scholarly journals TOWARD A MULTI-SOURCE REMOTE SENSING WETLAND INVENTORY OF THE USA: PRELIMINARY RESULTS ON WETLAND INVENTORY OF MINNESOTA

2021 ◽  
Vol V-3-2021 ◽  
pp. 97-100
Author(s):  
S. Adeli ◽  
B. Salehi ◽  
M. Mahidanpari ◽  
L. J. Quackenbush
Keyword(s):  
Field Data ◽  
Cost Effective ◽  
Google Earth ◽  
Machine Learning Techniques ◽  
Wetland Delineation ◽  
Observation Data ◽  
Alos Palsar ◽  
The Usa ◽  
Wetland Inventory ◽  
Sentinel 2

Abstract. Wetlands are highly productive ecosystems that offer unique services on regional and global scales including nutrient assimilation, carbon reduction, geochemical cycling, and water storage. In recent years, however, they are being lost or exploited as croplands due to natural or man-made stressors (1.4 percent in 5 years within the USA). This decline in the extent of wetlands began legislative activity at a national scale that mandate the regulate use of wetlands. As such, the need for cost-effective, robust, and semi-automated techniques for wetland preservation is ever-increasing in the current era. In this study, we developed a workflow for wetland inventorying on a state-wide scale using optimal incorporation of dual-polarimetry Sentinel-1, multi-spectral Sentinel-2 and dual polarimetry ALOS-PALSAR with the Random Forest (RF) classifier in Google Earth Engine (GEE). A total of 45 features from a stack of multi-season/multi-year SAR and Optical imagery (included more than 5000 imagery) was extracted over Minnesota state, USA. We followed the Cowardin classification scheme for clustering the field data. The classification was performed in two levels in 5 different ecozones that cover the Minnesota state. Depending on the availability field data for each ecozone overall accuracies changed from 77% to 85%. The variable importance analysis suggests that Sentinel-2 spectral features are dominant in terms of their capability for wetland delineation. Sentinel-1 backscattering coefficient was also superior among other SAR features. Ultimately, the results of this study shall illustrate the applicability of free of charge earth observation data coupled with the advanced machine learning techniques that are available in GEE for better restoration and management of wetlands.

scholarly journals The First Wetland Inventory Map of Newfoundland at a Spatial Resolution of 10 m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform

2018 ◽  
Vol 11 (1) ◽  
pp. 43 ◽  
Author(s):  
Masoud Mahdianpari ◽  
Bahram Salehi ◽  
Fariba Mohammadimanesh ◽  
Saeid Homayouni ◽  
Eric Gill
Keyword(s):  
Large Scale ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Wetland Mapping ◽  
Object Based ◽  
Wetland Inventory ◽  
Inventory Map ◽  
Google Earth Engine ◽  
Provincial Scale ◽  
Sentinel 2

Wetlands are one of the most important ecosystems that provide a desirable habitat for a great variety of flora and fauna. Wetland mapping and modeling using Earth Observation (EO) data are essential for natural resource management at both regional and national levels. However, accurate wetland mapping is challenging, especially on a large scale, given their heterogeneous and fragmented landscape, as well as the spectral similarity of differing wetland classes. Currently, precise, consistent, and comprehensive wetland inventories on a national- or provincial-scale are lacking globally, with most studies focused on the generation of local-scale maps from limited remote sensing data. Leveraging the Google Earth Engine (GEE) computational power and the availability of high spatial resolution remote sensing data collected by Copernicus Sentinels, this study introduces the first detailed, provincial-scale wetland inventory map of one of the richest Canadian provinces in terms of wetland extent. In particular, multi-year summer Synthetic Aperture Radar (SAR) Sentinel-1 and optical Sentinel-2 data composites were used to identify the spatial distribution of five wetland and three non-wetland classes on the Island of Newfoundland, covering an approximate area of 106,000 km2. The classification results were evaluated using both pixel-based and object-based random forest (RF) classifications implemented on the GEE platform. The results revealed the superiority of the object-based approach relative to the pixel-based classification for wetland mapping. Although the classification using multi-year optical data was more accurate compared to that of SAR, the inclusion of both types of data significantly improved the classification accuracies of wetland classes. In particular, an overall accuracy of 88.37% and a Kappa coefficient of 0.85 were achieved with the multi-year summer SAR/optical composite using an object-based RF classification, wherein all wetland and non-wetland classes were correctly identified with accuracies beyond 70% and 90%, respectively. The results suggest a paradigm-shift from standard static products and approaches toward generating more dynamic, on-demand, large-scale wetland coverage maps through advanced cloud computing resources that simplify access to and processing of the “Geo Big Data.” In addition, the resulting ever-demanding inventory map of Newfoundland is of great interest to and can be used by many stakeholders, including federal and provincial governments, municipalities, NGOs, and environmental consultants to name a few.

