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

2020 ◽  
Vol 12 (24) ◽  
pp. 4021
Author(s):  
Geethen Singh ◽  
Chevonne Reynolds ◽  
Marcus Byrne ◽  
Benjamin Rosman
Keyword(s):  
South Africa ◽  
Water Hyacinth ◽  
Aquatic Vegetation ◽  
Hierarchical Classification ◽  
Open Water ◽  
Google Earth ◽  
Species Discrimination ◽  
Landsat 8 ◽  
Forest Modelling ◽  
Alien Plant

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.

scholarly journals Improved Remote Sensing Methods to Detect Northern Wild Rice (Zizania palustris L.)

2020 ◽  
Vol 12 (18) ◽  
pp. 3023
Author(s):  
Kristen O’Shea ◽  
Jillian LaRoe ◽  
Anthony Vorster ◽  
Nicholas Young ◽  
Paul Evangelista ◽  
...  
Keyword(s):  
Aquatic Vegetation ◽  
Google Earth ◽  
Detection Accuracy ◽  
Landsat 8 ◽  
Zizania Palustris ◽  
Declining Populations ◽  
Vegetation Surveys ◽  
Time Periods ◽  
Sar Imagery ◽  
June July

Declining populations of Zizania palustris L. (northern wildrice, or wildrice) during the last century drives the demand for new and innovative techniques to support monitoring of this culturally and ecologically significant crop wild relative. We trained three wildrice detection models in R and Google Earth Engine using data from annual aquatic vegetation surveys in northern Minnesota. Three different training datasets, varying in the definition of wildrice presence, were combined with Landsat 8 Operational Land Imager (OLI) and Sentinel-1 C-band synthetic aperture radar (SAR) imagery to map wildrice in 2015 using random forests. Spectral predictors were derived from phenologically important time periods of emergence (June–July) and peak harvest (August–September). The range of the Vertical Vertical (VV) polarization between the two time periods was consistently the top predictor. Model outputs were evaluated using both point and area-based validation (polygon). While all models performed well in the point validation with percent correctly classified ranging from 83.8% to 91.1%, we found polygon validation necessary to comprehensively assess wildrice detection accuracy. Our practical approach highlights a variety of applications that can be applied to guide field excursions and estimate the extent of occurrence at landscape scales. Further testing and validation of the methods we present may support multiyear monitoring which is foundational for the preservation of wildrice for future generations.

scholarly journals Diverse trends in observed pan evaporation in South Africa suggest multiple interacting drivers

2021 ◽  
Vol 117 (7/8) ◽  
Author(s):  
R. Arthur Chapman ◽  
Guy F. Midgley ◽  
Kathleen Smart
Keyword(s):  
South Africa ◽  
Water Resource Management ◽  
Open Water ◽  
Surface Friction ◽  
Tree Cover ◽  
Pan Evaporation ◽  
Alien Plant ◽  
Local Conditions ◽  
Global Dimming ◽  
Global Drivers

Planning for future water resource management in a warming climate is confounded when an expectation of increasing evaporation from open water surfaces with global warming is contradicted by observations of secular declines of pan evaporation. Decreasing pan evaporation has been observed globally – a trend which has been attributed variously to declines in wind run (‘global stilling’), declines in radiation (‘global dimming’) and increases in ambient humidity. This contrast between expectation and observation is known as the ‘evaporation paradox’. We evaluated trends in Symons pan evaporation from 154 pans across South Africa. Whilst 59 pans (38% of the 154) showed a statistically significant decrease in observed evaporation rates (p≤0.05), 30 (20%) showed an increase, and 65 (42%) showed no change. These results do not support simple attributions of trends to a common global cause. There is no spatially coherent pattern to trends across South Africa, suggesting that shifts in local drivers of evaporation confound expectations of secular trends due to global drivers. Changes in fetch conditions of the Symons pan installations may be implicated, whereby increasing tree density (through afforestation, alien plant invasion and woody thickening) increases surface friction, reducing wind run, and/or irrigation nearby, increasing local humidity. Correct attribution of the evaporation paradox to reduced wind run in South Africa must consider changing local conditions. Increased tree cover has been observed near a third of the South African Symons pans. Observed evaporation increases for one fifth of pans may implicate expected global drivers for pans where local fetch conditions have remained relatively constant.

