scholarly journals The Google Earth Engine Mangrove Mapping Methodology (GEEMMM)

2020 ◽  
Vol 12 (22) ◽  
pp. 3758
Author(s):  
J. Maxwell M. Yancho ◽  
Trevor Gareth Jones ◽  
Samir R. Gandhi ◽  
Colin Ferster ◽  
Alice Lin ◽  
...  
Keyword(s):  
Global Climate ◽  
Anthropogenic Activities ◽  
Google Earth ◽  
Remotely Sensed Data ◽  
Wide Audience ◽  
Goods And Services ◽  
Unique Mapping ◽  
Google Earth Engine ◽  
Accuracy Assessments ◽  
And Storage

Mangroves are found globally throughout tropical and sub-tropical inter-tidal coastlines. These highly biodiverse and carbon-dense ecosystems have multi-faceted value, providing critical goods and services to millions living in coastal communities and making significant contributions to global climate change mitigation through carbon sequestration and storage. Despite their many values, mangrove loss continues to be widespread in many regions due primarily to anthropogenic activities. Accessible, intuitive tools that enable coastal managers to map and monitor mangrove cover are needed to stem this loss. Remotely sensed data have a proven record for successfully mapping and monitoring mangroves, but conventional methods are limited by imagery availability, computing resources and accessibility. In addition, the variable tidal levels in mangroves presents a unique mapping challenge, particularly over geographically large extents. Here we present a new tool—the Google Earth Engine Mangrove Mapping Methodology (GEEMMM)—an intuitive, accessible and replicable approach which caters to a wide audience of non-specialist coastal managers and decision makers. The GEEMMM was developed based on a thorough review and incorporation of relevant mangrove remote sensing literature and harnesses the power of cloud computing including a simplified image-based tidal calibration approach. We demonstrate the tool for all of coastal Myanmar (Burma)—a global mangrove loss hotspot—including an assessment of multi-date mapping and dynamics outputs and a comparison of GEEMMM results to existing studies. Results—including both quantitative and qualitative accuracy assessments and comparisons to existing studies—indicate that the GEEMMM provides an accessible approach to map and monitor mangrove ecosystems anywhere within their global distribution.

scholarly journals Long Term Monitoring of Ghana’s Forest Reserves Using Google Earth Engine

2019 ◽  
Author(s):  
Jeff Dacosta Osei ◽  
S. A. Andam-Akorful ◽  
Edward Matthew Osei jnr
Keyword(s):  
Structural Changes ◽  
Forest Cover ◽  
Anthropogenic Activities ◽  
Google Earth ◽  
Forest Monitoring ◽  
Forest Reserves ◽  
Deforestation Rate ◽  
Noaa Avhrr ◽  
Forestry Commission ◽  
Google Earth Engine

Farm activities continued sand winning operations and the allocation of plots of land to prospective developers in Ghana pose a serious threat to the forest covers and lifespan of the Forest and game reserves. With all the positive add ups to the country from forests, Ghana has lost more than 33.7%(equivalent to 2,500,000 hectares) of its forest, since the early 1990s between 2005 and 2010, the rate of deforestation in Ghana was estimated at 2.19% per annum; the sixth highest deforestation rate globally for that period. This shows how important forest monitoring can be to the forestry commission in Ghana. Despite the frameworks which have been developed to help Ghana to protect and restore its forest resources, inadequate monitoring systems remain a barrier to effective implementation. In this study, Google earth engine was used to map and analyze the structural changes of forest cover using JavaScript to query and compute Landsat, MODIS and NOAA AVHRR satellite imageries of the study area (Ghana) with spatial resolutions 30m, 250m and 7km respectively. A supervised classification was performed on three multi-temporal satellite imageries and a total of six major land use and land cover classes were identified and mapped. By using random Forest-classification technique, from 1985 to 2018 recorded by NOAA AVHRR, forest cover has decreased by 66% and 2000 to 2018 recorded by Landsat and MODIS 61% and 47% respectively. A decrease in the forest has been as a result of anthropogenic activities in Ghana. A change detection analysis was performed on these images and it was noted that Ghana is losing forest reserves in every 5years. Overlay of the reserved forest of the 2000 and the classified map of 2018 shows vegetation changed during 2000-2018 remarkably. Therefore, forest-related institutions like the Forestry Commission can employ and use this monitoring system on Google Earth Engine for processing satellite images particularly Landsat, MODIS and NOAA AVHRR for forest cover monitoring and analysis for fast, efficient and reliable results.

