suitable gain matrix. Experimental results over both simulated and real datasets demonstrate that the proposed HFWT method effectively generates the fused HS image with high spatial resolution while maintaining the spectral information of the original low spatial resolution HS image.

scholarly journals Evaluating the Impacts of Continuous and Rotational Grazing on Tallgrass Prairie Landscape Using High-Spatial-Resolution Imagery

Agronomy ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. 238 ◽  
Author(s):  
Shengfang Ma ◽  
Yuting Zhou ◽  
Prasanna H. Gowda ◽  
Liangfu Chen ◽  
Patrick J. Starks ◽  
...  
Keyword(s):  
United States ◽  
Land Cover ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Tallgrass Prairie ◽  
Landscape Metrics ◽  
High Spatial Resolution ◽  
Shrub Encroachment ◽  
Rotational Grazing ◽  
Grass Growth

This study evaluated the impacts of different grazing treatments (continuous (C) and rotational (R) grazing) on tallgrass prairie landscape, using high-spatial-resolution aerial imagery (1-m at RGB and near-infrared bands) of experimental C and R pastures within two replicates (Rep A and Rep B) in the southern Great Plains (SGP) of the United States. The imagery was acquired by the National Agriculture Imagery Program (NAIP) during the agricultural growing season of selected years (2010, 2013, 2015, and 2017) in the continental United States. Land cover maps were generated by combining visual interpolation, a support vector machine, and a decision tree classifier. Landscape metrics (class area, patch number, percentage of landscape, and fragmentation indices) were calculated from the FRAGSTATS (a computer software program designed to compute a wide variety of landscape metrics for categorical map patterns) based on land cover results. Both the metrics and land cover results were used to analyze landscape dynamics in the experiment pastures. Results showed that both grass and shrubs of different pastures differed largely in the same year and had significant annual dynamics controlled by climate. High stocking intensity delayed grass growth. A large proportion of bare soil occurred in sub-paddocks of rotational grazing that were just grazed or under grazing. Rep A experienced rapid shrub encroachment, with a large proportion of shrub at the beginning of the experiment. Shrub may occupy 41% of C and 15% of R in Rep A by 2030, as revealed by the linear regression analysis of shrub encroachment. In contrast, shrub encroachment was not significant in Rep B, which only had a small number of shrub patches at the beginning of the experiment. This result indicates that the shrub encroachment is mainly controlled by the initial status of the pastures instead of grazing management. However, the low temporal resolution of the NAIP imagery (one snapshot in two or three years) limits our comparison of the continuous and rotational grazing at the annual scale. Future studies need to combine NAIP imagery with other higher temporal resolution imagery (e.g., WorldView), in order to better evaluate the interannual variabilities of grass productivity and shrub encroachment.

scholarly journals SCENE CLASSFICATION BASED ON THE SEMANTIC-FEATURE FUSION FULLY SPARSE TOPIC MODEL FOR HIGH SPATIAL RESOLUTION REMOTE SENSING IMAGERY

2016 ◽  
Vol XLI-B7 ◽  
pp. 451-457
Author(s):  
Qiqi Zhu ◽  
Yanfei Zhong ◽  
Liangpei Zhang
Keyword(s):  
Spatial Resolution ◽  
Spatial Information ◽  
Topic Model ◽  
High Spatial Resolution ◽  
Feature Fusion ◽  
Spectral Feature ◽  
Semantic Feature ◽  
Scene Classification ◽  
Probabilistic Topic Model ◽  
Sparse Topic Model

Topic modeling has been an increasingly mature method to bridge the semantic gap between the low-level features and high-level semantic information. However, with more and more high spatial resolution (HSR) images to deal with, conventional probabilistic topic model (PTM) usually presents the images with a dense semantic representation. This consumes more time and requires more storage space. In addition, due to the complex spectral and spatial information, a combination of multiple complementary features is proved to be an effective strategy to improve the performance for HSR image scene classification. But it should be noticed that how the distinct features are fused to fully describe the challenging HSR images, which is a critical factor for scene classification. In this paper, a semantic-feature fusion fully sparse topic model (SFF-FSTM) is proposed for HSR imagery scene classification. In SFF-FSTM, three heterogeneous features – the mean and standard deviation based spectral feature, wavelet based texture feature, and dense scale-invariant feature transform (SIFT) based structural feature are effectively fused at the latent semantic level. The combination of multiple semantic-feature fusion strategy and sparse based FSTM is able to provide adequate feature representations, and can achieve comparable performance with limited training samples. Experimental results on the UC Merced dataset and Google dataset of SIRI-WHU demonstrate that the proposed method can improve the performance of scene classification compared with other scene classification methods for HSR imagery.

scholarly journals An Approach for the Pan Sharpening of Very High Resolution Satellite Images Using a CIELab Color Based Component Substitution Algorithm

