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.

Identification of Tectonic Activity and Fault Mechanism From Morphological Signatures

2019 ◽  
pp. 99-123
Author(s):  
Arunima Nandy
Keyword(s):  
Active Zone ◽  
Satellite Images ◽  
Google Earth ◽  
Tectonic Activity ◽  
Landsat 8 ◽  
North East India ◽  
North East ◽  
Gis And Rs ◽  
Fault Mechanism ◽  
Tectonic Activities

Agartala, the capital of Tripura, is one of the most important and populated cities of North-East India. From the aspect of geomorphology, the whole area is characterized by highlands (tilla) and lowlands (lunga). Tectonically, Tripura falls under very active zone (Zone V). Assessment of tectonic activities of this region is very significant. For identification of tectonic activity, morphological or geomorphic signatures play very important role. The chapter identifies the presence of tectonic activity from morphological signatures in and around Agartala city. Landsat 8 OLI, maps from Geological Survey of India, Google Earth imageries have been used in this study. The presence of some lineaments and sag ponds has been identified on the basis of which fault mechanism of Agartala and Baramura hills has been delineated. This study contains a brief note on the conceptual demonstration of application of GIS and RS technologies and how morphological signatures and satellite images can help us to recognize tectonic activities over a region.

scholarly journals Tree Diversity in Urban Parks and Gardens of Ibadan City, Nigeria

2020 ◽  
pp. 29-40
Author(s):  
O. S. Olokeogun ◽  
A. O. Oladoye ◽  
A. F. Aderounmu
Keyword(s):  
Species Richness ◽  
Species Diversity ◽  
Urban Area ◽  
Diversity Index ◽  
Urban Parks ◽  
Google Earth ◽  
Conservation Strategies ◽  
Evergreen Species ◽  
Essential Information ◽  
Wiener Diversity Index

Aims: To assess the species diversity of trees in parks and gardens across the urban area of the city of Ibadan. Study Design: Total enumeration was conducted for data collection; All the trees in the urban parks and gardens were identified. The species similarity and diversity of trees were computed. Place and Duration of Study: Parks and gardens within the urban area of Ibadan city, Nigeria, Department of Forestry Technology, Federal College of Forestry, Ibadan, Nigeria, Department of Forestry and Wildlife Management, Federal University of Agriculture, Abeokuta, Nigeria, between April, 2019 and March, 2020. Methodology: The parks and gardens were identified on google earth image. The species and family of all the trees within the parks and gardens were identified. The density, species richness, relative abundance, similarity, diversity and evenness were also estimated. The Shannon-Wiener diversity index, Simpson’s diversity index, and Pielou’s species evenness index were used in estimating the species diversity. Results: The findings reveal a significant species composition of 82 species distributed across 34 families, with Senna sesame and Fabaceae being the most abundant species and family respectively. The trees with a population of 2,471 trees are largely dominated by exotic and evergreen species. The species richness, similarity, and diversity were relatively high. Conclusion: The study provides an opportunity to evaluate the contribution of urban parks and gardens to the ecological integrity and health of a city, thereby serving as essential information for preparing workable conservation strategies.

Identifying multi-day to sub-daily supraglacial lake change in Greenland from Sentinel 2 and Landsat 8 imagery using Google Earth Engine

2021 ◽  
Author(s):  
Qiao Li ◽  
James Lea ◽  
Stephen Brough
Keyword(s):  
Google Earth ◽  
Landsat 8 ◽  
Lake Area ◽  
High Latitudes ◽  
Large Area ◽  
Large Lakes ◽  
North East ◽  
The North ◽  
Google Earth Engine ◽  
Sentinel 2

