scholarly journals Remote sensing of <i>Trichodesmium</i> spp. mats in the western tropical South Pacific

2018 ◽  
Vol 15 (16) ◽  
pp. 5203-5219 ◽  
Author(s):  
Guillaume Rousset ◽  
Florian De Boissieu ◽  
Christophe E. Menkes ◽  
Jérôme Lefèvre ◽  
Robert Frouin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
New Caledonia ◽  
Hot Spot ◽  
Regional Scale ◽  
Atmospheric Correction ◽  
South Pacific ◽  
Aggregation Processes ◽  
Modis Imagery ◽  
Generate Surface

Abstract. Trichodesmium is the major nitrogen-fixing species in the western tropical South Pacific (WTSP) region, a hot spot of diazotrophy. Due to the paucity of in situ observations, remote-sensing methods for detecting Trichodesmium presence on a large scale have been investigated to assess the regional-to-global impact of this organism on primary production and carbon cycling. A number of algorithms have been developed to identify Trichodesmium surface blooms from space, but determining with confidence their accuracy has been difficult, chiefly because of the scarcity of sea-truth information at the time of satellite overpass. Here, we use a series of new cruises as well as airborne surveys over the WTSP to evaluate their ability to detect Trichodesmium surface blooms in the satellite imagery. The evaluation, performed on MODIS data at 250 m and 1 km resolution acquired over the region, shows limitations due to spatial resolution, clouds, and atmospheric correction. A new satellite-based algorithm is designed to alleviate some of these limitations, by exploiting optimally spectral features in the atmospherically corrected reflectance at 531, 645, 678, 748, and 869 nm. This algorithm outperforms former ones near clouds, limiting false positive detection and allowing regional-scale automation. Compared with observations, 80 % of the detected mats are within a 2 km range, demonstrating the good statistical skill of the new algorithm. Application to MODIS imagery acquired during the February-March 2015 OUTPACE campaign reveals the presence of surface blooms northwest and east of New Caledonia and near 20∘ S–172∘ W in qualitative agreement with measured nitrogen fixation rates. Improving Trichodesmium detection requires measuring ocean color at higher spectral and spatial (<250 m) resolution than MODIS, taking into account environment properties (e.g., wind, sea surface temperature), fluorescence, and spatial structure of filaments, and a better understanding of Trichodesmium dynamics, including aggregation processes to generate surface mats. Such sub-mesoscale aggregation processes for Trichodesmium are yet to be understood.

scholarly journals Remote Sensing of <i>Trichodesmium</i> spp. mats in the Western Tropical South Pacific

2018 ◽  
Author(s):  
Guillaume Rousset ◽  
Florian De Boissieu ◽  
Christophe E. Menkes ◽  
Jérôme Lefèvre ◽  
Robert Frouin ◽  
...  
Keyword(s):  
Large Scale ◽  
New Caledonia ◽  
Regional Scale ◽  
Atmospheric Correction ◽  
South Pacific ◽  
The South ◽  
False Positive Detection ◽  
Modis Imagery ◽  
In Situ Observations

Abstract. Trichodesmium is the main nitrogen-fixing species in the South Pacific region, a hotspot for diazotrophy. Due to the paucity of in situ observations, methods for detecting Trichodesmium presence on a large scale have been investigated to assess the regional-to-global impact of these species on primary production and carbon cycling. A number of satellite-derived algorithms have been developed to identify Trichodesmium surface blooms, but determining with confidence their accuracy has been difficult, chiefly because of the scarcity of sea-truth information at time of satellite overpass. Here, we use a series of new cruises as well as airborne observational surveys in the South Pacific to quantify statistically the ability of these algorithms to discern correctly Trichodesmium surface blooms in the satellite imagery. The evaluation, performed on MODIS data at 250 m and 1 km resolution acquired over the South West Pacific, shows limitations due to spatial resolution, clouds, and atmospheric correction. A new satellite-based algorithm is designed to alleviate some of these limitations, by exploiting optimally spectral features in the atmospherically corrected reflectance at 531, 645, 678, 748, and 869 nm. This algorithm outperforms former ones near clouds, limiting false positive detection, and allowing regional scale automation. Compared with observations, 80 % of the detected mats are within a 2 km range, demonstrating the good statistical skill of the new algorithm. Application to MODIS imagery acquired during the February–March 2015 OUTPACE campaign reveals the presence of surface blooms Northwest and East of New Caledonia and near 20° S–172° W in qualitative agreement with measured nitrogen fixation rates. The new algorithm, however, fails to detect sub-surface booms evidenced in trichome counts. Improving Trichodesmium detection requires measuring ocean color at higher spectral and spatial (

scholarly journals Potential of Multiway PLS (N-PLS) Regression Method to Analyse Time-Series of Multispectral Images: A Case Study in Agriculture

