scholarly journals Spatial-temporal Distribution of Salt Marshes in Intertidal Zone of China during 1985-2019

2021 ◽  
Author(s):  
Guangwei Chen ◽  
Zhanjiang Ye ◽  
Runjie Jin ◽  
Junyu He ◽  
Jiaping Wu ◽  
...  
Keyword(s):  
Salt Marsh ◽  
Salt Marshes ◽  
Temporal Distribution ◽  
Google Earth ◽  
Driving Factors ◽  
Support Vector ◽  
Tamarix Chinensis ◽  
Ecological Functions ◽  
Cyperus Malaccensis ◽  
Sentinel 2

Based on the cloud platform of Google Earth Engine (GEE), this study selected Landsat 5/8 and Sentinel-2 remote sensing images and used Support Vector Machine (SVM) classification method to classify the 35 years of intertidal salt marshes in China, and verified the classification results in combination with field survey. Finally, combining with various driving factors, the reasons and laws affecting the changes of salt marshes species and area were discussed and analyzed. The main results of the study are as follows:The main types of salt marshes plants in China include Phragmites australis, Spartina alterniflora, Suaeda salsa, Scirpus mariquete, Tamarix chinensis, Cyperus malaccensis and Sesuvium portulacastrum. The results salt marshes classification indicated that 166999.32 ha in 1985, 172893.87 ha in 1990, 174952.29 ha in 1995, 125567.51 ha in 2000, 93257.97 ha in 2005, 102539.04 ha in 2010, 96302.92 ha in 2015, and 115722.75 ha in 2019. The main driving factors of salt marsh change from 1985 to 2015 are reclamation, mudflat aquaculture, climate change, coastal zone erosion, invasion of alien species, and natural competition and succession among salt marshes species. The results can be used to quantitatively analyze the salt marshes carbon storage in space and time, and provide data support for the protection of salt marsh wetlands, the restoration of ecological functions and the implementation of "carbon neutral".

scholarly journals Integrating SAR and Optical Remote Sensing for Conservation-Targeted Wetlands Mapping

2021 ◽  
Vol 14 (1) ◽  
pp. 159
Author(s):  
Hossein Sahour ◽  
Kaylan M. Kemink ◽  
Jessica O’Connell
Keyword(s):  
Surface Water ◽  
Google Earth ◽  
Machine Learning Algorithms ◽  
Emergent Vegetation ◽  
Water Bodies ◽  
Depressional Wetlands ◽  
Support Vector ◽  
Ground Truth Data ◽  
Synthetic Aperture Radar Data ◽  
Sentinel 2

The Prairie Pothole Region (PPR) contains numerous depressional wetlands known as potholes that provide habitats for waterfowl and other wetland-dependent species. Mapping these wetlands is essential for identifying viable waterfowl habitat and conservation planning scenarios, yet it is a challenging task due to the small size of the potholes, and the presence of emergent vegetation. This study develops an open-source process within the Google Earth Engine platform for mapping the spatial distribution of wetlands through the integration of Sentinel-1 C-band SAR (synthetic aperture radar) data with high-resolution (10-m) Sentinel-2 bands. We used two machine-learning algorithms (random forest (RF) and support vector machine (SVM)) to identify wetlands across the study area through supervised classification of the multisensor composite. We trained the algorithms with ground truth data provided through field studies and aerial photography. The accuracy was assessed by comparing the predicted and actual wetland and non-wetland classes using statistical coefficients (overall accuracy, Kappa, sensitivity, and specificity). For this purpose, we used four different out-of-sample test subsets, including the same year, next year, small vegetated, and small non-vegetated test sets to evaluate the methods on different spatial and temporal scales. The results were also compared to Landsat-derived JRC surface water products, and the Sentinel-2-derived normalized difference water index (NDWI). The wetlands derived from the RF model (overall accuracy 0.76 to 0.95) yielded favorable results, and outperformed the SVM, NDWI, and JRC products in all four testing subsets. To provide a further characterization of the potholes, the water bodies were stratified based on the presence of emergent vegetation using Sentinel-2-derived NDVI, and, after excluding permanent water bodies, using the JRC surface water product. The algorithm presented in the study is scalable and can be adopted for identifying wetlands in other regions of the world.

