MOD2SEA: A Coupled Atmosphere-Hydro-Optical Model for the Retrieval of Chlorophyll-a from Remote Sensing Observations in Complex Turbid Waters

Arabi, Behnaz; Salama, Mhd. Suhyb; Wernand, Marcel Robert; Verhoef, W.

doi:10.3390/rs8090722

Cited by 23 publications

(71 citation statements)

References 78 publications

(84 reference statements)

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“…Les corrections atmosphériques ont été réalisées à partir du module FLAASH (Fast Line-of-sight Atmospheric Analysis Hypercubes) basé sur le modèle atmosphérique MODTRAN (MODerate resolution atmospheric TRANsmission) (Arabi et al, 2016 ;Rosas et al, 2017). En premier lieu, un étalonnage a été réalisé pour définir le type de satellite, la date d'acquisition de l'image et l'angle d'élévation solaire.…”

Section: Déforestation De Yangambi / Le Point Sur…unclassified

Dynamique de la déforestation dans la Réserve de biosphère de Yangambi (République démocratique du Congo) : variabilité spatiale et temporelle au cours des 30 dernières années

et al. 2019

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D’une superficie de 225 000 ha, la Réserve de biosphère de Yangambi (République démocratique du Congo) est l’une des principales aires protégées menacées par l’anthropisation dans la région. Malgré son appartenance au réseau mondial des réserves de biosphère depuis les années 1970, les connaissances sur les dynamiques d’occupation du sol y sont encore lacunaires. Une démarche fondée sur l’analyse d’images (Landsat TM, ETM+ et OLI) et la réalité du terrain a été mise en œuvre pour étudier la dynamique d’occupation des sols de la réserve entre 1986 et 2016. Le taux annuel de déforestation est passé de 0,18 % au cours de la période 1986-2003 à 0,38 % en 2003-2016. Durant cette dernière période, la forêt primaire a connu un taux de déforestation plus élevé qu’en forêt secondaire (respectivement 4,5 % et 0,4 %). Quatre zones aux dynamiques socio-environnementales spécifiques ont été identifiées, nécessitant des approches de gestion différentes. Il s’agit notamment de la concession de l’INERA, des zones proches d’axes principaux de circulation, des zones situées sur des axes routiers plus ou moins abandonnés et des zones isolées du cœur de la réserve. Les principales causes de déforestation mentionnées par les habitants sont l’agriculture itinérante sur brûlis (54 % des ménages), l’exploitation artisanale des minerais (17 %) et la fabrication des pirogues (8 %). La pression démographique, sans amélioration du niveau de vie des communautés ou des techniques agricoles, et le développement du réseau routier, favorisant les échanges commerciaux, en sont les causes sous-jacentes. Cette dynamique de déforestation dans une forêt rattachée au réseau mondial des réserves de biosphère entame l’objectif fixé par l’État d’étendre le réseau d’aires protégées à 15 % du territoire national.

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Section: Déforestation De Yangambi / Le Point Sur…unclassified

Dynamique de la déforestation dans la Réserve de biosphère de Yangambi (République démocratique du Congo) : variabilité spatiale et temporelle au cours des 30 dernières années

et al. 2019

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“…Different from the conventional methods, remote sensing technology can acquire large-area water spectral information quickly, continuously and inexpensively and obtain by different methods the multiple components information of water from remote sensing images (Bukata & Bukata, 2005; Arabi et al, 2016; Campanelli et al, 2017; Deng et al, 2017). However, due to the influence of spatial and temporal variation and long transmission distance, about 90–98% of the signals obtained by the remote sensing are from the surface of the water and the atmosphere, and the remaining 2–10% of the signals are from the water components.…”

Section: Introductionmentioning

confidence: 99%

Quantitative estimation of wastewater quality parameters by hyperspectral band screening using GC, VIP and SPA