2022 ◽  
Vol 14 (1) ◽  
pp. 216
Author(s):  
Eva Lopez-Fornieles ◽  
Guilhem Brunel ◽  
Florian Rancon ◽  
Belal Gaci ◽  
Maxime Metz ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Heat Wave ◽  
Large Scale ◽  
Regional Scale ◽  
Pls Regression ◽  
Wave Impact ◽  
Sensing Applications ◽  
Sentinel 2

Recent literature reflects the substantial progress in combining spatial, temporal and spectral capacities for remote sensing applications. As a result, new issues are arising, such as the need for methodologies that can process simultaneously the different dimensions of satellite information. This paper presents PLS regression extended to three-way data in order to integrate multiwavelengths as variables measured at several dates (time-series) and locations with Sentinel-2 at a regional scale. Considering that the multi-collinearity problem is present in remote sensing time-series to estimate one response variable and that the dataset is multidimensional, a multiway partial least squares (N-PLS) regression approach may be relevant to relate image information to ground variables of interest. N-PLS is an extension of the ordinary PLS regression algorithm where the bilinear model of predictors is replaced by a multilinear model. This paper presents a case study within the context of agriculture, conducted on a time-series of Sentinel-2 images covering regional scale scenes of southern France impacted by the heat wave episode that occurred on 28 June 2019. The model has been developed based on available heat wave impact data for 107 vineyard blocks in the Languedoc-Roussillon region and multispectral time-series predictor data for the period May to August 2019. The results validated the effectiveness of the proposed N-PLS method in estimating yield loss from spectral and temporal attributes. The performance of the model was evaluated by the R2 obtained on the prediction set (0.661), and the root mean square of error (RMSE), which was 10.7%. Limitations of the approach when dealing with time-series of large-scale images which represent a source of challenges are discussed; however, the N–PLS regression seems to be a suitable choice for analysing complex multispectral imagery data with different spectral domains and with a clear temporal evolution, such as an extreme weather event.

scholarly journals Mesoscale Simulation of Tropical Cyclones in the South Pacific: Climatology and Interannual Variability

2011 ◽  
Vol 24 (1) ◽  
pp. 3-25 ◽  
Author(s):  
Nicolas C. Jourdain ◽  
Patrick Marchesiello ◽  
Christophe E. Menkes ◽  
Jérome Lefèvre ◽  
Emmanuel M. Vincent ◽  
...  
Keyword(s):  
Interannual Variability ◽  
Tropical Cyclones ◽  
Large Scale ◽  
Southern Oscillation ◽  
Regional Scale ◽  
South Pacific ◽  
South Pacific Convergence Zone ◽  
Dimensional Structure ◽  
The South ◽  
Wide Range

Abstract The Weather Research and Forecast model at ⅓° resolution is used to simulate the statistics of tropical cyclone (TC) activity in the present climate of the South Pacific. In addition to the large-scale conditions, the model is shown to reproduce a wide range of mesoscale convective systems. Tropical cyclones grow from the most intense of these systems formed along the South Pacific convergence zone (SPCZ) and sometimes develop into hurricanes. The three-dimensional structure of simulated tropical cyclones is in excellent agreement with dropsondes and satellite observations. The mean seasonal and spatial distributions of TC genesis and occurrence are also in good agreement with the Joint Typhoon Warning Center (JTWC) data. It is noted, however, that the spatial pattern of TC activity is shifted to the northeast because of a similar bias in the environmental forcing. Over the whole genesis area, 8.2 ± 3.5 cyclones are produced seasonally in the model, compared with 6.6 ± 3.0 in the JTWC data. Part of the interannual variability is associated with El Niño–Southern Oscillation (ENSO). ENSO-driven displacement of the SPCZ position produces a dipole pattern of correlation and results in a weaker correlation when the opposing correlations of the dipole are amalgamated over the entire South Pacific region. As a result, environmentally forced variability at the regional scale is relatively weak, that is, of comparable order to stochastic variability (±1.7 cyclones yr−1), which is estimated from a 10-yr climatological simulation. Stochastic variability appears essentially related to mesoscale interactions, which also affect TC tracks and the resulting occurrence.

