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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".

Deadline for manuscript submissions: closed (30 March 2019) | Viewed by 90713

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Special Issue Editors

Dr. Markus Hollaus

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Guest Editor

Department of Geodesy and Geoinformation, University of Technology, 1040 Vienna, Austria
Interests: lidar; forest; biomass; vegetation; change detection; environmental studies; forest inventories
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Xinlian Liang

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Guest Editor

State Key Laboratory of Surveying and Mapping Remote Sensing Information Engineering, Wuhan University, Wuhan 430072, China
Interests: geodesy and surveying; geoinformatics (GIS); remote sensing; laser scanning; terrestrial laser scanning; mobile laser scanning; 3D modelling; forest inventory
Special Issues, Collections and Topics in MDPI journals

Dr. Piotr Tompalski

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Department of Forest Resources Management, The University of British Columbia, 2424 Main Mall, Vancouver, BC V6T 1Z4, Canada
Interests: remote sensing; forestry; airborne laser scanning; digital aerial photogrammetry

Special Issue Information

Dear Colleagues,

In the last decade, an increased availability of remote sensing data with very high-temporal, spatial and spectral resolution can be observed. On a plot-level, forest in-situ measurement is experiencing a rapid change, characterized by the increasingly available point cloud data from LiDAR, image matching and structure light at an accuracy up to millimeter-level and user-demand temporal interval. On a state-level, the most known remote sensing data source is provided by digital aerial cameras with a spatial resolution of 10-50 cm and a temporal resolution of a few years. This data source can be used to derive forest information through delineating tree crown using image processing and also through deriving digital surface models using image matching. Additionally, LiDAR is increasingly used for scanning the Earth surface with high point densities (e.g. 1-20 pts/m²). The acquired point clouds are the basis for deriving high precision digital terrain as well as surface models for delineating forest resources. The availability of multi-temporal LiDAR data sets is increasing. On a global-scale, the satellite missions (e.g., European Sentinel-1 and Sentinel-2) acquire data with high spatial (e.g. 10-20 m) and temporal (e.g. with repetition rates up to few days) resolution which allows impressive multi-temporal forest analyses of, e.g., phenology, forest changes due to natural and man-made disturbances.

The main focus of this special issue is on multi-temporal analyses of remote sensing data with respect to forest applications. Scope includes but is not limited to thematic information extraction through multi-temporal analyses, sensor/data integration, the integration of in-situ measurements with terrestrial-, UAV-, airborne- and satellite remote sensing data for multi-temporal mapping and measuring forest environments, phenology, etc.

Dr. Markus Hollaus
Dr. Xinlian Liang
Dr. Piotr Tompalski
Guest Editors

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Keywords

Multitemporal Remote Sensing
LiDAR
Multispectral Remote Sensing
RADAR
Sensor integration
Change detection
Phenology
Forest growth
Thematic information extraction

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Published Papers (11 papers)

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24 pages, 9850 KiB

Open AccessArticle

Forest Stand Species Mapping Using the Sentinel-2 Time Series

by Ewa Grabska, Patrick Hostert, Dirk Pflugmacher and Katarzyna Ostapowicz

Remote Sens. 2019, 11(10), 1197; https://doi.org/10.3390/rs11101197 - 20 May 2019

Cited by 203 | Viewed by 15755

Abstract

Accurate information regarding forest tree species composition is useful for a wide range of applications, both for forest management and scientific research. Remote sensing is an efficient tool for collecting spatially explicit information on forest attributes. With the launch of the Sentinel-2 mission, new opportunities have arisen for mapping tree species owing to its spatial, spectral, and temporal resolution. The short revisit cycle (five days) is crucial in vegetation mapping because of the reflectance changes caused by phenological phases. In our study, we evaluated the utility of the Sentinel-2 time series for mapping tree species in the complex, mixed forests of the Polish Carpathian Mountains. We mapped the following nine tree species: common beech, silver birch, common hornbeam, silver fir, sycamore maple, European larch, grey alder, Scots pine, and Norway spruce. We used the Sentinel-2 time series from 2018, with 18 images included in the study. Different combinations of Sentinel-2 imagery were selected based on mean decrease accuracy (MDA) and mean decrease Gini (MDG) measures, in addition to temporal phonological pattern analysis. Tree species discrimination was performed using the Random Forest classification algorithm. Our results showed that the use of the Sentinel-2 time series instead of single date imagery significantly improved forest tree species mapping, by approximately 5–10% of overall accuracy. In particular, combining images from spring and autumn resulted in better species discrimination. Full article

