scholarly journals Modelling Aboveground Biomass Carbon Stock of the Bohai Rim Coastal Wetlands by Integrating Remote Sensing, Terrain, and Climate Data

2021 ◽  
Vol 13 (21) ◽  
pp. 4321
Author(s):  
Shaobo Sun ◽  
Yafei Wang ◽  
Zhaoliang Song ◽  
Chu Chen ◽  
Yonggen Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Aboveground Biomass ◽  
Coastal Wetlands ◽  
Carbon Stock ◽  
Coastal Wetland ◽  
Data Sets ◽  
Special Role ◽  
Climate Data ◽  
Bohai Rim

Remotely sensed vegetation indices (VIs) have been widely used to estimate the aboveground biomass (AGB) carbon stock of coastal wetlands by establishing Vis-related linear models. However, these models always have high uncertainties due to the large spatial variation and fragmentation of coastal wetlands. In this paper, an efficient coastal wetland AGB model for the Bohami Rim coastal wetlands was presented based on multiple data sets. The model was developed statistically with 7 independent variables from 23 metrics derived from remote sensing, topography, and climate data. Compared to previous models, it had better performance, with a root mean square error and r value of 188.32 g m−2 and 0.74, respectively. Using the model, we firstly generated a regional coastal wetland AGB map with a 10 m spatial resolution. Based on the AGB map, the AGB carbon stock of the Bohai Rim coastal wetland was 2.11 Tg C in 2019. The study demonstrated that integrating emerging high spatial resolution multi-remote sensing data and several auxiliary metrics can effectively improve VIs-based coastal wetland AGB models. Such models with emerging freely available data sets will allow for the rapid monitoring and better understanding of the special role that “blue carbon” plays in global carbon cycle.

scholarly journals Mapping Coastal Wetlands of the Bohai Rim at a Spatial Resolution of 10 m Using Multiple Open-Access Satellite Data and Terrain Indices

2020 ◽  
Vol 12 (24) ◽  
pp. 4114
Author(s):  
Shaobo Sun ◽  
Yonggen Zhang ◽  
Zhaoliang Song ◽  
Baozhang Chen ◽  
Yangjian Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Open Access ◽  
Spatial Resolution ◽  
Coastal Wetlands ◽  
Coastal Wetland ◽  
Regional Scale ◽  
Google Earth ◽  
Human Welfare ◽  
Bohai Rim ◽  
Terrain Indices

Coastal wetlands provide essential ecosystem services and are closely related to human welfare. However, they can experience substantial degradation, especially in regions in which there is intense human activity. To control these increasingly severe problems and to develop corresponding management policies in coastal wetlands, it is critical to accurately map coastal wetlands. Although remote sensing is the most efficient way to monitor coastal wetlands at a regional scale, it traditionally involves a large amount of work, high cost, and low spatial resolution when mapping coastal wetlands at a large scale. In this study, we developed a workflow for rapidly mapping coastal wetlands at a 10 m spatial resolution, based on the recently emergent Google Earth Engine platform, using a machine learning algorithm, open-access Synthetic Aperture Radar (SAR) and optical images from the Sentinel satellites, and two terrain indices. We then generated a coastal wetland map of the Bohai Rim (BRCW10) based on the workflow. It has a producer accuracy of 82.7%, according to validation using 150 wetland samples. The BRCW10 data reflected finer information when compared to wetland maps derived from two sets of global high-spatial-resolution land cover data, due to the fusion of multiple data sources. The study highlights the benefits of simultaneously merging SAR and optical remote sensing images when mapping coastal wetlands.

scholarly journals Land cover change analysis in Mexico using 30m Landsat and 250m MODIS data

2015 ◽  
Vol XL-7/W3 ◽  
pp. 367-374 ◽  
Author(s):  
R. R. Colditz ◽  
R. M. Llamas ◽  
R. A. Ressl
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Spatial Resolution ◽  
Spatial Scales ◽  
Time Interval ◽  
Data Sets ◽  
Change Analysis ◽  
Dimensional Error ◽  
Analysis Technique ◽  
Coarse Spatial Resolution

