scholarly journals Satellite remote sensing reveals a positive impact of living oyster reefs on microalgal biofilm development

2017 ◽  
Author(s):  
Caroline Echappé ◽  
Pierre Gernez ◽  
Vona Méléder ◽  
Bruno Jesus ◽  
Bruno Cognie ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Seasonal Variations ◽  
Satellite Remote Sensing ◽  
Vegetation Index ◽  
Positive Impact ◽  
Oyster Reefs ◽  
Inorganic Matter ◽  
Biofilm Development ◽  
The Impact

Abstract. Satellite remote sensing (RS) is routinely used for the large-scale monitoring of microphytobenthos (MPB) biomass in intertidal mudflats, and has greatly improved our knowledge of MPB spatio-temporal variability and its potential drivers. Processes operating at smaller scales however, such as the impact of benthic macrofauna on MPB development, to date remain underinvestigated. In this study, we analysed the influence of wild Crassostrea gigas oyster reefs on MPB biofilm development using multispectral RS. A 30-year time series (1985–2015) combining high resolution (30 m) Landsat and SPOT data was built in order to explore the relationship between C. gigas reefs and MPB spatial distribution and seasonal dynamics, using the normalized difference vegetation index (NDVI). Emphasis was placed on the analysis of a before after control impact (BACI) experiment designed to assess the effect of oyster killing on the surrounding MPB biofilms. Our RS data reveal that the presence of oyster reefs positively affects MPB biofilm development. Analysis of the historical time series first showed the presence of persistent, highly concentrated MPB patches around oyster reefs. This observation was then confirmed by the BACI experiment which showed that killing the oysters (while leaving the physical reef structure, i.e. oyster shells, intact) negatively affected both MPB biofilm biomass and spatial stability around the reef. As such, our results are consistent with the hypothesis of nutrient input as an explanation for the MPB growth promoting effect of oysters, whereby organic and inorganic matter released through oyster excretion and biodeposition stimulates MPB biomass accumulation. MPB also showed marked seasonal variations in biomass and patch shape, size and degree of aggregation around the oyster reefs. Seasonal variations in biomass, with higher NDVI during spring and fall, were consistent with those observed at broader scales in other European mudflats. Our study provides the first multi-sensor RS satellite evidence of the promoting and structuring effect of oyster reefs on MPB biofilms.

scholarly journals Satellite remote sensing reveals a positive impact of living oyster reefs on microalgal biofilm development

2018 ◽  
Vol 15 (3) ◽  
pp. 905-918 ◽  
Author(s):  
Caroline Echappé ◽  
Pierre Gernez ◽  
Vona Méléder ◽  
Bruno Jesus ◽  
Bruno Cognie ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Seasonal Variations ◽  
Satellite Remote Sensing ◽  
Vegetation Index ◽  
Positive Impact ◽  
Oyster Reefs ◽  
Inorganic Matter ◽  
Biofilm Development ◽  
The Impact

Abstract. Satellite remote sensing (RS) is routinely used for the large-scale monitoring of microphytobenthos (MPB) biomass in intertidal mudflats and has greatly improved our knowledge of MPB spatio-temporal variability and its potential drivers. Processes operating on smaller scales however, such as the impact of benthic macrofauna on MPB development, to date remain underinvestigated. In this study, we analysed the influence of wild Crassostrea gigas oyster reefs on MPB biofilm development using multispectral RS. A 30-year time series (1985–2015) combining high-resolution (30 m) Landsat and SPOT data was built in order to explore the relationship between C. gigas reefs and MPB spatial distribution and seasonal dynamics, using the normalized difference vegetation index (NDVI). Emphasis was placed on the analysis of a before–after control-impact (BACI) experiment designed to assess the effect of oyster killing on the surrounding MPB biofilms. Our RS data reveal that the presence of oyster reefs positively affects MPB biofilm development. Analysis of the historical time series first showed the presence of persistent, highly concentrated MPB patches around oyster reefs. This observation was supported by the BACI experiment which showed that killing the oysters (while leaving the physical reef structure, i.e. oyster shells, intact) negatively affected both MPB biofilm biomass and spatial stability around the reef. As such, our results are consistent with the hypothesis of nutrient input as an explanation for the MPB growth-promoting effect of oysters, whereby organic and inorganic matter released through oyster excretion and biodeposition stimulates MPB biomass accumulation. MPB also showed marked seasonal variations in biomass and patch shape, size and degree of aggregation around the oyster reefs. Seasonal variations in biomass, with higher NDVI during spring and autumn, were consistent with those observed on broader scales in other European mudflats. Our study provides the first multi-sensor RS satellite evidence of the promoting and structuring effect of oyster reefs on MPB biofilms.

