A mangrove forest map of China in 2015: Analysis of time series Landsat 7/8 and Sentinel-1A imagery in Google Earth Engine cloud computing platform

Chen, Bangqian; Xiao, Xiangming; Li, Xiangping; Pan, Lianghao; Dong, Jinwei; Qin, Yuanwei; Zhao, Bin; Wu, Zhixiang; Sun, Rui; Lan, Guoyu; Xie, Guishui; Clinton, Nicholas; Giri, Chandra

doi:10.1016/j.isprsjprs.2017.07.011

Cited by 318 publications

(159 citation statements)

References 70 publications

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“…This pattern may be associated with intra-annual oceanographic and climatic variables (rainy and dry seasons), which alter the prevailing tide and humidity conditions in the median composites [57][58][59][60]. On one-year intervals, especially over areas often covered by clouds such as the BCZ, the definition of additional image-selection parameters (e.g., the tide condition and the dominant climatic pattern) greatly reduces the number of images available, which makes spatial analysis over shorter time frames impractical.…”

Section: Discussionmentioning

confidence: 99%

Brazilian Mangrove Status: Three Decades of Satellite Data Analysis

Diniz

Cortinhas²,

Nerino³

et al. 2019

Remote Sensing

119

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Since the 1980s, mangrove cover mapping has become a common scientific task. However, the systematic and continuous identification of vegetation cover, whether on a global or regional scale, demands large storage and processing capacities. This manuscript presents a Google Earth Engine (GEE)-managed pipeline to compute the annual status of Brazilian mangroves from 1985 to 2018, along with a new spectral index, the Modular Mangrove Recognition Index (MMRI), which has been specifically designed to better discriminate mangrove forests from the surrounding vegetation. If compared separately, the periods from 1985 to 1998 and 1999 to 2018 show distinct mangrove area trends. The first period, from 1985 to 1998, shows an upward trend, which seems to be related more to the uneven distribution of Landsat data than to a regeneration of Brazilian mangroves. In the second period, from 1999 to 2018, a trend of mangrove area loss was registered, reaching up to 2% of the mangrove forest. On a regional scale,~85% of Brazil's mangrove cover is in the states of Maranhão, Pará, Amapá and Bahia. In terms of persistence,~75% of the Brazilian mangroves remained unchanged for two decades or more.Globally, mangrove forests are distributed in tropical and subtropical intertidal regions between approximately 30 • N and 30 • S [6]. In 2000, mangrove forests represented a total area of 137,760 km 2 , distributed in 118 countries and making up~1% of the tropical forests in the world [7]. Mangrove forests are an evergreen type of vegetation typically distributed from the mean sea level to the highest spring tide [8] and grow in extreme environmental conditions such as extreme tides, high salinity, high temperatures and muddy anaerobic soils [9].Mangrove systems play an essential role in human sustainability, providing a wide range of ecosystem services, including nutrient cycling, soil formation and wood production. They also provide fish spawning grounds and carbon (C) storage [10][11][12], being one of the most productive and biologically complex ecosystems on earth [13]. Mangroves and coastal wetlands sequester carbon at an annual rate two to four times greater than that of mature tropical forests and store three to five times more carbon per equivalent area than do tropical forests [10]. Despite its importance, this environment is still highly threatened due to population growth and urbanisation processes.Since the 1980s, mapping and change detection in mangrove areas at the global scale have been carried out [7,11,[14][15][16]. However, there are few studies in the current literature that include the systematic and continuous identification of mangroves and associated changes, whether on the global or regional scale. In Brazil, the first national mangrove map was published in 1991 [17], based on airborne real aperture radar data collected from 1972 to 1975. At that time, the national mangrove area was~13,800 km 2 . In the same period, Schaeffer-Novelli et al. [18] described the variability in the mangrove ecosystems along the Brazilian c...

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Section: Discussionmentioning

confidence: 99%

Brazilian Mangrove Status: Three Decades of Satellite Data Analysis

Diniz

Cortinhas²,

Nerino³

et al. 2019

Remote Sensing

119

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“…Due to the mixed distributions of water and grasses in the wetlands, the wetland vegetation is the main factor leading to the classification errors of surface water bodies [51]. It has been suggested that combining mNDWI, the normalized water body index (NDVI) and enhanced vegetation index (EVI) can perform better and more stable than the individual index in delineating water [17,18,[29][30][31]. Accordingly, this study used the combination of three indexes to extract surface water areas, which are EVI, NDVI, and mNDWI (Equations (1)-(3)).…”