scholarly journals A GIS based approach to determine the changes of water hyacinth (Eichhornia crassipes) cover and it’s relation with Lesser Whistling Teal (Dendrocygna javanica) assemblage at Santragachi Wetland, West Bengal

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Masuma Begam ◽  
Sudin Pal ◽  
Niranjita Mitra ◽  
Asitava Chatterjee ◽  
Anirban Mukhopadhyay ◽  
...  
Keyword(s):  
Water Hyacinth ◽  
Eichhornia Crassipes ◽  
Bird Species ◽  
Open Water ◽  
Water Area ◽  
Google Earth ◽  
Open Water Area ◽  
Marked Decline ◽  
The Relationship ◽  
Migratory Waterbirds

The present investigation is conducted to study the year wise (2011 to 2018) changes of water hyacinth (Eichhornia crassipes) cover at Santragachi Lake a Wetland under National Wetland Conservation Programme of India. Further the relationship between water hyacinth cover and the most abundant migratory waterbirds of Satragachi, Lesser Whistling Teal (LWT; Dendrocygna javanica) is assessed because this bird species is fully depended on water hyacinth mat for their roosting. The study comprises of eight satellite images procured from Google earth (2011 to 2018) to explore this relationship. A marked decline in the number of LWT at Santragachi wetland is observed in the year of 2017 and 2018. It is very interesting fact that from 2017-2018, the water hyacinth mat of this wetland is almost cleared before winter and the result of cluster analysis supports this fact. Significant positive correlation is also observed within LWT number and water hyacinth cover area (r = 0.7481 at p< 0.05) along with the total perimeter (r = 0.8648 at p< 0.05) of the water hyacinth islands at Santragachi wetland. However, open water area is also needed for diving, swimming, food searching for the LWT and other waterbirds. Therefore, more study is needed to optimize the clearing operations, focused on optimizing shape and size of water hyacinth islands for proper management of the waterbirds habitat. 

scholarly journals Pixel- vs. Object-Based Landsat 8 Data Classification in Google Earth Engine Using Random Forest: The Case Study of Maiella National Park

2021 ◽  
Vol 13 (12) ◽  
pp. 2299
Author(s):  
Andrea Tassi ◽  
Daniela Gigante ◽  
Giuseppe Modica ◽  
Luciano Di Martino ◽  
Marco Vizzari
Keyword(s):  
Land Cover ◽  
National Park ◽  
Time Window ◽  
Central Italy ◽  
Google Earth ◽  
Data Cube ◽  
Landsat 8 ◽  
Change Analysis ◽  
Object Based ◽  
Google Earth Engine

With the general objective of producing a 2018–2020 Land Use/Land Cover (LULC) map of the Maiella National Park (central Italy), useful for a future long-term LULC change analysis, this research aimed to develop a Landsat 8 (L8) data composition and classification process using Google Earth Engine (GEE). In this process, we compared two pixel-based (PB) and two object-based (OB) approaches, assessing the advantages of integrating the textural information in the PB approach. Moreover, we tested the possibility of using the L8 panchromatic band to improve the segmentation step and the object’s textural analysis of the OB approach and produce a 15-m resolution LULC map. After selecting the best time window of the year to compose the base data cube, we applied a cloud-filtering and a topography-correction process on the 32 available L8 surface reflectance images. On this basis, we calculated five spectral indices, some of them on an interannual basis, to account for vegetation seasonality. We added an elevation, an aspect, a slope layer, and the 2018 CORINE Land Cover classification layer to improve the available information. We applied the Gray-Level Co-Occurrence Matrix (GLCM) algorithm to calculate the image’s textural information and, in the OB approaches, the Simple Non-Iterative Clustering (SNIC) algorithm for the image segmentation step. We performed an initial RF optimization process finding the optimal number of decision trees through out-of-bag error analysis. We randomly distributed 1200 ground truth points and used 70% to train the RF classifier and 30% for the validation phase. This subdivision was randomly and recursively redefined to evaluate the performance of the tested approaches more robustly. The OB approaches performed better than the PB ones when using the 15 m L8 panchromatic band, while the addition of textural information did not improve the PB approach. Using the panchromatic band within an OB approach, we produced a detailed, 15-m resolution LULC map of the study area.