Zheng

Chen

Zhao

et al. 2019

PeerJ

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Water pollution has been hindering the world’s sustainable development. The accurate inversion of water quality parameters in sewage with visible-near infrared spectroscopy can improve the effectiveness and rational utilization and management of water resources. However, the accuracy of spectral models of water quality parameters is usually prone to noise information and high dimensionality of spectral data. This study aimed to enhance the model accuracy through optimizing the spectral models based on the sensitive spectral intervals of different water quality parameters. To this end, six kinds of sewage water taken from a biological sewage treatment plant went through laboratory physical and chemical tests. In total, 87 samples of sewage water were obtained by adding different amount of pure water to them. The raw reflectance (Rraw) of the samples were collected with analytical spectral devices. The Rraw-SNV were obtained from the Rraw processed with the standard normal variable. Then, the sensitive spectral intervals of each of the six water quality parameters, namely, chemical oxygen demand (COD), biological oxygen demand (BOD), NH3-N, the total dissolved substances (TDS), total hardness (TH) and total alkalinity (TA), were selected using three different methods: gray correlation (GC), variable importance in projection (VIP) and set pair analysis (SPA). Finally, the performance of both extreme learning machine (ELM) and partial least squares regression (PLSR) was investigated based on the sensitive spectral intervals. The results demonstrated that the model accuracy based on the sensitive spectral ranges screened through different methods appeared different. The GC method had better performance in reducing the redundancy and the VIP method was better in information preservation. The SPA method could make the optimal trade-offs between information preservation and redundancy reduction and it could retain maximal spectral band intervals with good response to the inversion parameters. The accuracy of the models based on varied sensitive spectral ranges selected by the three analysis methods was different: the GC was the highest, the SPA came next and the VIP was the lowest. On the whole, PLSR and ELM both achieved satisfying model accuracy, but the prediction accuracy of the latter was higher than the former. Great differences existed among the optimal inversion accuracy of different water quality parameters: COD, BOD and TN were very high; TA relatively high; and TDS and TH relatively low. These findings can provide a new way to optimize the spectral model of wastewater biochemical parameters and thus improve its prediction precision.

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“…The Landsat images were subject to radiometric and atmospheric correction in order to compensate for sensor noise, as well as atmospheric water vapor and aerosol contamination, using ENVI 5.1 software and Fast Line-ofsight Atmospheric Analysis of Spectral Hypercubes (FLAASH) [24][25]26].…”

Section: Pre-processingmentioning

confidence: 99%

Spatio-temporal Analysis Dynamics of the Landscape in the Classified Forest of Koulbi, Southwestern of Burkina Faso

Sanon¹,

Hien²,

Tankoano³

et al. 2019

IJNREM

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Most of the protected areas in Burkina Faso are affected by deforestation and forest degradation. This study is done in order to analysis the spatial and temporal dynamics of the classified forest of Koulbi and to identify the drivers of deforestation in the forest. For that, three Landsat images from 1986, 1998 and 2014 were exploited. The supervised classification based on the artificial neural network algorithm, under the ENVI software, was used. A transition matrix was established to analyze land use/land cover changes. The standardized precipitation index of 1985 to 2014 was used to access climate trend. Survey data from riverside villages and field observations were used to assess the human impact. The results show that between 1986 and 2014, the woodlands and tree savannas areas were reduced by 7% and 0.49% per year, respectively. However, substantial yearly increases in the areas of shrub savanna (2.8%) and agricultural fields (1.4%) were observed. During this period, the rainfall had a sharp trend towards aridity, with 18 years of drought against 12 years of humidity. This show that deficit of rainfall is increase. The socioeconomic survey revealed that the classified forest used to be highly occupied and 64% of the inhabitants were yam producers. Cattle breeders and gold diggers have also been observed. We conclude that human activities are the main cause of degradation of the vegetation cover of the Koulbi classified forest. And climatic factors to some extent. It will be necessary to take actions to better conservation.

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MOD2SEA: A Coupled Atmosphere-Hydro-Optical Model for the Retrieval of Chlorophyll-a from Remote Sensing Observations in Complex Turbid Waters

Cited by 23 publications

References 78 publications

Dynamique de la déforestation dans la Réserve de biosphère de Yangambi (République démocratique du Congo) : variabilité spatiale et temporelle au cours des 30 dernières années

Dynamique de la déforestation dans la Réserve de biosphère de Yangambi (République démocratique du Congo) : variabilité spatiale et temporelle au cours des 30 dernières années

Quantitative estimation of wastewater quality parameters by hyperspectral band screening using GC, VIP and SPA

Spatio-temporal Analysis Dynamics of the Landscape in the Classified Forest of Koulbi, Southwestern of Burkina Faso

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