scholarly journals Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery

2014 ◽  
Vol 11 (16) ◽  
pp. 4305-4320 ◽  
Author(s):  
S. T. Klosterman ◽  
K. Hufkens ◽  
J. M. Gray ◽  
E. Melaas ◽  
O. Sonnentag ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Curve Fitting ◽  
Satellite Remote Sensing ◽  
Vegetation Index ◽  
Deciduous Forest ◽  
Spatial Scales ◽  
Visual Assessment ◽  
Statistical Uncertainty ◽  
Near Surface

Abstract. Plant phenology regulates ecosystem services at local and global scales and is a sensitive indicator of global change. Estimates of phenophase transition dates, such as the start of spring or end of fall, can be derived from sensor-based time series, but must be interpreted in terms of biologically relevant events. We use the PhenoCam archive of digital repeat photography to implement a consistent protocol for visual assessment of canopy phenology at 13 temperate deciduous forest sites throughout eastern North America, and to perform digital image analysis for time-series-based estimation of phenophase transition dates. We then compare these results to remote sensing metrics of phenophase transition dates derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR) sensors. We present a new type of curve fit that uses a generalized sigmoid function to estimate phenology dates, and we quantify the statistical uncertainty of phenophase transition dates estimated using this method. Results show that the generalized sigmoid provides estimates of dates with less statistical uncertainty than other curve-fitting methods. Additionally, we find that dates derived from analysis of high-frequency PhenoCam imagery have smaller uncertainties than satellite remote sensing metrics of phenology, and that dates derived from the remotely sensed enhanced vegetation index (EVI) have smaller uncertainty than those derived from the normalized difference vegetation index (NDVI). Near-surface time-series estimates for the start of spring are found to closely match estimates derived from visual assessment of leaf-out, as well as satellite remote-sensing-derived estimates of the start of spring. However late spring and fall phenology metrics exhibit larger differences between near-surface and remote scales. Differences in late spring phenology between near-surface and remote scales are found to correlate with a landscape metric of deciduous forest cover. These results quantify the effect of landscape heterogeneity when aggregating to the coarser spatial scales of remote sensing, and demonstrate the importance of accurate curve fitting and vegetation index selection when analyzing and interpreting phenology time series.

Assessing and monitoring the vulnerability to drought and climate anomalies of Mediterranenan oak forests by using NDVI

2020 ◽  
Author(s):  
Maria Castellaneta ◽  
Angelo Rita ◽  
J. Julio Camarero ◽  
Michele Colangelo ◽  
Angelo Nolè ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Tree Mortality ◽  
Vegetation Index ◽  
Google Earth ◽  
Oak Forests ◽  
Summer Drought ◽  
Area Index ◽  
Ndvi Time Series ◽  
The Impact

<p>Several die-off episodes related to heat weaves and drought spells have evidenced the high vulnerability of Mediterranean oak forests. These events consisted in the loss in tree vitality and manifested as growths decline, elevated crown transparency (defoliation) and rising tree mortality rate. In this context, the changes in vegetation productivity and canopy greenness may represent valuable proxies to analyze how extreme climatic events trigger forest die-off. Such changes in vegetation status may be analyzed using remote-sensing data, specifically multi-temporal spectral information. For instance, the Normalized Difference Vegetation Index (NDVI) measures changes in vegetation greenness and is a proxy of changes in leaf area index (LAI), forest aboveground biomass and productivity. In this study, we analyzed the temporal patterns of vegetation in three Mediterranean oak forests showing recent die-off in response to the 2017 severe summer drought. For this purpose, we used an open-source platform (Google Earth Engine) to extract collections of MODIS NDVI time-series from 2000 to 2019. The analysis of both NDVI trends and anomalies were used to infer differential patterns of vegetation phenology among sites comparing plots where most trees were declining and showed high defoliation (test) versus plots were most trees were considered healthy (ctrl) and showed low or no defoliation. Here we discuss: i) the likely offset in NDVI time-series between test- versus ctrl- sites; and ii) the impact of summer droughts  on NDVI.</p><p><strong>Keywords</strong>: climate change, forest vulnerability, time series, remote sensing.</p>

Timely monitoring of Asian Migratory locust habitats in the Amudarya delta, Uzbekistan using time series of satellite remote sensing vegetation index

2016 ◽  
Vol 183 ◽  
pp. 562-575 ◽  
Author(s):  
Fabian Löw ◽  
François Waldner ◽  
Alexandre Latchininsky ◽  
Chandrashekhar Biradar ◽  
Maximilian Bolkart ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Satellite Remote Sensing ◽  
Vegetation Index ◽  
Migratory Locust

scholarly journals Grouping-Based Time-Series Model for Monitoring of Fall Peak Coloration Dates Using Satellite Remote Sensing Data

2020 ◽  
Vol 12 (2) ◽  
pp. 274
Author(s):  
Qu Zhou ◽  
Xianghan Sun ◽  
Liqiao Tian ◽  
Jian Li ◽  
Wenkai Li
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Satellite Remote Sensing ◽  
Vegetation Index ◽  
Transition Period ◽  
Scale Effects ◽  
Remote Sensing Data ◽  
Temporal Scale ◽  
Absolute Difference ◽  
Wide Range