<p>Supraglacial lakes (SGLs) are a major component of Greenland’s surface hydrology and mass balance. Monitoring their evolution at multi-day to sub-daily timescales has traditionally been performed by relatively low-resolution sensors such as MODIS Terra, though opportunities exist for using higher spatial resolution sensors at high latitudes.</p><p>In this study, we take advantage of frequent orbital crossovers of Sentinel 2 and Landsat 8 imagery at high latitudes to monitor lakes at multi-day to sub-day temporal resolution, and spatial resolutions up to/over an order of magnitude higher than MODIS Terra (10 m to 30 m, compared to ~250 m for MODIS Terra). Through leveraging the cloud computing resources of Google Earth Engine (GEE), we have developed a workflow to track the evolution of lakes for all available Sentinel 2 and Landsat 8 images over a melt season.</p><p>Our workflow builds on the approach of Moussavi et al. (2020) that was developed for Antarctica, implementing it within GEE to explore its sensitivity and suitability for application to the catchment of the North East Greenland Ice Stream (NEGIS) for the 2019 melt season. To improve the efficiency of analysis, we analyse 282 large lakes (>0.125 km^2) that were previously identified through analysis of MODIS Terra imagery. All lake outlines are appended with image ID and lake area metadata to facilitate subsequent analysis, and allow each lake outline to be traced back to the original image that it was derived from. Our approach is able to monitor lake growth and drainage at unprecedented spatial and temporal resolutions over a large area, allowing the widespread characterization of seasonal lake evolution.</p>

scholarly journals Development of a global 30 m impervious surface map using multisource and multitemporal remote sensing datasets with the Google Earth Engine platform

2020 ◽  
Vol 12 (3) ◽  
pp. 1625-1648 ◽  
Author(s):  
Xiao Zhang ◽  
Liangyun Liu ◽  
Changshan Wu ◽  
Xidong Chen ◽  
Yuan Gao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Random Forest ◽  
Human Activity ◽  
Impervious Surface ◽  
Google Earth ◽  
Landsat 8 ◽  
Impervious Surfaces ◽  
Sar Images ◽  
Training Samples ◽  
Google Earth Engine

Abstract. The amount of impervious surface is an important indicator in the monitoring of the intensity of human activity and environmental change. The use of remote sensing techniques is the only means of accurately carrying out global mapping of impervious surfaces covering large areas. Optical imagery can capture surface reflectance characteristics, while synthetic-aperture radar (SAR) images can be used to provide information on the structure and dielectric properties of surface materials. In addition, nighttime light (NTL) imagery can detect the intensity of human activity and thus provide important a priori probabilities of the occurrence of impervious surfaces. In this study, we aimed to generate an accurate global impervious surface map at a resolution of 30 m for 2015 by combining Landsat 8 Operational Land Image (OLI) optical images, Sentinel-1 SAR images and Visible Infrared Imaging Radiometer Suite (VIIRS) NTL images based on the Google Earth Engine (GEE) platform. First, the global impervious and nonimpervious training samples were automatically derived by combining the GlobeLand30 land-cover product with VIIRS NTL and MODIS enhanced vegetation index (EVI) imagery. Then, the local adaptive random forest classifiers, allowing for a regional adjustment of the classification parameters to take into account the regional characteristics, were trained and used to generate regional impervious surface maps for each 5∘×5∘ geographical grid using local training samples and multisource and multitemporal imagery. Finally, a global impervious surface map, produced by mosaicking numerous 5∘×5∘ regional maps, was validated by interpretation samples and then compared with five existing impervious products (GlobeLand30, FROM-GLC, NUACI, HBASE and GHSL). The results indicated that the global impervious surface map produced using the proposed multisource, multitemporal random forest classification (MSMT_RF) method was the most accurate of the maps, having an overall accuracy of 95.1 % and kappa coefficient (one of the most commonly used statistics to test interrater reliability; Olofsson et al., 2014) of 0.898 as against 85.6 % and 0.695 for NUACI, 89.6 % and 0.780 for FROM-GLC, 90.3 % and 0.794 for GHSL, 88.4 % and 0.753 for GlobeLand30, and 88.0 % and 0.745 for HBASE using all 15 regional validation data. Therefore, it is concluded that a global 30 m impervious surface map can accurately and efficiently be generated by the proposed MSMT_RF method based on the GEE platform. The global impervious surface map generated in this paper is available at https://doi.org/10.5281/zenodo.3505079 (Zhang and Liu, 2019).