scholarly journals Validation of the Accuracy of Different Precipitation Datasets over Tianshan Mountainous Area

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Chuancheng Zhao ◽  
Shuxia Yao ◽  
Shiqiang Zhang ◽  
Haidong Han ◽  
Qiudong Zhao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Large Scale ◽  
Regional Scale ◽  
Precipitation Amount ◽  
Rain Gauge ◽  
Small Scale ◽  
Accurate Information ◽  
Mountainous Area ◽  
Basin Scale

Precipitation is one of the important water supplies in the arid and semiarid regions of northwestern China, playing a vital role in maintaining the fragile ecosystem. In remote mountainous area, it is difficult to obtain an accurate and reliable spatialization of the precipitation amount at the regional scale due to the inaccessibility, the sparsity of observation stations, and the complexity of relationships between precipitation and topography. Furthermore, accurate precipitation is important driven data for hydrological models to assess the water balance and water resource for hydrologists. Therefore, the use of satellite remote sensing becomes an important means over mountainous area. Precipitation datasets based on station data or pure satellite data have been increasingly available in spite of several weaknesses. This paper evaluates the usefulness of three precipitation datasets including TRMM 3B43_V6, 3B43_V7, and Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation with rain gauge data over Tianshan mountainous area where precipitation data is scarce. The results suggest that precipitation measurements only provided accurate information on a small scale, while the satellite remote sensing of precipitation had obvious advantages in basin scale or large scale especially over remote mountainous area.

scholarly journals The use of remote sensing to quantify wetland loss in the Choke Mountain range, Upper Blue Nile basin, Ethiopia

2010 ◽  
Vol 7 (4) ◽  
pp. 6243-6284 ◽  
Author(s):  
E. Teferi ◽  
S. Uhlenbrook ◽  
W. Bewket ◽  
J. Wenninger ◽  
B. Simane
Keyword(s):  
Remote Sensing ◽  
Regional Scale ◽  
Atmospheric Correction ◽  
Wetland Loss ◽  
Climatic Conditions ◽  
Mountain Range ◽  
Nile Basin ◽  
Blue Nile ◽  
Blue Nile Basin ◽  
Upper Blue Nile Basin

Abstract. Wetlands provide multiple ecosystem services such as storing and regulating water flows and water quality, providing unique habitats to flora and fauna, and regulating micro-climatic conditions. Conversion of wetlands for agricultural use is a widespread practice in Ethiopia, particularly in the southwestern part where wetlands cover large areas. Although there are many studies on land cover and land use changes in this region, comprehensive studies on wetlands are still missing. Hence, extent and rate of wetland loss at regional scale is unknown. The objective of this paper is to quantify wetland dynamics and estimate wetland loss in the Choke Mountain range (area covering 17 443 km2) in the Upper Blue Nile basin, a key headwater region of the river Nile. Therefore, satellite remote sensing images of the period 1986–2005 were considered. To create images of surface reflectance that are radiometrically consistent, a combination of cross-calibration and atmospheric correction (Vogelman-DOS3) methods was used. A hybrid supervised/unsupervised classification approach was used to classify the images. Overall accuracies of 94.1% and 93.5% and Kappa Coefficients of 0.908 and 0.913 for the 1986 and 2005 imageries, respectively were obtained. The results showed that 607 km2 of seasonal wetland with low moisture and 22.4 km2 of open water are lost in the study area during the period 1986 to 2005. The current situation in the wetlands of Choke Mountain is characterized by further degradation which calls for wetland conservation and rehabilitation efforts through incorporating wetlands into watershed management plans.

Italian network of four permanent observatories: implementation of background data selection (BaDSfit) and 5-year analysis of the atmospheric CO2 mixing ratio.

2020 ◽  
Author(s):  
Pamela Trisolino ◽  
Alcide di Sarra ◽  
Damiano Sferlazzo ◽  
Salvatore Piacentino ◽  
Francesco Monteleone ◽  
...  
Keyword(s):  
Radiative Forcing ◽  
Mediterranean Basin ◽  
Large Scale ◽  
Hot Spot ◽  
Regional Scale ◽  
Data Selection ◽  
Background Data ◽  
The Mediterranean ◽  
The Mediterranean Basin

