scholarly journals Automated satellite remote sensing of giant kelp at the Falkland Islands (Islas Malvinas)

PLoS ONE ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. e0257933
Author(s):  
Henry F. Houskeeper ◽  
Isaac S. Rosenthal ◽  
Katherine C. Cavanaugh ◽  
Camille Pawlak ◽  
Laura Trouille ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Satellite Imagery ◽  
Google Earth ◽  
Nitrate Content ◽  
Falkland Islands ◽  
Kelp Forests ◽  
Giant Kelp ◽  
Canopy Area ◽  
Islas Malvinas

Giant kelp populations that support productive and diverse coastal ecosystems at temperate and subpolar latitudes of both hemispheres are vulnerable to changing climate conditions as well as direct human impacts. Observations of giant kelp forests are spatially and temporally uneven, with disproportionate coverage in the northern hemisphere, despite the size and comparable density of southern hemisphere kelp forests. Satellite imagery enables the mapping of existing and historical giant kelp populations in understudied regions, but automating the detection of giant kelp using satellite imagery requires approaches that are robust to the optical complexity of the shallow, nearshore environment. We present and compare two approaches for automating the detection of giant kelp in satellite datasets: one based on crowd sourcing of satellite imagery classifications and another based on a decision tree paired with a spectral unmixing algorithm (automated using Google Earth Engine). Both approaches are applied to satellite imagery (Landsat) of the Falkland Islands or Islas Malvinas (FLK), an archipelago in the southern Atlantic Ocean that supports expansive giant kelp ecosystems. The performance of each method is evaluated by comparing the automated classifications with a subset of expert-annotated imagery (8 images spanning the majority of our continuous timeseries, cumulatively covering over 2,700 km of coastline, and including all relevant sensors). Using the remote sensing approaches evaluated herein, we present the first continuous timeseries of giant kelp observations in the FLK region using Landsat imagery spanning over three decades. We do not detect evidence of long-term change in the FLK region, although we observe a recent decline in total canopy area from 2017–2021. Using a nitrate model based on nearby ocean state measurements obtained from ships and incorporating satellite sea surface temperature products, we find that the area of giant kelp forests in the FLK region is positively correlated with the nitrate content observed during the prior year. Our results indicate that giant kelp classifications using citizen science are approximately consistent with classifications based on a state-of-the-art automated spectral approach. Despite differences in accuracy and sensitivity, both approaches find high interannual variability that impedes the detection of potential long-term changes in giant kelp canopy area, although recent canopy area declines are notable and should continue to be monitored carefully.

scholarly journals Automated satellite remote sensing of giant kelp at the Falkland Islands (Islas Malvinas)

2021 ◽  
Author(s):  
Henry F. Houskeeper ◽  
Isaac S. Rosenthal ◽  
Katherine C. Cavanaugh ◽  
Camille Pawlak ◽  
Laura Trouille ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Satellite Imagery ◽  
Google Earth ◽  
Nitrate Content ◽  
Falkland Islands ◽  
Kelp Forests ◽  
Giant Kelp ◽  
Canopy Area ◽  
Islas Malvinas

AbstractGiant kelp populations support productive and diverse coastal ecosystems in both hemispheres at temperate and subpolar latitudes but are vulnerable to changing climate conditions as well as direct human impacts. Observations of giant kelp forests are spatially and temporally patchy, with disproportionate coverage in the northern hemisphere, despite the size and comparable density of southern hemisphere kelp forests. Satellite imagery enables the mapping of existing and historical giant kelp populations in understudied regions, but automating the detection of giant kelp in large satellite datasets requires approaches that are robust to the optical complexity of the shallow, nearshore environment. We present and compare two approaches for automating the detection of giant kelp in satellite datasets: one based on crowd sourcing of satellite imagery classifications and another based on a decision tree paired with a spectral unmixing algorithm (automated using Google Earth Engine). Both approaches are applied to satellite imagery (Landsat) of the Falkland Islands or Islas Malvinas (FLK), an archipelago in the southern Atlantic Ocean that supports expansive giant kelp ecosystems. The performance of each method is evaluated by comparing the automated classifications with a subset of expert-annotated imagery cumulatively spanning over 2,700km of coastline. Using the remote sensing approaches evaluated herein, we present the first continuous timeseries of giant kelp observations in the FLK region using Landsat imagery spanning over three decades. We do not detect evidence of long-term change in the FLK region, although we observe a recent decline in total canopy area from 2017-2021. Using a nitrate model based on nearby ocean state measurements obtained from ships and incorporating satellite sea surface temperature products, we find that the area of giant kelp forests in the FLK region is positively correlated with the nitrate content observed during the prior year. Our results indicate that giant kelp classifications using citizen science are approximately consistent with classifications based on a state-of-the-art automated spectral approach. Despite differences in accuracy and sensitivity, both approaches find high interannual variability that impedes the detection of potential long-term changes in giant kelp canopy area, although recent canopy area declines are notable and should continue to be monitored carefully.

