scholarly journals CARTOGRAPHY OF MOROCCAN ARGAN TREE USING COMBINED OPTICAL AND SAR IMAGERY INTEGRATED WITH DIGITAL ELEVATION MODEL

2021 ◽  
Vol XLVI-4/W5-2021 ◽  
pp. 211-217
Author(s):  
E. Elmoussaoui ◽  
A. Moumni ◽  
A. Lahrouni
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Digital Elevation Model ◽  
Optical Data ◽  
Support Vector ◽  
Ground Truth Data ◽  
Argan Tree ◽  
Digital Elevation ◽  
Elevation Model ◽  
Sentinel 2

Abstract. Forest tree species mapping became easier due to the global availability of high spatio-temporal resolution images acquired from multiple sensors. Such data can lead to better forest resources management. Machine-learning pixel based analysis was performed to multi-spectral Sentinel-2 and Synthetic Aperture Radar Sentinel-1 time series integrated with Digital Elevation Model acquired over Argan forest of Essaouira province, Morocco. The argan tree constitutes a fundamental resource for the populations of this arid area of Morocco. This research aims to use the potential of the combination of multi-sensor data to detect, map and identify argan tree from other forest species using three Machine Learning algorithms: Support Vector Machine (SVM), Maximum Likelihood (ML) and Artificial Neural Networks (ANN). The exploited datasets included Sentinel-1 (S1), Sentinel-2 (S2) time series, Shuttle Radar Topographic Missing Digital Elevation Model (DEM) layer and Ground truth data. We tested several sets of scenarios, including single S1 derived features, single S2 time series and combined S1 and S2 derived layers with DEM scene acquisition. The best results (overall accuracy OA and Kappa coefficient K) obtained from time series of optical data (NDVI): OA = 86.87%, K = 0.84, from time series of SAR data (VV+VH/VV): OA = 45.90%, K = 0.36, from the combination of optical and SAR time series (NDVI+VH+DEM): OA = 93.01%, K = 0.914, and from the fusion of optical time series and DEM layer (NDVI+DEM): OA = 93.25%, K = 0.91. These results indicate that single-sensor (S2) integrated with the DEM layer led us to obtain the highest classification results.

Detection of irrigated and rainfed crop fields in temperate areas using Sentinel-1, Sentinel-2 and rainfall time series

2021 ◽  
Author(s):  
Yann Pageot ◽  
Frédéric Baup ◽  
Jordi Inglada ◽  
Valérie Demarez
Keyword(s):  
Time Series ◽  
Adaptation Strategies ◽  
Optical Data ◽  
Spectral Information ◽  
Agricultural Activity ◽  
Reference Scenario ◽  
Data Set ◽  
Radar Rainfall ◽  
Ground Truth Data ◽  
Sentinel 2

<p><span>Human activities have an impact on the different components of the hydrosphere and 80 % of the world's population is now facing water shortages that will worsen with global warming. Faced with this emergency situation, it is necessary to develop adaptation strategies to monitor and manage water resources for the entire population and to maintain agricultural activity. One of the adaptation strategies that has been favoured is the management of crop irrigation to optimize the use of scarce water ressources. </span></p><p><span>To meet this objective, it is necessary to have explicit information on irrigated areas. However, up to now, this information is missing or imprecise at the field scale (it is only produced as aggregated statistics or maps at the regional or national scales). In this work, we propose a method for detecting irrigated and rainfed plots in a temperate areas (Adour-Amont watershed of 1500 km² located in south-western France) jointly using optical (Sentinel-2), radar (Sentinel-1) and rainfall (SAFRAN) time series, through the random forest classification algorithm. This spectral information was synthesized in the form of cumulative monthly indices corresponding to the sum of the spectral information for each element (optical, radar, rainfall). This cumulative approach makes it possible to reduce the redundancy of the spectral information and the calculation time of the classification process.</span></p><p><span>The summer crops studied were maize, soybean and sunflower, representing respectively 82%, 9% and 8% of the crops cultivated of the studied area, but only part of these crops were irrigated. In order to make the distinction for the same crop, we assume that the speed and amplitude of canopy development differs between irrigated and rainfed crop. Five scenarios were used to evaluate the performance of classification models. They have been built according to the different spatialized data, i.e (Optic; Radar; Optic & Radar; Optic, Radar & Rainfall and 10-day images, which is reference scenario without the cumulative monthly indices). Finally, generated classification maps were evaluated using ground truth data collected during 2 years with contrasted meteorological conditions. </span></p><p> <span>The use of separate radar and optical data gives low results (Overall Accuracy (OA) < 0.5) compared to the combined classifications of the cumulated data set (optical & radar), which gives good results (OA ± 0.7). The use of the monthly cumulated rainfall allows a significant improvement of the Fscore of the irrigated and rainfed crop classes. Our study also reveals that the use of cumulative monthly indices leads to performances similar to those of the use of 10-day images while considerably reducing computational resources.</span></p>

