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.

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.

scholarly journals Statistical Stability and Spatial Instability in Mapping Forest Tree Species by Comparing 9 Years of Satellite Image Time Series

2019 ◽  
Vol 11 (21) ◽  
pp. 2512 ◽  
Author(s):  
Nicolas Karasiak ◽  
Jean-François Dejoux ◽  
Mathieu Fauvel ◽  
Jérôme Willm ◽  
Claude Monteil ◽  
...  
Keyword(s):  
Time Series ◽  
Tree Species ◽  
Cross Validation ◽  
Satellite Image ◽  
Support Vector ◽  
Validation Data ◽  
Spatial Instability ◽  
Classification Errors ◽  
Leave One Out ◽  
The Impact

Mapping forest composition using multiseasonal optical time series remains a challenge. Highly contrasted results are reported from one study to another suggesting that drivers of classification errors are still under-explored. We evaluated the performances of single-year Formosat-2 time series to discriminate tree species in temperate forests in France and investigated how predictions vary statistically and spatially across multiple years. Our objective was to better estimate the impact of spatial autocorrelation in the validation data on measurement accuracy and to understand which drivers in the time series are responsible for classification errors. The experiments were based on 10 Formosat-2 image time series irregularly acquired during the seasonal vegetation cycle from 2006 to 2014. Due to lot of clouds in the year 2006, an alternative 2006 time series using only cloud-free images has been added. Thirteen tree species were classified in each single-year dataset based on the Support Vector Machine (SVM) algorithm. The performances were assessed using a spatial leave-one-out cross validation (SLOO-CV) strategy, thereby guaranteeing full independence of the validation samples, and compared with standard non-spatial leave-one-out cross-validation (LOO-CV). The results show relatively close statistical performances from one year to the next despite the differences between the annual time series. Good agreements between years were observed in monospecific tree plantations of broadleaf species versus high disparity in other forests composed of different species. A strong positive bias in the accuracy assessment (up to 0.4 of Overall Accuracy (OA)) was also found when spatial dependence in the validation data was not removed. Using the SLOO-CV approach, the average OA values per year ranged from 0.48 for 2006 to 0.60 for 2013, which satisfactorily represents the spatial instability of species prediction between years.

scholarly journals Predicting Forest Cover in Distinct Ecosystems: The Potential of Multi-Source Sentinel-1 and -2 Data Fusion

2020 ◽  
Vol 12 (2) ◽  
pp. 302 ◽  
Author(s):  
Kai Heckel ◽  
Marcel Urban ◽  
Patrick Schratz ◽  
Miguel Mahecha ◽  
Christiane Schmullius
Keyword(s):  
South Africa ◽  
Time Series ◽  
Forest Cover ◽  
Short Wave ◽  
Optical Data ◽  
Biophysical Parameters ◽  
Data Set ◽  
Area Index ◽  
The Individual ◽  
Sentinel 2

The fusion of microwave and optical data sets is expected to provide great potential for the derivation of forest cover around the globe. As Sentinel-1 and Sentinel-2 are now both operating in twin mode, they can provide an unprecedented data source to build dense spatial and temporal high-resolution time series across a variety of wavelengths. This study investigates (i) the ability of the individual sensors and (ii) their joint potential to delineate forest cover for study sites in two highly varied landscapes located in Germany (temperate dense mixed forests) and South Africa (open savanna woody vegetation and forest plantations). We used multi-temporal Sentinel-1 and single time steps of Sentinel-2 data in combination to derive accurate forest/non-forest (FNF) information via machine-learning classifiers. The forest classification accuracies were 90.9% and 93.2% for South Africa and Thuringia, respectively, estimated while using autocorrelation corrected spatial cross-validation (CV) for the fused data set. Sentinel-1 only classifications provided the lowest overall accuracy of 87.5%, while Sentinel-2 based classifications led to higher accuracies of 91.9%. Sentinel-2 short-wave infrared (SWIR) channels, biophysical parameters (Leaf Area Index (LAI), and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)) and the lower spectrum of the Sentinel-1 synthetic aperture radar (SAR) time series were found to be most distinctive in the detection of forest cover. In contrast to homogenous forests sites, Sentinel-1 time series information improved forest cover predictions in open savanna-like environments with heterogeneous regional features. The presented approach proved to be robust and it displayed the benefit of fusing optical and SAR data at high spatial resolution.

