Monitoring Invasive Plant Species Using Hyperspectral Remote 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.

Download Full-text

Detecting subcanopy invasive plant species in tropical rainforest by integrating optical and microwave (InSAR/PolInSAR) remote sensing data, and a decision tree algorithm

ISPRS Journal of Photogrammetry and Remote Sensing ◽

10.1016/j.isprsjprs.2013.12.007 ◽

2014 ◽

Vol 88 ◽

pp. 174-192 ◽

Cited By ~ 32

Author(s):

Abduwasit Ghulam ◽

Ingrid Porton ◽

Karen Freeman

Keyword(s):

Remote Sensing ◽

Decision Tree ◽

Plant Species ◽

Invasive Plant ◽

Tropical Rainforest ◽

Remote Sensing Data ◽

Invasive Plant Species ◽

Decision Tree Algorithm ◽

Tree Algorithm ◽

Sensing Data

Download Full-text

Multispectral Approach for Identifying Invasive Plant Species Based on Flowering Phenology Characteristics

Remote Sensing ◽

10.3390/rs11080953 ◽

2019 ◽

Vol 11 (8) ◽

pp. 953 ◽

Cited By ~ 5

Author(s):

Tarin Paz-Kagan ◽

Micha Silver ◽

Natalya Panov ◽

Arnon Karnieli

Keyword(s):

Plant Species ◽

Species Distribution ◽

Invasive Plant ◽

Remote Sensing Data ◽

Hyperspectral Data ◽

Support Vector ◽

Temperature Pattern ◽

Multispectral Images ◽

Invasive Plant Species ◽

Distribution Maps

Invasive plant species (IPS) are the second biggest threat to biodiversity after habitat loss. Since the spatial extent of IPS is essential for managing the invaded ecosystem, the current study aims at identifying and mapping the aggressive IPS of Acacia salicina and Acacia saligna, to understand better the key factors influencing their distribution in the coastal plain of Israel. This goal was achieved by integrating airborne-derived hyperspectral imaging and multispectral earth observation for creating species distribution maps. Hyperspectral data, in conjunction with high spatial resolution species distribution maps, were used to train the multispectral images at the species level. We incorporated a series of statistical models to classify the IPS location and to recognize their distribution and density. We took advantage of the phenological flowering stages of Acacia trees, as obtained by the multispectral images, for the support vector machine classification procedure. The classification yielded an overall Kappa coefficient accuracy of 0.89. We studied the effect of various environmental and human factors on IPS density by using a random forest machine learning model, to understand the mechanisms underlying successful invasions, and to assess where IPS have a higher likelihood of occurring. This algorithm revealed that the high density of Acacia most closely related to elevation, temperature pattern, and distances from rivers, settlements, and roads. Our results demonstrate how the integration of remote-sensing data with different data sources can assist in determining IPS proliferation and provide detailed geographic information for conservation and management efforts to prevent their future spread.

Download Full-text

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

10.5194/egusphere-egu2020-1869 ◽

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>

Download Full-text

The Support Vector Machine (SVM) Algorithm for Supervised Classification of Hyperspectral Remote Sensing Data

Kernel Methods for Remote Sensing Data Analysis ◽

10.1002/9780470748992.ch3 ◽

2009 ◽

pp. 49-83 ◽

Cited By ~ 3

Author(s):

J. Anthony Gualtieri

Keyword(s):

Remote Sensing ◽

Support Vector Machine ◽

Supervised Classification ◽

Remote Sensing Data ◽

Hyperspectral Remote Sensing ◽

Support Vector ◽

Sensing Data ◽

Svm Algorithm

Download Full-text

Improved Classification of Mangroves Health Status Using Hyperspectral Remote Sensing Data

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-8-667-2014 ◽

2014 ◽

Vol XL-8 ◽

pp. 667-670 ◽

Cited By ~ 5

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.

Download Full-text