scholarly journals Mapping the Global Mangrove Forest Aboveground Biomass Using Multisource Remote Sensing Data

2020 ◽  
Vol 12 (10) ◽  
pp. 1690 ◽  
Author(s):  
Tianyu Hu ◽  
YingYing Zhang ◽  
Yanjun Su ◽  
Yi Zheng ◽  
Guanghui Lin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Aboveground Biomass ◽  
Mangrove Forest ◽  
Remote Sensing Data ◽  
Dimensional Structure ◽  
Coefficient Of Determination ◽  
Free Access ◽  
Mangrove Forests ◽  
Estimation Model ◽  
Mangrove Biomass

Mangrove forest ecosystems are distributed at the land–sea interface in tropical and subtropical regions and play an important role in carbon cycles and biodiversity. Accurately mapping global mangrove aboveground biomass (AGB) will help us understand how mangrove ecosystems are affected by the impacts of climatic change and human activities. Light detection and ranging (LiDAR) techniques have been proven to accurately capture the three-dimensional structure of mangroves and LiDAR can estimate forest AGB with high accuracy. In this study, we produced a global mangrove forest AGB map for 2004 at a 250-m resolution by combining ground inventory data, spaceborne LiDAR, optical imagery, climate surfaces, and topographic data with random forest, a machine learning method. From the published literature and free-access datasets of mangrove biomass, we selected 342 surface observations to train and validate the mangrove AGB estimation model. Our global mangrove AGB map showed that average global mangrove AGB density was 115.23 Mg/ha, with a standard deviation of 48.89 Mg/ha. Total global AGB storage within mangrove forests was 1.52 Pg. Cross-validation with observed data demonstrated that our mangrove AGB estimates were reliable. The adjusted coefficient of determination (R2) and root-mean-square error (RMSE) were 0.48 and 75.85 Mg/ha, respectively. Our estimated global mangrove AGB storage was similar to that predicted by previous remote sensing methods, and remote sensing approaches can overcome overestimates from climate-based models. This new biomass map provides information that can help us understand the global mangrove distribution, while also serving as a baseline to monitor trends in global mangrove biomass.

scholarly journals Application of Remote Sensing in Mangroves at the Surrounding of Sungai Selangor Estuary in Kuala Selangor

2021 ◽  
Vol 18 (2) ◽  
pp. 38
Author(s):  
Syed Muhammad Iqbal Sayad Romli ◽  
Illyani Ibrahim ◽  
MZainora Asmawi ◽  
Azizan Abu Samah
Keyword(s):  
Remote Sensing ◽  
River Basin ◽  
Mangrove Forest ◽  
Remote Sensing Data ◽  
Coastal Areas ◽  
Food Sources ◽  
Mangrove Forests ◽  
Sediment Retention ◽  
Management Policies ◽  
Basin Area

The mangrove forest ecosystem protects the land area from the tidal wave hence preventing the coastal areas and properties from severe damage. Mangroves provide valuable ecological services and goods, sediment retention, food sources of some animals, and stabilisation of the coastal areas. Unfortunately, the species have been experiencing an extensive loss in many parts of the world. This paper aims to detect the changes in mangrove forests and possible changes in the Selangor river basin area. The methodology uses remote sensing data via supervised classification on a maximum likelihood algorithm to analyse the distribution of mangrove forests at the Selangor River basin for a thirty-two-year period, from 1989 to 2021. The findings indicate that the percentage of mangroves in the study area has reduced over the study period. The coverage of mangroves has reduced from 24.29 percent (1989) to 15.57 percent in 2008, and continued to reduce to 13.12 percent in 2021. The research finding indicates a decrease in mangroves due to aquaculture, tourism, agriculture, and other human activities. Such a trend may risk coastal and river erosion, thus necessitating a revision of the management policies for environmental protection. Keywords: mangrove, forest, remote sensing, Selangor river basin

scholarly journals OPEN: A New Estimation of Global Ocean Heat Content for Upper 2000 Meters from Remote Sensing Data

2020 ◽  
Vol 12 (14) ◽  
pp. 2294
Author(s):  
Hua Su ◽  
Haojie Zhang ◽  
Xupu Geng ◽  
Tian Qin ◽  
Wenfang Lu ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Network Architecture ◽  
Climate Changes ◽  
Heat Content ◽  
Remote Sensing Data ◽  
Ocean Heat Content ◽  
Global Ocean ◽  
Coefficient Of Determination ◽  
Sensing Data

