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.

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 Use of SAR and Optical Time Series for Tropical Forest Disturbance Mapping

2020 ◽  
Vol 12 (4) ◽  
pp. 727 ◽  
Author(s):  
Manuela Hirschmugl ◽  
Janik Deutscher ◽  
Carina Sobe ◽  
Alexandre Bouvet ◽  
Stéphane Mermoz ◽  
...  
Keyword(s):  
Time Series ◽  
Patch Size ◽  
Forest Disturbance ◽  
Optical Data ◽  
Landsat 8 ◽  
Data Sets ◽  
Disturbed Forest ◽  
Optical Time ◽  
The Individual ◽  
Sentinel 2

Frequent cloud cover and fast regrowth often hamper topical forest disturbance monitoring with optical data. This study aims at overcoming these limitations by combining dense time series of optical (Sentinel-2 and Landsat 8) and SAR data (Sentinel-1) for forest disturbance mapping at test sites in Peru and Gabon. We compare the accuracies of the individual disturbance maps from optical and SAR time series with the accuracies of the combined map. We further evaluate the detection accuracies by disturbance patch size and by an area-based sampling approach. The results show that the individual optical and SAR based forest disturbance detections are highly complementary, and their combination improves all accuracy measures. The overall accuracies increase by about 3% in both areas, producer accuracies of the disturbed forest class increase by up to 25% in Peru when compared to only using one sensor type. The assessment by disturbance patch size shows that the amount of detections of very small disturbances (< 0.2 ha) can almost be doubled by using both data sets: for Gabon 30% as compared to 15.7–17.5%, for Peru 80% as compared to 48.6–65.7%.

scholarly journals Comparison of LAI Estimates from High Resolution Satellite Observations Using Different Biophysical Processors

2021 ◽  
Vol 3 (1) ◽  
pp. 5
Author(s):  
Federico Filipponi
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Agricultural Land ◽  
Spectral Characteristics ◽  
Earth Observation ◽  
Estimation Accuracy ◽  
Landsat 8 ◽  
Biophysical Parameters ◽  
Area Index ◽  
Satellite Sensors

Earth observation provides timely and spatially explicit information about crop phenology and vegetation dynamics that can support decision making and sustainable agricultural land management. Vegetation spectral indices calculated from optical multispectral satellite sensors have been largely used to monitor vegetation status. In addition, techniques to retrieve biophysical parameters from satellite acquisitions, such as the Leaf Area Index (LAI), have allowed to assimilate Earth observation time series in numerical modeling for the analysis of several land surface processes related to agroecosystem dynamics. More recently, biophysical processors used to estimate biophysical parameters from satellite acquisitions have been calibrated for retrieval from sensors with different high spatial resolution and spectral characteristics. Virtual constellations of satellite sensors allow the generation of denser LAI time series, contributing to improve vegetation phenology estimation accuracy and, consequently, enhancing agroecosystems monitoring capacity. This research study compares LAI estimates over croplands using different biophysical processors from Sentinel-2 MSI and Landsat-8 OLI satellite sensors. The results are used to demonstrate the capacity of virtual satellite constellation to strengthen LAI time series to derive important cropland use information over large areas.

Monitoring of water table dynamics in peatlands with OPTRAM: Towards globally applicable algorithms in Google Earth Engine using Landsat and Sentinel-2

2021 ◽  
Author(s):  
Iuliia Burdun ◽  
Michel Bechtold ◽  
Viacheslav Komisarenko ◽  
Annalea Lohila ◽  
Elyn Humphreys ◽  
...  
Keyword(s):  
Time Series ◽  
Water Table ◽  
Input Data ◽  
Short Wave ◽  
Google Earth ◽  
Google Earth Engine ◽  
Short Wave Infrared ◽  
Northern Peatlands ◽  
Sentinel 2

&lt;p&gt;Fluctuations of water table depth (WTD) affect many processes in peatlands, such as vegetation development and emissions of greenhouse gases. Here, we present the OPtical TRApezoid Model (OPTRAM) as a new method for satellite-based monitoring of the temporal variation of WTD in peatlands. OPTRAM is based on the response of short-wave infrared reflectance to the vegetation water status. For five northern peatlands with long-term in-situ WTD records, and with diverse vegetation cover and hydrological regimes, we generate a suite of OPTRAM index time series using (a) different procedures to parametrise OPTRAM (peatland-specific manual vs. globally applicable automatic parametrisation in Google Earth Engine), and (b) different satellite input data (Landsat vs. Sentinel-2). The results based on the manual parametrisation of OPTRAM indicate a high correlation with in-situ WTD time-series for pixels with most suitable vegetation for OPTRAM application (mean Pearson correlation of 0.7 across sites), and we will present the performance differences when moving from a manual to an automatic procedure. Furthermore, for the overlap period of Landsat and Sentinel-2, which have different ranges and widths of short-wave infrared bands used for OPTRAM calculation, the impact of the satellite input data to OPTRAM will be analysed. Eventually, the challenge of merging different satellite missions in the derivation of OPTRAM time series will be explored as an important step towards a global application of OPTRAM for the monitoring of WTD dynamics in northern peatlands.&lt;/p&gt;

scholarly journals Kc and LAI Estimations Using Optical and SAR Remote Sensing Imagery for Vineyards Plots