scholarly journals Remote Sensing Is Changing Our View of the Coast: Insights from 40 Years of Monitoring at Narrabeen-Collaroy, Australia

2018 ◽  
Vol 10 (11) ◽  
pp. 1744 ◽  
Author(s):  
Kristen Splinter ◽  
Mitchell Harley ◽  
Ian Turner
Keyword(s):  
Remote Sensing ◽  
Data Collection ◽  
Temporal Resolution ◽  
Monitoring Program ◽  
Google Earth ◽  
Airborne Lidar ◽  
Data Collection Program ◽  
Insight Into ◽  
Collection Program

Narrabeen-Collaroy Beach, located on the Northern Beaches of Sydney along the Pacific coast of southeast Australia, is one of the longest continuously monitored beaches in the world. This paper provides an overview of the evolution and international scientific impact of this long-term beach monitoring program, from its humble beginnings over 40 years ago using the rod and tape measure Emery field survey method; to today, where the application of remote sensing data collection including drones, satellites and crowd-sourced smartphone images, are now core aspects of this continuing and much expanded monitoring effort. Commenced in 1976, surveying at this beach for the first 30 years focused on in-situ methods, whereby the growing database of monthly beach profile surveys informed the coastal science community about fundamental processes such as beach state evolution and the role of cross-shore and alongshore sediment transport in embayment morphodynamics. In the mid-2000s, continuous (hourly) video-based monitoring was the first application of routine remote sensing at the site, providing much greater spatial and temporal resolution over the traditional monthly surveys. This implementation of video as the first of a now rapidly expanding range of remote sensing tools and techniques also facilitated much wider access by the international research community to the continuing data collection program at Narrabeen-Collaroy. In the past decade the video-based data streams have formed the basis of deeper understanding into storm to multi-year response of the shoreline to changing wave conditions and also contributed to progress in the understanding of estuary entrance dynamics. More recently, ‘opportunistic’ remote sensing platforms such as surf cameras and smartphones have also been used for image-based shoreline data collection. Commencing in 2011, a significant new focus for the Narrabeen-Collaroy monitoring program shifted to include airborne lidar (and later Unmanned Aerial Vehicles (UAVs)), in an enhanced effort to quantify the morphological impacts of individual storm events, understand key drivers of erosion, and the placing of these observations within their broader regional context. A fixed continuous scanning lidar installed in 2014 again improved the spatial and temporal resolution of the remote-sensed data collection, providing new insight into swash dynamics and the often-overlooked processes of post-storm beach recovery. The use of satellite data that is now readily available to all coastal researchers via Google Earth Engine continues to expand the routine data collection program and provide key insight into multi-decadal shoreline variability. As new and expanding remote sensing technologies continue to emerge, a key lesson from the long-term monitoring at Narrabeen-Collaroy is the importance of a regular re-evaluation of what data is most needed to progress the science.

scholarly journals Garlic and Winter Wheat Identification Based on Active and Passive Satellite Imagery and the Google Earth Engine in Northern China

2020 ◽  
Vol 12 (21) ◽  
pp. 3539
Author(s):  
Haifeng Tian ◽  
Jie Pei ◽  
Jianxi Huang ◽  
Xuecao Li ◽  
Jian Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Winter Wheat ◽  
Satellite Imagery ◽  
Remote Sensing Data ◽  
Northern China ◽  
Google Earth ◽  
Kappa Coefficient ◽  
Landsat 8 ◽  
Winter Crops ◽  
Sentinel 2