scholarly journals Towards Global-Scale Seagrass Mapping and Monitoring Using Sentinel-2 on Google Earth Engine: The Case Study of the Aegean and Ionian Seas

2018 ◽  
Vol 10 (8) ◽  
pp. 1227 ◽  
Author(s):  
Dimosthenis Traganos ◽  
Bharat Aggarwal ◽  
Dimitris Poursanidis ◽  
Konstantinos Topouzelis ◽  
Nektarios Chrysoulakis ◽  
...  
Keyword(s):  
Large Scale ◽  
Anthropogenic Impacts ◽  
Global Scale ◽  
Google Earth ◽  
Support Vector ◽  
Learning Approaches ◽  
Data Set ◽  
Independent Field ◽  
Google Earth Engine ◽  
Sentinel 2

Seagrasses are traversing the epoch of intense anthropogenic impacts that significantly decrease their coverage and invaluable ecosystem services, necessitating accurate and adaptable, global-scale mapping and monitoring solutions. Here, we combine the cloud computing power of Google Earth Engine with the freely available Copernicus Sentinel-2 multispectral image archive, image composition, and machine learning approaches to develop a methodological workflow for large-scale, high spatiotemporal mapping and monitoring of seagrass habitats. The present workflow can be easily tuned to space, time and data input; here, we show its potential, mapping 2510.1 km2 of P. oceanica seagrasses in an area of 40,951 km2 between 0 and 40 m of depth in the Aegean and Ionian Seas (Greek territorial waters) after applying support vector machines to a composite of 1045 Sentinel-2 tiles at 10-m resolution. The overall accuracy of P. oceanica seagrass habitats features an overall accuracy of 72% following validation by an independent field data set to reduce bias. We envision that the introduced flexible, time- and cost-efficient cloud-based chain will provide the crucial seasonal to interannual baseline mapping and monitoring of seagrass ecosystems in global scale, resolving gain and loss trends and assisting coastal conservation, management planning, and ultimately climate change mitigation.

scholarly journals Mapping the Forest Canopy Height in Northern China by Synergizing ICESat-2 with Sentinel-2 Using a Stacking Algorithm

2021 ◽  
Vol 13 (8) ◽  
pp. 1535
Author(s):  
Fugen Jiang ◽  
Feng Zhao ◽  
Kaisen Ma ◽  
Dongsheng Li ◽  
Hua Sun
Keyword(s):  
Forest Canopy ◽  
Nearest Neighbor ◽  
Critical Role ◽  
Continuous Distribution ◽  
Continuous Mapping ◽  
Google Earth ◽  
Canopy Height ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Sentinel 2

The forest canopy height (FCH) plays a critical role in forest quality evaluation and resource management. The accurate and rapid estimation and mapping of the regional forest canopy height is crucial for understanding vegetation growth processes and the internal structure of the ecosystem. A stacking algorithm consisting of multiple linear regression (MLR), support vector machine (SVM), k-nearest neighbor (kNN), and random forest (RF) was used in this paper and demonstrated optimal performance in predicting the forest canopy height by synergizing Sentinel-2 images acquired from the cloud-based computation platform Google Earth Engine (GEE) with data from ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2). This research was conducted to achieve continuous mapping of the canopy height of plantations in Saihanba Mechanical Forest Plantation, which is located in Chengde City, northern Hebei province, China. The results show that stacking achieved the best prediction accuracy for the forest canopy height, with an R2 of 0.77 and a root mean square error (RMSE) of 1.96 m. Compared with MLR, SVM, kNN, and RF, the RMSE obtained by stacking was reduced by 25.2%, 24.9%, 22.8%, and 18.7%, respectively. Since Sentinel-2 images and ICESat-2 data are publicly available, this opens the door for the accurate mapping of the continuous distribution of the forest canopy height globally in the future.