2019 ◽  
Vol 9 (23) ◽  
pp. 5234 ◽  
Author(s):  
Rahimzadeganasl ◽  
Alganci ◽  
Goksel
Keyword(s):  
Spatial Resolution ◽  
Satellite Images ◽  
Spatial Information ◽  
High Spatial Resolution ◽  
Critical Role ◽  
Study Region ◽  
High Resolution Satellite Images ◽  
Very High ◽  
Component Substitution ◽  
Cielab Color

Recent very high spatial resolution (VHR) remote sensing satellites provide high spatial resolution panchromatic (Pan) images in addition to multispectral (MS) images. The pan sharpening process has a critical role in image processing tasks and geospatial information extraction from satellite images. In this research, CIELab color based component substitution Pan sharpening algorithm was proposed for Pan sharpening of the Pleiades VHR images. The proposed method was compared with the state-of-the-art Pan sharpening methods, such as IHS, EHLERS, NNDiffuse and GIHS. The selected study region included ten test sites, each of them representing complex landscapes with various land categories, to evaluate the performance of Pan sharpening methods in varying land surface characteristics. The spatial and spectral performance of the Pan sharpening methods were evaluated by eleven accuracy metrics and visual interpretation. The results of the evaluation indicated that proposed CIELab color-based method reached promising results and improved the spectral and spatial information preservation.

Analysis of Layered Structures at High Spatial Resolution Using Energy Filtered Imaging

1998 ◽  
Vol 523 ◽  
Author(s):  
P.L. Flaitz ◽  
J. Bruley
Keyword(s):  
Spatial Resolution ◽  
Spatial Information ◽  
High Spatial Resolution ◽  
Spatial Dimension ◽  
Imaging Systems ◽  
Multivariate Statistical ◽  
Edge Structure ◽  
Small Probe ◽  
High Spatial Dimension ◽  
Energy Information

AbstractThe development of energy filtered imaging systems for the TEM has opened new approaches to analyzing structures with very small dimensions. One of the benefits of such systems is the ability to form an EELS spectrum image containing energy information on one axis and spatial information on the other axis. By dissecting such an image in the spatial dimension, it is possible to generate a line profile across a layered structure at high spatial dimension without the need for a small probe. We have applied this approach to two structures typical of semiconductors, the Si/SiO2/Si interfaces in a gate oxide and the Ti/SiO2 interface for Al barrier metallization, which illustrate the spatial resolution possible with this technique. To analyze the spectrum images, the element specific near edge structure (ELNES) data are processed by conventional EELS background routines and multivariate statistical techniques using MATLAB software to extract both profiles of principal bonding components and composition.

scholarly journals High-Resolution Reef Bathymetry and Coral Habitat Complexity from Airborne Imaging Spectroscopy

2020 ◽  
Vol 12 (2) ◽  
pp. 310 ◽  
Author(s):  
Gregory P. Asner ◽  
Nicholas R. Vaughn ◽  
Christopher Balzotti ◽  
Philip G. Brodrick ◽  
Joseph Heckler
Keyword(s):  
Coral Reef ◽  
Spatial Resolution ◽  
Habitat Complexity ◽  
Spatial Information ◽  
High Spatial Resolution ◽  
Measurement Techniques ◽  
Imaging Spectroscopy ◽  
Three Dimensional ◽  
Reef Habitat ◽  
Persistent Problem

Coral reef ecosystems are rapidly changing, and a persistent problem with monitoring changes in reef habitat complexity rests in the spatial resolution and repeatability of measurement techniques. We developed a new approach for high spatial resolution (<1 m) mapping of nearshore bathymetry and three-dimensional habitat complexity (rugosity) using airborne high-fidelity imaging spectroscopy. Using this new method, we mapped coral reef habitat throughout two bays to a maximum depth of 25 m and compared the results to the laser-based SHOALS bathymetry standard. We also compared the results derived from imaging spectroscopy to a more conventional 4-band multispectral dataset. The spectroscopic approach yielded consistent results on repeat flights, despite variability in viewing and solar geometries and sea state conditions. We found that the spectroscopy-based results were comparable to those derived from SHOALS, and they were a major improvement over the multispectral approach. Yet, spectroscopy provided much finer spatial information than that which is available with SHOALS, which is valuable for analyzing changes in benthic composition at the scale of individual coral colonies. Monitoring temporal changes in reef 3D complexity at high spatial resolution will provide an improved means to assess the impacts of climate change and coastal processes that affect reef complexity.

scholarly journals SCENE CLASSFICATION BASED ON THE SEMANTIC-FEATURE FUSION FULLY SPARSE TOPIC MODEL FOR HIGH SPATIAL RESOLUTION REMOTE SENSING IMAGERY

