Understanding seasonal dynamics of invasive water hyacinth (
            <i>Eichhornia crassipes</i>
            ) in the Greater Letaba river system using Sentinel-2 satellite data

Thamaga, Kgabo Humphrey; Dube, Timothy

doi:10.1080/15481603.2019.1646988

Cited by 48 publications

(27 citation statements)

References 80 publications

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“…Average temperature ranges from 18.9 to 26.1°C. Rainfall is generally low, less than 400 mm per annum (Thamaga & Dube, 2019). Mankweng is predominantly semi‐rural and the community still practice livestock and small‐scale crop farming at a communal scale.…”

Section: Methodsmentioning

confidence: 99%

Remote sensing of aboveground grass biomass between protected and non‐protected areas in savannah rangelands

2021

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Accurate information on the amount of aboveground grass biomass (AGBg) stored in savannah rangelands remains unknown. Therefore, in this study we assessed the capability of a new multispectral sensor (Landsat‐8 OLI) in estimating aboveground grass biomass stored within two different land use management units in savannah rangelands using the stepwise multiple linear regression methods. Specifically, we evaluated the performance of different Landsat‐8 OLI derivatives (i.e. (a) spectral bands, (b) derived vegetation indices and (c) a combination of spectral bands and vegetation indices) to quantify aboveground grass biomass. The results highlighted that the use of spectral bands as stand‐alone model variables yielded considerably high accuracies in terms of the coefficient of determination (r2) and the root mean square error (RMSE). An r2 of 0.73 (72.81%) and RMSE of 37.88% for the protected area and an r2 of 0.75 (75.18%) and with a RMSE of 33.16% for the nonprotected area were attained. The use of vegetation indices, however, demonstrated a very weak model performance yielding an r2 of 0.31 (30.68%) and a RMSE of 51.51%, for the protected area and an r2 of 0.50 (50.43%) and RMSE of 40% for the nonprotected areas. Comparatively, combining Landsat‐8 OLI derived spectral bands and vegetation indices demonstrated improved model performance, yielding an r2 of 0.92 (91.90%), and RMSE of 41.3% for the protected area and an r2 of 0.94 (93.82%) with a RMSE of 33.14% for the nonprotected area. Therefore based on the observed results, AGBg in savannah rangelands can be satisfactorily estimated using broadband multispectral derivatives.

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Section: Methodsmentioning

confidence: 99%

Remote sensing of aboveground grass biomass between protected and non‐protected areas in savannah rangelands

2021

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“…For example, previous studies aimed to generalize within fewer study sites distributed across smaller study areas [20][21][22]35], whilst this study aimed to generalize across different study sites distributed across a large study area. In addition, most previous studies [20][21][22]35] aimed to discriminate surrounding terrestrial land cover and water from water hyacinth and Cyperus papyrus [19], whereas this study focused on discriminating water hyacinth from other aquatic vegetation. As a result of the easier classification goal, our method was able to map water hyacinth at superior accuracies for at least one accuracy metric (user accuracy or producer accuracy), despite the study area being 67 times greater than previous attempts (Table 3).…”

Section: Stage 3-species Discriminationmentioning

confidence: 99%

“…Together, these technological and algorithm advancements may prove beneficial for the monitoring of typically highly variable aquatic vegetation cover [16], and for the detection of newly spreading IAAP invasions, at a lower cost than equivalent field work [17,18]. Moreover, these advancements may allow for the monitoring of satellite-detectable waterbodies and their associated infestations across national levels, surpassing the capacity of previous methods for large scale water hyacinth (Pontederia crassipes, previously Eichhornia crassipes) discrimination [19][20][21][22][23]. Subsequently, if IAAP infestations and their associated management efforts are closely monitored across large areas, effective management that allows for the efficient allocation of limited resources may be promoted [24][25][26].…”

Section: Introductionmentioning

confidence: 99%

“…Thus, despite the benefits to be gained from EO-based IAAP monitoring at large extents [21,39], such an approach has yet to be realized. This may largely be attributed to previous studies that rely on a single supervised model to simultaneously discriminate water hyacinth from multiple surrounding land cover types [19][20][21][22][23]. Consequently, these studies highlight the erroneous detection of surrounding land cover as water hyacinth [19][20][21]23,35].…”