crops planting area identification and analysis based on multi-source high resolution remote sensing data

2020 ◽  
Author(s):  
Lei Wang ◽  
Haoran Sun ◽  
Wenjun Li ◽  
Liang Zhou
Keyword(s):  
Remote Sensing ◽  
Random Forest ◽  
Classification Accuracy ◽  
Kappa Coefficient ◽  
Optical Data ◽  
Accurate Estimation ◽  
Support Vector ◽  
Random Forest Method ◽  
Multi Temporal ◽  
Sar Data

<p>Crop planting structure is of great significance to the quantitative management of agricultural water and the accurate estimation of crop yield. With the increasing spatial and temporal resolution of remote sensing optical and SAR(Synthetic Aperture Radar) images,  efficient crop mapping in large area becomes possible and the accuracy is improved. In this study, Qingyijiang Irrigation District in southwest of China is selected for crop identification methods comparison, which has heterogeneous terrain and complex crop structure . Multi-temporal optical (Sentinel-2) and SAR (Sentinel-1) data were used to calculate NDVI and backscattering coefficient as the main classification indexes. The multi-spectral and SAR data showed significant change in different stages of the whole crop growth period and varied with different crop types. Spatial distribution and texture analysis was also made. Classification using different combinations of indexes were performed using neural network, support vector machine and random forest method. The results showed that, the use of multi-temporal optical data and SAR data in the key growing periods of main crops can both provide satisfactory classification accuracy. The overall classification accuracy was greater than 82% and Kappa coefficient was greater than 0.8. SAR data has high accuracy and much potential in rice identification. However optical data had more accuracy in upland crops classification. In addition, the classification accuracy can be effectively improved by combination of classification indexes from optical and SAR data, the overall accuracy was up to 91.47%. The random forest method was superior to the other two methods in terms of the overall accuracy and the kappa coefficient.</p>

scholarly journals Monitoring Forest Loss in ALOS/PALSAR Time-Series with Superpixels

2019 ◽  
Vol 11 (5) ◽  
pp. 556 ◽  
Author(s):  
Charlie Marshak ◽  
Marc Simard ◽  
Michael Denbina
Keyword(s):  
Time Series ◽  
Landsat Imagery ◽  
Optical Data ◽  
Support Vector ◽  
Forest Loss ◽  
Alos Palsar ◽  
Square Grid ◽  
Radar Images ◽  
L Band ◽  
Single Pixel

We present a flexible methodology to identify forest loss in synthetic aperture radar (SAR) L-band ALOS/PALSAR images. Instead of single pixel analysis, we generate spatial segments (i.e., superpixels) based on local image statistics to track homogeneous patches of forest across a time-series of ALOS/PALSAR images. Forest loss detection is performed using an ensemble of Support Vector Machines (SVMs) trained on local radar backscatter features derived from superpixels. This method is applied to time-series of ALOS-1 and ALOS-2 radar images over a boreal forest within the Laurentides Wildlife Reserve in Québec, Canada. We evaluate four spatial arrangements including (1) single pixels, (2) square grid cells, (3) superpixels based on segmentation of the radar images, and (4) superpixels derived from ancillary optical Landsat imagery. Detection of forest loss using superpixels outperforms single pixel and regular square grid cell approaches, especially when superpixels are generated from ancillary optical imagery. Results are validated with official Québec forestry data and Hansen et al. forest loss products. Our results indicate that this approach can be applied to monitor forest loss across large study areas using L-band radar instruments such as ALOS/PALSAR, particularly when combined with superpixels generated from ancillary optical data.

