scholarly journals ANALYSIS OF TEMPORAL AND SPATIAL CHARACTERISTICS OF MANGROVE PHYSIOLOGICAL STRUCTURE PARAMETERS QUANTITATIVE INVERSION BASED ON TIME SERIES SENTINEL-2 – TAKING SHANKOU MANGROVE NATURE RESERVE AS AN EXAMPLE

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 487-494
Author(s):  
S. W. Wang ◽  
H. C. He ◽  
B. L. Fu ◽  
H. L. Sun
Keyword(s):  
Health Status ◽  
Vegetation Change ◽  
Nature Reserve ◽  
Image Data ◽  
Structure Parameters ◽  
Ecological Health ◽  
Area Index ◽  
Mangrove Vegetation ◽  
Physiological Structure ◽  
Sentinel 2

Abstract. The physiological structure parameter of vegetation is an important index to measure the ecological health status of mangroves, and it is of great value to measure the ecological health status. Taking Shankou Mangrove Nature Reserve of Guangxi as the study area, The Chlorophyll-A/B(CAB),Leaf Area Index (LAI),Fractional Vegetation Cover (FVC) and Fraction of absorbed photo synthetically active radiation (FAPAR) were calculated by using Sentinel-2 image data to calculate the chlorophyll content of mangrove vegetation in the study area. The BP neural network algorithm is used to verify the accuracy difference between the inversion results and the corresponding products of MODIS, and the dynamic changes of physiological structure parameters of vegetation in mangroves are further studied. Results showed that: (1)The correlation coefficients between LAI, CAB, FAPAR, FVC and MODIS products were higher than 0.71 in 95% confidence interval in mangrove years, it is proved that Sentinel-2A/B multispectral image inversion of mangrove physiological structure parameters has high accuracy and quality. (2)The physiological structure parameters of mangroves fluctuated during the year. In February, the lowest values of LAI, CAB, FAPAR and FVC were 0.30, 0.08, 0.08 and 0.13, respectively. The highest values were 0.69 in October, 0.29 in December, 0.27 in August, 0.40 in April, 0.24 and 0.24 in September, and 0.27 and 0.33 in LAI in November, respectively, with the highest LAI in October, 0.29 in December, 0.27 in August, 0.40 in April, 0.24 and 0.24 in September, and 0.27 and 0.33 in November. The results provide the basis for the monitoring of mangrove vegetation change and provide a reference for ecological assessment and protection.

scholarly journals Short-Term Effects of Fire Severity on Vegetation Based on Sentinel-2 Satellite Data

2021 ◽  
Vol 13 (1) ◽  
pp. 432
Author(s):  
Aru Han ◽  
Song Qing ◽  
Yongbin Bao ◽  
Li Na ◽  
Yuhai Bao ◽  
...  
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Vegetation Change ◽  
Fire Severity ◽  
Dynamic Change ◽  
Burned Area ◽  
Fire Event ◽  
Vegetation Dynamic ◽  
Area Index ◽  
Sentinel 2

An important component in improving the quality of forests is to study the interference intensity of forest fires, in order to describe the intensity of the forest fire and the vegetation recovery, and to improve the monitoring ability of the dynamic change of the forest. Using a forest fire event in Bilahe, Inner Monglia in 2017 as a case study, this study extracted the burned area based on the BAIS2 index of Sentinel-2 data for 2016–2018. The leaf area index (LAI) and fractional vegetation cover (FVC), which are more suitable for monitoring vegetation dynamic changes of a burned area, were calculated by comparing the biophysical and spectral indices. The results showed that patterns of change of LAI and FVC of various land cover types were similar post-fire. The LAI and FVC of forest and grassland were high during the pre-fire and post-fire years. During the fire year, from the fire month (May) through the next 4 months (September), the order of areas of different fire severity in terms of values of LAI and FVC was: low > moderate > high severity. During the post fire year, LAI and FVC increased rapidly in areas of different fire severity, and the ranking of areas of different fire severity in terms of values LAI and FVC was consistent with the trend observed during the pre-fire year. The results of this study can improve the understanding of the mechanisms involved in post-fire vegetation change. By using quantitative inversion, the health trajectory of the ecosystem can be rapidly determined, and therefore this method can play an irreplaceable role in the realization of sustainable development in the study area. Therefore, it is of great scientific significance to quantitatively retrieve vegetation variables by remote sensing.

scholarly journals Spaceborne Estimation of Leaf Area Index in Cotton, Tomato, and Wheat Using Sentinel-2

Land ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 505
Author(s):  
Gregoriy Kaplan ◽  
Offer Rozenstein
Keyword(s):  
Water Vapor ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Near Infrared ◽  
Linear Regression Models ◽  
Area Index ◽  
Estimation Performance ◽  
Red Edge ◽  
Sentinel 2