Change detection is one of the most important and widely requested applications of terrestrial remote sensing. Despite a wealth of techniques and successful studies, there is still a need for research in remote sensing science. This paper addresses two important issues: the temporal and spatial scales of change maps. Temporal scales relate to the time interval between observations for successful change detection. We compare annual change detection maps accumulated over five years against direct change detection over that period. Spatial scales relate to the spatial resolution of remote sensing products. We compare fractions from 30m Landsat change maps to 250m grid cells that match MODIS change products. Results suggest that change detection at annual scales better detect abrupt changes, in particular those that do not persist over a longer period. The analysis across spatial scales strongly recommends the use of an appropriate analysis technique, such as change fractions from fine spatial resolution data for comparison with coarse spatial resolution maps. Plotting those results in bi-dimensional error space and analyzing various criteria, the “lowest cost”, according to a user defined (here hyperbolic) cost function, was found most useful. In general, we found a poor match between Landsat and MODIS-based change maps which, besides obvious differences in the capabilities to detect change, is likely related to change detection errors in both data sets.

scholarly journals Prediction Potential of Remote Sensing-Related Variables in the Topsoil Organic Carbon Density of Liaohekou Coastal Wetlands, Northeast China

2021 ◽  
Vol 13 (20) ◽  
pp. 4106
Author(s):  
Shuai Wang ◽  
Mingyi Zhou ◽  
Qianlai Zhuang ◽  
Liping Guo
Keyword(s):  
Remote Sensing ◽  
Organic Carbon ◽  
Coastal Wetlands ◽  
Environmental Variables ◽  
Northeast China ◽  
Vegetation Index ◽  
Coastal Wetland ◽  
Wetland Ecosystems ◽  
Flat Terrain ◽  
Soc Density

Wetland ecosystems contain large amounts of soil organic carbon. Their natural environment is often both at the junction of land and water with good conditions for carbon sequestration. Therefore, the study of accurate prediction of soil organic carbon (SOC) density in coastal wetland ecosystems of flat terrain areas is the key to understanding their carbon cycling. This study used remote sensing data to study SOC density potentials of coastal wetland ecosystems in Northeast China. Eleven environmental variables including normalized difference vegetation index (NDVI), difference vegetation index (DVI), soil adjusted vegetation index (SAVI), renormalization difference vegetation index (RDVI), ratio vegetation index (RVI), topographic wetness index (TWI), elevation, slope aspect (SA), slope gradient (SG), mean annual temperature (MAT), and mean annual precipitation (MAP) were selected to predict SOC density. A total of 193 soil samples (0–30 cm) were divided into two parts, 70% of the sampling sites data were used to construct the boosted regression tree (BRT) model containing three different combinations of environmental variables, and the remaining 30% were used to test the predictive performance of the model. The results show that the full variable model is better than the other two models. Adding remote sensing-related variables significantly improved the model prediction. This study revealed that SAVI, NDVI and DVI were the main environmental factors affecting the spatial variation of topsoil SOC density of coastal wetlands in flat terrain areas. The mean (±SD) SOC density of full variable models was 18.78 (±1.95) kg m−2, which gradually decreased from northeast to southwest. We suggest that remote sensing-related environmental variables should be selected as the main environmental variables when predicting topsoil SOC density of coastal wetland ecosystems in flat terrain areas. Accurate prediction of topsoil SOC density distribution will help to formulate soil management policies and enhance soil carbon sequestration.

scholarly journals Water vapor mapping by fusing InSAR and GNSS remote sensing data and atmospheric simulations

2015 ◽  
Vol 19 (12) ◽  
pp. 4747-4764 ◽  
Author(s):  
F. Alshawaf ◽  
B. Fersch ◽  
S. Hinz ◽  
H. Kunstmann ◽  
M. Mayer ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Vapor ◽  
Data Fusion ◽  
Spatial Resolution ◽  
Heterogeneous Data ◽  
Spatial Density ◽  
Data Sets ◽  
Data Set ◽  
Fixed Rank Kriging ◽  
Fixed Rank

Abstract. Data fusion aims at integrating multiple data sources that can be redundant or complementary to produce complete, accurate information of the parameter of interest. In this work, data fusion of precipitable water vapor (PWV) estimated from remote sensing observations and data from the Weather Research and Forecasting (WRF) modeling system are applied to provide complete grids of PWV with high quality. Our goal is to correctly infer PWV at spatially continuous, highly resolved grids from heterogeneous data sets. This is done by a geostatistical data fusion approach based on the method of fixed-rank kriging. The first data set contains absolute maps of atmospheric PWV produced by combining observations from the Global Navigation Satellite Systems (GNSS) and Interferometric Synthetic Aperture Radar (InSAR). These PWV maps have a high spatial density and a millimeter accuracy; however, the data are missing in regions of low coherence (e.g., forests and vegetated areas). The PWV maps simulated by the WRF model represent the second data set. The model maps are available for wide areas, but they have a coarse spatial resolution and a still limited accuracy. The PWV maps inferred by the data fusion at any spatial resolution show better qualities than those inferred from single data sets. In addition, by using the fixed-rank kriging method, the computational burden is significantly lower than that for ordinary kriging.