scholarly journals Performance Evaluation of Sentinel-2 and Landsat 8 OLI Data for Land Cover/Use Classification Using a Comparison between Machine Learning Algorithms

2021 ◽  
Vol 13 (7) ◽  
pp. 1349
Author(s):  
Laleh Ghayour ◽  
Aminreza Neshat ◽  
Sina Paryani ◽  
Himan Shahabi ◽  
Ataollah Shirzadi ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Satellite Data ◽  
Google Earth ◽  
Kernel Functions ◽  
Support Vector ◽  
Landsat 8 ◽  
Penalty Parameter ◽  
Tuning Parameters ◽  
Sentinel 2

With the development of remote sensing algorithms and increased access to satellite data, generating up-to-date, accurate land use/land cover (LULC) maps has become increasingly feasible for evaluating and managing changes in land cover as created by changes to ecosystem and land use. The main objective of our study is to evaluate the performance of Support Vector Machine (SVM), Artificial Neural Network (ANN), Maximum Likelihood Classification (MLC), Minimum Distance (MD), and Mahalanobis (MH) algorithms and compare them in order to generate a LULC map using data from Sentinel 2 and Landsat 8 satellites. Further, we also investigate the effect of a penalty parameter on SVM results. Our study uses different kernel functions and hidden layers for SVM and ANN algorithms, respectively. We generated the training and validation datasets from Google Earth images and GPS data prior to pre-processing satellite data. In the next phase, we classified the images using training data and algorithms. Ultimately, to evaluate outcomes, we used the validation data to generate a confusion matrix of the classified images. Our results showed that with optimal tuning parameters, the SVM classifier yielded the highest overall accuracy (OA) of 94%, performing better for both satellite data compared to other methods. In addition, for our scenes, Sentinel 2 date was slightly more accurate compared to Landsat 8. The parametric algorithms MD and MLC provided the lowest accuracy of 80.85% and 74.68% for the data from Sentinel 2 and Landsat 8. In contrast, our evaluation using the SVM tuning parameters showed that the linear kernel with the penalty parameter 150 for Sentinel 2 and the penalty parameter 200 for Landsat 8 yielded the highest accuracies. Further, ANN classification showed that increasing the hidden layers drastically reduces classification accuracy for both datasets, reducing zero for three hidden layers.

scholarly journals Machine Learning Classification of Mediterranean Forest Habitats in Google Earth Engine Based on Seasonal Sentinel-2 Time-Series and Input Image Composition Optimisation

2021 ◽  
Vol 13 (4) ◽  
pp. 586
Author(s):  
Salvatore Praticò ◽  
Francesco Solano ◽  
Salvatore Di Fazio ◽  
Giuseppe Modica
Keyword(s):  
Time Series ◽  
Input Image ◽  
Google Earth ◽  
Classification And Regression Tree ◽  
Support Vector ◽  
Mountain Forest ◽  
Machine Learning Classification ◽  
Free Data ◽  
Google Earth Engine ◽  
Sentinel 2

The sustainable management of natural heritage is presently considered a global strategic issue. Owing to the ever-growing availability of free data and software, remote sensing (RS) techniques have been primarily used to map, analyse, and monitor natural resources for conservation purposes. The need to adopt multi-scale and multi-temporal approaches to detect different phenological aspects of different vegetation types and species has also emerged. The time-series composite image approach allows for capturing much of the spectral variability, but presents some criticalities (e.g., time-consuming research, downloading data, and the required storage space). To overcome these issues, the Google Earth engine (GEE) has been proposed, a free cloud-based computational platform that allows users to access and process remotely sensed data at petabyte scales. The application was tested in a natural protected area in Calabria (South Italy), which is particularly representative of the Mediterranean mountain forest environment. In the research, random forest (RF), support vector machine (SVM), and classification and regression tree (CART) algorithms were used to perform supervised pixel-based classification based on the use of Sentinel-2 images. A process to select the best input image (seasonal composition strategies, statistical operators, band composition, and derived vegetation indices (VIs) information) for classification was implemented. A set of accuracy indicators, including overall accuracy (OA) and multi-class F-score (Fm), were computed to assess the results of the different classifications. GEE proved to be a reliable and powerful tool for the classification process. The best results (OA = 0.88 and Fm = 0.88) were achieved using RF with the summer image composite, adding three VIs (NDVI, EVI, and NBR) to the Sentinel-2 bands. SVM and RF produced OAs of 0.83 and 0.80, respectively.

