| ... | @@ -42,6 +42,12 @@ The morning session will be dedicated to short talks from users and contributors |
... | @@ -42,6 +42,12 @@ The morning session will be dedicated to short talks from users and contributors |
|
|
|
|
|
|
|
> Monitoring vegetation dynamics in nature reserves requires accurate mapping of habitats. In August 2017, we used a drone to acquire LAS images and high-resolution (2cm)orthophotos on islands of nature reserve of St-Mesmin along the Loire river. At the same time, we mapped habitats based on vegetation sampling every 15 m and the phytosociological nomenclature published by the Botanical Conservatory of the Paris Basin. The OTB workflow allowed us to map 9 vegetation types but with an overall kappa of 0.60 only. If the results were satisfactory for the differents ripparian forest types (min F-score = 0.70) , the various tools tested under OTB failed to correctly classify open areas as reed-bed (F-score = 0.31) and grassland (F-score = 0.18). Two hypotheses can explain this problem: the low accuracy of the field GPS data and some subjectivity in the interpretation of the phytosociological nomenclature.
|
|
> Monitoring vegetation dynamics in nature reserves requires accurate mapping of habitats. In August 2017, we used a drone to acquire LAS images and high-resolution (2cm)orthophotos on islands of nature reserve of St-Mesmin along the Loire river. At the same time, we mapped habitats based on vegetation sampling every 15 m and the phytosociological nomenclature published by the Botanical Conservatory of the Paris Basin. The OTB workflow allowed us to map 9 vegetation types but with an overall kappa of 0.60 only. If the results were satisfactory for the differents ripparian forest types (min F-score = 0.70) , the various tools tested under OTB failed to correctly classify open areas as reed-bed (F-score = 0.31) and grassland (F-score = 0.18). Two hypotheses can explain this problem: the low accuracy of the field GPS data and some subjectivity in the interpretation of the phytosociological nomenclature.
|
|
|
|
|
|
|
|
|
* **[CONFIRMED]** Deforestation map in French Guyana, using Sentinel-1 and OTB through QGIS scripts.
|
|
|
|
|
|
|
|
> Included in the Guiana Shield ecosystem which is one of the largest blocks of intact tropical forest worldwide, French Guiana play a critical role in mitigating climate change, preserving biodiversity and regulating water of the Amazon basin. Under low pressure in the past, degradation of this fragile ecosystem is growing, especially driven by gold mining activities or illegal agricultural activities.
|
|
|
|
In this context and for a project provided by the french forest national office (ONF), in order to deliver a near real tile deforestation monitoring process, we develop a set of tools allowing to monitor deforestation using Sentinel-1 data. The main process of the approach are (i) Calibration and Orthorectification over user study area (shape file), (ii) Temporal Adaptive speckle filtering, (iii) Adjust time series level and smooth it, (iv) Deforestation analysis. Deforestation analysis is based on applying a threshold of the differences between a reference date (usually the oldest date) to the current date. If this change is significant we expect a deforestation process and give the current date as deforested label in the deforestation map.
|
|
|
|
This open source tool box is mainly based on Orfeo ToolBox for Sentinel-1 pre-processing and is based on specific python script. All the toolbox is integrated in QGIS and available in a QGIS RemoteSensing toolbox for windows or using the following for Ubuntu users https://gitlab.com/clardeux/FOSS4G-fr-2018-Atelier-Radar.
|
|
|
|
|
|
|
## 10:30 - 10:50 : Coffee break
|
|
## 10:30 - 10:50 : Coffee break
|
|
|
|
|
|
|
|
## 10:50 - 12:30 : Talks Part 2
|
|
## 10:50 - 12:30 : Talks Part 2
|
| ... | | ... | |
