scholarly journals Improved Classification of Mangroves Health Status Using Hyperspectral Remote Sensing Data

2014 ◽  
Vol XL-8 ◽  
pp. 667-670 ◽  
Author(s):  
R. Vidhya ◽  
D. Vijayasekaran ◽  
M. Ahamed Farook ◽  
S. Jai ◽  
M. Rohini ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Health Status ◽  
Remote Sensing Data ◽  
Principal Component ◽  
Hyperspectral Remote Sensing ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Spectral Angle Mapper ◽  
Svm Classification ◽  
Sensing Data

Mangrove ecosystem plays a crucial role in costal conservation and provides livelihood supports to humans. It is seriously affected by the various climatic and anthropogenic induced changes. The continuous monitoring is imperative to protect this fragile ecosystem. In this study, the mangrove area and health status has been extracted from Hyperspectral remote sensing data (EO- 1Hyperion) using support vector machine classification (SVM). The principal component transformation (PCT) technique is used to perform the band reduction in Hyperspectral data. The soil adjusted vegetation Indices (SAVI) were used as additional parameters. The mangroves are classified into three classes degraded, healthy and sparse. The SVM classification is generated overall accuracy of 73 % and kappa of 0.62. The classification results were compared with the results of spectral angle mapper classification (SAM). The SAVI also included in SVM classification and the accuracy found to be improved to 82 %. The sparse and degraded mangrove classes were well separated. The results indicate that the mapping of mangrove health is accurate when the machine learning classifier like SVM combined with different indices derived from hyperspectral remote sensing data.

scholarly journals Agricultural Stress Monitoring using Remote Sensing Data

2020 ◽  
Vol 9 (3) ◽  
pp. 335-337
Keyword(s):  
Remote Sensing ◽  
Stress Level ◽  
Hyperspectral Image ◽  
Atmospheric Correction ◽  
Remote Sensing Data ◽  
Hyperspectral Remote Sensing ◽  
Hyperspectral Data ◽  
Stress Monitoring ◽  
Chlorophyll Contents ◽  
Sensing Data

This study consist of experiments on Hyperspectral remote sensing data for monitoring field stress using remote sensing tools. We have segmented Hyperspectral image and then calculated stress level using ENVI tool. EO-I hyperspectral remote sensing data from hyperion space born sensor has been used as the key input. QUACK (Quick Atmospheric Correction) algorithm has been used for atmospheric correction of hyperspectral data. EO-1, hyperion sensors data It has been observed that stress level depends on chlorophyll contents of a leaf. It has been observed that green field is with less stress and rock where no chlorophyll contents have most stress. We have also shown stress level in the scale of 1 to 9.

scholarly journals Monitoring Invasive Plant Species Using Hyperspectral Remote Sensing Data

Land ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 29
Author(s):  
Levente Papp ◽  
Boudewijn van Leeuwen ◽  
Péter Szilassi ◽  
Zalán Tobak ◽  
József Szatmári ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Plant Species ◽  
Invasive Plant ◽  
Reference Data ◽  
Remote Sensing Data ◽  
Hyperspectral Remote Sensing ◽  
Support Vector ◽  
Invasive Plant Species ◽  
Natural Habitats ◽  
Sensing Data

The species richness and biodiversity of vegetation in Hungary are increasingly threatened by invasive plant species brought in from other continents and foreign ecosystems. These invasive plant species have spread aggressively in the natural and semi-natural habitats of Europe. Common milkweed (Asclepias syriaca) is one of the species that pose the greatest ecological menace. Therefore, the primary purpose of the present study is to map and monitor the spread of common milkweed, the most common invasive plant species in Europe. Furthermore, the possibilities to detect and validate this special invasive plant by analyzing hyperspectral remote sensing data were investigated. In combination with field reference data, high-resolution hyperspectral aerial images acquired by an unmanned aerial vehicle (UAV) platform in 138 spectral bands in areas infected by common milkweed were examined. Then, support vector machine (SVM) and artificial neural network (ANN) classification algorithms were applied to the highly accurate field reference data. As a result, common milkweed individuals were distinguished in hyperspectral images, achieving an overall accuracy of 92.95% in the case of supervised SVM classification. Using the ANN model, an overall accuracy of 99.61% was achieved. To evaluate the proposed approach, two experimental tests were conducted, and in both cases, we managed to distinguish the individual specimens within the large variety of spreading invasive species in a study area of 2 ha, based on centimeter spatial resolution hyperspectral UAV imagery.