(This article belongs to the Special Issue Multitemporal Remote Sensing for Forestry)

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24 pages, 5651 KiB

Open AccessArticle

Rubber Identification Based on Blended High Spatio-Temporal Resolution Optical Remote Sensing Data: A Case Study in Xishuangbanna

by Shupeng Gao, Xiaolong Liu, Yanchen Bo, Zhengtao Shi and Hongmin Zhou

Remote Sens. 2019, 11(5), 496; https://doi.org/10.3390/rs11050496 - 1 Mar 2019

Cited by 17 | Viewed by 5545

Abstract

As an important economic resource, rubber has rapidly grown in Xishuangbanna of Yunnan Province, China, since the 1990s. Tropical rainforests have been replaced by extensive rubber plantations, which has resulted in ecological problems such as the loss of biodiversity and local water shortages. It is vitally important to accurately map the rubber plantations in this region. Although several rubber mapping methods have been proposed, few studies have investigated methods based on optical remote sensing time series data with high spatio-temporal resolution due to the cloudy and foggy weather conditions in this area. This study presented a rubber plantation identification method that used spatio-temporal optical remote sensing data fusion technology to obtain vegetation index data at high spatio-temporal resolution within the optical remote sensing window in Xishuangbanna. The analysis of the proposed method shows that (1) fused optical remote sensing data with high spatio-temporal resolution could map the rubber distribution with high accuracy (overall accuracy of up to 89.51% and kappa of 0.86). (2) Fused indices have high R² (R² greater than 0.8, where R is the correlation coefficient) with the indices that were derived from the Landsat observed data, which indicates that fusion results are dependable. However, the fusion accuracy is affected by terrain factors including elevation, slope, and slope aspects. These factors have obvious negative effects on the fusion accuracy of high spatio-temporal resolution optical remote sensing data: the highest fusion accuracy occurred in areas with elevations between 1201 and 1400 m.a.s.l., and the lowest accuracy occurred in areas with elevations less than 600 m.a.s.l. For the 5 fused time series indices (normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), normalized difference moisture index (NDMI), normalized burn ratio (NBR), and tasseled cap angle (TCA)), the fusion accuracy decreased with increasing slope, and increasing slope had the least impact on the EVI, but the greatest negative impact on the NDVI; the slope aspect had a limited influence on the fusion accuracies of the 5 time series indices, but fusion accuracy was lowest on the northwest slope. (3) EVI had the highest accuracy of rubber plantation classification among the 5 time series indices, and the overall classification accuracies of the time series EVI for the four different years (2000, 2005, 2010, and 2015) reached 87.20% (kappa 0.82), 86.91% (kappa 0.81), 88.85% (kappa 0.84), and 89.51% (kappa 0.86), respectively. The results indicate that the method is a promising approach for rubber plantation mapping and the detection of changes in rubber plantations in this tropical area. Full article

(This article belongs to the Special Issue Multitemporal Remote Sensing for Forestry)

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21 pages, 4767 KiB

Open AccessArticle

Mapping Annual Forest Change Due to Afforestation in Guangdong Province of China Using Active and Passive Remote Sensing Data

by Wenjuan Shen, Mingshi Li, Chengquan Huang, Xin Tao, Shu Li and Anshi Wei

Remote Sens. 2019, 11(5), 490; https://doi.org/10.3390/rs11050490 - 27 Feb 2019

Cited by 21 | Viewed by 7266

Abstract

Accurate acquisition of spatial distribution of afforestation in a large area is of great significance to contributing to the sustainable utilization of forest resources and the evaluation of the carbon accounting. Annual forest maps (1986–2016) of Guangdong, China were generated using time series Landsat images and PALSAR data. Initially, four PALSAR-based classifiers were used to classify land cover types. Then, the optimal mapping algorithm was determined. Next, an accurate identification of forest and non-forest was carried out by combining Landsat-based phenological variables and PALSAR-based land cover classifications. Finally, the spatio-temporal distribution of forest cover change due to afforestation was created and its forest biomass dynamics changes were detected. The results indicated that the overall accuracy of forest classification of the improved model based on the PALSAR-based stochastic gradient boosting (SGB) classification and the maximum value of normalized difference vegetation index (NDVI; SGB-NDVI) were approximately 75–85% in 2005, 2010, and 2016. Compared with the Japan Aerospace Exploration Agency (JAXA) PALSAR-forest/non-forest, the SGB-NDVI-based forest product showed great improvement, while the SGB-NDVI product was the same or slightly inferior to the Global Land Cover (GLC) and vegetation tracker change (VCT)-based land cover types, respectively. Although this combination of multiple sources contained some errors, the SGB-NDVI model effectively identified the distribution of forest cover changes by afforestation events. By integrating aboveground biomass dynamics (AGB) change with forest cover, the trend in afforestation area closely corresponded with the trend in forest AGB. This technique can provide an essential data baseline for carbon assessment in the planted forests of southern China. Full article