scholarly journals GOOGLE EARTH ENGINE: APPLICATION OF ALGORITHMS FOR REMOTE SENSING OF CROPS IN TUSCANY (ITALY)

2020 ◽  
Vol XLII-3/W12-2020 ◽  
pp. 291-296
Author(s):  
J. P. Clemente ◽  
G. Fontanelli ◽  
G. G. Ovando ◽  
Y. L. B. Roa ◽  
A. Lapini ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Google Earth ◽  
Optical Data ◽  
Support Vector ◽  
Use Of Time ◽  
Crop Types ◽  
Google Earth Engine ◽  
Agricultural Areas ◽  
Sentinel 2

Abstract. Remote sensing has become an important mean to assess crop areas, specially for the identification of crop types. Google Earth Engine (GEE) is a free platform that provides a large number of satellite images from different constellations. Moreover, GEE provides pixel-based classifiers, which are used for mapping agricultural areas. The objective of this work is to evaluate the performance of different classification algorithms such as Minimum Distance (MD), Random Forest (RF), Support Vector Machine (SVM), Classification and Regression Trees (CART) and Na¨ıve Bayes (NB) on an agricultural area in Tuscany (Italy). Four different scenarios were implemented in GEE combining different information such as optical and Synthetic Aperture Radar (SAR) data, indices and time series. Among the five classifiers used the best performers were RF and SVM. Integrating Sentinel-1 (S1) and Sentinel-2 (S2) slightly improves the classification in comparison to the only S2 image classifications. The use of time series substantially improves supervised classifications. The analysis carried out so far lays the foundation for the integration of time series of SAR and optical data.

scholarly journals OPTIMAL DATES FOR DECIDUOUS TREE SPECIES MAPPING USING FULL YEARS SENTINEL-2 TIME SERIES IN SOUTH WEST FRANCE

2020 ◽  
Vol V-3-2020 ◽  
pp. 469-476
Author(s):  
N. Karasiak ◽  
M. Fauvel ◽  
J.-F. Dejoux ◽  
C. Monteil ◽  
D. Sheeren
Keyword(s):  
Time Series ◽  
Species Distribution ◽  
Tree Species ◽  
Satellite Image ◽  
Deciduous Tree ◽  
Support Vector ◽  
Red Oak ◽  
Spectral Bands ◽  
One Year ◽  
Sentinel 2

Abstract. The free to use Sentinel-2 (S2) sensors with 5-day revisit time at high spatial resolution in 10 spectral bands is a revolution in the remote sensing domain. Including 6 spectral bands in the near infrared, with 3 dedicated for the red-edge (where the vegetation significatively increases), these european satellites are very promising for mapping tree species distribution at a national scale. Here, we study the contribution of three one-year S2 Satellite Image Time Series (SITS) for mapping deciduous species distribution in the southwest of France. The annual cycle of vegetation (called phenology) can contribute to the identification of tree species. For some specific dates, species can have different phenological behaviours (senesence, flowering…). To train and validate the maps, we used the Support Vector Machine algorithm with a spatial cross-validation method. To train the algorithm with the same number of samples per species, we decided to undersample each class to the smallest class using a K-means clustering method. Moreover, a Sequential Feature Selection (SFS) has been implemented to detect the optimal dates per species. Our results are promising with high accuracy for Red oak andWillow (average score of the three one-year respectively F1 = 0.99, F1 = 0.94) based on the optimal dates. However, it appears that the performances when using the each full SITS are far below the optimal dates models (average ΔF1 = 0.32). We did not find, except for Willow and Red oak, that the optimal dates were the same for each year. Perspectives is to find an algorithm robust to temporal or spectral noise and to smooth the time series.

scholarly journals Tree Species (Genera) Identification with GF-1 Time-Series in A Forested Landscape, Northeast China

