SATELLITE MONITORING OF THE DROUGHT CONSEQUENCES VIA MEDIUM AND HIGH RESOLUTION MULTISPECTRAL IMAGES

2018 ◽  
Author(s):  
Dmitriy Mozgovoy
Keyword(s):  
High Resolution ◽  
Satellite Monitoring ◽  
Multispectral Images

scholarly journals A global satellite-assisted precipitation climatology

2015 ◽  
Vol 7 (2) ◽  
pp. 275-287 ◽  
Author(s):  
C. Funk ◽  
A. Verdin ◽  
J. Michaelsen ◽  
P. Peterson ◽  
D. Pedreros ◽  
...  
Keyword(s):  
High Resolution ◽  
Complex Terrain ◽  
Poor Performance ◽  
Satellite Observations ◽  
Satellite Monitoring ◽  
Monthly Precipitation ◽  
Climate Conditions ◽  
Precipitation Climatology ◽  
Polar Orbiting Satellite ◽  
Observation Networks

Abstract. Accurate representations of mean climate conditions, especially in areas of complex terrain, are an important part of environmental monitoring systems. As high-resolution satellite monitoring information accumulates with the passage of time, it can be increasingly useful in efforts to better characterize the earth's mean climatology. Current state-of-the-science products rely on complex and sometimes unreliable relationships between elevation and station-based precipitation records, which can result in poor performance in food and water insecure regions with sparse observation networks. These vulnerable areas (like Ethiopia, Afghanistan, or Haiti) are often the critical regions for humanitarian drought monitoring. Here, we show that long period of record geo-synchronous and polar-orbiting satellite observations provide a unique new resource for producing high-resolution (0.05°) global precipitation climatologies that perform reasonably well in data-sparse regions. Traditionally, global climatologies have been produced by combining station observations and physiographic predictors like latitude, longitude, elevation, and slope. While such approaches can work well, especially in areas with reasonably dense observation networks, the fundamental relationship between physiographic variables and the target climate variables can often be indirect and spatially complex. Infrared and microwave satellite observations, on the other hand, directly monitor the earth's energy emissions. These emissions often correspond physically with the location and intensity of precipitation. We show that these relationships provide a good basis for building global climatologies. We also introduce a new geospatial modeling approach based on moving window regressions and inverse distance weighting interpolation. This approach combines satellite fields, gridded physiographic indicators, and in situ climate normals. The resulting global 0.05° monthly precipitation climatology, the Climate Hazards Group's Precipitation Climatology version 1 (CHPclim v.1.0, doi:10.15780/G2159X), is shown to compare favorably with similar global climatology products, especially in areas with complex terrain and low station densities.

scholarly journals AGB Estimation in a Tropical Mountain Forest (TMF) by Means of RGB and Multispectral Images Using an Unmanned Aerial Vehicle (UAV)

2019 ◽  
Vol 11 (12) ◽  
pp. 1413 ◽  
Author(s):  
Víctor González-Jaramillo ◽  
Andreas Fries ◽  
Jörg Bendix
Keyword(s):  
High Resolution ◽  
Unmanned Aerial Vehicle ◽  
Tree Level ◽  
Mountain Forest ◽  
Multispectral Images ◽  
Individual Tree ◽  
Tropical Mountain ◽  
Aerial Vehicle ◽  
Rgb Images ◽  
Very High

The present investigation evaluates the accuracy of estimating above-ground biomass (AGB) by means of two different sensors installed onboard an unmanned aerial vehicle (UAV) platform (DJI Inspire I) because the high costs of very high-resolution imagery provided by satellites or light detection and ranging (LiDAR) sensors often impede AGB estimation and the determination of other vegetation parameters. The sensors utilized included an RGB camera (ZENMUSE X3) and a multispectral camera (Parrot Sequoia), whose images were used for AGB estimation in a natural tropical mountain forest (TMF) in Southern Ecuador. The total area covered by the sensors included 80 ha at lower elevations characterized by a fast-changing topography and different vegetation covers. From the total area, a core study site of 24 ha was selected for AGB calculation, applying two different methods. The first method used the RGB images and applied the structure for motion (SfM) process to generate point clouds for a subsequent individual tree classification. Per the classification at tree level, tree height (H) and diameter at breast height (DBH) could be determined, which are necessary input parameters to calculate AGB (Mg ha−1) by means of a specific allometric equation for wet forests. The second method used the multispectral images to calculate the normalized difference vegetation index (NDVI), which is the basis for AGB estimation applying an equation for tropical evergreen forests. The obtained results were validated against a previous AGB estimation for the same area using LiDAR data. The study found two major results: (i) The NDVI-based AGB estimates obtained by multispectral drone imagery were less accurate due to the saturation effect in dense tropical forests, (ii) the photogrammetric approach using RGB images provided reliable AGB estimates comparable to expensive LiDAR surveys (R2: 0.85). However, the latter is only possible if an auxiliary digital terrain model (DTM) in very high resolution is available because in dense natural forests the terrain surface (DTM) is hardly detectable by passive sensors due to the canopy layer, which impedes ground detection.

