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Satellite data have been widely used in the detection of vegetation area changes, however, the lack of historical training samples seriously limits detection accuracy. In this research, an iterative intersection analysis algorithm (IIAA) is proposed to solve this problem, and employed to improve the change detection accuracy of Phragmites area in t...
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The study utilizes Landsat-7 ETM+ 2001and Landsat TM5 2009 based on Normalized Differences Vegetation Index (NDVI) and 457 colour composite at the study area located in Leihitu Peninsula, Ambon City District, Ambon Island, Moluccas Province. The classified satellite data under NDVI and 457 colour composite of 2001 and 2009 of 2001 and 2009 were use...
Vegetation change detection is needed for conserve of quality and water cycle in Cidanau watershed. The NDVI was applied to quantify the vegetation changes of Cidanau watershed for three different years 1989, 2001, and 2015. Using NDVI we mapped the reflectance from chlorophyll and distinguished varying amounts of vegetation at the pixel level by i...
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... Additionally, NAIP data is freely available and accessible through Google Earth Engine (GEE), making it easily accessible for efforts of Phragmites control. Previous research has used NAIP images to manually identify Phragmites to create a dataset to train a machine-learning model with moderate-resolution Landsat images as input (Liu et al., 2016a). Other studies have also mapped Phragmites by using NAIP imagery as an input into machine learning classifiers (Correll et al., 2019;Liu et al., 2016b;Xie et al., 2015). ...
Phragmites australis is a widespread invasive plant species in the USA that greatly impacts estuarine wetlands by creating dense patches and outcompeting other plants. The invasion of Phragmites into wetland ecosystems is known to decrease biodiversity, destroy the habitat of threatened and endangered bird species, and alter biogeochemistry. While the impact of Phragmites is known, the spatial extent of this species is challenging to document due to its fragmented occurrence. Using high-resolution imagery from the National Agriculture Imagery Program (NAIP) from 2017, we evaluated a geospatial method of mapping the spatial extent of Phragmites across the state of DE. Normalized difference vegetation index (NDVI) and principal component analysis (PCA) bands are generated from the NAIP data and used as inputs in a random forest classifier to achieve a high overall accuracy for the Phragmites classification of around 95%. The classified gridded dataset has a spatial resolution of 1 m and documents the spatial distribution of Phragmites throughout the state’s estuarine wetlands (around 11%). Such detailed classification could aid in monitoring the spread of this invasive species over space and time and would inform the decision-making process for landscape managers.
... Most current mapping methods for invasive P. australis focus on imagery acquired from one of three platforms: satellites, crewed aircraft, and uncrewed aerial vehicles (UAVs). Satellite-based mapping of invasive P. australis has been done with a variety of multispectral imagery (e.g., Arzandeh and Wang 2003;Labda et al. 2007Labda et al. , 2008Labda et al. , 2010Ghioca-Robrecht et al. 2008;Gilmore et al. 2008;Lantz and Wang 2013;Brooks et al. 2015;Xie et al. 2015;Liu et al. 2016;Marcaccio and Chow-Fraser 2016;Rupasinghe and Chow-Fraser 2019), hyperspectral data (e.g., Pengra et al. 2007), fusion methods that incorporate optical and radar imagery (e.g., Bourgeau-Chavez et al. 2004Bourgeau-Chavez et al. 2008a, b;Brooks et al. 2015), and methods that use radar alone (e.g., Bourgeau-Chavez et al. 2009Marcaccio and Chow-Fraser 2016). ...
Wetland managers in North America spend a great deal of time and money trying to control invasive Phragmites australis. Accurate mapping with remote sensing imagery is key to these efforts, which are increasingly employing uncrewed aerial vehicle (UAV) imagery. We mapped P. australis on the Crow Island State Game Area using UAV-derived single-date and multi-date RGB imagery combined with a Digital Surface Model (DSM). In addition to a traditional maximum likelihood classification (MLC), we used two machine-learning (ML) classification algorithms: support vector machine (SVM) and neural network (NN). We assessed accuracy based on both the traditional global model (overall accuracy [OA], omission [OE] and commission [CE] errors for the Phragmites class, and Kappa statistic) and local, per-patch accuracy broken down across 5 density classes and 3 size classes. Our global accuracy assessment for single-date imagery found that SVM (72% OA, 10% OE, 16% CE) performed similar to MLC (70% OA, 17% OE, 8% CE), while NN (33% OA, 7% OE, 41% CE) performed worse. The use of multi-date imagery had little effect on accuracy (MLC 64% OA, 21% OE, 12% CE; SVM 71% OA, 11% OE, 17% CE) except with NN, where the additional bands led to much higher accuracy (67% OA, 7% OE, 22% CE). These results were largely mirrored in the per-patch accuracy assessment, where SVM performed slightly better than MLC and NN performed poorly due to high commission errors. Regarding patch size and density, both larger and medium sized patches, as well as denser patches, were identified relatively accurately, but smaller patches tended to be overestimated and lower-density patches exhibited high omission errors. These results show that wetland managers can achieve very acceptable mapping accuracies with simple methods that require little in the way of resources and expertise.
... However, the methods are more diverse than those in Table 1. Change detection algorithms have been used in many applications such as arid environment monitoring [100], submerged biomasses in shallow coastal water [101], phragmites australis distribution [102], etc. We will mention some other applications in the following paragraphs. ...
Multispectral (MS) and hyperspectral (HS) images have been successfully and widely used in remote sensing applications such as target detection, change detection, and anomaly detection. In this paper, we aim at reviewing recent change detection papers and raising some challenges and opportunities in the field from a practitioner’s viewpoint using MS and HS images. For example, can we perform change detection using synthetic hyperspectral images? Can we use temporally-fused images to perform change detection? Some of these areas are ongoing and will require more research attention in the coming years. Moreover, in order to understand the context of our paper, some recent and representative algorithms in change detection using MS and HS images are included, and their advantages and disadvantages will be highlighted.