scholarly journals EXPLORATION OF IOCG MINERALIZATIONS USING INTEGRATION OF SPACE-BORNE REMOTE SENSING DATA WITH AIRBORNE GEOPHYSICAL DATA

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 9-16
Author(s):  
M. Abdolmaleki ◽  
T. M. Rasmussen ◽  
M. K. Pal
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Principal Component ◽  
Support Vector ◽  
Mineral Potential Mapping ◽  
Band Ratio ◽  
Sensing Data ◽  
Mineral Potential ◽  
Potential Mapping ◽  
Sentinel 2

Abstract. Nowadays, remote sensing technologies are playing a significant role in mineral potential mapping. To optimize the exploration approach along with a cost-effective way, narrow down the target areas for a more detailed study for mineral exploration using suitable data selection and accurate data processing approaches are crucial. To establish optimum procedures by integrating space-borne remote sensing data with other earth sciences data (e.g., airborne magnetic and electromagnetic) for exploration of Iron Oxide Copper Gold (IOCG) mineralization is the objective of this study. Further, the project focus is to test the effectiveness of Copernicus Sentinel-2 data in mineral potential mapping from the high Arctic region. Thus, Inglefield Land from northwest Greenland has been chosen as a study area to evaluate the developed approach. The altered minerals, including irons and clays, were mapped utilizing Sentinel-2 data through band ratio and principal component analysis (PCA) methods. Lineaments of the study area were extracted from Sentinel-2 data using directional filters. Self-Organizing Maps (SOM) and Support Vector Machines (SVM) were used for classification and analysing the available data. Further, various thematic maps (e.g., geological, geophysical, geochemical) were prepared from the study area. Finally, a mineral prospectively map was generated by integrating the above mentioned information using the Fuzzy Analytic Hierarchy Process (FAHP). The prepared potential map for IOCG mineralization using the above approach of Inglefield Land shows a good agreement with the previous geological field studies.

Retrieval of Water Quality Parameters in Lake Ontario Based on Hyperspectral Remote Sensing Data and Intelligent Algorithms

2020 ◽  
Author(s):  
Yu Li ◽  
Youyue Sun ◽  
Jinhui Jeanne Huang ◽  
Edward McBean
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Water Quality ◽  
Remote Sensing Data ◽  
Quality Parameters ◽  
Hyperspectral Remote Sensing ◽  
Optically Active ◽  
Water Quality Parameters ◽  
Support Vector ◽  
Sensing Data

<p>With the increasingly prominent ecological and environmental problems in lakes, the monitoring water quality in lakes by satellite remote sensing is becoming more and more high demanding. Traditional water quality sampling is normally conducted manually and are time-consuming and labor-costly. It could not provide a full picture of the waterbodies over time due to limited sampling points and low sampling frequency. A novel attempt is proposed to use hyperspectral remote sensing in conjunction with machine learning technologies to retrieve water quality parameters and provide mapping for these parameters in a lake. The retrieval of both optically active parameters: Chlorophyll-a (CHLA) and dissolved oxygen concentration (DO), as well as non-optically active parameters: total phosphorous (TP), total nitrogen (TN), turbidity (TB), pH were studied in this research. A comparison of three machine learning algorithms including Random Forests (RF), Support Vector Regression (SVR) and Artificial Neural Networks were conducted. These water parameters collected by the Environment and Climate Change Canada agency for 20 years were used as the ground truth for model training and validation. Two set of remote sensing data from MODIS and Sentinel-2 were utilized and evaluated. This research proposed a new approach to retrieve both optically active parameters and non-optically active parameters for water body and provide new strategy for water quality monitoring.</p>

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