scholarly journals Aircraft and Ship Velocity Determination in Sentinel-2 Multispectral Images

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2873 ◽  
Author(s):  
Henning Heiselberg
Keyword(s):  
High Resolution ◽  
Jet Stream ◽  
Multispectral Images ◽  
Jet Streams ◽  
Band Offsets ◽  
Sentinel 2

The Sentinel-2 satellites in the Copernicus program provide high resolution multispectral images, which are recorded with temporal offsets up to 2.6 s. Moving aircrafts and ships are therefore observed at different positions due to the multispectral band offsets, from which velocities can be determined. We describe an algorithm for detecting aircrafts and ships, and determining their speed, heading, position, length, etc. Aircraft velocities are also affected by the parallax effect and jet streams, and we show how the altitude and the jet stream speed can be determined from the geometry of the aircraft and/or contrail heading. Ship speeds are more difficult to determine as wakes affect the average ship positions differently in the various multispectral bands, and more advanced corrections methods are shown to improve the velocity determination.

scholarly journals Semantic Segmentation of Natural Materials on a Point Cloud Using Spatial and Multispectral Features

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2244
Author(s):  
J. M. Jurado ◽  
J. L. Cárdenas ◽  
C. J. Ogayar ◽  
L. Ortega ◽  
F. R. Feito
Keyword(s):  
High Resolution ◽  
Point Cloud ◽  
Near Infrared ◽  
Automatic Segmentation ◽  
Hierarchical Cluster ◽  
Semantic Segmentation ◽  
Heterogeneous Data ◽  
Multispectral Images ◽  
Three Dimensional Model ◽  
Natural Materials

The characterization of natural spaces by the precise observation of their material properties is highly demanded in remote sensing and computer vision. The production of novel sensors enables the collection of heterogeneous data to get a comprehensive knowledge of the living and non-living entities in the ecosystem. The high resolution of consumer-grade RGB cameras is frequently used for the geometric reconstruction of many types of environments. Nevertheless, the understanding of natural spaces is still challenging. The automatic segmentation of homogeneous materials in nature is a complex task because there are many overlapping structures and an indirect illumination, so the object recognition is difficult. In this paper, we propose a method based on fusing spatial and multispectral characteristics for the unsupervised classification of natural materials in a point cloud. A high-resolution camera and a multispectral sensor are mounted on a custom camera rig in order to simultaneously capture RGB and multispectral images. Our method is tested in a controlled scenario, where different natural objects coexist. Initially, the input RGB images are processed to generate a point cloud by applying the structure-from-motion (SfM) algorithm. Then, the multispectral images are mapped on the three-dimensional model to characterize the geometry with the reflectance captured from four narrow bands (green, red, red-edge and near-infrared). The reflectance, the visible colour and the spatial component are combined to extract key differences among all existing materials. For this purpose, a hierarchical cluster analysis is applied to pool the point cloud and identify the feature pattern for every material. As a result, the tree trunk, the leaves, different species of low plants, the ground and rocks can be clearly recognized in the scene. These results demonstrate the feasibility to perform a semantic segmentation by considering multispectral and spatial features with an unknown number of clusters to be detected on the point cloud. Moreover, our solution is compared to other method based on supervised learning in order to test the improvement of the proposed approach.

Object-based change detection in wind storm-damaged forest using high-resolution multispectral images

2014 ◽  
Vol 35 (13) ◽  
pp. 4758-4777 ◽  
Author(s):  
N. Chehata ◽  
C. Orny ◽  
S. Boukir ◽  
D. Guyon ◽  
J.P. Wigneron
Keyword(s):  
High Resolution ◽  
Change Detection ◽  
Multispectral Images ◽  
Object Based

An Optimized Approach for Pansharpening Very High Resolution Multispectral Images

2012 ◽  
Vol 9 (4) ◽  
pp. 735-739 ◽  
Author(s):  
Zhiqiang Zhou ◽  
Silong Peng ◽  
Bo Wang ◽  
Zhihui Hao ◽  
Shaolin Chen
Keyword(s):  
High Resolution ◽  
Multispectral Images ◽  
Very High
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