(This article belongs to the Special Issue Multitemporal Remote Sensing for Forestry)

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17 pages, 4016 KiB

Open AccessArticle

Tree Species Classification with Multi-Temporal Sentinel-2 Data

by Magnus Persson, Eva Lindberg and Heather Reese

Remote Sens. 2018, 10(11), 1794; https://doi.org/10.3390/rs10111794 - 12 Nov 2018

Cited by 187 | Viewed by 18538

Abstract

The Sentinel-2 program provides the opportunity to monitor terrestrial ecosystems with a high temporal and spectral resolution. In this study, a multi-temporal Sentinel-2 data set was used to classify common tree species over a mature forest in central Sweden. The tree species to be classified were Norway spruce (Picea abies), Scots pine (Pinus silvestris), Hybrid larch (Larix × marschlinsii), Birch (Betula sp.) and Pedunculate oak (Quercus robur). Four Sentinel-2 images from spring (7 April and 27 May), summer (9 July) and fall (19 October) of 2017 were used along with the Random Forest (RF) classifier. A variable selection approach was implemented to find fewer and uncorrelated bands resulting in the best model for tree species identification. The final model resulting in the highest overall accuracy (88.2%) came from using all bands from the four image dates. The single image that gave the most accurate classification result (80.5%) was the late spring image (27 May); the 27 May image was always included in subsequent image combinations that gave the highest overall accuracy. The five tree species were classified with a user’s accuracy ranging from 70.9% to 95.6%. Thirteen of the 40 bands were selected in a variable selection procedure and resulted in a model with only slightly lower accuracy (86.3%) than that using all bands. Among the highest ranked bands were the red edge bands 2 and 3 as well as the narrow NIR (near-infrared) band 8a, all from the 27 May image, and SWIR (short-wave infrared) bands from all four image dates. This study shows that the red-edge bands and SWIR bands from Sentinel-2 are of importance, and confirms that spring and/or fall images capturing phenological differences between the species are most useful to tree species classification. Full article

(This article belongs to the Special Issue Multitemporal Remote Sensing for Forestry)

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20 pages, 6398 KiB

Open AccessArticle

Relating Spatiotemporal Patterns of Forest Fires Burned Area and Duration to Diurnal Land Surface Temperature Anomalies

by Carmine Maffei, Silvia Maria Alfieri and Massimo Menenti

Remote Sens. 2018, 10(11), 1777; https://doi.org/10.3390/rs10111777 - 9 Nov 2018

Cited by 33 | Viewed by 6206

Abstract

Forest fires are a major source of ecosystem disturbance. Vegetation reacts to meteorological factors contributing to fire danger by reducing stomatal conductance, thus leading to an increase of canopy temperature. The latter can be detected by remote sensing measurements in the thermal infrared as a deviation of observed land surface temperature (LST) from climatological values, that is as an LST anomaly. A relationship is thus expected between LST anomalies and forest fires burned area and duration. These two characteristics are indeed controlled by a large variety of both static and dynamic factors related to topography, land cover, climate, weather (including those affecting LST) and anthropic activity. To investigate the predicting capability of remote sensing measurements, rather than constructing a comprehensive model, it would be relevant to determine whether anomalies of LST affect the probability distributions of burned area and fire duration. This research approached the outlined knowledge gap through the analysis of a dataset of forest fires in Campania (Italy) covering years 2003–2011 against estimates of LST anomaly. An LST climatology was first computed from time series of daily Aqua-MODIS LST data (product MYD11A1, collection 6) over the longest available sequence of complete annual datasets (2003–2017), through the Harmonic Analysis of Time Series (HANTS) algorithm. HANTS was also used to create individual annual models of LST data, to minimize the effect of varying observation geometry and cloud contamination on LST estimates while retaining its seasonal variation. LST anomalies where thus quantified as the difference between LST annual models and LST climatology. Fire data were intersected with LST anomaly maps to associate each fire with the LST anomaly value observed at its position on the day previous to the event. Further to this step, the closest probability distribution function describing burned area and fire duration were identified against a selection of parametric models through the maximization of the Anderson-Darling goodness-of-fit. Parameters of the identified distributions conditional to LST anomaly where then determined along their confidence intervals. Results show that in the study area log-transformed burned area is described by a normal distribution, whereas log-transformed fire duration is closer to a generalized extreme value (GEV) distribution. The parameters of these distributions conditional to LST anomaly show clear trends with increasing LST anomaly; significance of this observation was verified through a likelihood ratio test. This confirmed that LST anomaly is a covariate of both burned area and fire duration. As a consequence, it was observed that conditional probabilities of extreme events appear to increase with increasing positive deviations of LST from its climatology values. This confirms the stated hypothesis that LST anomalies affect forest fires burned area and duration and highlights the informative content of time series of LST with respect to fire danger. Full article