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 Assessing an Atmospheric Correction Algorithm for Time Series of Satellite-Based Water-Leaving Reflectance Using Match-Up Sites in Australian Coastal Waters

2021 ◽  
Vol 13 (10) ◽  
pp. 1927
Author(s):  
Fuqin Li ◽  
David Jupp ◽  
Thomas Schroeder ◽  
Stephen Sagar ◽  
Joshua Sixsmith ◽  
...  
Keyword(s):  
Coastal Waters ◽  
Atmospheric Correction ◽  
Correction Method ◽  
In Situ Measurements ◽  
Landsat 8 ◽  
Correction Algorithm ◽  
Simple Method ◽  
North East ◽  
The Impact

An atmospheric correction algorithm for medium-resolution satellite data over general water surfaces (open/coastal, estuarine and inland waters) has been assessed in Australian coastal waters. In situ measurements at four match-up sites were used with 21 Landsat 8 images acquired between 2014 and 2017. Three aerosol sources (AERONET, MODIS ocean aerosol and climatology) were used to test the impact of the selection of aerosol optical depth (AOD) and Ångström coefficient on the retrieved accuracy. The initial results showed that the satellite-derived water-leaving reflectance can have good agreement with the in situ measurements, provided that the sun glint is handled effectively. Although the AERONET aerosol data performed best, the contemporary satellite-derived aerosol information from MODIS or an aerosol climatology could also be as effective, and should be assessed with further in situ measurements. Two sun glint correction strategies were assessed for their ability to remove the glint bias. The most successful one used the average of two shortwave infrared (SWIR) bands to represent sun glint and subtracted it from each band. Using this sun glint correction method, the mean all-band error of the retrieved water-leaving reflectance at the Lucinda Jetty Coastal Observatory (LJCO) in north east Australia was close to 4% and unbiased over 14 acquisitions. A persistent bias in the other strategy was likely due to the sky radiance being non-uniform for the selected images. In regard to future options for an operational sun glint correction, the simple method may be sufficient for clear skies until a physically based method has been established.

Redescription of Pseudogilquinia pillersi (Southwell, 1929) (Cestoda, Trypanorhyncha) from serranid and lethrinid fishes from New Caledonia and Australia

2007 ◽  
Vol 52 (3) ◽  
Author(s):  
Ian Beveridge ◽  
Claude Chauvet ◽  
Jean-Lou Justine
Keyword(s):  
Sri Lanka ◽  
Host Range ◽  
East Coast ◽  
New Caledonia ◽  
Epinephelus Coioides ◽  
North East ◽  
The North ◽  
Specific Variation ◽  
Epinephelus Malabaricus

AbstractPseudogilquinia pillersi (Southwell, 1929), a poorly known species of trypanorhynch, is redescribed from plerocerci collected from Epinephelus coioides (Hamilton, 1922), Epinephelus malabaricus (Bloch et Schneider, 1801) (Serranidae) and Plectropomus laevis (Lacépède, 1801) (Serranidae) off New Caledonia. These were compared with specimens from Lethrinus atkinsoni Seale, 1910 and Lethrinus miniatus (Forster, 1801) (Lethrinidae) off the north-east coast of Australia as well as syntypes from Protonibea diacantha (Lacépède, 1802) from Sri Lanka. Although size differences were found in parts of the scolex as well as in the sizes of the tentacular hooks, the hook arrangements were identical in all specimens. The differences observed were attributed provisionally to intra-specific variation across a wide geographic and host range.

Google Earth: A New Resource for Shoreline Change Estimation—Case Study from Jaffna Peninsula, Sri Lanka

2018 ◽  
Vol 41 (6) ◽  
pp. 546-580 ◽  
Author(s):  
T. W. S. Warnasuriya ◽  
Kuddithamby Gunaalan ◽  
S. S. Gunasekara
Keyword(s):  
Sri Lanka ◽  
Shoreline Change ◽  
Google Earth

scholarly journals Fusing Geostationary Satellite Observations with Harmonized Landsat-8 and Sentinel-2 Time Series for Monitoring Field-Scale Land Surface Phenology