scholarly journals GOOGLE EARTH ENGINE: APPLICATION OF ALGORITHMS FOR REMOTE SENSING OF CROPS IN TUSCANY (ITALY)

2020 ◽  
Vol XLII-3/W12-2020 ◽  
pp. 291-296
Author(s):  
J. P. Clemente ◽  
G. Fontanelli ◽  
G. G. Ovando ◽  
Y. L. B. Roa ◽  
A. Lapini ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Google Earth ◽  
Optical Data ◽  
Support Vector ◽  
Use Of Time ◽  
Crop Types ◽  
Google Earth Engine ◽  
Agricultural Areas ◽  
Sentinel 2

Abstract. Remote sensing has become an important mean to assess crop areas, specially for the identification of crop types. Google Earth Engine (GEE) is a free platform that provides a large number of satellite images from different constellations. Moreover, GEE provides pixel-based classifiers, which are used for mapping agricultural areas. The objective of this work is to evaluate the performance of different classification algorithms such as Minimum Distance (MD), Random Forest (RF), Support Vector Machine (SVM), Classification and Regression Trees (CART) and Na¨ıve Bayes (NB) on an agricultural area in Tuscany (Italy). Four different scenarios were implemented in GEE combining different information such as optical and Synthetic Aperture Radar (SAR) data, indices and time series. Among the five classifiers used the best performers were RF and SVM. Integrating Sentinel-1 (S1) and Sentinel-2 (S2) slightly improves the classification in comparison to the only S2 image classifications. The use of time series substantially improves supervised classifications. The analysis carried out so far lays the foundation for the integration of time series of SAR and optical data.

scholarly journals Testing the efficacy of hyperspectral (AVIRIS-NG), multispectral (Sentinel-2) and radar (Sentinel-1) remote sensing images to detect native and invasive non-native trees

2021 ◽  
Author(s):  
M Arasumani ◽  
Aditya Singh ◽  
Milind Bunyan ◽  
V.V. Robin
Keyword(s):  
Remote Sensing ◽  
Native Species ◽  
Ground Truth ◽  
Google Earth ◽  
Support Vector ◽  
Svm Classification ◽  
Machine Learning Classification ◽  
Tropical Grasslands ◽  
Exotic Tree ◽  
Sentinel 2

