scholarly journals COMPARISON OF SPOT AND LANDSAT DATA IN CLASSIFYING WETLAND VEGETATION TYPES

2017 ◽  
Vol XLII-3/W2 ◽  
pp. 131-135
Author(s):  
M. T. Mosime ◽  
S. G. Tesfamichael
Keyword(s):  
Spatial Resolution ◽  
Field Data ◽  
Landsat Imagery ◽  
Google Earth ◽  
Wetland Vegetation ◽  
High Temporal Resolution ◽  
Vegetation Types ◽  
Landsat Images ◽  
Vegetation Diversity ◽  
Spot Imagery

The aim of this study was to compare the performances of Landsat and SPOT imagery to map wetland vegetation types in the Klipsriviersberg Nature Reserve, South Africa. The Gauteng Conservation Plan 3.3 (C-Plan 3) was used to delineate the boundaries of the wetlands in the study area. According to the plan, the proposed study area falls within the Critical Biodiversity Areas (CBA) and Ecological Support Areas (ESA). Limited field data were collected within the boundaries of the wetlands during summer 2015 when the vegetation cover was relatively high. These data identified features including sparse vegetation, dense vegetation, grassland and bare land.Additional samples were added from Google Earth image to increase sample size. Both the field data and Google Earth data were used as reference against which the performances of SPOT and Landsat product were compared. Unsupervised classification was used to classify SPOT and Landsat images acquired in summer 2015. The results showed that overall accuracy of SPOT images is higher than Landsat images. This is attributed to its high spatial resolution of 1.5 m compared to 30 m spatial resolution of Landsat imagery. This indicates that SPOT imagery is recommended to map wetland vegetation diversity in a localised area such as the study area. The current high temporal resolution of the image has also an added advantage that conservationists should exploit.

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.

National-scale mangrove forest mapping by using Sentinel-1 SAR and Sentinel-2 MSI imagery on the Google Earth Engine Platform

2020 ◽  
Author(s):  
Luojia Hu ◽  
Wei Yao ◽  
Zhitong Yu ◽  
Lei Wang
Keyword(s):  
Spatial Resolution ◽  
Mangrove Forest ◽  
Large Scale ◽  
Landsat Imagery ◽  
Google Earth ◽  
Accuracy Evaluation ◽  
Mangrove Forests ◽  
Forest Mapping ◽  
Google Earth Engine ◽  
Sentinel 2

<p>Mangrove forest is considered as one of the pivotal ecosystems to near-shore environment health, adjacent terrestrial ecosystems and even global climate change migration. However, for past two decades, they are declining rapidly. In order to take effective steps to prevent the extinction of mangroves, high spatial resolution information of large-scale mangrove distribution is urgent. Recent study has indicated that a suitable pixel size for extracting mangroves should be at least equal to 10 m. Hence, Sentinel imagery (Sentinel-1 C-band synthetic aperture radar (SAR) and Sentinel-2 Multi-Spectral Instrument (MSI) imagery) whose spatial resolution is 10 m may hold great potentials to achieve this goal, but there are limited researches investigating it. Therefore, in this study, we will explore the potential of Sentinel imagery to extract mangrove forests in China on the Google Earth Engine platform. Specifically, our study was mainly conducted around 3 questions: (1) Which Sentinel imagery provides a higher accuracy for mangrove forest mapping, Sentinel-1 SAR data or Sentinel-2 multi-spectral data? (2) which combination of features from Sentinel imagery provides the most accurate mangrove forest map? (3) Compared to 30-m resolution mangrove products derived from Landsat imagery, how does 10-m resolution map improve our knowledge about the distribution of mangrove forest in China?</p><p> </p><p>Our results show that: (1) The highest producer’s accuracies (the reason why using producer’s accuracy as an accuracy evaluation indicator here is that the omission errors in mangrove forest extent map are much larger than commission errors) of mangrove forest maps derived from Sentinel-1 and Sentinel-2 imagery are 91.76% and 90.39%, respectively, which means that the contributions of Sentinel-1 SAR and Sentinel-2 MSI imagery to mangrove mapping are similar; (2) The highest producer’s accuracy of mangrove forest map at 10-m resolution is 95.4%. The mangrove forest map with the highest accuracy is obtained by combining quantiles of spectral and backscatter bands, spectral index, and texture index derived from time series of Sentinel-1 and Sentinel-2 imagery, indicating that the combination of Sentinel-1 SAR and Sentinel-2 MSI imagery is more useful in mangrove forest mapping than using them separately; (3) In China, the total area of mangrove forest extent at 10-m resolution is similar to that at 30-m resolution (20003 ha vs. 19220 ha). However, compared to 30-m resolution mangrove products, the 10-m resolution mangrove map identifies 1741 ha (occupying 8.7% of total mangrove forest area in China) mangrove forests in size smaller than 1 ha, which are especially important to low-lying coastal zone. This study demonstrates the feasibility of Sentinel imagery in large-scale mangrove forest mapping and gives guidance to map global mangrove forest at 10-m resolution in the future.  </p><p> </p>

scholarly journals Crop Area Mapping in Southern and Central Malawi With Google Earth Engine