Section: Introductionmentioning

confidence: 99%

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A Remote Sensing Method to Monitor Water, Aquatic Vegetation, and Invasive Water Hyacinth at National Extents

et al. 2020

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Diverse freshwater biological communities are threatened by invasive aquatic alien plant (IAAP) invasions and consequently, cost countries millions to manage. The effective management of these IAAP invasions necessitates their frequent and reliable monitoring across a broad extent and over a long-term. Here, we introduce and apply a monitoring approach that meet these criteria and is based on a three-stage hierarchical classification to firstly detect water, then aquatic vegetation and finally water hyacinth (Pontederia crassipes, previously Eichhornia crassipes), the most damaging IAAP species within many regions of the world. Our approach circumvents many challenges that restricted previous satellite-based water hyacinth monitoring attempts to smaller study areas. The method is executable on Google Earth Engine (GEE) extemporaneously and utilizes free, medium resolution (10–30 m) multispectral Earth Observation (EO) data from either Landsat-8 or Sentinel-2. The automated workflow employs a novel simple thresholding approach to obtain reliable boundaries for open-water, which are then used to limit the area for aquatic vegetation detection. Subsequently, a random forest modelling approach is used to discriminate water hyacinth from other detected aquatic vegetation using the eight most important variables. This study represents the first national scale EO-derived water hyacinth distribution map. Based on our model, it is estimated that this pervasive IAAP covered 417.74 km2 across South Africa in 2013. Additionally, we show encouraging results for utilizing the automatically derived aquatic vegetation masks to fit and evaluate a convolutional neural network-based semantic segmentation model, removing the need for detection of surface water extents that may not always be available at the required spatio-temporal resolution or accuracy. The water hyacinth species discrimination has a 0.80, or greater, overall accuracy (0.93), F1-score (0.87) and Matthews correlation coefficient (0.80) based on 98 widely distributed field sites across South Africa. The results suggest that the introduced workflow is suitable for monitoring changes in the extent of open water, aquatic vegetation, and water hyacinth for individual waterbodies or across national extents. The GEE code can be accessed here.

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“…Target detection locates pixels in an image that resemble a reference spectrum and is usually employed to verify if and where a material of interest occurs in a scene (Shaw and Manolakis 2002). Classification and target detection can be used to determine SAV extent, canopy density, stressors (such as disease and salinity), the taxonomic composition of a canopy, and where a SAV species occurs (Ackleson and Klemas 1987;Chen et al 2018;Peñuelas et al 1993;Thamaga and Dube 2019;Tian et al 2010;Tilley et al 2007).…”

Section: Classification and Target Detectionmentioning

confidence: 99%

Remote sensing of submerged aquatic vegetation: an introduction and best practices review

Rowan¹,

Kalácska²

2020

Preprint

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Submerged aquatic vegetation (SAV) is a critical component of aquatic ecosystems. It is however understudied and rapidly changing due to global climate change and anthropogenic disturbances. Remote sensing can provide the efficient, accurate and large-scale monitoring needed to ensure proper SAV management. Our objective is to introduce remote sensing to researchers in the field of aquatic ecology. Applying remote sensing to the underwater environment is more complex in comparison to terrestrial studies due to the water column. A wide range of sensors and platforms from remotely operated vehicles to satellites are available for use in the underwater environment, a sample of which being presented herein. The utility of any sensor/platform combination varies depending on the aquatic conditions being observed. An overview of the required corrections, processing, and analysis methods for passive optical imagery is presented and discussed. Previous applications of remote sensing to identify and detecting SAV are briefly presented and notable results and lessons are discussed.

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Understanding seasonal dynamics of invasive water hyacinth ( Eichhornia crassipes ) in the Greater Letaba river system using Sentinel-2 satellite data

Cited by 48 publications

References 80 publications

Remote sensing of aboveground grass biomass between protected and non‐protected areas in savannah rangelands

Remote sensing of aboveground grass biomass between protected and non‐protected areas in savannah rangelands

A Remote Sensing Method to Monitor Water, Aquatic Vegetation, and Invasive Water Hyacinth at National Extents

Remote sensing of submerged aquatic vegetation: an introduction and best practices review

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