AbstractInvasive alien species (IAS) threaten tropical grasslands and native biodiversity and impact ecosystem service delivery, ecosystem function, and associated human livelihoods. Tropical grasslands have been dramatically and disproportionately lost to invasion by trees. The invasion continues to move rapidly into the remaining fragmented grasslands impacting various native grassland-dependent species and water streamflow in tropical montane habitats. The Shola Sky Islands of the Western Ghats host a mosaic of native grasslands and forests; of which the grasslands have been lost to exotic tree invasion (Acacias, Eucalyptus and Pines) since the 1950s. The invasion intensities, however, differ between these species wherein Acacia mearnsii and Pinus patula are highly invasive in contrast to Eucalyptus globulus. These disparities necessitate distinguishing these species for effective grassland restoration. Further, these invasive alien trees are highly intermixed with native species, thus requiring high discrimination abilities to native species apart from the non-native species.Here we assess the accuracy of various satellite and airborne remote sensing sensors and machine learning classification algorithms to identify the spatial extent of native habitats and invasive trees. Specifically, we test Sentinel-1 SAR and Sentinel-2 multispectral data and assess high spatial and spectral resolution AVIRIS-NG imagery identifying invasive species across this landscape. Sensor combinations thus include hyperspectral, multispectral and radar data and present tradeoffs in associated costs and ease of procurement. Classification methods tested include Support Vector Machine (SVM), Classification and Regression Trees (CART) and Random Forest (RF) algorithms implemented on the Google Earth Engine platform. Results indicate that AVIRIS-NG data in combination with SVM recover the highest classification skill (Overall −98%, Kappa-0.98); while CART and RF yielded < 90% accuracy. Fused Sentinel-1 and Sentinel-2 produce 91% accuracy, while Sentinel-2 alone yielded 91% accuracy with RF and SVM classification; but only with higher coverage of ground control points. AVIRIS-NG imagery was able to accurately (97%) demarcate the Acacia invasion front while Sentinel-1 and Sentinel-2 data failed. Our results suggest that Sentinel-2 images could be useful for detecting the native and non-native forests with more ground truth points, but hyperspectral data (AVIRIS-NG) permits distinguishing, native and non-native tree species and recent invasions with high precision using limited ground truth points. We suspect that large areas will have to be mapped and assessed in the coming years by conservation managers, NGOs to plan restoration, or to assess the success of restoration activities, and several data procurement and analysis steps may have to be simplified.

scholarly journals INVESTIGATING THE PERFORMANCE OF RANDOM FOREST AND SUPPORT VECTOR REGRESSION FOR ESTIMATION OF CLOUD-FREE NDVI USING SENTINEL-1 SAR DATA

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1379-1383
Author(s):  
J. D. Mohite ◽  
S. A. Sawant ◽  
A. Pandit ◽  
S. Pappula
Keyword(s):  
Random Forest ◽  
Support Vector Regression ◽  
Cloud Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Incidence Angle ◽  
Google Earth ◽  
Support Vector ◽  
Optical Remote Sensing ◽  
Sentinel 2

Abstract. The current study focuses on the estimation of cloud-free Normalized Difference Vegetation Index (NDVI) using the Synthetic Aperture Radar (SAR) observations obtained from Sentinel-1 (A and B) sensor. South-West Summer Monsoon over the Indian sub-continent lasts for four months (mid-June to mid-October). During this time, optical remote sensing observations are affected by dense cloud cover. Therefore, there is a need for methodology to estimate state of vegetation during the cloud cover. The crops considered in this study are Paddy (Rice) from Punjab and Haryana, whereas Cotton, Turmeric, and Banana from Andhra Pradesh, India. We have considered, observations of Sentinel-1 and Sentinel-2 sensors with the same overpass day and non-cloudy pixels for each crop. We used Google Earth Engine to extract surface reflectance for the Sentinel-2 and Ground Range Detected (GRD) backscatter for Sentinel-1. The Red and NIR bands of Sentinel 2 were used to estimate NDVI. Sentinel-1 based VV, and VH backscatter was used for estimation of Normalized Ratio Procedure between Bands (NRPB). Regression analysis was performed by using NDVI as an independent variable, and VV, VH, NRPB, and radar incidence angle as dependant variables. We evaluated the performance of Linear regression with tuned Support Vector Regression (SVR) as well as tuned Random Forest Regression (RFR) using the independent data. Results showed that the RFR produced the lowest RMSE for all the crops in the study. The average RMSE using the RFR was 0.08, 0.09, 0.11, and 0.10 for Rice, Cotton, Banana, and Turmeric, respectively. Similarly, we have obtained R2 values of 0.79, 0.76, 0.69, and 0.71 for the same crops using the RFR. A model with 80 trees produced the best results for Rice and Cotton, whereas the model with 90 trees produced the best results for Banana and Turmeric. Analysis with NDVI threshold of 0.25 showed improved R2 and RMSE. We found that for grown crop canopy, SAR based NDVI estimates are reasonably matching with the optical NDVI. A good agreement was observed between the actual and estimated NDVI using the tuned RFR model.

