Mapping potential, existing and efficient wetlands using free remote sensing data

Rapinel, Sébastien; Fabre, Elodie; Dufour, Simon; Arvor, Damien; Mony, Cendrine; Hubert‐Moy, Laurence

doi:10.1016/j.jenvman.2019.06.098

Cited by 52 publications

(31 citation statements)

References 45 publications

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“…Remote sensing is a potentially very useful source of information for spatial monitoring of natural or cultivated vegetation. Many approaches have been developed recently for monitoring and mapping of vegetation (Roberts et al, 2015;Niculescu et al, 2016 ;Niculescu et al, 2017 ;Shivers et al, 2018;Muller-Karger et al, 2018;Rapinel et al, 2019). We noted the new opportunities for the monitoring of the vegetation with the arrival the European multi-sensor images in time series and namely with the increasing number of satellites and the availability of free data.…”

Section: Introductionmentioning

confidence: 86%

Rotation Forests and Random Forest Classifiers for Monitoring of Vegetation in Pays De Brest (France)

Niculescu

Xia

Roberts³

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Remote sensing is a potentially very useful source of information for spatial monitoring of natural or cultivated vegetation. The latest advances, in particular the arrival of new image acquisition programs, are changing the temporal approach to monitoring vegetation. The latest European satellites launched, delivering an image every 5 days for each point on the globe, allow the end of a growing season to be monitored. The main objective of this work is to identify and map the vegetation in the Pays de Brest area by using a multi sensors stacking of Sentinel-1 and Sentinel-2 satellites data via Random Forest, Rotation forests (RoF) and Canonical Correlation Forests (CCFs). RoF and CCF create diverse base learners using data transformation and subset features. Twenty four radar images and optical dataa representing different dates in 2017 were processed in time series stacks. The results of RoF and CCF were compared with the ones of RF.

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Section: Introductionmentioning

confidence: 86%

Rotation Forests and Random Forest Classifiers for Monitoring of Vegetation in Pays De Brest (France)

Niculescu

Xia

Roberts³

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…The model supported interrelationships between soil, vegetation, and remotely sensed information and established reasonable predictions for soil organic carbon and bulk density in a coastal wetland. On the other hand, Potential, Existing, Efficient Wetland (PEEW) approach as an operational method was developed and utilized to delineate three wetland components using remote‐sensing data (Rapinel et al, 2019). The potential wetlands derived from light detection and ranging (LiDAR) data, existing wetlands from Sentinel‐1/2 times series, and existing wetlands from Moderate Resolution Imaging Spectroradiometer (MODIS) annual time series provided a new perspective for wetland management by prioritizing the sites for restoration measures.…”

Section: Wetland Modelingmentioning

confidence: 99%

Wetlands for environmental protection

Martinez‐Guerra

Ghimire

Nandimandalam

et al. 2020

Water Environment Research

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This article presents an update on the research and practical demonstration of wetland-based treatment technologies for protecting water resources and environment covering papers published in 2019. Wetland applications in wastewater treatment, stormwater management, and removal of nutrients, metals, and emerging pollutants including pathogens are highlighted. A summary of studies focusing on the effects of vegetation, wetland design and operation strategies, and process configurations and modeling, for efficient treatment of various municipal and industrial wastewaters, is included. In addition, hybrid and innovative processes with wetlands as a platform treatment technology are presented.

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“…The results showed that the condition of the wetland has deteriorated over the past three decades. Rapinel, et al [38] mapped and monitored wetlands condition on the Couesnon watershed, located in northeastern Brittany, France, using LiDAR, Sentinel-1/2 and Moderate Resolution Imaging Spectroradiometer (MODIS) time-series data. The results showed that 44 % of the existing wetlands have been destroyed.…”

Section: Introductionmentioning

confidence: 99%

A Remotely Sensed Assessment of Surface Ecological Change over the Gomishan Wetland, Iran

et al. 2020

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Due to the excessive use of natural resources in the contemporary world, the importance of ecological and environmental condition modeling has increased. Wetlands and cities represent the natural and artificial strategic areas that affect ecosystem conditions. Changes in the ecological conditions of these areas have a great impact on the conditions of the global ecosystem. Therefore, modeling spatiotemporal variations of the ecological conditions in these areas is critical. This study was aimed at comparing degrees of variation among surface ecological conditions due to natural and unnatural factors. Consequently, the surface ecological conditions of Gomishan city and Gomishan wetland in Iran were modeled for a period of 30 years, and the spatiotemporal variations were evaluated and compared with each other. To this end, 20 Landsat 5, 7, and 8, and 432 Moderate Resolution Imaging Spectroradiometer (MODIS), monthly land surface temperature (LST) (MOD11C3) and normalized difference vegetation index (NDVI) (MOD13C3) products were utilized. The surface ecological conditions were modeled according to the Remote Sensing-based Ecological Index (RSEI), and the spatiotemporal variation of the RSEI values in the study area (Gomishan city, Gomishan wetland) were evaluated and compared with each other. According to MODIS products, the mean of the LST and NDVI variance values for the study area (Gomishan city, Gomishan wetland) were obtained to be 6.5 °C (2.1, 12.1) and 0.009 (0.005, 0.013), respectively. The highest LST and NDVI temporal variations were found for Gomishan wetland near the Caspian Sea. According to Landsat images, Gomishan wetland and Gomishan city have the highest and lowest temporal variations in surface biophysical characteristics, respectively. The mean RSEI for the study area (Gomishan city, Gomishan wetland) was 0.43 (0.65, 0.29), respectively. Additionally, the mean Coefficient of Variation (CV) of RSEI for the study area (Gomishan city, Gomishan wetland) was 0.10 (0.88, 0.51), respectively. The surface ecological conditions of Gomishan city were worse than those of the Gomishan wetland at all dates. Temporal variations in the surface ecological conditions of Gomishan wetland were greater than those of the study area and Gomishan city. These results can provide useful and effective information for environmental planning and decision-making to improve ecological conditions, protect the environment, and support sustainable ecosystem development.

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Mapping potential, existing and efficient wetlands using free remote sensing data

Cited by 52 publications

References 45 publications

Rotation Forests and Random Forest Classifiers for Monitoring of Vegetation in Pays De Brest (France)

Rotation Forests and Random Forest Classifiers for Monitoring of Vegetation in Pays De Brest (France)

Wetlands for environmental protection

A Remotely Sensed Assessment of Surface Ecological Change over the Gomishan Wetland, Iran

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