scholarly journals How canopy shadow affects invasive plant species classification in high spatial resolution remote sensing

2019 ◽  
Vol 5 (4) ◽  
pp. 302-317 ◽  
Author(s):  
Javier Lopatin ◽  
Klara Dolos ◽  
Teja Kattenborn ◽  
Fabian E. Fassnacht
Keyword(s):  
Remote Sensing ◽  
Plant Species ◽  
Spatial Resolution ◽  
Invasive Plant ◽  
High Spatial Resolution ◽  
Invasive Plant Species ◽  
Species Classification

scholarly journals DOES THE DATA RESOLUTION/ORIGIN MATTER? SATELLITE, AIRBORNE AND UAV IMAGERY TO TACKLE PLANT INVASIONS

2016 ◽  
Vol XLI-B7 ◽  
pp. 903-908 ◽  
Author(s):  
Jana Müllerová ◽  
Josef Brůna ◽  
Petr Dvořák ◽  
Tomáš Bartaloš ◽  
Michaela Vítková
Keyword(s):  
Remote Sensing ◽  
Plant Species ◽  
Spectral Resolution ◽  
Invasive Plant ◽  
Plant Invasions ◽  
Economic Feasibility ◽  
Invasive Plant Species ◽  
Trade Offs ◽  
Very High ◽  
Resolution Data

Invasive plant species represent a serious threat to biodiversity and landscape as well as human health and socio-economy. To successfully fight plant invasions, new methods enabling fast and efficient monitoring, such as remote sensing, are needed. In an ongoing project, optical remote sensing (RS) data of different origin (satellite, aerial and UAV), spectral (panchromatic, multispectral and color), spatial (very high to medium) and temporal resolution, and various technical approaches (object-, pixelbased and combined) are tested to choose the best strategies for monitoring of four invasive plant species (giant hogweed, black locust, tree of heaven and exotic knotweeds). In our study, we address trade-offs between spectral, spatial and temporal resolutions required for balance between the precision of detection and economic feasibility. For the best results, it is necessary to choose best combination of spatial and spectral resolution and phenological stage of the plant in focus. For species forming distinct inflorescences such as giant hogweed iterative semi-automated object-oriented approach was successfully applied even for low spectral resolution data (if pixel size was sufficient) whereas for lower spatial resolution satellite imagery or less distinct species with complicated architecture such as knotweed, combination of pixel and object based approaches was used. High accuracies achieved for very high resolution data indicate the possible application of described methodology for monitoring invasions and their long-term dynamics elsewhere, making management measures comparably precise, fast and efficient. This knowledge serves as a basis for prediction, monitoring and prioritization of management targets.

scholarly journals DOES THE DATA RESOLUTION/ORIGIN MATTER? SATELLITE, AIRBORNE AND UAV IMAGERY TO TACKLE PLANT INVASIONS

2016 ◽  
Vol XLI-B7 ◽  
pp. 903-908 ◽  
Author(s):  
Jana Müllerová ◽  
Josef Brůna ◽  
Petr Dvořák ◽  
Tomáš Bartaloš ◽  
Michaela Vítková
Keyword(s):  
Remote Sensing ◽  
Plant Species ◽  
Spectral Resolution ◽  
Invasive Plant ◽  
Plant Invasions ◽  
Economic Feasibility ◽  
Invasive Plant Species ◽  
Trade Offs ◽  
Very High ◽  
Resolution Data

Invasive plant species represent a serious threat to biodiversity and landscape as well as human health and socio-economy. To successfully fight plant invasions, new methods enabling fast and efficient monitoring, such as remote sensing, are needed. In an ongoing project, optical remote sensing (RS) data of different origin (satellite, aerial and UAV), spectral (panchromatic, multispectral and color), spatial (very high to medium) and temporal resolution, and various technical approaches (object-, pixelbased and combined) are tested to choose the best strategies for monitoring of four invasive plant species (giant hogweed, black locust, tree of heaven and exotic knotweeds). In our study, we address trade-offs between spectral, spatial and temporal resolutions required for balance between the precision of detection and economic feasibility. For the best results, it is necessary to choose best combination of spatial and spectral resolution and phenological stage of the plant in focus. For species forming distinct inflorescences such as giant hogweed iterative semi-automated object-oriented approach was successfully applied even for low spectral resolution data (if pixel size was sufficient) whereas for lower spatial resolution satellite imagery or less distinct species with complicated architecture such as knotweed, combination of pixel and object based approaches was used. High accuracies achieved for very high resolution data indicate the possible application of described methodology for monitoring invasions and their long-term dynamics elsewhere, making management measures comparably precise, fast and efficient. This knowledge serves as a basis for prediction, monitoring and prioritization of management targets.