2016 ◽  
Vol XLI-B7 ◽  
pp. 451-457
Author(s):  
Qiqi Zhu ◽  
Yanfei Zhong ◽  
Liangpei Zhang
Keyword(s):  
Spatial Resolution ◽  
Spatial Information ◽  
Topic Model ◽  
High Spatial Resolution ◽  
Feature Fusion ◽  
Spectral Feature ◽  
Semantic Feature ◽  
Scene Classification ◽  
Probabilistic Topic Model ◽  
Sparse Topic Model

Topic modeling has been an increasingly mature method to bridge the semantic gap between the low-level features and high-level semantic information. However, with more and more high spatial resolution (HSR) images to deal with, conventional probabilistic topic model (PTM) usually presents the images with a dense semantic representation. This consumes more time and requires more storage space. In addition, due to the complex spectral and spatial information, a combination of multiple complementary features is proved to be an effective strategy to improve the performance for HSR image scene classification. But it should be noticed that how the distinct features are fused to fully describe the challenging HSR images, which is a critical factor for scene classification. In this paper, a semantic-feature fusion fully sparse topic model (SFF-FSTM) is proposed for HSR imagery scene classification. In SFF-FSTM, three heterogeneous features &ndash; the mean and standard deviation based spectral feature, wavelet based texture feature, and dense scale-invariant feature transform (SIFT) based structural feature are effectively fused at the latent semantic level. The combination of multiple semantic-feature fusion strategy and sparse based FSTM is able to provide adequate feature representations, and can achieve comparable performance with limited training samples. Experimental results on the UC Merced dataset and Google dataset of SIRI-WHU demonstrate that the proposed method can improve the performance of scene classification compared with other scene classification methods for HSR imagery.

scholarly journals Land use land cover classification using local multiple pattern from very high resolution satellite imagery

2014 ◽  
Vol XL-8 ◽  
pp. 971-976 ◽  
Author(s):  
R. Suresh Kumar ◽  
A. R. Mahesh Balaji
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Spatial Resolution ◽  
Spatial Information ◽  
High Spatial Resolution ◽  
Spectral Variation ◽  
Land Use Land Cover ◽  
Texture Information ◽  
Very High Spatial Resolution ◽  
Very High

The recent development in satellite sensors provide images with very high spatial resolution that aids detailed mapping of Land Use Land Cover (LULC). But the heterogeneity in the landscapes often results in spectral variation within the same and spectral confusion among different LU/LC classes at finer spatial resolution. This leads to poor classification performances based on traditional spectral-based classification. Many studies have been addressed to improve this classification by incorporating texture information with multispectral images. Although different methods are available to extract textures from the satellite images, only a limited number of studies compared their performance in classification. The major problem with the existing texture measures is either scale/orientation/illumination variant (Haralick textures) or computationally difficult (Gabor textures) or less informative (Local binary pattern). This paper explores the use of texture information captured by Local Multiple Patterns (LMP) for LULC classification in a spectral-spatial classifier framework. LMP preserve more structural information and involves less computational efforts. Thus LMP is expected to be more promising for capturing spatial information from very high spatial resolution images. The proposed method is implemented with spectral bands and LMP derived from WorldView-2 multispectral imagery acquired for Madurai, India study area. The Multi-Layer-Perceptron neural network is used as a classifier. The proposed classification method is compared with LBP and conventional Maximum Likelihood Classification (MLC) separately. The classification results with 89.5% clarify the improvement offered by the LMP for LULC classification in comparison with the conventional approaches.

scholarly journals A high-spatial-resolution dataset of human thermal stress indices over South and East Asia

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yechao Yan ◽  
Yangyang Xu ◽  
Shuping Yue
Keyword(s):  
Public Health ◽  
Thermal Stress ◽  
East Asia ◽  
Spatial Resolution ◽  
Work Performance ◽  
Spatial Information ◽  
High Spatial Resolution ◽  
Population Group ◽  
Stress Indices ◽  
Warming World

AbstractThermal stress poses a major public health threat in a warming world, especially to disadvantaged communities. At the population group level, human thermal stress is heavily affected by landscape heterogeneities such as terrain, surface water, and vegetation. High-spatial-resolution thermal-stress indices, containing more detailed spatial information, are greatly needed to characterize the spatial pattern of thermal stress to enable a better understanding of its impacts on public health, tourism, and study and work performance. Here, we present a 0.1° × 0.1° gridded dataset of multiple thermal stress indices derived from the newly available ECMWF ERA5-Land and ERA5 reanalysis products over South and East Asia from 1981 to 2019. This high-spatial-resolution database of human thermal stress indices over South and East Asia (HiTiSEA), which contains the daily mean, maximum, and minimum values of UTCI, MRT, and eight other widely adopted indices, is suitable for both indoor and outdoor applications and allows researchers and practitioners to investigate the spatial and temporal evolution of human thermal stress and its impacts on densely populated regions over South and East Asia at a finer scale.

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