2020 ◽  
Vol 12 (10) ◽  
pp. 1554
Author(s):  
Kaijian Xu ◽  
Qingjiu Tian ◽  
Zhaoying Zhang ◽  
Jibo Yue ◽  
Chung-Te Chang
Keyword(s):  
Time Series ◽  
Species Identification ◽  
Tree Species ◽  
Northeast China ◽  
Forest Canopy ◽  
Pinus Tabulaeformis ◽  
Wide Field ◽  
Study Region ◽  
Forest Mapping ◽  
Phenological Metrics

Forests are the most important component of terrestrial ecosystem; the accurate mapping of tree species is helpful for the management of forestry resources. Moderate- and high-resolution multispectral images have been commonly utilized to identify regional tree species in forest ecosystem, but the accuracy of recognition is still unsatisfactory. To enhance the forest mapping accuracy, this study integrated the land surface phenological metrics and text features of forest canopy on tree species identification based on Gaofen-1 (GF-1) wide field of view (WFV) and time-series images (36 10-day NDVI data), conducted at a forested landscape in Harqin Banner, Northeast China in 2017. The dominant tree species include Pinus tabulaeformis, Larix gmelinii, Populus davidiana, Betula platyphylla, and Quercus mongolica in the study region. The result of forest mapping derived from a 10-day dataset was also compared with the outcome based upon a commonly utilized 30-day dataset in tree species identification. The results indicate that tree species identification accuracy is significantly (p < 0.05) improved with higher temporal resolution (10-day, 79.4%) of images than commonly used monthly data (30-day, 76.14%), and the accuracy can be further increased to 85.13% with a combination of the information derived from principal component analysis (PCA) transformation, phenological metrics (standing for the information of growing season) and texture features. The integration of higher dimensional NDVI data, vegetation growth dynamics and feature of canopy simultaneously will be beneficial to map tree species at the landscape scale.

scholarly journals Reconstructing historical forest cover change in the Lower Amazon floodplains using the LandTrendr algorithm

2016 ◽  
Vol 46 (1) ◽  
pp. 13-24 ◽  
Author(s):  
Everton Hafemann FRAGAL ◽  
Thiago Sanna Freire SILVA ◽  
Evlyn Márcia Leão de Moraes NOVO
Keyword(s):  
Time Series ◽  
Forest Cover ◽  
Ecosystem Functions ◽  
Support Vector ◽  
Forest Loss ◽  
Forest Cover Change ◽  
Disturbance And Recovery ◽  
Global Classification ◽  
Natural Losses ◽  
Historical Forest

ABSTRACTThe Amazon várzeas are an important component of the Amazon biome, but anthropic and climatic impacts have been leading to forest loss and interruption of essential ecosystem functions and services. The objectives of this study were to evaluate the capability of the Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) algorithm to characterize changes in várzeaforest cover in the Lower Amazon, and to analyze the potential of spectral and temporal attributes to classify forest loss as either natural or anthropogenic. We used a time series of 37 Landsat TM and ETM+ images acquired between 1984 and 2009. We used the LandTrendr algorithm to detect forest cover change and the attributes of "start year", "magnitude", and "duration" of the changes, as well as "NDVI at the end of series". Detection was restricted to areas identified as having forest cover at the start and/or end of the time series. We used the Support Vector Machine (SVM) algorithm to classify the extracted attributes, differentiating between anthropogenic and natural forest loss. Detection reliability was consistently high for change events along the Amazon River channel, but variable for changes within the floodplain. Spectral-temporal trajectories faithfully represented the nature of changes in floodplain forest cover, corroborating field observations. We estimated anthropogenic forest losses to be larger (1.071 ha) than natural losses (884 ha), with a global classification accuracy of 94%. We conclude that the LandTrendr algorithm is a reliable tool for studies of forest dynamics throughout the floodplain.

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