(This article belongs to the Special Issue Multitemporal Remote Sensing for Forestry)

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16 pages, 4307 KiB

Open AccessArticle

Four-Stage Inversion Algorithm for Forest Height Estimation Using Repeat Pass Polarimetric SAR Interferometry Data

by Tayebe Managhebi, Yasser Maghsoudi and Mohammad Javad Valadan Zoej

Remote Sens. 2018, 10(8), 1174; https://doi.org/10.3390/rs10081174 - 25 Jul 2018

Cited by 14 | Viewed by 3724

Abstract

This paper proposes a new method for forest height estimation using single-baseline single frequency polarimetric synthetic aperture radar interferometry (PolInSAR) data. The new algorithm estimates the forest height based on the random volume over the ground with a volume temporal decorrelation (RVoG+VTD) model. We approach the problem using a four-stage geometrical method without the need for any prior information. In order to decrease the number of unknown parameters in the RVoG+VTD model, the mean extinction coefficient is estimated in an independent procedure. In this respect, the suggested algorithm estimates the mean extinction coefficient as a function of a geometrical index based on the signal penetration in the volume layer. As a result, the proposed four-stage algorithm can be used for forest height estimation using the repeat pass PolInSAR data, affected by temporal decorrelation, without the need for any auxiliary data. The suggested algorithm was applied to the PolInSAR data of the European Space Agency (ESA), BioSAR 2007 campaign. For the performance analysis of the proposed approach, repeat pass experimental SAR (ESAR) L-band data, acquired over the Remningstorp test site in Southern Sweden, is employed. The experimental result shows that the four-stage method estimates the volume height with an average root mean square error (RMSE) of 2.47 m against LiDAR heights. It presents a significant improvement of forest height accuracy, i.e., 5.42 m, compared to the three-stage method result, which ignores the temporal decorrelation effect. Full article

(This article belongs to the Special Issue Multitemporal Remote Sensing for Forestry)

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12 pages, 3945 KiB

Open AccessArticle

Temporal Variability of MODIS Phenological Indices in the Temperate Rainforest of Northern Patagonia

by Carlos Lara, Gonzalo S. Saldías, Alvaro L. Paredes, Bernard Cazelles and Bernardo R. Broitman

Remote Sens. 2018, 10(6), 956; https://doi.org/10.3390/rs10060956 - 15 Jun 2018

Cited by 18 | Viewed by 4708

Abstract

Western Patagonia harbors unique and sparsely studied terrestrial ecosystems that are threatened by land use changes and exposure to basin-scale climatic variability. We assessed the performance of two satellite vegetation indices derived from MODIS–Terra, EVI (Enhanced Vegetation Index) and NDVI (Normalized Difference Vegetation Index), over the northern and southern sectors of the Chiloé Island System (CIS) to advance our understanding of vegetation dynamics in the region. Then we examined their time-varying relationships with two climatic indices indicative of tropical and extratropical influence, the ENSO (El Niño–Southern Oscillation) and the Antarctic Oscillation (AAO) index, respectively. The 17-year time series showed that only EVI captured the seasonal pattern characteristic of temperate regions, with low (high) phenological activity during Autumn-Winter (Spring–Summer). NDVI saturated during the season of high productivity and failed to capture the seasonal cycle. Temporal patterns in productivity showed a weakened seasonal cycle during the past decade, particularly over the northern sector. We observed a non-stationary association between EVI and both climatic indices. Significant co-variation between EVI and the Niño–Southern Oscillation index in the annual band persisted from 2001 until 2008–2009; annual coherence with AAO prevailed from 2013 onwards and the 2009–2012 period was characterized by coherence between EVI and both climate indices over longer temporal scales. Our results suggest that the influence of large-scale climatic variability on local weather patterns drives phenological responses in the northern and southern regions of the CIS. The imprint of climatic variability on patterns of primary production across the CIS may be underpinned by spatial differences in the anthropogenic modification of this ecosystem, as the northern sector is strongly modified by forestry and agriculture. We highlight the need for field validation of satellite indices around areas of high biomass and high endemism, located in the southern sector of the island, in order to enhance the utility of satellite vegetation indices in the conservation and management of austral ecosystems. Full article