Garlic and winter wheat are major economic and grain crops in China, and their boundaries have increased substantially in recent decades. Updated and accurate garlic and winter wheat maps are critical for assessing their impacts on society and the environment. Remote sensing imagery can be used to monitor spatial and temporal changes in croplands such as winter wheat and maize. However, to our knowledge, few studies are focusing on garlic area mapping. Here, we proposed a method for coupling active and passive satellite imagery for the identification of both garlic and winter wheat in Northern China. First, we used passive satellite imagery (Sentinel-2 and Landsat-8 images) to extract winter crops (garlic and winter wheat) with high accuracy. Second, we applied active satellite imagery (Sentinel-1 images) to distinguish garlic from winter wheat. Third, we generated a map of the garlic and winter wheat by coupling the above two classification results. For the evaluation of classification, the overall accuracy was 95.97%, with a kappa coefficient of 0.94 by eighteen validation quadrats (3 km by 3 km). The user’s and producer’s accuracies of garlic are 95.83% and 95.85%, respectively; and for the winter wheat, these two accuracies are 97.20% and 97.45%, respectively. This study provides a practical exploration of targeted crop identification in mixed planting areas using multisource remote sensing data.

scholarly journals Remote sensing and conservation of isolated indigenous villages in Amazonia

2014 ◽  
Vol 1 (3) ◽  
pp. 140246 ◽  
Author(s):  
Robert S. Walker ◽  
Marcus J. Hamilton ◽  
Aaron A. Groth
Keyword(s):  
Remote Sensing ◽  
Satellite Imagery ◽  
Term Survival ◽  
Surveillance Programme ◽  
Non Invasive ◽  
External Threats ◽  
Indigenous Groups ◽  
Order Of Magnitude ◽  
Population Sizes

The vast forests on the border between Brazil and Peru harbour a number of indigenous groups that have limited contact with the outside world. Accurate estimates of population sizes and village areas are essential to begin assessing the immediate conservation needs of such isolated groups. In contrast to overflights and encounters on the ground, remote sensing with satellite imagery offers a safe, inexpensive, non-invasive and systematic approach to provide demographic and land-use information for isolated peoples. Satellite imagery can also be used to understand the growth of isolated villages over time. There are five isolated villages in the headwaters of the Envira River confirmed by overflights that are visible with recent satellite imagery further confirming their locations and allowing measurement of their cleared gardens, village areas and thatch roofed houses. These isolated villages appear to have population densities that are an order of magnitude higher than averages for other Brazilian indigenous villages. Here, we report on initial results of a remote surveillance programme designed to monitor movements and assess the demographic health of isolated peoples as a means to better mitigate against external threats to their long-term survival.

scholarly journals Changes in Mesopotamian Wetlands: Investigations Using Diverse Remote Sensing Datasets

2021 ◽  
Author(s):  
Ali K. M. Al-Nasrawi ◽  
Ignacio Fuentes ◽  
Dhahi Al-Shammari
Keyword(s):  
Remote Sensing ◽  
Drainage System ◽  
Meteorological Drought ◽  
Significant Loss ◽  
Google Earth ◽  
Social Communities ◽  
Term Change ◽  
Supported Living ◽  
Mesopotamian Marshes

Abstract Early civilizations have inhabited stable-water-resourced areas that supported living needs and activities, including agriculture. The Mesopotamian marshes, recognised as the most ancient human-inhabited area (~6000 years ago) and refuge of rich biodiversity, have experienced dramatic changes during the past five decades, starting to fail in providing adequate environmental functioning and support of social communities as they used to for thousands of years. The aim of this study is to observe, analyse and report the extent of changes in these marshes from 1972 to 2020. Data from various remote sensing sources were acquired through Google Earth Engine (GEE) including climate variables, land cover, surface reflectance, and surface water occurrence collections. Results show a clear wetlands dynamism over time and a significant loss in marshlands extent, even though no significant long-term change was observed in lumped rainfall from 1982, and even during periods where no meteorological drought had been recorded. Human interventions have disturbed the ecosystems, which is evident when studying water occurrence changes. These show that the diversion of rivers and the building of a new drainage system caused the migration and spatiotemporal changes of marshlands. Nonetheless, restoration plans (after 2003) and strong wet conditions (period 2018 - 2020) have helped to recover the ecosystems, these have not led the marshlands to regain their former extent. Further studies should pay more attention to the drainage network within the study area as well as the neighboring regions and their impact on the streamflow that feeds the study area.

scholarly journals Long-Term Mapping of a Greenhouse in a Typical Protected Agricultural Region Using Landsat Imagery and the Google Earth Engine