Retrieving information concerning the interior of the ocean using satellite remote sensing data has a major impact on studies of ocean dynamic and climate changes; however, the lack of information within the ocean limits such studies about the global ocean. In this paper, an artificial neural network, combined with satellite data and gridded Argo product, is used to estimate the ocean heat content (OHC) anomalies over four different depths down to 2000 m covering the near-global ocean, excluding the polar regions. Our method allows for the temporal hindcast of the OHC to other periods beyond the 2005–2018 training period. By applying an ensemble technique, the hindcasting uncertainty could also be estimated by using different 9-year periods for training and then calculating the standard deviation across six ensemble members. This new OHC product is called the Ocean Projection and Extension neural Network (OPEN) product. The accuracy of the product is accessed using the coefficient of determination (R2) and the relative root-mean-square error (RRMSE). The feature combinations and network architecture are optimized via a series of experiments. Overall, intercomparison with several routinely analyzed OHC products shows that the OPEN OHC has an R2 larger than 0.95 and an RRMSE of <0.20 and presents notably accurate trends and variabilities. The OPEN product can therefore provide a valuable complement for studies of global climate changes.

scholarly journals Understanding Dynamics of Mangrove Forest on Protected Areas of Hainan Island, China: 30 Years of Evidence from Remote Sensing

2019 ◽  
Vol 11 (19) ◽  
pp. 5356 ◽  
Author(s):  
Liao ◽  
Zhen ◽  
Zhang ◽  
Metternicht
Keyword(s):  
Remote Sensing ◽  
Protected Areas ◽  
Mangrove Forest ◽  
Public Awareness ◽  
Hainan Island ◽  
Current Data ◽  
Support Vector ◽  
Mangrove Forests ◽  
Visual Interpretation ◽  
Wastewater Disposal

Implementation of the UN Sustainable Development Goals requires countries to determine targets for the protection, conservation, or restoration of coastal ecosystems such as mangrove forests by 2030. Satellite remote sensing provides historical and current data on the distribution and dynamics of mangrove forests, essential baseline data that are needed to design suitable policy interventions. In this study, Landsat time series were used to map trends and dynamics of mangrove change over a time span of 30 years (1987–2017) in protected areas of Hainan Island (China). A support vector machine algorithm was combined with visual interpretation of imagery and result showed alternating periods of expansion and loss of mangrove forest at seven selected sites on Hainan Island. Over this period, there was a net decrease in mangrove area of 9.3%, with anthropic activities such as land conversion for aquaculture, wastewater disposal and discharge, and tourism development appearing to be the likely drivers of this decline in cover. Long-term studies examining trends in land use cover change coupled with assessments of drivers of loss or gain enable the development of evidence based on policy and legislation. This forms the basis of financing of natural reserves of management and institutional capacity building, and facilitates public awareness and participation, including co-management.

Predicting stem diameters and aboveground biomass of individual trees using remote sensing data

2018 ◽  
Vol 85 ◽  
pp. 367-376 ◽  
Author(s):  
Michele Dalponte ◽  
Lorenzo Frizzera ◽  
Hans Ole Ørka ◽  
Terje Gobakken ◽  
Erik Næsset ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Aboveground Biomass ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Individual Trees

scholarly journals Impacts of exotic mangrove forests and mangrove deforestation on carbon remineralization and ecosystem functioning in marine sediments

2010 ◽  
Vol 7 (2) ◽  
pp. 2631-2671 ◽  
Author(s):  
A. K. Sweetman ◽  
J. J. Middelburg ◽  
A. M. Berle ◽  
A. F. Bernardino ◽  
C. Schander ◽  
...  
Keyword(s):  
Community Structure ◽  
Ecosystem Functioning ◽  
Mangrove Forest ◽  
Mangrove Forests ◽  
Short Term ◽  
Benthic Respiration ◽  
Faunal Abundance ◽  
Carbon Remineralization ◽  
And Control ◽  
Mangrove Biomass

Abstract. To evaluate how mangrove invasion and removal can modify benthic carbon cycling processes and ecosystem functioning, we used stable-isotopically labelled algae as a deliberate tracer to quantify benthic respiration and C-flow through macrofauna and bacteria in sediments collected from (1) an invasive mangrove forest, (2) deforested mangrove sites 2 and 6 years after removal of above-sediment mangrove biomass, and (3) two mangrove-free, control sites in the Hawaiian coastal zone. Sediment oxygen consumption (SOC) rates were significantly greater in the mangrove and mangrove removal site experiments than in controls and were significantly correlated with total benthic (macrofauna and bacteria) biomass and sedimentary mangrove biomass (SMB). Bacteria dominated short-term C-processing of added microalgal-C and benthic biomass in sediments from the invasive mangrove forest habitat. In contrast, macrofauna were the most important agents in the short-term processing of microalgal-C in sediments from the mangrove removal and control sites. Mean faunal abundance and short term C-uptake rates in sediments from both removal sites were significantly higher than in control cores, which collectively suggest that community structure and short-term C-cycling dynamics in habitats where mangroves have been cleared can remain fundamentally different from un-invaded mudflat sediments for at least 6-yrs following above-sediment mangrove removal. In summary, invasion by mangroves can lead to large shifts in benthic ecosystem function, with sediment metabolism, benthic community structure and short-term C-remineralization dynamics being affected for years following invader removal.