scholarly journals Identifying Mangrove Deforestation Hotspots in South Asia, Southeast Asia and Asia-Pacific

2019 ◽  
Vol 11 (6) ◽  
pp. 728 ◽  
Author(s):  
Samir Gandhi ◽  
Trevor Jones
Keyword(s):  
Southeast Asia ◽  
South Asia ◽  
Global Climate ◽  
Remotely Sensed ◽  
The Philippines ◽  
Asia Pacific ◽  
Accurate Information ◽  
Mangrove Forests ◽  
Remotely Sensed Data ◽  
Goods And Services

Mangroves inhabit highly productive inter-tidal ecosystems in >120 countries in the tropics and subtropics providing critical goods and services to coastal communities and contributing to global climate change mitigation owing to substantial carbon stocks. Despite their importance, global mangrove distribution continues to decline primarily due to anthropogenic drivers which vary by region/country. South Asia, Southeast Asia and Asia-Pacific contain approximately 46% of the world’s mangrove ecosystems, including the most biodiverse mangrove forests. This region also exhibits the highest global rates of mangrove loss. Remotely sensed data provides timely and accurate information on mangrove distribution and dynamics critical for targeting loss hotspots and guiding intervention. This report inventories, describes and compares all known single- and multi-date remotely sensed datasets with regional coverage and provides areal mangrove extents by country. Multi-date datasets were used to estimate dynamics and identify loss hotspots (i.e., countries that exhibit greatest proportional loss). Results indicate Myanmar is the primary mangrove loss hotspot, exhibiting 35% loss from 1975–2005 and 28% between 2000–2014. Rates of loss in Myanmar were four times the global average from 2000–2012. The Philippines is additionally identified as a loss hotspot, with secondary hotspots including Malaysia, Cambodia and Indonesia. This information helps inform and guide mangrove conservation, restoration and managed-use within the region.

scholarly journals Enhancing Animal Movement Analyses: Spatiotemporal Matching of Animal Positions with Remotely Sensed Data Using Google Earth Engine and R

2021 ◽  
Vol 13 (20) ◽  
pp. 4154
Author(s):  
Ramiro D. Crego ◽  
Majaliwa M. Masolele ◽  
Grant Connette ◽  
Jared A. Stabach
Keyword(s):  
Vegetation Index ◽  
Animal Movement ◽  
Remotely Sensed ◽  
Google Earth ◽  
High Temporal Resolution ◽  
Remotely Sensed Data ◽  
First Case ◽  
Data Products ◽  
Google Earth Engine

Movement ecologists have witnessed a rapid increase in the amount of animal position data collected over the past few decades, as well as a concomitant increase in the availability of ecologically relevant remotely sensed data. Many researchers, however, lack the computing resources necessary to incorporate the vast spatiotemporal aspects of datasets available, especially in countries with less economic resources, limiting the scope of ecological inquiry. We developed an R coding workflow that bridges the gap between R and the multi-petabyte catalogue of remotely sensed data available in Google Earth Engine (GEE) to efficiently extract raster pixel values that best match the spatiotemporal aspects (i.e., spatial location and time) of each animal’s GPS position. We tested our approach using movement data freely available on Movebank (movebank.org). In a first case study, we extracted Normalized Difference Vegetation Index information from the MOD13Q1 data product for 12,344 GPS animal locations by matching the closest MODIS image in the time series to each GPS fix. Data extractions were completed in approximately 3 min. In a second case study, we extracted hourly air temperature from the ERA5-Land dataset for 33,074 GPS fixes from 12 different wildebeest (Connochaetes taurinus) in approximately 34 min. We then investigated the relationship between step length (i.e., the net distance between sequential GPS locations) and temperature and found that animals move less as temperature increases. These case studies illustrate the potential to explore novel questions in animal movement research using high-temporal-resolution, remotely sensed data products. The workflow we present is efficient and customizable, with data extractions occurring over relatively short time periods. While computing times to extract remotely sensed data from GEE will vary depending on internet speed, the approach described has the potential to facilitate access to computationally demanding processes for a greater variety of researchers and may lead to increased use of remotely sensed data in the field of movement ecology. We present a step-by-step tutorial on how to use the code and adapt it to other data products that are available in GEE.