2021 ◽  
Vol 13 (21) ◽  
pp. 4465
Author(s):  
Yu Shen ◽  
Xiaoyang Zhang ◽  
Weile Wang ◽  
Ramakrishna Nemani ◽  
Yongchang Ye ◽  
...  
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Cloud Cover ◽  
Landsat 8 ◽  
Field Scale ◽  
Spatiotemporal Resolution ◽  
Essential Information ◽  
Land Surface Phenology ◽  
The Earth ◽  
Sentinel 2

Accurate and timely land surface phenology (LSP) provides essential information for investigating the responses of terrestrial ecosystems to climate changes and quantifying carbon and surface energy cycles on the Earth. LSP has been widely investigated using daily Visible Infrared Imaging Radiometer Suite (VIIRS) or Moderate Resolution Imaging Spectroradiometer (MODIS) observations, but the resultant phenometrics are frequently influenced by surface heterogeneity and persistent cloud contamination in the time series observations. Recently, LSP has been derived from Landsat-8 and Sentinel-2 time series providing detailed spatial pattern, but the results are of high uncertainties because of poor temporal resolution. With the availability of data from Advanced Baseline Imager (ABI) onboard a new generation of geostationary satellites that observe the earth every 10–15 min, daily cloud-free time series could be obtained with high opportunities. Therefore, this study investigates the generation of synthetic high spatiotemporal resolution time series by fusing the harmonized Landsat-8 and Sentinel-2 (HLS) time series with the temporal shape of ABI data for monitoring field-scale (30 m) LSP. The algorithm is verified by detecting the timings of greenup and senescence onsets around north Wisconsin/Michigan states, United States, where cloud cover is frequent during spring rainy season. The LSP detections from HLS-ABI are compared with those from HLS or ABI alone and are further evaluated using PhenoCam observations. The result indicates that (1) ABI could provide ~3 times more high-quality observations than HLS around spring greenup onset; (2) the greenup and senescence onsets derived from ABI and HLS-ABI are spatially consistent and statistically comparable with a median difference less than 1 and 10-days, respectively; (3) greenup and senescence onsets derived from HLS data show sharp boundaries around the orbit-overlapped areas and shifts of ~13 days delay and ~15 days ahead, respectively, relative to HLS-ABI detections; and (4) HLS-ABI greenup and senescence onsets align closely to PhenoCam observations with an absolute average difference of less than 2 days and 5 days, respectively, which are much better than phenology detections from ABI or HLS alone. The result suggests that the proposed approach could be implemented the monitor of 30 m LSP over regions with persistent cloud cover.

scholarly journals Spatiotemporal change analysis of long time series inland water in Sri Lanka based on remote sensing cloud computing

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Jianfeng Li ◽  
Jiawei Wang ◽  
Liangyan Yang ◽  
Huping Ye
Keyword(s):  
Remote Sensing ◽  
Cloud Computing ◽  
Surface Water ◽  
Water Resources ◽  
Sri Lanka ◽  
Water Resource Management ◽  
Google Earth ◽  
Water Extraction ◽  
Inland Lakes ◽  
Lakes And Reservoirs

AbstractSri Lanka is an important hub connecting Asia-Africa-Europe maritime routes. It receives abundant but uneven spatiotemporal distribution of rainfall and has evident seasonal water shortages. Monitoring water area changes in inland lakes and reservoirs plays an important role in guiding the development and utilisation of water resources. In this study, a rapid surface water extraction model based on the Google Earth Engine remote sensing cloud computing platform was constructed. By evaluating the optimal spectral water index method, the spatiotemporal variations of reservoirs and inland lakes in Sri Lanka were analysed. The results showed that Automated Water Extraction Index (AWEIsh) could accurately identify the water boundary with an overall accuracy of 99.14%, which was suitable for surface water extraction in Sri Lanka. The area of the Maduru Oya Reservoir showed an overall increasing trend based on small fluctuations from 1988 to 2018, and the monthly area of the reservoir fluctuated significantly in 2017. Thus, water resource management in the dry zone should focus more on seasonal regulation and control. From 1995 to 2015, the number and area of lakes and reservoirs in Sri Lanka increased to different degrees, mainly concentrated in arid provinces including Northern, North Central, and Western Provinces. Overall, the amount of surface water resources have increased.

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