scholarly journals VEGETATION COVER MAPPING BASED ON REMOTE SENSING AND DIGITAL ELEVATION MODEL DATA

2016 ◽  
Vol XLI-B8 ◽  
pp. 699-704 ◽  
Author(s):  
M. A. Korets ◽  
V. A. Ryzhkova ◽  
I. V. Danilova ◽  
A. S. Prokushkin
Keyword(s):  
Digital Elevation Model ◽  
Boreal Forests ◽  
Forest Cover ◽  
Ground Truth ◽  
Test Site ◽  
Ground Truth Data ◽  
Digital Elevation ◽  
Forest Cover Mapping ◽  
Elevation Model ◽  
Digital Elevation Model Data

An algorithm of forest cover mapping based on combined GIS-based analysis of multi-band satellite imagery, digital elevation model, and ground truth data was developed. Using the classification principles and an approach of Russian forest scientist Kolesnikov, maps of forest types and forest growing conditions (FGC) were build. The first map is based on RS-composite classification, while the second map is constructed on the basis of DEM-composite classification. The spatial combination of this two layers were also used for extrapolation and mapping of ecosystem carbon stock values (kgC/m<sup>2</sup>). The proposed approach was applied for the test site area (~3600 km<sup>2</sup>), located in the Northern Siberia boreal forests of Evenkia near Tura settlement.

Sentinel-2 Multitemporal Bare Soil Composites for predicting soil properties using machine learning methods

2020 ◽  
Author(s):  
Daniel Zizala
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Surface Roughness ◽  
Soil Properties ◽  
Bare Soil ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Support Vector ◽  
Composite Formation ◽  
Sentinel 2

&lt;p&gt;Previous studies have shown that remote sensing data can be very useful input into soil prediction models. This input usually represents reflectance from bare soils, which, however, make up only a small part of the total area in a given part of the year. For eliminating masking effect of vegetation time series of individual images (&amp;#381;&amp;#237;&amp;#382;ala et al. 2019; Shabou et al. 2015; Dematt&amp;#234; et al. 2016; Blasch et al. 2015a) or multitemporal composites of spectral data can be used. Exposed Soil Composite Mapping Processor (SCMaP) (Rogge et al. 2018), Geospatial Soil Sensing System (GEOS3) (Dematt&amp;#234; et al. 2018), Bare Soil Composite Image (Gallo et al. 2018), and Barest Pixel Composite for Agricultural Areas (Diek et al. 2017), all developed from Landsat time series, multitemporal bare soil image developed from RapidEye time series (Blasch et al. 2015b), or bare soil mosaic (Loiseau et al. 2019) derived from Sentinel-2 data can serve as examples of such composites. However, only some of the composite products have been used yet to predict soil properties. Promising results were achieved; however, the potential of these spectral composites has not yet been tested in a relevant number of studies. Further research is needed for its evaluation.&lt;/p&gt;&lt;p&gt;Aims of this study are to analyze and to compare the prediction ability of models using different types of multitemporal bare soil composites derived from Sentinel-2 images and their applicability for mapping soil properties in large areas. The study was conducted on a regional scale in the soil heterogeneous region of central Czechia with dissected relief and variable soil properties, where data from 100 soil profiles with soil analytics were available. Sentinel-2 images from 2016-2019 were used for composite formation in the python numpy environment. Different methods of cloud masking, bare soil identification and data aggregation (both already used in previous studies and newly derived) have been tested to compare which is the most suitable for prediction of soil properties. The principles of digital soil mapping and machine learning algorithms (random forest and support vector machine multivariate methods) were used for prediction.&lt;/p&gt;&lt;p&gt;Results reveal that Sentinel-2 multitemporal bare soil composites can be successfully applied in the prediction of soil properties. The setting of basic parameters of composite creation is very complex and challenging and it requires to use exact algorithms for masking clouds and bare soil. Soil moisture and surface roughness also greatly affect spectral characteristics of bare soil and thus a very important aspect of compositing is finding appropriate statistics to derive final pixel values of reflectance (minimum, mean, median, ...). One possible way to minimize the effect of moisture and surface roughness may be incorporation radar backscatter information from Sentinel-1. However, it further complicates the processing of data and makes the composite creation more complex.&lt;/p&gt;&lt;p&gt;The research has been supported by the project no. QK1820389 &quot; Production of actual detailed maps of soil properties in the Czech Republic based on database of Large-scale Mapping of Agricultural Soils in Czechoslovakia and application of digital soil mapping&quot; funding by Ministry of Agriculture.&lt;/p&gt;