Satellite remote sensing is a useful tool for estimating crop variables, particularly Leaf Area Index (LAI), which plays a pivotal role in monitoring crop development. The goal of this study was to identify the optimal Sentinel-2 bands for LAI estimation and to derive Vegetation Indices (VI) that are well correlated with LAI. Linear regression models between time series of Sentinel-2 imagery and field-measured LAI showed that Sentinel-2 Band-8A—Narrow Near InfraRed (NIR) is more accurate for LAI estimation than the traditionally used Band-8 (NIR). Band-5 (Red edge-1) showed the lowest performance out of all red edge bands in tomato and cotton. A novel finding was that Band 9 (Water vapor) showed a very high correlation with LAI. Bands 1, 2, 3, 4, 5, 11, and 12 were saturated at LAI ≈ 3 in cotton and tomato. Bands 6, 7, 8, 8A, and 9 were not saturated at high LAI values in cotton and tomato. The tomato, cotton, and wheat LAI estimation performance of ReNDVI (R2 = 0.79, 0.98, 0.83, respectively) and two new VIs (WEVI (Water vapor red Edge Vegetation Index) (R2 = 0.81, 0.96, 0.71, respectively) and WNEVI (Water vapor narrow NIR red Edge Vegetation index) (R2 = 0.79, 0.98, 0.79, respectively)) were higher than the LAI estimation performance of the commonly used NDVI (R2 = 0.66, 0.83, 0.05, respectively) and other common VIs tested in this study. Consequently, reNDVI, WEVI, and WNEVI can facilitate more accurate agricultural monitoring than traditional VIs.

scholarly journals A Critical Comparison of Remote Sensing Leaf Area Index Estimates over Rice-Cultivated Areas: From Sentinel-2 and Landsat-7/8 to MODIS, GEOV1 and EUMETSAT Polar System

2018 ◽  
Vol 10 (5) ◽  
pp. 763 ◽  
Author(s):  
Manuel Campos-Taberner ◽  
Francisco García-Haro ◽  
Lorenzo Busetto ◽  
Luigi Ranghetti ◽  
Beatriz Martínez ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Area Index ◽  
Polar System ◽  
Critical Comparison ◽  
Landsat 7 ◽  
Sentinel 2

Crop Yield Modelling Applying Leaf Area Index Estimated from Sentinel-2 and Proba-V Data at JECAM site in Poland

2018 ◽  
Author(s):  
Katarzyna Dabrowska-Zielinska ◽  
Maciej Bartold ◽  
Radoslaw Gurdak ◽  
Martyna Gatkowska ◽  
Wojciech Kiryla ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Crop Yield ◽  
Area Index ◽  
Sentinel 2

scholarly journals Characterization of macro-benthic fauna for ecological health status of the Fosu and Benya lagoons in coastal Ghana

2012 ◽  
Vol 35 (4) ◽  
pp. 279-289
Author(s):  
Frederick A. Armah ◽  
Benjamin Ason ◽  
Isaac Luginaah ◽  
Paul K. Essandoh
Keyword(s):  
Health Status ◽  
Benthic Fauna ◽  
Ecological Health

Vegetation change in response to grazing and water level decline in the Enot Zukim Nature Reserve (En Fescha), Israel

2009 ◽  
Vol 57 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Linda Olsvig-Whittaker ◽  
Margareta Walczak ◽  
Amos Sabach ◽  
Eli Dror
Keyword(s):  
Water Level ◽  
Vegetation Change ◽  
Nature Reserve ◽  
Water Level Decline

Improvements to the Simplified Level 2 Prototype Processor for Retrieving Canopy Biophysical Variables from Sentinel 2 Multispectral Instrument Data

2021 ◽  
Author(s):  
Richard Fernandes ◽  
Fred Baret ◽  
Luke Brown ◽  
Francis Canisius ◽  
Jadu Dash ◽  
...  
Keyword(s):  
North America ◽  
Google Earth ◽  
Model Complexity ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Wide Field ◽  
Area Index ◽  
Product Generation ◽  
Level 2 ◽  
Sentinel 2

<p>The Sentinel 2 (S2) constellation mission was designed to facilitate the systematic mapping canopy biophysical variables at medium resolution on a global basis and in a free and open manner.  The mission concept requires the development of downstream services to map variables such as the fraction of absorbed photosynthetically active radiation (fAPAR), fraction of canopy cover (fCOVER) and leaf area index (LAI) using Level 2A surface reflectance inputs from the S2 ground segment.  Currently, free and open products generation can be performed using the Simplified Level 2 Prototype Processor (SL2P) applied on a product granule basis.  Considering that the processor is a prototype this study addresses three questions: 1) Can the SL2P algorithm, or subsequent versions, be engineered to facilitate systematic product generation over large extents in a free and open manner? 2) What is the uncertainty of SL2P products over North America during the growing season? 3) Can the uncertainty be reduced by changing the calibration database used within SL2P?  </p><p><br><br></p><p>To facilitate validation and product generation, SL2P was ported to a Google Earth Engine application (the Landscape Evolution and Forecasting Toolbox).  This now allows mapping of up to one million square kilometers in near real time using either the original SL2P algorithm or updated versions.  SL2P uncertainty was quantified over North America using direct comparison to 20 in-situ sites within the National Environmental Observing Network in the continental United States of America and within a Canada wide field campaign over forests and shrublands conducted by Canada Centre for Remote Sensing. SL2P outputs were also compared to MODIS and Copernicus Global Land Service products over the Belmanip II regional sites and 30 additional forested regions in North America.  Results from NEON validation indicate SL2P is generally within uncertainty requirements except for forests; where it underestimates fAPAR, fCOVER and LAI.  Results for other sites will also be presented.  To address the forest bias, SL2P was recalibrated using simulations from the FLIGHT 3D radiative transfer model representative of North American forests.  The uncertainty of the recalibrated SL2P algorithm will be compared to baseline SL2P estimates to determine if increased model complexity is warranted.</p>

scholarly journals Estimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images

2019 ◽  
Vol 154 ◽  
pp. 189-201 ◽  
Author(s):  
Jie Wang ◽  
Xiangming Xiao ◽  
Rajen Bajgain ◽  
Patrick Starks ◽  
Jean Steiner ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Aboveground Biomass ◽  
Landsat Images ◽  
Area Index ◽  
Sentinel 2
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