2021 ◽  
Vol 3 ◽  
Author(s):  
Seth Peterson ◽  
Greg Husak
Keyword(s):  
Spatial Resolution ◽  
Test Data ◽  
Google Earth ◽  
Reference Points ◽  
Sub Saharan Africa ◽  
High Temporal Resolution ◽  
Smallholder Farms ◽  
Google Earth Engine ◽  
Crop Area ◽  
Sentinel 2

Agriculture in sub-Saharan Africa consists primarily of smallholder farms of rainfed crops. Historically, satellite data were too coarse to account for the heterogeneity in these landscapes. Sentinel-2 data have improved spectral resolution and much higher spatial resolution (10 m) than previously available satellites with global coverage, such as Landsat or MODIS, making mapping smallholder farms possible. Spectral mixture analysis was used to convert the Sentinel-2 signal into fractions of green vegetation, non-photosynthetic vegetation, soil, and shade endmembers. Very high spatial resolution imagery in Google Earth Pro was used to identify locations of crop and natural vegetation classes, with over 20,000 reference points interpreted. The high temporal resolution of Sentinel-2 (5 days repeat) allows for classification of landcover based on the phenological signal, with natural areas having smoothly varying amounts of photosynthetic vegetation annually, while cropped areas show more abrupt changes, and also the presence of bare soil due to agricultural activity at some point during the year. We summarized the endmember values using monthly medians, extracted values for the reference data points, randomly split them into training and test data sets, and input the training data into the random forests algorithm in Google Earth Engine to map crop area. We divided southern and central Malawi into tiles, and found crop/no crop classification accuracies on the test data for each tile to be between 87 and 93%. The 10 m map of crop area was aggregated to the district level and showed an R2 of 0.74 with ground-based statistics from the Malawi government and 0.79 with a remotely sensed product developed by the USGS.

scholarly journals Automated Inundation Mapping Over Large Areas Using Landsat Data and Google Earth Engine

2020 ◽  
Vol 12 (8) ◽  
pp. 1348 ◽  
Author(s):  
Victoria L. Inman ◽  
Mitchell B. Lyons
Keyword(s):  
Spatial Resolution ◽  
Aquatic Vegetation ◽  
Landsat Imagery ◽  
Google Earth ◽  
Okavango Delta ◽  
Infrared Band ◽  
Inundation Maps ◽  
Noaa Avhrr ◽  
Inundation Mapping ◽  
Google Earth Engine

Accurate inundation maps for flooded wetlands and rivers are a critical resource for their management and conservation. In this paper, we automate a method (thresholding of the short-wave infrared band) for classifying peak inundation in the Okavango Delta, northern Botswana, using Landsat imagery and Google Earth Engine. Inundation classification in the Okavango Delta is complex owing to the spectral overlap between inundated areas covered with aquatic vegetation and dryland vegetation classes on satellite imagery, and classifications have predominately been implemented on broad spatial resolution imagery. We present the longest time series to date (1990–2019) of inundation maps for the peak flood season at a high spatial resolution (30 m) for the Okavango Delta. We validated the maps using image-based and in situ data accuracy assessments, with overall accuracy ranging from 91.5% to 98.1%. Use of Landsat imagery resulted in consistently lower (on average, 692 km2) estimates of inundation extent than previous studies that used Moderate Resolution Imaging Spectroradiometer (MODIS) and National Oceanic and Atmospheric Administration Advanced Very-High-Resolution Radiometer (NOAA AVHRR) imagery, likely owing to the increased number of mixed pixels that occur when using broad spatial resolution imagery, which can lead to overestimations of the size of inundated areas. We provide the inundation maps and Google Earth Engine code for public use. This classification method can likely be adapted for inundation mapping in other regions.

scholarly journals A Google Earth Engine Tool to Investigate, Map and Monitor Volcanic Thermal Anomalies at Global Scale by Means of Mid-High Spatial Resolution Satellite Data

2020 ◽  
Vol 12 (19) ◽  
pp. 3232
Author(s):  
Nicola Genzano ◽  
Nicola Pergola ◽  
Francesco Marchese
Keyword(s):  
Spatial Resolution ◽  
Satellite Data ◽  
Hot Spot ◽  
Global Scale ◽  
Google Earth ◽  
High Temporal Resolution ◽  
Landsat 8 ◽  
Thermal Anomalies ◽  
Google Earth Engine ◽  
Sentinel 2