scholarly journals Patterns, Trends and Drivers of Water Transparency in Sri Lanka Using Landsat 8 Observations and Google Earth Engine

2021 ◽  
Vol 13 (11) ◽  
pp. 2193
Author(s):  
Deepakrishna Somasundaram ◽  
Fangfang Zhang ◽  
Sisira Ediriweera ◽  
Shenglei Wang ◽  
Ziyao Yin ◽  
...  
Keyword(s):  
Sri Lanka ◽  
Human Activity ◽  
Google Earth ◽  
Water Transparency ◽  
Conservation Strategies ◽  
Landsat 8 ◽  
Essential Information ◽  
Annual Variations ◽  
North East ◽  
South West Monsoon

Addressing inland water transparency and driver effects to ensure the sustainability and provision of good quality water in Sri Lanka has been a timely prerequisite, especially under the Sustainable Development Goals 2030 agenda. Natural and anthropogenic changes lead to significant variations in water quality in the country. Therefore, an urgent need has emerged to understand the variability, spatiotemporal patterns, changing trends and impact of drivers on transparency, which are unclear to date. This study used all available Landsat 8 images from 2013 to 2020 and a quasi-analytical approach to assess the spatiotemporal Secchi disk depth (ZSD) variability of 550 reservoirs and its relationship with natural (precipitation, wind and temperature) and anthropogenic (human activity and population density) drivers. ZSD varied from 9.68 cm to 199.47 with an average of 64.71 cm and 93% of reservoirs had transparency below 100 cm. Overall, slightly increasing trends were shown in the annual mean ZSD. Notable intra-annual variations were also indicating the highest and lowest ZSD during the north-east monsoon and south-west monsoon, respectively. The highest ZSD was found in wet zone reservoirs, while dry zone showed the least. All of the drivers were significantly affecting the water transparency in the entire island. The combined impact of natural factors on ZSD changes was more significant (77.70%) than anthropogenic variables, whereas, specifically, human activity accounted for the highest variability across all climatic zones. The findings of this study provide the first comprehensive estimation of the ZSD of entire reservoirs and driver contribution and also provides essential information for future sustainable water management and conservation strategies.

scholarly journals Vegetation Types Mapping Using Multi-Temporal Landsat Images in the Google Earth Engine Platform

2021 ◽  
Vol 13 (22) ◽  
pp. 4683
Author(s):  
Masoumeh Aghababaei ◽  
Ataollah Ebrahimi ◽  
Ali Asghar Naghipour ◽  
Esmaeil Asadi ◽  
Jochem Verrelst
Keyword(s):  
Vegetation Index ◽  
Google Earth ◽  
Optimal Time ◽  
Quantitative Detection ◽  
Landsat 8 ◽  
Vegetation Types ◽  
Landsat Images ◽  
Multi Temporal ◽  
Google Earth Engine ◽  
Land Covers

Vegetation Types (VTs) are important managerial units, and their identification serves as essential tools for the conservation of land covers. Despite a long history of Earth observation applications to assess and monitor land covers, the quantitative detection of sparse VTs remains problematic, especially in arid and semiarid areas. This research aimed to identify appropriate multi-temporal datasets to improve the accuracy of VTs classification in a heterogeneous landscape in Central Zagros, Iran. To do so, first the Normalized Difference Vegetation Index (NDVI) temporal profile of each VT was identified in the study area for the period of 2018, 2019, and 2020. This data revealed strong seasonal phenological patterns and key periods of VTs separation. It led us to select the optimal time series images to be used in the VTs classification. We then compared single-date and multi-temporal datasets of Landsat 8 images within the Google Earth Engine (GEE) platform as the input to the Random Forest classifier for VTs detection. The single-date classification gave a median Overall Kappa (OK) and Overall Accuracy (OA) of 51% and 64%, respectively. Instead, using multi-temporal images led to an overall kappa accuracy of 74% and an overall accuracy of 81%. Thus, the exploitation of multi-temporal datasets favored accurate VTs classification. In addition, the presented results underline that available open access cloud-computing platforms such as the GEE facilitates identifying optimal periods and multitemporal imagery for VTs classification.

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