2020 ◽  
Vol 12 (21) ◽  
pp. 3478
Author(s):  
Ofer Beeri ◽  
Yishai Netzer ◽  
Sarel Munitz ◽  
Danielle Ferman Mintz ◽  
Ran Pelta ◽  
...  
Keyword(s):  
Time Series ◽  
Temporal Resolution ◽  
Crop Coefficient ◽  
Landsat 8 ◽  
Area Index ◽  
Management Zone ◽  
Growing Seasons ◽  
Crop Parameters ◽  
Better Than ◽  
Sentinel 2

Daily or weekly irrigation monitoring conducted per sub-field or management zone is an important factor in vine irrigation decision-making. The objective is to determine the crop coefficient (Kc) and the leaf area index (LAI). Since the 1990s, optic satellite imagery has been utilized for this purpose, yet cloud-cover, as well as the desire to increase the temporal resolution, raise the need to integrate more imagery sources. The Sentinel-1 (a C-band synthetic aperture radar—SAR) can solve both issues, but its accuracy for LAI and Kc mapping needs to be determined. The goals of this study were as follows: (1) to test different methods for integrating SAR and optic sensors for increasing temporal resolution and creating seamless time-series of LAI and Kc estimations; and (2) to evaluate the ability of Sentinel-1 to estimate LAI and Kc in comparison to Sentinel-2 and Landsat-8. LAI values were collected at two vineyards, over three (north plot) and four (south plot) growing seasons. These values were converted to Kc, and both parameters were tested against optic and SAR indices. The results present the two Sentinel-1 indices that achieved the best accuracy in estimating the crop parameters and the best method for fusing the optic and the SAR data. Utilizing these achievements, the accuracy of the Kc and LAI estimations from Sentinel-1 were slightly better than the Sentinel-2′s and the Landsat-8′s accuracy. The integration of all three sensors into one seamless time-series not only increases the temporal resolution but also improves the overall accuracy.

Estimation of the seasonal leaf area index in urban oak forests using Sentinel-2 time series data

2019 ◽  
Author(s):  
A. Kozlova ◽  
I. Piestova ◽  
L. Patrusheva ◽  
M. Lubsky ◽  
A. Nikulina ◽  
...  
Keyword(s):  
Time Series ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Time Series Data ◽  
Series Data ◽  
Oak Forests ◽  
Area Index ◽  
Sentinel 2

scholarly journals NEW VEGETATION INDICES FOR FULL AND COMPACT POLARIMETRIC SAR DATA: IN PREPARATION FOR THE RADARSAT CONSTELLATION MISSION (RCM)

2020 ◽  
Vol IV-3/W2-2020 ◽  
pp. 41-46
Author(s):  
D. Ratha ◽  
D. Mandal ◽  
S. Dey ◽  
A. Bhattacharya ◽  
A. Frery ◽  
...  
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Time Series Data ◽  
Geodesic Distance ◽  
Vegetation Indices ◽  
Series Data ◽  
Biophysical Parameters ◽  
Area Index ◽  
Plant Area Index ◽  
Sar Data

Abstract. In this paper, we present two radar vegetation indices for full-pol and compact-pol SAR data, respectively. Both are derived using the notion of a geodesic distance between observation and well-known scattering models available in the literature. While the full-pol version depends on a generalized volume scattering model, the compact-pol version uses the ideal depolariser to model the randomness in the vegetation. We have utilized the RADARSAT Constellation Mission (RCM) time-series data from the SAMPVEX16-MB campaign in the Manitoba region of Canada for comparing and assessing the indices in terms of the change in the biophysical parameters as well. The compact-pol data for comparison is simulated from the full-pol RCM time series. Both the indices show better performance at correlating with biophysical parameters such as Plant Area Index (PAI) and Volumetric Water Content (VWC) for wheat and soybean crops, in comparison to the state-of-art Radar Vegetation Index (RVI) of Kim and van Zyl. These indices are timely for the upcoming release of the data from the RCM, which will provide data in both full and compact-pol modes, aimed at better crop monitoring from space.

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.

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