scholarly journals A Fusion of Feature-Oriented Principal Components of Multispectral Data to Map Granite Exposures of Pakistan

2021 ◽  
Vol 11 (23) ◽  
pp. 11486
Author(s):  
Shahab Ud Din ◽  
Khan Muhammad ◽  
Muhammad Fawad Akbar Khan ◽  
Shahid Bashir ◽  
Muhammad Sajid ◽  
...  
Keyword(s):  
Principal Component ◽  
Cost Effective ◽  
Thermal Infrared ◽  
Google Earth ◽  
Engineering Properties ◽  
Petrographic Analysis ◽  
Landsat 8 ◽  
Khyber Pakhtunkhwa ◽  
Multispectral Data ◽  
Sentinel 2

Despite low spatial resolutions, thermal infrared bands (TIRs) are generally more suitable for mineral mapping due to fundamental tones and high penetration in vegetated areas compared to shortwave infrared (SWIR) bands. However, the weak overtone combinations of SWIR bands for minerals can be compensated by fusing SWIR-bearing data (Sentinel-2 and Landsat-8) with other multispectral data containing fundamental tones from TIR bands. In this paper, marble in a granitic complex in Mardan District (Khyber Pakhtunkhwa) in Pakistan is discriminated by fusing feature-oriented principal component selection (FPCS) obtained from the ASTER, Landsat-8 Operational Land Imager (OLI), Thermal Infrared Sensor (TIRS) and Sentinel-2 MSI data. Cloud computing from Google Earth Engine (GEE) was used to apply FPCS before and after the decorrelation stretching of Landsat-8, ASTER, and Sentinel-2 MSI data containing five (5) bands in the Landsat-8 OLI and TIRS and six (6) bands each in the ASTER and Sentinel-2 MSI datasets, resulting in 34 components (i.e., 2 × 17 components). A weighted linear combination of selected three components was used to map granite and marble. The samples collected during field visits and petrographic analysis confirmed the remote sensing results by revealing the region’s precise contact and extent of marble and granite rock types. The experimental results reflected the theoretical advantages of the proposed approach compared with the conventional stacking of band data for PCA-based fusion. The proposed methodology was also applied to delineate granite deposits in Karoonjhar Mountains, Nagarparker (Sindh province) and the Kotah Dome, Malakand (Khyber Pakhtunkhwa Province) in Pakistan. The paper presents a cost-effective methodology by the fusion of FPCS components for granite/marble mapping during mineral resource estimation. The importance of SWIR-bearing components in fusion represents minor minerals present in granite that could be used to model the engineering properties of the rock mass.

scholarly journals Comparison of BG-Sentinel Prototype, BG-Sentinel-1, and BG-Sentinel-2: Better Results With Modification of Earlier Design