(This article belongs to the Special Issue Multitemporal Remote Sensing for Forestry)

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22 pages, 9117 KiB

Open AccessArticle

Spatiotemporal Estimation of Bamboo Forest Aboveground Carbon Storage Based on Landsat Data in Zhejiang, China

by Yangguang Li, Ning Han, Xuejian Li, Huaqiang Du, Fangjie Mao, Lu Cui, Tengyan Liu and Luqi Xing

Remote Sens. 2018, 10(6), 898; https://doi.org/10.3390/rs10060898 - 7 Jun 2018

Cited by 42 | Viewed by 6036

Abstract

China is one of the countries with the most abundant bamboo forest resources in the world, and Zhejiang province is among the top-3 Chinese provinces with richest bamboo forests. For rational bamboo forests management, it is of great significance to study the spatiotemporal dynamic changes of Aboveground Carbon (AGC) stocks of bamboo forest in Zhejiang. In this study, remote sensing variables, such as spectral, vegetation indices and texture features of bamboo forest in Zhejiang, were extracted from 32 Landsat TM and OLI images got from four different years (2000, 2004, 2008 and 2014). These variables were subsequently selected with stepwise regression method to build an estimation model of AGC of the bamboo forests. The results showed that (1) the accuracy of bamboo forest remote sensing information extracted from the four different years was high with a classification accuracy of >76.26% and an accuracy of users of >91.62%. The classification area of bamboo forest was highly consistent with the area from forest resource inventory, and the area accuracy was over 96.50%; (2) the estimation model performed well in predicting the AGC in Zhejiang for different years. The correlation coefficient for estimated and measured AGC was between 63% and 72% with low root mean square error; (3) the derived AGC of the bamboo forests in Zhejiang province increased gradually from 2000 to 2014, with the AGC density of 6.75 Mg·ha⁻¹, 10.95 Mg·ha⁻¹, 15.25 Mg·ha⁻¹ and 19.07 Mg·ha⁻¹ respectively, and the average annual growth of 0.88 Mg·ha⁻¹. The spatiotemporal evolution of bamboo forest AGC in Zhejiang province had a close relationship with the gradual expansion of bamboo forest in the province and the differentiation of management levels in different regions. Full article

(This article belongs to the Special Issue Multitemporal Remote Sensing for Forestry)

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24 pages, 6410 KiB

Open AccessArticle

Assessing the Defoliation of Pine Forests in a Long Time-Series and Spatiotemporal Prediction of the Defoliation Using Landsat Data

by Chenghao Zhu, Xiaoli Zhang, Ning Zhang, Mohammed Abdelmanan Hassan and Lin Zhao

Remote Sens. 2018, 10(3), 360; https://doi.org/10.3390/rs10030360 - 26 Feb 2018

Cited by 15 | Viewed by 4856

Abstract

Pine forests (Pinus tabulaeformis) have been in danger of defoliation by a caterpillar in the west Liaoning province of China for more than thirty years. This paper aims to assess and predict the degree of damage to pine forests by using remote sensing and ancillary data. Through regression analysis of the pine foliage remaining ratios of field plots with several vegetation indexes of Landsat data, a feasible inversion model was obtained to detect the degree of damage using the Normalized Difference Infrared Index of 5th band (NDII5). After comparing the inversion result of the degree of damage to the pine in 29 years and the historical damage record, quantized results of damage assessment in a long time-series were accurately obtained. Based on the correlation analysis between meteorological variables and the degree of damage from 1984 to 2015, the average degree of damage was predicted in temporal scale. By adding topographic and other variables, a linear prediction model in spatiotemporal scale was constructed. The spatiotemporal model was based on 5015 public pine points for 24 years and reached 0.6169 in the correlation coefficient. This paper provided a feasible and quantitative method in the spatiotemporal prediction of forest pest occurrence by remote sensing. Full article