scholarly journals ESTIMATION OF REVISED CAPACITY IN GOBIND SAGAR RESERVOIR USING GOOGLE EARTH ENGINE AND GIS

2018 ◽  
Vol XLII-5 ◽  
pp. 589-595
Author(s):  
S. Singh ◽  
M. K. Dhasmana ◽  
V. Shrivastava ◽  
V. Sharma ◽  
N. Pokhriyal ◽  
...  
Keyword(s):  
Google Earth ◽  
Water Levels ◽  
Landsat 8 ◽  
Remotely Sensed Data ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Trapezoidal Formula ◽  
Spread Area ◽  
Google Earth Engine ◽  
Water Spread

<p><strong>Abstract.</strong> Capacity studies of reservoirs are important to evaluate sedimentation and optimize reservoir operation schedule based on realistic assessment of available storage. Capacity study at regular interval provide information about rate and pattern of sedimentation between various levels, loss of capacity due to sedimentation, remaining time span of reservoir, etc. In the present study, evaluation of revised capacity of Gobind Sagar reservoir in Bilaspur district, Himachal Pradesh, India has been done using Google Earth Engine. Landsat 8 (OLI) data for September 2015 and for the period September 2017 to May 2018 covering full extent of Gobind Sagar reservoir is taken to compute the water spread area of this reservoir at different dates. Subsequently, the reservoir water level and volume of water stored on the corresponding dates is acquired from India-WRIS. By using trapezoidal formula capacity between two elevations is determined using water spread area obtained from Google Earth Engine analysis and elevation data obtained from India-WRIS. The comparison of water spread areas of different water levels, as obtained from remotely sensed data from September 2017 to May, 2018 with those from survey carried out during 1996/97, indicates a reduction in the capacity by 10.71% and sedimentation rate was estimated to be 14.24<span class="thinspace"></span>Mm<sup>3</sup>/year.</p>

Monitoreo de los efectos de los incendios forestales mediante el uso de sensores remotos en el Área de Conservación Guanacaste, Costa Rica

2019 ◽  
Author(s):  
Mauricio Vega-Araya
Keyword(s):  
Costa Rica ◽  
Google Earth ◽  
Google Earth Engine