2018 ◽  
Vol 34 (3) ◽  
pp. 237-239 ◽  
Author(s):  
Isik Unlu ◽  
Mark Baker
Keyword(s):  
Gold Standard ◽  
Cost Effective ◽  
Industrial Area ◽  
Significant Difference ◽  
Field Efficacy ◽  
The Usa ◽  
Trapping Performance ◽  
Bg Sentinel ◽  
The City ◽  
Sentinel 2

ABSTRACT The BG-Sentinel® (BGS) trap is considered “the gold standard” for Aedes albopictus surveillance. With the risk of dengue, chikungunya, and Zika viruses in the USA, it is imperative our best surveillance asset is as dependable and cost-effective as possible. Biogents AG (Regensburg, Germany) in recent years has manufactured 3 generations of BGS traps in an effort to optimize trapping performance of invasive Aedes species. We evaluated the field efficacy of BG-Sentinel 2 prototype (BGS2P), BG-Sentinel (BGS1), and BG-Sentinel 2® (BGS2). The field experiment was conducted between July 1 and September 21, 2016, on the outskirts of an abandoned industrial area in the city of Trenton, NJ (40°13′58.0″N, 74°44′21.6″W). All 3 traps were compared with 3-compound BG cartridge lures. There was no significant difference in total Ae. albopictus collections among BGS1 and BGS2. However, the number of Ae. albopictus collected from the BGS2P was significantly lower than BGS1 (P = 0.016) and BGS2 (P = 0.025). Our results indicate BGS2, encompassing the latest technology aimed with improved durability and efficacy, will yield the highest capture rates of adult Ae. albopictus mosquitoes.

scholarly journals A COMPARISON OF MACHINE LEARNING MODELS FOR SOIL SALINITY ESTIMATION USING MULTI-SPECTRAL EARTH OBSERVATION DATA

2021 ◽  
Vol V-3-2021 ◽  
pp. 257-263
Author(s):  
A. Zarei ◽  
M. Hasanlou ◽  
M. Mahdianpari
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Soil Salinity ◽  
Field Data ◽  
Observation Data ◽  
Mean Square ◽  
Sentinel 2

Abstract. Soil salinity, a significant environmental indicator, is considered one of the leading causes of land degradation, especially in arid and semi-arid regions. In many cases, this major threat leads to loss of arable land, reduces crop productivity, groundwater resources loss, increases economic costs for soil management, and ultimately increases the probability of soil erosion. Monitoring soil salinity distribution and degree of salinity and mapping the electrical conductivity (EC) using remote sensing techniques are crucial for land use management. Salt-effected soil is a predominant phenomenon in the Eshtehard Salt Lake located in Alborz, Iran. In this study, the potential of Sentinel-2 imagery was investigated for mapping and monitoring soil salinity. According to the satellite's pass, different salt properties were measured for 197 soil samples in the field data study. Therefore several spectral features, such as satellite band reflectance, salinity indices, and vegetation indices, were extracted from Sentinel-2 imagery. To build an optimum machine learning regression model for soil salinity estimation, three different regression models, including Gradient Boost Machine (GBM), Extreme Gradient Boost (XGBoost), and Random Forest (RF), were used. The XGBoostmethod outperformed GBM and RF with the coefficient of determination (R2) more than 76%, Root Mean Square Error (RMSE) about 0.84 dS m−1, and Normalized Root Mean Square Error (NRMSE) about 0.33 dS m−1. The results demonstrated that the integration of remote sensing data, field data, and using an appropriate machine learning model could provide high-precision salinity maps to monitor soil salinity as an environmental problem.

scholarly journals High throughput estimates of Wolbachia, Zika and chikungunya infection in Aedes aegypti by near-infrared spectroscopy to improve arbovirus surveillance

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Lilha M. B. Santos ◽  
Mathijs Mutsaers ◽  
Gabriela A. Garcia ◽  
Mariana R. David ◽  
Márcio G. Pavan ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Aedes Aegypti ◽  
Near Infrared Spectroscopy ◽  
Field Data ◽  
Near Infrared ◽  
Wild Population ◽  
Infection Type ◽  
Cost Effective ◽  
Full Capacity ◽  
Arbovirus Surveillance