Accurate monitoring of plant phenology is vital to effective understanding and prediction of the response of vegetation ecosystems to climate change. Satellite remote sensing is extensively employed to monitor vegetation phenology. However, fall phenology, such as peak foliage coloration, is less well understood compared with spring phenological events, and is mainly determined using the vegetation index (VI) time-series. Each VI only emphasizes a single vegetation property. Thus, selecting suitable VIs and taking advantage of multiple spectral signatures to detect phenological events is challenging. In this study, a novel grouping-based time-series approach for satellite remote sensing was proposed, and a wide range of spectral wavelengths was considered to monitor the complex fall foliage coloration process with simultaneous changes in multiple vegetation properties. The spatial and temporal scale effects of satellite data were reduced to form a reliable remote sensing time-series, which was then divided into groups, namely pre-transition, transition and post-transition groups, to represent vegetation dynamics. The transition period of leaf coloration was correspondingly determined to divisions with the smallest intra-group and largest inter-group distances. Preliminary results using a time-series of Moderate Resolution Imaging Spectroradiometer (MODIS) data from 2002 to 2013 at the Harvard Forest (spatial scale: ~3500 m; temporal scale: ~8 days) demonstrated that the method can accurately determine the coloration period (correlation coefficient: 0.88; mean absolute difference: 3.38 days), and that the peak coloration periods displayed a shifting trend to earlier dates. The grouping-based approach shows considerable potential in phenological monitoring using satellite time-series.

scholarly journals Remote Detection of Large-Area Crop Types: The Role of Plant Phenology and Topography

Agriculture ◽  
2019 ◽  
Vol 9 (7) ◽  
pp. 150 ◽  
Author(s):  
Yanfei Wei ◽  
Xinhua Tong ◽  
Gang Chen ◽  
Deqiang Liu ◽  
Zhenfeng Han
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Vegetation Index ◽  
Plant Phenology ◽  
Superior Performance ◽  
Mountainous Area ◽  
Large Area ◽  
Modis Evi ◽  
Crop Types ◽  
The Impact

Sustainable agricultural practices necessitate accurate baseline data of crop types and their detailed spatial distribution. Compared with field surveys, remote sensing has demonstrated superior performance, offering spatially explicit crop distribution in a timely manner. Recent studies have taken advantage of remote sensing time series to capture the variation in plant phenology, inferring major crop types. However, such an approach was rarely used to extract detailed, multiple crop types spanning a large area, and the impact of topography has yet to be well analyzed in mountainous regions. This study aims to answer two questions in crop type extraction: (i) Is it feasible to accurately map multiple crop types over a large mountainous area with phenology-based modeling? (ii) What are the effects of topography in such modeling? To answer the questions, phenological metrics were extracted from MODIS (Moderate Resolution Imaging Spectroradiometer) satellite time series, and the random forests classifier was used to map 12 crop types in South China (236,700 km2), featuring a subtropical monsoon climate and high topographic variation. Our study revealed promising results using MODIS EVI (Enhanced Vegetation Index) and NDVI (Normalized Difference Vegetation Index) time series, although EVI outperformed NDVI (overall accuracy: 85% versus 81%). The spectral and temporal metrics of plant phenology significantly contributed to crop identification, where the spectral information exhibited greater importance. The increase of slope led to a decrease in model accuracy in general. However, uniformly distributed tree plantations (e.g., tea-oil camellia, gum, and tea trees) being cultivated on large slopes (>15 degrees) achieved accuracies greater than 80%.

Welfare Use in Japan: Trends and Determinants

2007 ◽  
pp. 88
Author(s):  
Wataru Suzuki ◽  
Yanfei Zhou
Keyword(s):  
Time Series ◽  
Longitudinal Data ◽  
Time Series Data ◽  
Positive Impact ◽  
Series Data ◽  
Negative Effects ◽  
Long Time ◽  
Welfare Use ◽  
The Impact

This article represents the first step in filling a large gap in knowledge concerning why Public Assistance (PA) use recently rose so fast in Japan. Specifically, we try to address this problem not only by performing a Blanchard and Quah decomposition on long-term monthly time series data (1960:04-2006:10), but also by estimating prefecturelevel longitudinal data. Two interesting findings emerge from the time series analysis. The first is that permanent shock imposes a continuously positive impact on the PA rate and is the main driving factor behind the recent increase in welfare use. The second finding is that the impact of temporary shock will last for a long time. The rate of the use of welfare is quite rigid because even if the PA rate rises due to temporary shocks, it takes about 8 or 9 years for it to regain its normal level. On the other hand, estimations of prefecture-level longitudinal data indicate that the Financial Capability Index (FCI) of the local government2 and minimum wage both impose negative effects on the PA rate. We also find that the rapid aging of Japan's population presents a permanent shock in practice, which makes it the most prominent contribution to surging welfare use.

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