scholarly journals The New Landsat Collection-2 Digital Elevation Model

2020 ◽  
Vol 12 (23) ◽  
pp. 3909
Author(s):  
Shannon Franks ◽  
James Storey ◽  
Rajagopalan Rengarajan
Keyword(s):  
Digital Elevation Model ◽  
Optical Data ◽  
Line Of Sight ◽  
The Public ◽  
Mountainous Regions ◽  
Shuttle Radar Topographic Mission ◽  
National Elevation Dataset ◽  
Digital Elevation ◽  
Seamless Transition ◽  
Elevation Model

The Landsat Collection-2 distribution introduces a new global Digital Elevation Model (DEM) for scene orthorectification. The new global DEM is a composite of the latest and most accurate freely available DEM sources and will include reprocessed Shuttle Radar Topographic Mission (SRTM) data (called NASADEM), high-resolution stereo optical data (ArcticDEM), a new National Elevation Dataset (NED) and various publicly available national datasets including the Canadian Digital Elevation Model (CDEM) and DEMs for Sweden, Norway and Finland (SNF). The new DEM will be available world-wide with few exceptions. It is anticipated that the transition from the Collection-1 DEM at 3 arcsecond to the new DEM will be seamless because processing methods to maintain a seamless transition were employed, void filling techniques were used, where persistent gaps were found, and the pixel spacing is the same between the two collections. Improvements to the vertical accuracy were realized by differencing accuracies of other elevation datasets to the new DEM. The greatest improvement occurred where ArcticDEM data were used, where an improvement of 35 m was measured. By using theses improved vertical values in a line of sight algorithm, horizontal improvements were noted in some of the most mountainous regions over multiple 30-m Landsat pixels. This new DEM will be used to process all of the scenes from Landsat 1-8 in Collection-2 processing and will be made available to the public by the end of 2020.

scholarly journals Argan Tree (Argania spinosa (L.) Skeels) Mapping Based on Multisensor Fusion of Satellite Imagery in Essaouira Province, Morocco

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Aicha Moumni ◽  
Tarik Belghazi ◽  
Brahim Maksoudi ◽  
Abderrahman Lahrouni
Keyword(s):  
Time Series ◽  
Species Identification ◽  
Tree Species ◽  
Forest Cover ◽  
Kappa Coefficient ◽  
Optical Data ◽  
Actual State ◽  
Support Vector ◽  
Argania Spinosa ◽  
Argan Tree

Tree species identification and their geospatial distribution mapping are crucial for forest monitoring and management. The satellite-based remote sensing time series of Sentinel missions (Sentinel-1 and Sentinel-2) are a perfect tool to map the type, location, and extent of forest cover over large areas at local or global scale. This study is focused on the geospatial mapping of the endemic argan tree (Argania spinosa (L.) Skeels) and the identification of two other tree species (sandarac gum and olive trees) using optical and synthetic aperture radar (SAR) time series. The objective of the present work is to detect the actual state of forest species trees, more specifically the argan tree, in order to be able to study and analyze forest changes (degradation) and make new strategies to protect this endemic tree. The study was conducted over an area located in Essaouira province, Morocco. The support vector machine (SVM) algorithm was used for the classification of the two types of data. We first classified the optical data for tree species identification and mapping. Second, the SAR time series were used to identify the argan tree and distinguish it from other species. Finally, the two types of satellite images were combined to improve and compare the results of classification with those obtained from single-source data. The overall accuracy (OA) of optical classification reached 86.9% with a kappa coefficient of 0.84 and declined strongly to 37.22% (kappa of 0.29) for SAR classification. The fusion of multisensor data (optical and SAR images) reached an OA of 86.51%. A postclassification was performed to improve the results. The classified images were smoothed, and therefore, the quantitative and qualitative results showed an improvement, in particular for optical classification with a highest OA of 89.78% (kappa coefficient of 0.88). The study confirmed the potential of the multitemporal optical data for accurate forest cover mapping and endemic species identification.

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