(This article belongs to the Special Issue Multitemporal Remote Sensing for Forestry)

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21 pages, 11686 KiB

Open AccessFeature PaperArticle

Combining Multi-Date Airborne Laser Scanning and Digital Aerial Photogrammetric Data for Forest Growth and Yield Modelling

by Piotr Tompalski, Nicholas C. Coops, Peter L. Marshall, Joanne C. White, Michael A. Wulder and Todd Bailey

Remote Sens. 2018, 10(2), 347; https://doi.org/10.3390/rs10020347 - 24 Feb 2018

Cited by 53 | Viewed by 8251

Abstract

The increasing availability of highly detailed three-dimensional remotely-sensed data depicting forests, including airborne laser scanning (ALS) and digital aerial photogrammetric (DAP) approaches, provides a means for improving stand dynamics information. The availability of data from ALS and DAP has stimulated attempts to link these datasets with conventional forestry growth and yield models. In this study, we demonstrated an approach whereby two three-dimensional point cloud datasets (one from ALS and one from DAP), acquired over the same forest stands, at two points in time (circa 2008 and 2015), were used to derive forest inventory information. The area-based approach (ABA) was used to predict top height (H), basal area (BA), total volume (V), and stem density (N) for Time 1 and Time 2 (T1, T2). We assigned individual yield curves to 20 × 20 m grid cells for two scenarios. The first scenario used T1 estimates only (approach 1, single date), while the second scenario combined T1 and T2 estimates (approach 2, multi-date). Yield curves were matched by comparing the predicted cell-level attributes with a yield curve template database generated using an existing growth simulator. Results indicated that the yield curves using the multi-date data of approach 2 were matched with slightly higher accuracy; however, projections derived using approach 1 and 2 were not significantly different. The accuracy of curve matching was dependent on the ABA prediction error. The relative root mean squared error of curve matching in approach 2 for H, BA, V, and N, was 18.4, 11.5, 25.6, and 27.53% for observed (plot) data, and 13.2, 44.6, 50.4 and 112.3% for predicted data, respectively. The approach presented in this study provides additional detail on sub-stand level growth projections that enhances the information available to inform long-term, sustainable forest planning and management. Full article

(This article belongs to the Special Issue Multitemporal Remote Sensing for Forestry)

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10 pages, 8208 KiB

Open AccessLetter

Potential of Sentinel-1 Data for Monitoring Temperate Mixed Forest Phenology

by Pierre-Louis Frison, Bénédicte Fruneau, Syrine Kmiha, Kamel Soudani, Eric Dufrêne, Thuy Le Toan, Thierry Koleck, Ludovic Villard, Eric Mougin and Jean-Paul Rudant

Remote Sens. 2018, 10(12), 2049; https://doi.org/10.3390/rs10122049 - 17 Dec 2018

Cited by 80 | Viewed by 8710

Abstract

In this study, the potential of Sentinel-1 data to seasonally monitor temperate forests was investigated by analyzing radar signatures observed from plots in the Fontainebleau Forest of the Ile de France region, France, for the period extending from March 2015 to January 2016. Radar backscattering coefficients, σ⁰ and the amplitude of temporal interferometric coherence profiles in relation to environmental variables are shown, such as in situ precipitation and air temperature. The high temporal frequency of Sentinel-1 acquisitions (i.e., twelve days, or six, if both Sentinel-1A and B are combined over Europe) and the dual polarization configuration (VV and VH over most land surfaces) made a significant contribution. In particular, the radar backscattering coefficient ratio of VV to VH polarization,

σ_{V V}^{0} / σ_{V H}^{0}

, showed a well-pronounced seasonality that was correlated with vegetation phenology, as confirmed in comparison to NDVI profiles derived from Landsat-8 (r = 0.77) over stands of deciduous trees. These results illustrate the high potential of Sentinel-1 data for monitoring vegetation, and as these data are not sensitive to the atmosphere, the phenology could be estimated with more accuracy than optical data. These observations will be quantitatively analyzed with the use of electromagnetic models in the near future. Full article

(This article belongs to the Special Issue Multitemporal Remote Sensing for Forestry)

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