&lt;p&gt;The Mediterranean basin is considered a global hot-spot region for climate change and air-quality. CO&lt;sub&gt;2&lt;/sub&gt; is the single most-important anthropogenic greenhouse gas (GHG) in the atmosphere, accounting approximatively for &amp;#8764;63% of the anthropogenic radiative forcing by long-lived GHG.&amp;#160;According to Le Qu&amp;#233;r&amp;#233;e et al. (2018), the increasing of the atmospheric CO&lt;sub&gt;2&lt;/sub&gt; mixing ratios in the global atmosphere is driven by fossil fuel and cement production.&lt;br&gt;In order to reduce GHG emissions and taking into account the needs for economy and society development, schemes of regulation and emission trading have been adopted at international, national, and city levels. The implementation of these regulation, to achieve the goal successfully, needs scientific evidence and information provided on consistent datasets. In the last year, efforts are dedicated to set up harmonized reference networks at difference scales (WMO/GAW, AGAGE, ICOS).&lt;br&gt;In this work, we analysed a set of continuous long-term measurements of CO&lt;sub&gt;2&lt;/sub&gt; carried out at 4 atmospheric observatories in Italy belonging to the WMO/GAW network and spanning from the Alpine region to central Mediterranean Sea: Plateau Rosa (western Italian Alps, 3480 m a.s.l.), Mt. Cimone (northern Apennines, 2165 m a.s.l.), Capo Granitola (southern Sicily coastline) and Lampedusa Island. Mt. Cimone is also a &amp;#8220;class-2&amp;#8221; ICOS station, while Plateau Rosa and Lampedusa are in the labelling process. Starting time of GHG observations range from 1979 for Mt. Cimone to 2015 for Capo Granitola. Due to their different locations and ecosystems, they provide useful hints to investigate CO&lt;sub&gt;2&lt;/sub&gt; variability on different latitudinal and altitudinal ranges in the Mediterranean basin and to study of natural and anthropogenic-related processes able to affect the observed variability.&lt;br&gt;The study addresses primarily differences in daily and seasonal cycles at the different sites, and implemented a procedure to identify background conditions called BaDSfit (Background Data Selection for Italian stations; Trisolino et al., submitted). This methodology was originally used at Plateau Rosa station (Apadula, 2019) and it is based on the Mauna Loa data selection method (Tans and Thoning, 2008). BaDSfit consist of three steps and an optimization of the procedure was carried out with a sensitivity study.&amp;#160; Marked differences among the daily cycles at the various sites exist. The effect of the data selection on the seasonal and diurnal cycle and long-term evolution is investigated. The BaDSfit lead to a more coherent diurnal and seasonal evolution of the different datasets, is able to identify background condition and allows the separation of local/regional scale from large scale phenomena in the CO&lt;sub&gt;2&lt;/sub&gt; time series.&lt;/p&gt;

scholarly journals Aphotic N<sub>2</sub> fixation along an oligotrophic to ultraoligotrophic transect in the western tropical South Pacific Ocean

2018 ◽  
Vol 15 (10) ◽  
pp. 3107-3119 ◽  
Author(s):  
Mar Benavides ◽  
Katyanne M. Shoemaker ◽  
Pia H. Moisander ◽  
Jutta Niggemann ◽  
Thorsten Dittmar ◽  
...  
Keyword(s):  
N2 Fixation ◽  
Water Mass ◽  
New Caledonia ◽  
Hot Spot ◽  
Nifh Gene ◽  
South Pacific ◽  
French Polynesia ◽  
Mode Water ◽  
Fixation Activity ◽  
Nitrogen Inputs

Abstract. The western tropical South Pacific (WTSP) Ocean has been recognized as a global hot spot of dinitrogen (N2) fixation. Here, as in other marine environments across the oceans, N2 fixation studies have focused on the sunlit layer. However, studies have confirmed the importance of aphotic N2 fixation activity, although until now only one had been performed in the WTSP. In order to increase our knowledge of aphotic N2 fixation in the WTSP, we measured N2 fixation rates and identified diazotrophic phylotypes in the mesopelagic layer along a transect spanning from New Caledonia to French Polynesia. Because non-cyanobacterial diazotrophs presumably need external dissolved organic matter (DOM) sources for their nutrition, we also identified DOM compounds using Fourier transform ion cyclotron resonance mass spectrometry (FTICRMS) with the aim of searching for relationships between the composition of DOM and non-cyanobacterial N2 fixation in the aphotic ocean. N2 fixation rates were low (average 0.63 ± 0.07 nmol N L−1 d−1) but consistently detected across all depths and stations, representing ∼ 6–88 % of photic N2 fixation. N2 fixation rates were not significantly correlated with DOM compounds. The analysis of nifH gene amplicons revealed a wide diversity of non-cyanobacterial diazotrophs, mostly matching clusters 1 and 3. Interestingly, a distinct phylotype from the major nifH subcluster 1G dominated at 650 dbar, coinciding with the oxygenated Subantarctic Mode Water (SAMW). This consistent pattern suggests that the distribution of aphotic diazotroph communities is to some extent controlled by water mass structure. While the data available are still too scarce to elucidate the distribution and controls of mesopelagic non-cyanobacterial diazotrophs in the WTSP, their prevalence in the mesopelagic layer and the consistent detection of active N2 fixation activity at all depths sampled during our study suggest that aphotic N2 fixation may contribute significantly to fixed nitrogen inputs in this area and/or areas downstream of water mass circulation.