scholarly journals Fractional Cover Mapping of Invasive Plant Species by Combining Very High-Resolution Stereo and Multi-Sensor Multispectral Imageries

Forests ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 540 ◽  
Author(s):  
Siddhartha Khare ◽  
Hooman Latifi ◽  
Sergio Rossi ◽  
Sanjay Kumar Ghosh
Keyword(s):  
High Resolution ◽  
Plant Species ◽  
Spatial Resolution ◽  
Spectral Reflectance ◽  
Invasive Plant ◽  
Training Data ◽  
Landsat 8 ◽  
Invasive Plant Species ◽  
Fractional Cover ◽  
Very High

Invasive plant species are major threats to biodiversity. They can be identified and monitored by means of high spatial resolution remote sensing imagery. This study aimed to test the potential of multiple very high-resolution (VHR) optical multispectral and stereo imageries (VHRSI) at spatial resolutions of 1.5 and 5 m to quantify the presence of the invasive lantana (Lantana camara L.) and predict its distribution at large spatial scale using medium-resolution fractional cover analysis. We created initial training data for fractional cover analysis by classifying smaller extent VHR data (SPOT-6 and RapidEye) along with three dimensional (3D) VHRSI derived digital surface model (DSM) datasets. We modelled the statistical relationship between fractional cover and spectral reflectance for a VHR subset of the study area located in the Himalayan region of India, and finally predicted the fractional cover of lantana based on the spectral reflectance of Landsat-8 imagery of a larger spatial extent. We classified SPOT-6 and RapidEye data and used the outputs as training data to create continuous field layers of Landsat-8 imagery. The area outside the overlapping region was predicted by fractional cover analysis due to the larger extent of Landsat-8 imagery compared with VHR datasets. Results showed clear discrimination of understory lantana from upperstory vegetation with 87.38% (for SPOT-6), and 85.27% (for RapidEye) overall accuracy due to the presence of additional VHRSI derived DSM information. Independent validation for lantana fractional cover estimated root-mean-square errors (RMSE) of 11.8% (for RapidEye) and 7.22% (for SPOT-6), and R2 values of 0.85 and 0.92 for RapidEye (5 m) and SPOT-6 (1.5 m), respectively. Results suggested an increase in predictive accuracy of lantana within forest areas along with increase in the spatial resolution for the same Landsat-8 imagery. The variance explained at 1.5 m spatial resolution to predict lantana was 64.37%, whereas it decreased by up to 37.96% in the case of 5 m spatial resolution data. This study revealed the high potential of combining small extent VHR and VHRSI- derived 3D optical data with larger extent, freely available satellite data for identification and mapping of invasive species in mountainous forests and remote regions.

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 Remote sensing object-oriented approaches coupled with ecological informatics to map invasive plant species

2016 ◽  
Vol 5 (3) ◽  
pp. 285
Author(s):  
Sanjay Gairola ◽  
Şerban Procheş ◽  
Michael T. Gebreslasie ◽  
Duccio Rocchini
Keyword(s):  
Remote Sensing ◽  
Plant Species ◽  
Invasive Plant ◽  
Object Oriented ◽  
Invasive Plant Species

scholarly journals Advancements in satellite remote sensing for mapping and monitoring of alien invasive plant species (AIPs)

2019 ◽  
Vol 112 ◽  
pp. 237-245 ◽  
Author(s):  
Lwando Royimani ◽  
Onisimo Mutanga ◽  
John Odindi ◽  
Timothy Dube ◽  
Trylee Nyasha Matongera
Keyword(s):  
Remote Sensing ◽  
Plant Species ◽  
Satellite Remote Sensing ◽  
Invasive Plant ◽  
Invasive Plant Species
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