AbstractDeployment of Wolbachia to mitigate dengue (DENV), Zika (ZIKV) and chikungunya (CHIKV) transmission is ongoing in 12 countries. One way to assess the efficacy of Wolbachia releases is to determine invasion rates within the wild population of Aedes aegypti following their release. Herein we evaluated the accuracy, sensitivity and specificity of the Near Infrared Spectroscopy (NIRS) in estimating the time post death, ZIKV-, CHIKV-, and Wolbachia-infection in trapped dead female Ae. aegypti mosquitoes over a period of 7 days. Regardless of the infection type, time post-death of mosquitoes was accurately predicted into four categories (fresh, 1 day old, 2–4 days old and 5–7 days old). Overall accuracies of 93.2, 97 and 90.3% were observed when NIRS was used to detect ZIKV, CHIKV and Wolbachia in dead Ae. aegypti female mosquitoes indicating NIRS could be potentially applied as a rapid and cost-effective arbovirus surveillance tool. However, field data is required to demonstrate the full capacity of NIRS for detecting these infections under field conditions.

scholarly journals A Possible Role of Copernicus Sentinel-2 Data to Support Common Agricultural Policy Controls in Agriculture

Agronomy ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 110
Author(s):  
Filippo Sarvia ◽  
Elena Xausa ◽  
Samuele De Petris ◽  
Gianluca Cantamessa ◽  
Enrico Borgogno-Mondino
Keyword(s):  
Agricultural Policy ◽  
Common Agricultural Policy ◽  
Observation Data ◽  
Key Factor ◽  
Ndvi Time Series ◽  
Earth Observation Data ◽  
Four Levels ◽  
Nw Italy ◽  
Sentinel 2

Farmers that intend to access Common Agricultural Policy (CAP) contributions must submit an application to the territorially competent Paying Agencies (PA). Agencies are called to verify consistency of CAP contributions requirements through ground campaigns. Recently, EU regulation (N. 746/2018) proposed an alternative methodology to control CAP applications based on Earth Observation data. Accordingly, this work was aimed at designing and implementing a prototype of service based on Copernicus Sentinel-2 (S2) data for the classification of soybean, corn, wheat, rice, and meadow crops. The approach relies on the classification of S2 NDVI time-series (TS) by “user-friendly” supervised classification algorithms: Minimum Distance (MD) and Random Forest (RF). The study area was located in the Vercelli province (NW Italy), which represents a strategic agricultural area in the Piemonte region. Crop classes separability proved to be a key factor during the classification process. Confusion matrices were generated with respect to ground checks (GCs); they showed a high Overall Accuracy (>80%) for both MD and RF approaches. With respect to MD and RF, a new raster layer was generated (hereinafter called Controls Map layer), mapping four levels of classification occurrences, useful for administrative procedures required by PA. The Control Map layer highlighted that only the eight percent of CAP 2019 applications appeared to be critical in terms of consistency between farmers’ declarations and classification results. Only for these ones, a GC was warmly suggested, while the 12% must be desirable and the 80% was not required. This information alone suggested that the proposed methodology is able to optimize GCs, making possible to focus ground checks on a limited number of fields, thus determining an economic saving for PA and/or a more effective strategy of controls.

scholarly journals Continental-Scale Land Cover Mapping at 10 m Resolution Over Europe (ELC10)

2021 ◽  
Vol 13 (12) ◽  
pp. 2301
Author(s):  
Zander Venter ◽  
Markus Sydenham
Keyword(s):  
Land Cover ◽  
Atmospheric Correction ◽  
Google Earth ◽  
Tree Planting ◽  
Training Data ◽  
Classification Model ◽  
Night Time ◽  
Continental Scale ◽  
Land Cover Maps ◽  
Sentinel 2