scholarly journals PENGARUH PERUBAHAN LUAS HUTAN MANGROVE TERHADAP KONSENTRASI TOTAL SUSPENDED MATTER (TSM) DI MUARA PERANCAK, JEMBRANA – BALI

2019 ◽  
Vol 14 (1) ◽  
Author(s):  
Komang Iwan Suniada
Keyword(s):  
Suspended Matter ◽  
Mangrove Forest ◽  
Satellite Image ◽  
River Mouth ◽  
Remote Sensing Data ◽  
Image Data ◽  
Mangrove Ecosystem ◽  
Total Suspended Matter ◽  
Forest Area ◽  
Mangrove Forests

Study of the function of mangrove forests as a sediment trap has been largely undertaken using field measurement methods, but only a few researches that fully utilize remote sensing data to find out the influence of mangrove forest’s area changes against the Total Suspended Matter (TSM) making this study very interesting and important to do.  This research was conducted in Perancak estuary area which is one of mangrove ecosystem area in Bali besides West Bali National Park, Benoa Forest Park and Nusa Lembongan. The data used to generate TSM information and change of mangrove forest area in this research is medium resolution satellite image data, Landsat.  Tidal data and rainfall data were used as a supporting data. The information of TSM concentration obtained by using Budhiman (2004) algorithm, shows that along with the increasing of mangrove forest area has caused the decreasing of TSM concentration at mouth Perancak river. The decline was caused by sediments trapped and settled around trees or mangrove roots, especially the Rhizophora mangroves. In addition to the increasing of mangrove forest area, the tidal oceanography factor also greatly influences the TSM fluctuation around Perancak river mouth. 

scholarly journals Modeling regional evaporation through ANFIS incorporated solely with remote sensing data

2013 ◽  
Vol 10 (5) ◽  
pp. 6153-6192
Author(s):  
F.-J. Chang ◽  
W. Sun
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Vegetation Index ◽  
Remote Sensing Data ◽  
Data Allocation ◽  
Remotely Sensed Data ◽  
Estimation Model ◽  
Landsat Images ◽  
Inference System ◽  
Sensing Data

Abstract. The study aims to model regional evaporation that possesses the ability to present the spatial distribution of evaporation across the whole Taiwan by the adaptive network-based fuzzy inference system (ANFIS) based solely on remote sensing data. The remote sensing data used in this study consist of Landsat image products including Enhanced Vegetation Index (EVI) and land surface temperature (LST). The model construction is designed through two types of data allocation (temporal and spatial) driven with the same ten-year data of EVI and LST derived from Landsat images. Evidences indicate the estimation model based solely on remotely sensed data can effectively detect the spatial variation of evaporation and appropriately capture the evaporation trend with acceptable errors of about 1 mm day−1. The results also demonstrate the composite of EVI and LST input to the proposed estimation model improves the accuracy of estimated evaporation values as compared with the model using LST as the only input, which reveals EVI indeed benefits the estimation process. The results suggest Model-T (temporal input allocation) is suitable for making island-wide evaporation estimation while Model-S (spatial input allocation) is suitable for making evaporation estimation at ungauged sites. An island-wide evaporation map for the whole study area (Taiwan Island) is then derived. It concludes the proposed ANFIS model incorporated solely with remote sensing data can reasonably well generate evaporation estimation and is reliable as well as easily applicable for operational estimation of evaporation over large areas where the network of ground-based meteorological gauging stations is not dense enough or readily available.

scholarly journals Assessment of the environmental situation in Krasnoyarsk using remote sensing data

2021 ◽  
Author(s):  
K.V. Krasnoshchekov ◽  
O.E. Yakubailik
Keyword(s):  
Remote Sensing ◽  
Monitoring System ◽  
Monitoring Network ◽  
Remote Sensing Data ◽  
Air Monitoring ◽  
Coefficient Of Determination ◽  
Scientific Center ◽  
Sensing Data ◽  
Environmental Situation ◽  
Analytical System

The data on ground concentrations of aerosols and small gas components (particulate matter PM2.5 and sulfur dioxide NO2) were compared with remote sensing data obtained over the territory of Krasnoyarsk from June to August 2020. We use the air monitoring system of the Krasnoyarsk Scientific Center of the Siberian Branch of the Russian Academy of Sciences (KSC SB RAS) to determine the concentration of PM2.5. NO2 concentrations were taken according to the data of the State departmental information and analytical system of the Ministry of Ecology of the region. It is shown that the remote sensing data of the MODIS MAIAC algorithm with a spatial resolution of 1 km can be used to determine the concentration of PM2.5 as an addition to the data obtained by the ground-based air monitoring system of the KSC SB RAS. The MAIAC data were calculated using two different models and are given to the measurement system used in the KSC SB RAS monitoring network. A high coefficient of determination between satellite and ground monitoring data was obtained. Determination coefficients were also obtained for NO2, showing how applicable the remote sensing data are for assessing the environmental situation in Krasnoyarsk.

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