Characterising essential breeding habitat for whales informs the development of large-scale Marine Protected Areas in the South Pacific

2016 ◽  
Vol 548 ◽  
pp. 263-275 ◽  
Author(s):  
RE Lindsay ◽  
R Constantine ◽  
J Robbins ◽  
DK Mattila ◽  
A Tagarino ◽  
...  
Keyword(s):  
Protected Areas ◽  
Marine Protected Areas ◽  
Large Scale ◽  
South Pacific ◽  
Breeding Habitat ◽  
The South

MILL: Channel Attention–based Deep Multiple Instance Learning for Landslide Recognition

2021 ◽  
Vol 17 (2s) ◽  
pp. 1-11
Author(s):  
Xiaochuan Tang ◽  
Mingzhe Liu ◽  
Hao Zhong ◽  
Yuanzhen Ju ◽  
Weile Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Large Scale ◽  
Remote Sensing Image ◽  
Disaster Risk ◽  
Multiple Instance Learning ◽  
Remote Sensing Images ◽  
Loess Area ◽  
Remote Sensing Image Classification ◽  
Natural Disaster Risk

Landslide recognition is widely used in natural disaster risk management. Traditional landslide recognition is mainly conducted by geologists, which is accurate but inefficient. This article introduces multiple instance learning (MIL) to perform automatic landslide recognition. An end-to-end deep convolutional neural network is proposed, referred to as Multiple Instance Learning–based Landslide classification (MILL). First, MILL uses a large-scale remote sensing image classification dataset to build pre-train networks for landslide feature extraction. Second, MILL extracts instances and assign instance labels without pixel-level annotations. Third, MILL uses a new channel attention–based MIL pooling function to map instance-level labels to bag-level label. We apply MIL to detect landslides in a loess area. Experimental results demonstrate that MILL is effective in identifying landslides in remote sensing images.

scholarly journals Remote Sensing Methods for the Biophysical Characterization of Protected Areas Globally: Challenges and Opportunities

2021 ◽  
Vol 10 (6) ◽  
pp. 384
Author(s):  
Javier Martínez-López ◽  
Bastian Bertzky ◽  
Simon Willcock ◽  
Marine Robuchon ◽  
María Almagro ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Protected Areas ◽  
Large Scale ◽  
Anthropogenic Activities ◽  
Global Scale ◽  
Area Network ◽  
Biophysical Characterization ◽  
Challenges And Opportunities ◽  
Scale Characterization

Protected areas (PAs) are a key strategy to reverse global biodiversity declines, but they are under increasing pressure from anthropogenic activities and concomitant effects. Thus, the heterogeneous landscapes within PAs, containing a number of different habitats and ecosystem types, are in various degrees of disturbance. Characterizing habitats and ecosystems within the global protected area network requires large-scale monitoring over long time scales. This study reviews methods for the biophysical characterization of terrestrial PAs at a global scale by means of remote sensing (RS) and provides further recommendations. To this end, we first discuss the importance of taking into account the structural and functional attributes, as well as integrating a broad spectrum of variables, to account for the different ecosystem and habitat types within PAs, considering examples at local and regional scales. We then discuss potential variables, challenges and limitations of existing global environmental stratifications, as well as the biophysical characterization of PAs, and finally offer some recommendations. Computational and interoperability issues are also discussed, as well as the potential of cloud-based platforms linked to earth observations to support large-scale characterization of PAs. Using RS to characterize PAs globally is a crucial approach to help ensure sustainable development, but it requires further work before such studies are able to inform large-scale conservation actions. This study proposes 14 recommendations in order to improve existing initiatives to biophysically characterize PAs at a global scale.

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