Using Eddy-Covariance to understand CO2 and CH4 flux dynamics within a temperate, coastal wetland on French Island, Victoria, Australia

2020 ◽  
Author(s):  
Matthew Peck ◽  
Ruth Reef ◽  
Nigel Tapper ◽  
Edoardo Daly ◽  
Leigh Burgess ◽  
...  
Keyword(s):  
Greenhouse Gases ◽  
Eddy Covariance ◽  
Coastal Wetlands ◽  
Coastal Wetland ◽  
Climate Data ◽  
Flux Dynamics ◽  
Tropical Regions ◽  
Full Dataset ◽  
Inland Wetlands ◽  
Temperate Wetlands

<p>Coastal wetlands play a pivotal role in regulating both carbon (CO<sub>2</sub>) and methane (CH<sub>4</sub>) concentrations across the globe. The amount of CO<sub>2</sub> and CH<sub>4 </sub>stored and released by these ecosystems is becoming more understood, in particular, within each aspect of the ecosystem. However, how the dynamics of the ecosystem affect CO<sub>2</sub> and CH<sub>4 </sub>fluxes on a microclimate level is poorly understood, as well as the overall flux of these Greenhouse Gases (GHGs) within temperate, coastal wetlands. Current research primarily focuses on inland wetlands and coastal wetlands in sub-tropical and tropical regions. Thus, this research aims to investigate CO<sub>2</sub> and CH<sub>4 </sub>fluxes within coastal, temperate wetlands, and improve the understanding of how environmental dynamics impact the flux of these critically important Greenhouse Gases (GHGs).</p><p> </p><p>To satisfy this aim, the use of the Eddy-Covariance (EC) method was employed. An EC station was installed on the South-West tip of French Island, Victoria, Australia in late February 2018. The collected data demonstrates the challenges with collecting micro-climate data in an ecosystem with ever-changing environmental conditions. The preliminary results indicate how sensitive flux dynamics are within coastal, temperate wetlands, in particular, to factors such as: tidal and seasonal inundation, seasonal vegetation dynamics, and shifting ecological gradients. The data obtained by the EC station provides a preliminary indication of the complexities of accounting for, and understanding, carbon and methane movement through coastal wetlands in general. The full dataset will aid in improving this understanding, specifically for rare, temperate wetland environments, increasing the knowledge base on how flux dynamics of carbon and methane are affected when collected via open-source methods in dynamic environments.</p>

scholarly journals Assessing the Resilience of Coastal Wetlands to Extreme Hydrologic Events Using Vegetation Indices: A Review

2018 ◽  
Vol 10 (9) ◽  
pp. 1390 ◽  
Author(s):  
Subrina Tahsin ◽  
Stephen C. Medeiros ◽  
Arvind Singh
Keyword(s):  
Remote Sensing ◽  
Coastal Wetlands ◽  
Coastal Wetland ◽  
Assessment Tool ◽  
Anthropogenic Impacts ◽  
Vegetation Indices ◽  
Heavy Precipitation ◽  
Life Forms ◽  
Climatic Fluctuations ◽  
Wetland Ecosystems

Coastal wetlands (CWs) offer numerous imperative functions that support a diverse array of life forms that are poorly adapted for other environments and provide an economic base for human communities. Unfortunately, CWs have been experiencing significant threats due to meteorological and climatic fluctuations as well as anthropogenic impacts. The wetlands and marshes in Apalachicola Bay, Florida have endured the impacts of several extreme hydrologic events (EHEs) over the past few decades. These extreme hydrologic events include drought, hurricane, heavy precipitation and fluvial flooding. Remote sensing has been used and continues to demonstrate promise for acquiring spatial and temporal information about CWs thereby making it easier to track and quantify long term changes driven by EHEs. These wetland ecosystems are also adversely impacted by increased human activities such as wetland conversion to agricultural, aquaculture, industrial or residential use; construction of dikes along the shoreline; and sprawl of built areas. In this paper, we review previous works on coastal wetland resilience to EHEs. We synthesize these concepts in the context of remote sensing as the primary assessment tool with focus on derived vegetation indices to monitor CWs at regional and global scales.