2019 ◽  
Vol 12 (1) ◽  
pp. 55 ◽  
Author(s):  
Cong Ou ◽  
Jianyu Yang ◽  
Zhenrong Du ◽  
Yiming Liu ◽  
Quanlong Feng ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Landsat Imagery ◽  
Google Earth ◽  
Spatiotemporal Dynamics ◽  
Landscape Patterns ◽  
Agricultural Region ◽  
Multi Temporal ◽  
Google Earth Engine ◽  
Production Approach

The greenhouse is the fastest growing food production approach and has become the symbol of protected agriculture with the development of agricultural modernization. Previous studies have verified the effectiveness of remote sensing techniques for mono-temporal greenhouse mapping. In practice, long-term monitoring of greenhouse from remote sensing data is vital for the sustainable management of protected agriculture and existing studies have been limited in understanding its spatiotemporal dynamics. This study aimed to generate multi-temporal greenhouse maps in a typical protected agricultural region (Shouguang region, north China) from 1990 to 2018 using Landsat imagery and the Google Earth Engine and quantify its spatiotemporal dynamics that occur as a consequence of the development of protected agriculture in the study area. The multi-temporal greenhouse maps were produced using random forest supervised classification at seven-time intervals, and the overall accuracy of the results greater than 90%. The total area of greenhouses in the study area expanded by 1061.94 km 2 from 1990 to 2018, with the largest growth occurring in 1995–2010. And a large number of increased greenhouses occurred in 10–35 km northwest and 0–5 km primary roads buffer zones. Differential change trajectories between the total area and number of patches of greenhouses were revealed using global change metrics. Results of five landscape metrics showed that various landscape patterns occurred in both spatial and temporal aspects. According to the value of landscape expansion index in each period, the growth mode of greenhouses was from outlying to edge-expansion and then gradually changed to infilling. Spatial heterogeneity, which measured by Shannon’s entropy, of the increased greenhouses was different between the global and local levels. These results demonstrated the advantage of utilizing Landsat imagery and Google Earth Engine for monitoring the development of greenhouses in a long-term period and provided a more intuitive perspective to understand the process of this special agricultural production approach than relevant social science studies.

A Multi-Level Approach for Change Detection of Buildings Using Satellite Imagery

2018 ◽  
Vol 27 (08) ◽  
pp. 1850031 ◽  
Author(s):  
Md. Abdul Alim Sheikh ◽  
Alok Kole ◽  
Tanmoy Maity
Keyword(s):  
Remote Sensing ◽  
Mutual Information ◽  
Change Detection ◽  
Satellite Imagery ◽  
Image Matching ◽  
Google Earth ◽  
Remote Sensing Images ◽  
Normalized Mutual Information ◽  
Novel Technique ◽  
Multi Level

In this paper a novel technique for building change detection from remote sensing imagery is presented. It includes two main stages: (1) Object-specific discriminative features are extracted using Morphological Building Index (MBI) to automatically detect the existence of buildings in remote sensing images. (2) Pixel-based image matching is measured on the basis of Mutual Information (MI) of the images by Normalized Mutual Information (NMI). Here, the MBI features values are computed for each of the pair images taken over the same region at two different times and then changes in these two MBI images are measured to indicate the building change. MI is estimated locally for all the pixels for image matching and then thresholding is applied for eliminating those pixels which are responsible for strong similarity. Finally, after getting the MBI and NMI images, a further fusion of these two images is done for refinement of the change result. For evaluation purpose, the experiments are carried on QuickBird, IKONOS images and images taken from Google Earth. The results show that the proposed technique can attain acceptable correctness rates above 90% with Overall Accuracy (OA) 89.52%.

scholarly journals Effect of Satellite Temporal Resolution on Long-Term Suspended Particulate Matter in Inland Lakes

2019 ◽  
Vol 11 (23) ◽  
pp. 2785 ◽  
Author(s):  
Zhigang Cao ◽  
Ronghua Ma ◽  
Hongtao Duan ◽  
Kun Xue ◽  
Ming Shen
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Particulate Matter ◽  
Temporal Resolution ◽  
Suspended Particulate Matter ◽  
Google Earth ◽  
Inland Lakes ◽  
Suspended Particulate ◽  
The Impact