scholarly journals Multiscale Mapping of the Salt Marshes Using Sentinel-2, Dove and UAV Imagery in the Bay of Mont-Saint-Michel

2021 ◽  
Author(s):  
Antoine Collin ◽  
Dorothée James ◽  
Antoine Mury ◽  
Mathilde Letard ◽  
Thomas Houet ◽  
...  
Keyword(s):  
Salt Marsh ◽  
Salt Marshes ◽  
Vegetation Index ◽  
Near Infrared ◽  
Regional Scale ◽  
Great Promise ◽  
High Temporal Resolution ◽  
Multiscale Approach ◽  
Marsh Vegetation ◽  
Sentinel 2

The salt marshes, lying at the land-sea temperate interface, furnish a plethora of ecosystems services such as biodiversity niche support, ocean-climate change regulation, ornithology recreo-tourism or plant gathering by hand. They undergo significant worldwide losses due to their conversion into crop fields and to their spatial compression between the rising sea-level and the armoring shoreline. Their monitoring however requires to use a suite of remote sensing sensors to embrace the regional scale while capturing the plant details. This research innovatively adopts a multiscale approach using a cascading spaceborne and airborne process, from the 10-m Sentinel-2, through the 3-m Dove, to the 0.03-m unmanned airborne vehicle (UAV) imageries. The high to very high temporal resolution of the Sentinel-2 and Dove enabled to cover twenties and tens of km2 over five and four years, respectively, in the form of normalized difference vegetation index (NDVI) classes, associated with microphytobenthos, low, medium and high salt marsh vegetation, including the opportunistic Elyma genus. The NDVI was then modelled at the UAV scale (a few km2) using a three-layered NN prediction, providing the final near-infrared (NIR), and the intermediate red, green and blue reflectance imageries, calibrated/validated/tested with the Dove reflectance imageries (R2NIR=0.98, R2red=0.88, R2green=0.84, and R2blue=0.90). The 100fold increase in pixel size allowed to detect the decimeter-scale objects of the tidal flats and salt marshes, to enlarge the NDVI class ranges, and hold great promise to model other spectral bands at the UAV scale for further deeply enhancing the salt marsh mapping.

scholarly journals Broad-Leaved and Coniferous Forest Classification in Google Earth Engine Using Sentinel Imagery

2020 ◽  
Vol 3 (1) ◽  
pp. 64
Author(s):  
Gordana Kaplan
Keyword(s):  
Forest Cover ◽  
Accuracy Assessment ◽  
Coniferous Forest ◽  
Google Earth ◽  
Support Vector ◽  
Svm Classifier ◽  
Sample Collection ◽  
Forest Classification ◽  
Google Earth Engine ◽  
Sentinel 2

Forest structures knowledge is fundamental to understanding, managing, and preserving the biodiversity of forests. With the well-established need within the remote sensing community for better understanding of canopy structure, in this paper, the effectiveness of Sentinel-2 imagery for broad-leaved and coniferous forest classification within the Google Earth Engine (GEE) platform has been assessed. Here, we used Sentinel-2 image collection from the summer period over North Macedonia, when the canopy is fully developed. For the sample collection of the coniferous areas and the accuracy assessment of the classification, we used imagery from the spring period, when the broad-leaved forests are in the early green stage. A Support Vector Machine (SVM) classifier has been used for discriminating forest cover groups, namely, broadleaved and coniferous forests. According to the results, more than 90% of the canopy in North Macedonia is broad-leaved, while less than 10% is conifers. The results in this study show that, with the use of GEE, Sentinel-2 data alone can be effectively used to obtain rapid and accurate mapping of main forest types (conifers-broadleaved) with a fine resolution.

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