Several satellite-based systems have been developed over the years to study and monitor thermal volcanic activity. Most of them use high temporal resolution satellite data, provided by sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS) that if on the one hand guarantee a continuous monitoring of active volcanic areas on the other hand are less suited to map thermal anomalies, and to provide accurate information about their features. The Multispectral Instrument (MSI) and the Operational Land Imager (OLI), respectively, onboard the Sentinel-2 and Landsat-8 satellites, providing Short-Wave Infrared (SWIR) data at 20 m (MSI) and 30 m (OLI) spatial resolution, may make an important contribution in this area. In this work, we present the first Google Earth Engine (GEE) App to investigate, map and monitor volcanic thermal anomalies at global scale, integrating Landsat-8 OLI and Sentinel-2 MSI observations. This open tool, which implements the Normalized Hot spot Indices (NHI) algorithm, enables the analysis of more than 1400 active volcanoes, with very low processing times, thanks to the high GEE computational resources. Performance and limitations of the tool, such as its next upgrades, aiming at increasing the user-friendly experience and extending the temporal range of data analyses, are analyzed and discussed.

scholarly journals Automated Inundation History Mapping over Large Areas using Landsat and Google Earth Engine

2020 ◽  
Author(s):  
Victoria L Inman ◽  
Mitchell B Lyons
Keyword(s):  
Spatial Resolution ◽  
Aquatic Vegetation ◽  
Landsat Imagery ◽  
Short Wave ◽  
Google Earth ◽  
Okavango Delta ◽  
Complex Case ◽  
Infrared Band ◽  
Inundation Maps ◽  
Google Earth Engine

Accurate inundation maps for flooded wetlands and rivers are a critical resource for their management and conservation. In this paper we automate a method (thresholding of the short-wave infrared band) for classifying inundation, using Landsat imagery and Google Earth Engine. We demonstrate the method in the Okavango Delta, northern Botswana, a complex case study due to the spectral overlap between inundated areas covered with aquatic vegetation and dryland vegetation classes on satellite imagery. Inundation classifications in the Okavango Delta have predominately been implemented on broad spatial resolution images. We present the longest time series to date (1990-2019) of inundation maps at high spatial resolution (30m) for the Okavango Delta. We validated the maps using image-based and in situ data accuracy assessments, with accuracy ranging from 91.5 - 98.1%. Use of Landsat imagery resulted in consistently lower estimates of inundation extent than previous studies, likely due to the increased number of mixed pixels that occur when using broad spatial resolution imagery, which can lead to overestimations of the size of inundated areas. We provide the inundation maps and Google Earth Engine code for public use.

Application of Google Earth Engine (GEE) to obtain calibration data for a large-scale hydrosedimentological model: a test case in South America

2021 ◽  
Author(s):  
Renata Rossoni ◽  
Fernando Fan ◽  
Leonardo Laipelt
Keyword(s):  
Remote Sensing ◽  
Temporal Resolution ◽  
Field Data ◽  
Large Scale ◽  
Google Earth ◽  
Limiting Factor ◽  
High Temporal Resolution ◽  
Test Case ◽  
Calibration Data ◽  
Google Earth Engine

<p>In hydrosedimentological modelling, the lack of high temporal resolution field data is a limiting factor for the assessment of the performance of models. This way, the remote sensing images have been studied to correlate imagery information with suspended sediment concentration (SSC) in the last decades, aiming to complement field data, by improving the SSC information temporal and spatial resolution. Thus, the present work used the Google Earth Engine (GEE), a cloud-based platform, to systematically obtain red band reflectance information from Landsat 5 imagery to support large-scale hydrosedimentological modelling. The test case was to the Rio Grande do Sul state hydrological region in Brazil, a South American region with scarce SSC data. The methodology applied consisted in nine steps using GEE code: (1) river width analysis using remote sensing imagery to localize the virtual gauge stations (VGS) from the intersection between the discretization of hydrosedimentological model and the chosen rivers, (2) TM sensor definition, onboard of Landsat 5 satellite, (3) collection of red reflectance information between 1990 and 2010, based on previous works that presented better correlation between red reflectance and SSC, (4) in each VGS, we created a circle of radius equal to 1000 m, (5) to each image, we removed clouded-pixels, using the Landsat 5 quality bands, (6) we generated a dynamic water mask to each image to ensure that only pixels with water would be used to collected reflectance information, (7) finally, we calculated the mean of red band reflectance inside the intersection of water mask and circle buffer, removing the clouded-pixels, (8) we calculated a filter to remove remnants clouded-pixels and random errors from imagery, (9) we used the MGB-SED model to simulate long-term SSC in the region and we calibrated the model with the GEE data based on a correlation approach. The results found were: (i) 1267 virtual gauge stations, approximately 20 times the number of in situ SSC gauging stations available in the region, (ii) a larger area of data and greater temporal resolution, (iii) improvement in the correlation between model results and red reflectance, when we assess the model with SSC observed data. In conclusion, the work shows the potential of GEE to simply obtain large-scale reflectance data that could be used to improve the calibration processes of large-scale hydrosedimentological modelling.</p>

scholarly journals A comparison of mono-seasonal and multiseasonal Landsat images for vegetation cover classification in the Mediterranean region: a case study in Latakia, Syria