The temporal resolution of satellite determines how well remote sensing products represent changes in the lake environments and influences the practical applications by end-users. Here, a resampling method was used to reproduce the suspended particulate matter (SPM) dataset in 43 large lakes (>50 km2) on the eastern China plain during 2003–2017 at different temporal resolutions using MODIS Aqua (MODISA) based on Google Earth Engine platform, then to address the impact of temporal resolution on the long-term SPM dataset. Differences between the MODISA-derived and reproduced SPM dataset at longer temporal resolution were higher in the areas with large water dynamics. The spatial and temporal distributions of the differences were driven by unfavorable observation environments during satellite overpasses such as high cloud cover, and rapid changes in water quality, such as water inundation, algae blooms, and macrophytes. Furthermore, the annual mean difference in SPM ranged from 5–10% when the temporal difference was less than 10 d, and the differences in summer and autumn were higher than that of other seasons and surpassed 20% when the temporal resolution was more than 16 d. To assure that difference were less than 10% for long-term satellite-derived SPM datasets, the minimal requirement of temporal resolution should be within 5 d for most of the inland lakes and 3 d for lakes with large changes in water quality. This research can be used to not only evaluate the reliability of historically remote sensing products but also provide a reference for planning field campaigns and applying of high spatial resolution satellite missions to monitor aquatic systems in the future.

scholarly journals Estimation of Total Suspended Sediment Solid in Porong River Waters Using Multitemporal Satellite Imagery

2021 ◽  
Vol 936 (1) ◽  
pp. 012006
Author(s):  
Z N Ghuvita Hadi ◽  
T Hariyanto ◽  
N Hayati
Keyword(s):  
Remote Sensing ◽  
Suspended Sediment ◽  
Satellite Imagery ◽  
River Estuary ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Landsat 8 ◽  
Sample Point ◽  
Sensing Data ◽  
Google Earth Engine

Abstract Monitoring the concentration of Total Suspended Solid (TSS) is one method to determine water quality, because a high TSS value indicates a high level of pollution. Remote sensing data can be used effectively in generating suspended sediment concentrations. Nowdays, Google Earth Engine platform has provided a large collection of remote sensing data. Therefore, this study uses Google Earth Engine which is processed for free and aims to calculate the TSS value in the Kali Porong area. This research was conducted multitemporal in the last ten years, namely from 2013-2021 using multitemporal satellite imagery landsat-8 and sentinel-2 by applying empirical algorithms for calculating TSS. The results of this study are the value of TSS concentration at each sample point and a multitemporal TSS concentration distribution map. The year 2016, 2017, and 2021, the distribution of TSS concentration values was higher than in other years. At the sample point, the lowest TSS concentration value was 16.55 mg/L in 2013. Meanwhile, the highest TSS concentration value of 266.33 mg/L occurred in 2014 precisely in the Porong River estuary area which is the border area between land and water. the sea so that a lot of TSS material is concentrated in the area due to waves and ocean currents.

scholarly journals A Google Earth Engine-enabled Python approach to improve identification of anthropogenic palaeo-landscape features

2021 ◽  
Vol 1 ◽  
pp. 22
Author(s):  
Filippo Brandolini ◽  
Guillem Domingo-Ribas ◽  
Andrea Zerboni ◽  
Sam Turner
Keyword(s):  
Remote Sensing ◽  
Satellite Imagery ◽  
Cultural Landscape ◽  
Management Strategies ◽  
Landscape Management ◽  
Decomposition Analysis ◽  
Holistic Approach ◽  
Google Earth ◽  
Google Earth Engine ◽  
Landscape Heritage

The necessity of sustainable development for landscapes has emerged as an important theme in recent decades. Current methods take a holistic approach to landscape heritage and promote an interdisciplinary dialogue to facilitate complementary landscape management strategies. With the socio-economic values of the “natural” and “cultural” landscape heritage increasingly recognised worldwide, remote sensing tools are being used more and more to facilitate the recording and management of landscape heritage. Satellite remote sensing technologies have enabled significant improvements in landscape research. The advent of the cloud-based platform of Google Earth Engine (GEE) has allowed the rapid exploration and processing of satellite imagery such as the Landsat and Copernicus Sentinel datasets. In this paper, the use of Sentinel-2 satellite data in the identification of palaeo-riverscape features has been assessed in the Po Plain, selected because it is characterized by human exploitation since the Mid-Holocene. A multi-temporal approach has been adopted to investigate the potential of satellite imagery to detect buried hydrological and anthropogenic features along with spectral index and spectral decomposition analysis. This research represents one of the first applications of the GEE Python application programming interface (API) in landscape studies. The complete free and open-source software (FOSS) cloud protocol proposed here consists of a Python code script developed in Google Colab which could be simply adapted and replicated in different areas of the world.

Sign in / Sign up

Export Citation Format

Share Document