La Tierra y su biosfera están cambiando constantemente, por lo tanto, es fundamental detectar los cambios con el fin de entender su impacto en los ecosistemas terrestres. Los esquemas de monitoreo de ecosistemas han evolucionado rápidamente en las ultimas décadas. En el caso del monitoreo forestal, los métodos y herramientas que facilitan la utilización de imágenes satelitales permiten realizar este monitoreo con el cual se puede detectar donde y cuando un bosque es eliminado o afectado debido a un evento de deforestación o bien de fuego, lo anterior casi en tiempo real. Estas nuevas herramientas están disponibles para su implementación, sin embargo, ningún paı́s de la región centroamericana y el Caribe ha implementado un sistema como herramienta de decisión dentro de una estructura de gobierno central o federal debido a la ausencia de programas de transferencia de tecnologı́a o programas de capacitación de talento local. Los sensores remotos proporcionan mediciones consistentes y repetibles que permiten la captura de los efectos de muchos procesos que causan el cambio, incluyendo, por ejemplo, incendios, ataques de insectos, agentes de cambio naturales y antropogénicas como por ejemplo, la deforestación, la urbanización, la agricultura, etc. Las series temporales de imágenes de satélite proporcionan maneras para detectar y vigilar cambios en el tiempo y en el espacio, esto consistentemente durante los últimos 30 años a nivel mundial. Los incendios forestales afectan el proceso de sucesión del bosque, no obstante, es muy limitada la existencia de estudios locales que relacionen el efecto de los incendios forestales con las diferencias en la información espectral a partir de sensoramiento remoto. En el presente estudio se plantea y propone la utilización y aprovechamiento de lo que se ha denominado grandes datos, especialmente con el advenimiento muchas plataformas de sensores remotos como Landsat, MODIS y recientemente Sentinel, para identificar cuál es el efecto de los incendios forestales en la sucesión y sus elementos perturbadores, como por ejemplo, la presencia de lianas. Se procesaron las series temporales se usó la plataforma digital Google Earth Engine, que permitió la selección y reducción de la información espacial de los ı́ndices de vegetación en tendencia, estacionalidad y residuos. Se analizó la respuesta de estos ı́ndices en sitios con diferente afectación por incendios forestales. Con estos índices se pretende desarrollar modelos de clasificación de series espaciales de tiempo de los ı́ndices y poder ası́ comprender los cambios en el tiempo y el espacio de los ecosistemas afectados por incendios forestales. Preliminarmente, se encontró una relación entre la incidencia de los incendios forestales y el fenómeno del Niño-Oscilación del Sur para el índice de vegetación denominado índice de área foliar. Además, la evidencia indica que el índice normalizado de vegetación si presenta diferencias respecto a los sitios que tienen un historial de fuegos diferente. El establecer esta relación implica estudiar también los regı́menes de precipitación y temperatura. El descomponer las series de tiempo facilitó la correlación con otras series de tiempo, permitiendo establecer las bases de un monitoreo y a su vez, relacionar las índices de vegetación y su variación con otros elementos climáticos, como por ejemplo, el efecto ENOS.

Phát triển thuật toán giám sát lũ lụt vùng Đồng bằng sông cửu Long dựa vào nền tảng Google Earth Engine

2018 ◽  
Vol 54(9) ◽  
pp. 29
Author(s):  
Võ Quốc Tuấn ◽  
Nguyễn Thiên Hoa ◽  
Huỳnh Thị Kim Nhân ◽  
Đặng Hoàng Khải
Keyword(s):  
Google Earth ◽  
Google Earth Engine

scholarly journals Assessing the Effect of Training Sampling Design on the Performance of Machine Learning Classifiers for Land Cover Mapping Using Multi-Temporal Remote Sensing Data and Google Earth Engine

2021 ◽  
Vol 13 (8) ◽  
pp. 1433
Author(s):  
Shobitha Shetty ◽  
Prasun Kumar Gupta ◽  
Mariana Belgiu ◽  
S. K. Srivastav
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Random Sampling ◽  
Sampling Design ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Multi Temporal ◽  
Google Earth Engine

Machine learning classifiers are being increasingly used nowadays for Land Use and Land Cover (LULC) mapping from remote sensing images. However, arriving at the right choice of classifier requires understanding the main factors influencing their performance. The present study investigated firstly the effect of training sampling design on the classification results obtained by Random Forest (RF) classifier and, secondly, it compared its performance with other machine learning classifiers for LULC mapping using multi-temporal satellite remote sensing data and the Google Earth Engine (GEE) platform. We evaluated the impact of three sampling methods, namely Stratified Equal Random Sampling (SRS(Eq)), Stratified Proportional Random Sampling (SRS(Prop)), and Stratified Systematic Sampling (SSS) upon the classification results obtained by the RF trained LULC model. Our results showed that the SRS(Prop) method favors major classes while achieving good overall accuracy. The SRS(Eq) method provides good class-level accuracies, even for minority classes, whereas the SSS method performs well for areas with large intra-class variability. Toward evaluating the performance of machine learning classifiers, RF outperformed Classification and Regression Trees (CART), Support Vector Machine (SVM), and Relevance Vector Machine (RVM) with a >95% confidence level. The performance of CART and SVM classifiers were found to be similar. RVM achieved good classification results with a limited number of training samples.

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