2021 ◽  
Vol 310 ◽  
pp. 05001
Author(s):  
Vasiliy Malinnikov ◽  
Assem Khatib
Keyword(s):  
Vegetation Cover ◽  
Mediterranean Region ◽  
Satellite Images ◽  
Landsat Imagery ◽  
Google Earth ◽  
Landsat Images ◽  
Significance Level ◽  
The Mediterranean ◽  
Spectral Channels

Providing constantly updated information on vegetation serves as a basis for studies of natural resources and ecological issues. This paper discusses the question related to an appropriate season(s) for classification vegetation cover in the Mediterranean region and detecting its changes using Landsat imagery. Autumn, spring, and multi-seasonal satellite images, captured in 2017, were used to classify vegetation cover in a part of the Lattakia province, Syria. The satellite images were classified using the random forest algorithm, and high spatial resolution satellite images Google Earth Pro were used as reference data. The results indicate better effectiveness of the autumn images over spring ones for vegetation cover classification with 73.6% and 62.4% overall accuracy, respectively. In addition, a comparison of autumn and multi-seasonal Landsat images indicates no significant statistical difference in the accuracy of vegetation cover classification at the significance level of 0.05, which illustrates the effectiveness of using autumn images to classify the vegetation cover of the Mediterranean region. Furthermore, the obtained results show the necessity of using additional features as the spectral channels may not be sufficient for mapping vegetation cover in the Mediterranean region with high accuracy.

Coastline Vulnerability Assessment through Landsat and Cubesats in a Coastal Mega City

2020 ◽  
Vol 12 (5) ◽  
pp. 749 ◽  
Author(s):  
Majid Nazeer ◽  
Muhammad Waqas ◽  
Muhammad Imran Shahzad ◽  
Ibrahim Zia ◽  
Weicheng Wu
Keyword(s):  
Spatial Resolution ◽  
Land Reclamation ◽  
Anthropogenic Activities ◽  
Monsoon Season ◽  
Coastal Protection ◽  
Landsat Images ◽  
Intergovernmental Panel ◽  
Eastern Zone ◽  
Stable Conditions ◽  
Western Zone

According to the Intergovernmental Panel on Climate Change (IPCC), global mean sea levels may rise from 0.43 m to 0.84 m by the end of the 21st century. This poses a significant threat to coastal cities around the world. The shoreline of Karachi (a coastal mega city located in Southern Pakistan) is vulnerable mainly due to anthropogenic activities near the coast. Therefore, the present study investigates rates and susceptibility to shoreline change using a 76-year multi-temporal dataset (1942 to 2018) through the Digital Shoreline Analysis System (DSAS). Historical shoreline positions were extracted from the topographic sheets (1:250,000) of 1942 and 1966, the medium spatial resolution (30 m) multi-sensor Landsat images of 1976, 1990, 2002, 2011, and a high spatial resolution (3 m) Planet Scope image from 2018, along the 100 km coast of Karachi. The shoreline was divided into two zones, namely eastern (25 km) and western (29 km) zones, to track changes in development, movement, and dynamics of the shoreline position. The analysis revealed that 95% of transects drawn for the eastern zone underwent accretion (i.e., land reclamation) with a mean rate of 14 m/year indicating that the eastern zone faced rapid shoreline progression, with the highest rates due to the development of coastal areas for urban settlement. Similarly, 74% of transects drawn for the western zone experienced erosion (i.e., land loss) with a mean rate of −1.15 m/year indicating the weathering and erosion of rocky and sandy beaches by marine erosion. Among the 25 km length of the eastern zone, 94% (23.5 km) of the shoreline was found to be highly vulnerable, while the western zone showed much more stable conditions due to anthropogenic inactivity. Seasonal hydrodynamic analysis revealed approximately a 3% increase in the average wave height during the summer monsoon season and a 1% increase for the winter monsoon season during the post-land reclamation era. Coastal protection and management along the Sindh coastal zone should be adopted to defend against natural wave erosion and the government must take measures to stop illegal sea encroachments.

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