Section: Waterbody Area Extraction Algorithmmentioning

confidence: 99%

“…The multi-band water indices are superior to the single-band because it takes advantage of different reflectivity differences of spectrum information between water and other land covers. Meanwhile, the water indices also have the advantages in accurate, easy, rapid and reproducible extraction of the surface water information to capture the dramatic intra-annual and inter-annual water variability, and were successfully used to extract surface water using remotely sensed data [17][18][19][29][30][31].The surface areas of water bodies have significant inter-annual and intra-annual variations. Therefore, there are a lot of uncertainties using a single image to map water surface areas [32][33][34].…”

mentioning

confidence: 99%

Changes in Water Surface Area during 1989–2017 in the Huai River Basin using Landsat Data and Google Earth Engine

et al. 2019

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The dynamics of surface water play a crucial role in the hydrological cycle and are sensitive to climate change and anthropogenic activities, especially for the agricultural zone. As one of the most populous areas in China's river basins, the surface water in the Huai River Basin has significant impacts on agricultural plants, ecological balance, and socioeconomic development. However, it is unclear how water areas responded to climate change and anthropogenic water exploitation in the past decades. To understand the changes in water surface areas in the Huai River Basin, this study used the available 16,760 scenes Landsat TM, ETM+, and OLI images in this region from 1989 to 2017 and processed the data on the Google Earth Engine (GEE) platform. The vegetation index and water index were used to quantify the spatiotemporal variability of the surface water area changes over the years. The major results include: (1) The maximum area, the average area, and the seasonal variation of surface water in the Huai River Basin showed a downward trend in the past 29 years, and the year-long surface water areas showed a slight upward trend; (2) the surface water area was positively correlated with precipitation (p < 0.05), but was negatively correlated with the temperature and evapotranspiration; (3) the changes of the total area of water bodies were mainly determined by the 216 larger water bodies (>10 km 2 ). Understanding the variations in water body areas and the controlling factors could support the designation and implementation of sustainable water management practices in agricultural, industrial, and domestic usages. ranks the first among the major river basins in China, and it plays an important role in China's economic and social development. According to the 2016 Huai River Water Resources Bulletin, the surface water resource supply in the Huai River Basin accounts for 74.6% of the total water supply of various water supply projects. Therefore, the temporal and spatial variation characteristics of surface waters need to be accurately mapped to ensure the sustainable economic and social development of the river basin and the stability of the ecosystem.Previous studies mapped the surface water using different data, algorithms and produced different spatial scale production. Satellite-based methods have advantages compared to the traditional methods in surface water mapping due to the low cost, high frequency, and repeatable observations. In recent decades, regional, continental, and global-scales surface water areas have been investigated using the advanced very high resolution radiometer (AVHRR) [5,6], the moderate-resolution imaging spectro-radiometer (MODIS) [7], Landsat [8][9][10][11][12][13][14][15][16][17][18], Sentinel satellite images and so on. Meanwhile, many satellite-based approaches have been developed to detect surface water. The surface water detection algorithms can be roughly divided into general feature classification methods and thematic water surface extraction algorithms [17,19]. General feature cl...

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“…The capabilities of GEE as a platform which can deliver at a planetary scale are detailed in Gorelick, et al [28]. Various studies have been carried out using the GEE at a variety of scales for different purposes (see e.g., [29][30][31]…”

Section: Computational Platformmentioning

confidence: 99%

An Operational Before-After-Control-Impact (BACI) Designed Platform for Vegetation Monitoring at Planetary Scale

Poortinga¹,

Clinton

Saah

et al. 2018

Remote Sensing

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In this study, we develop a vegetation monitoring framework which is applicable at a planetary scale, and is based on the BACI (Before-After, Control-Impact) design. This approach utilizes Google Earth Engine, a state-of-the-art cloud computing platform. A web-based application for users named EcoDash was developed. EcoDash maps vegetation using Enhanced Vegetation Index (EVI) from Moderate Resolution Imaging Spectroradiometer (MODIS) products (the MOD13A1 and MYD13A1 collections) from both Terra and Aqua sensors from the years 2000 and 2002, respectively. to detect change in vegetation, we define an EVI baseline period, and then draw results at a planetary scale using the web-based application by measuring improvement or degradation in vegetation based on the user-defined baseline periods. We also used EcoDash to measure the impact of deforestation and mitigation efforts by the Vietnam Forests and Deltas (VFD) program for the Nghe An and Thanh Hoa provinces in Vietnam. Using the period before 2012 as a baseline, we found that as of March 2017, 86% of the geographical area within the VFD program shows improvement, compared to only a 24% improvement in forest cover for all of Vietnam. Overall, we show how using satellite imagery for monitoring vegetation in a cloud-computing environment could be a cost-effective and useful tool for land managers and other practitioners

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A mangrove forest map of China in 2015: Analysis of time series Landsat 7/8 and Sentinel-1A imagery in Google Earth Engine cloud computing platform

Cited by 318 publications

References 70 publications

Brazilian Mangrove Status: Three Decades of Satellite Data Analysis

Brazilian Mangrove Status: Three Decades of Satellite Data Analysis

Changes in Water Surface Area during 1989–2017 in the Huai River Basin using Landsat Data and Google Earth Engine

An Operational Before-After-Control-Impact (BACI) Designed Platform for Vegetation Monitoring at Planetary Scale

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