Land cover maps are important tools for quantifying the human footprint on the environment and facilitate reporting and accounting to international agreements addressing the Sustainable Development Goals. Widely used European land cover maps such as CORINE (Coordination of Information on the Environment) are produced at medium spatial resolutions (100 m) and rely on diverse data with complex workflows requiring significant institutional capacity. We present a 10 m resolution land cover map (ELC10) of Europe based on a satellite-driven machine learning workflow that is annually updatable. A random forest classification model was trained on 70K ground-truth points from the LUCAS (Land Use/Cover Area Frame Survey) dataset. Within the Google Earth Engine cloud computing environment, the ELC10 map can be generated from approx. 700 TB of Sentinel imagery within approx. 4 days from a single research user account. The map achieved an overall accuracy of 90% across eight land cover classes and could account for statistical unit land cover proportions within 3.9% (R2 = 0.83) of the actual value. These accuracies are higher than that of CORINE (100 m) and other 10 m land cover maps including S2GLC and FROM-GLC10. Spectro-temporal metrics that capture the phenology of land cover classes were most important in producing high mapping accuracies. We found that the atmospheric correction of Sentinel-2 and the speckle filtering of Sentinel-1 imagery had a minimal effect on enhancing the classification accuracy (< 1%). However, combining optical and radar imagery increased accuracy by 3% compared to Sentinel-2 alone and by 10% compared to Sentinel-1 alone. The addition of auxiliary data (terrain, climate and night-time lights) increased accuracy by an additional 2%. By using the centroid pixels from the LUCAS Copernicus module polygons we increased accuracy by <1%, revealing that random forests are robust against contaminated training data. Furthermore, the model requires very little training data to achieve moderate accuracies—the difference between 5K and 50K LUCAS points is only 3% (86 vs. 89%). This implies that significantly less resources are necessary for making in situ survey data (such as LUCAS) suitable for satellite-based land cover classification. At 10 m resolution, the ELC10 map can distinguish detailed landscape features like hedgerows and gardens, and therefore holds potential for aerial statistics at the city borough level and monitoring property-level environmental interventions (e.g., tree planting). Due to the reliance on purely satellite-based input data, the ELC10 map can be continuously updated independent of any country-specific geographic datasets.

scholarly journals Mapping National Mangrove Cover for Belize Using Google Earth Engine and Sentinel-2 Imagery

2021 ◽  
Vol 11 (9) ◽  
pp. 4258
Author(s):  
Jordan R. Cissell ◽  
Steven W. J. Canty ◽  
Michael K. Steinberg ◽  
Loraé T. Simpson
Keyword(s):  
Google Earth ◽  
Mangrove Forests ◽  
Random Forest Classification ◽  
High Resolution Mapping ◽  
Mangrove Ecosystems ◽  
Forest Classification ◽  
Resolution Mapping ◽  
Google Earth Engine ◽  
Terrestrial Biodiversity ◽  
Sentinel 2

In this paper, we present the highest-resolution-available (10 m) national map of the mangrove ecosystems of Belize. These important ecosystems are increasingly threatened by human activities and climate change, support both marine and terrestrial biodiversity, and provide critical ecosystem services to coastal communities in Belize and throughout the Mesoamerican Reef ecoregion. Previous national- and international-level inventories document Belizean mangrove forests at spatial resolutions of 30 m or coarser, but many mangrove patches and loss events may be too small to be accurately mapped at these resolutions. Our 10 m map addresses this need for a finer-scale national mangrove inventory. We mapped mangrove ecosystems in Belize as of 2020 by performing a random forest classification of Sentinel-2 Multispectral Instrument imagery in Google Earth Engine. We mapped a total mangrove area of 578.54 km2 in 2020, with 372.04 km2 located on the mainland and 206.50 km2 distributed throughout the country’s islands and cayes. Our findings are substantially different from previous, coarser-resolution national mangrove inventories of Belize, which emphasizes the importance of high-resolution mapping efforts for ongoing conservation efforts.

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