scholarly journals The CMEMS GlobColour chlorophyll <i>a</i> product based on satellite observation: multi-sensor merging and flagging strategies

Ocean Science ◽  
2019 ◽  
Vol 15 (3) ◽  
pp. 819-830 ◽  
Author(s):  
Philippe Garnesson ◽  
Antoine Mangin ◽  
Odile Fanton d'Andon ◽  
Julien Demaria ◽  
Marine Bretagnon
Keyword(s):  
Remote Sensing ◽  
Chlorophyll A ◽  
Spatial Resolution ◽  
Remote Sensing Data ◽  
Satellite Observation ◽  
Data Sets ◽  
In Situ Data ◽  
Long Time ◽  
The One ◽  
Monitoring Service

Abstract. This paper concerns the GlobColour-merged chlorophyll a products based on satellite observation (SeaWiFS, MERIS, MODIS, VIIRS and OLCI) and disseminated in the framework of the Copernicus Marine Environmental Monitoring Service (CMEMS). This work highlights the main advantages provided by the Copernicus GlobColour processor which is used to serve CMEMS with a long time series from 1997 to present at the global level (4 km spatial resolution) and for the Atlantic level 4 product (1 km spatial resolution). To compute the merged chlorophyll a product, two major topics are discussed: The first of these topics is the strategy for merging remote-sensing data, for which two options are considered. On the one hand, a merged chlorophyll a product computed from a prior merging of the remote-sensing reflectance of a set of sensors. On the other hand, a merged chlorophyll a product resulting from a combination of chlorophyll a products computed for each sensor. The second topic is the flagging strategy used to discard non-significant observations (e.g. clouds, high glint and so on). These topics are illustrated by comparing the CMEMS GlobColour products provided by ACRI-ST (Garnesson et al., 2019) with the OC-CCI/C3S project (Sathyendranath et al., 2018). While GlobColour merges chlorophyll a products with a specific flagging, the OC-CCI approach is based on a prior reflectance merging before chlorophyll a derivation and uses a more constrained flagging approach. Although this work addresses these two topics, it does not pretend to provide a full comparison of the two data sets, which will require a better characterisation and additional inter-comparison with in situ data.

scholarly journals Two Decades Progress on The Application of Remote Sensing for Monitoring Tropical and Sub-Tropical Natural Forests: A Review

Forests ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 739
Author(s):  
Enoch Gyamfi-Ampadu ◽  
Michael Gebreslasie
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Aboveground Biomass ◽  
Tree Species ◽  
Forest Cover ◽  
High Spatial Resolution ◽  
Remote Sensing Data ◽  
Natural Forests ◽  
Sensing Applications ◽  
Remote Sensing Applications

Forest covers about a third of terrestrial land surface, with tropical and subtropical zones being a major part. Remote sensing applications constitute a significant approach to monitoring forests. Thus, this paper reviews the progress made by remote sensing data applications to tropical and sub-tropical natural forest monitoring over the last two decades (2000–2020). The review focuses on the thematic areas of aboveground biomass and carbon estimations, tree species identification, tree species diversity, and forest cover and change mapping. A systematic search of articles was performed on Web of Science, Science Direct, and Google Scholar by applying a Boolean operator and using keywords related to the thematic areas. We identified 50 peer-reviewed articles that studied tropical and subtropical natural forests using remote sensing data. Asian and South American natural forests are the most highly researched natural forests, while African natural forests are the least studied. Medium spatial resolution imagery was extensively utilized for forest cover and change mapping as well as aboveground biomass and carbon estimation. In the latest studies, high spatial resolution imagery and machine learning algorithms, such as Random Forest and Support Vector Machine, were jointly utilized for tree species identification. In this review, we noted the promising potential of the emerging high spatial resolution satellite imagery for the monitoring of natural forests. We recommend more research to identify approaches to overcome the challenges of remote sensing applications to these thematic areas so that further and sustainable progress can be made to effectively monitor and manage sustainable forest benefits.

Estimation of forest aboveground biomass using high spatial resolution remote sensing imagery

2013 ◽  
Vol 33 (20) ◽  
pp. 6497-6508 ◽  
Author(s):  
黄金龙 HUANG Jinlong ◽  
居为民 JU Weimin ◽  
郑光 ZHENG Guang ◽  
康婷婷 KANG Tingting
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Aboveground Biomass ◽  
High Spatial Resolution ◽  
Remote Sensing Imagery ◽  
Forest Aboveground Biomass
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