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Operational Forest Monitoring in Siberia Using Multi-Source Earth Observation Data
Abstract
Forest cover disturbance rates are increasing in the forests of Siberia due to intensification of human activities and climate change. In this paper two satellite data sources were used for auto-mated forest cover change detection. Annual ALOS PALSAR backscatter mosaics (2007–2010) were used for yearly forest loss monitoring. Time series of the Enhanced Vegetation Index (EVI, 2000–2014) from the Moderate Resolution Imaging Spectroradiometer (MODIS) were in-tegrated in a web-based data middleware system to assess the capabilities of a near-real time de-tection of forest disturbances using the break point detection by additive season and trends (Bfast) method. The SAR-based average accuracy of the forest loss detection was 70 %, whereas the MODIS-based change assessment using breakpoint detection achieved average ac-curacies of 50 % for trend-based breakpoints and 43.4 % for season-based breakpoints. It was demonstrated that SAR remote sensing is a highly accurate tool for up-to-date forest monitoring. Web-based data middleware systems like the Earth Observation Monitor, linked with MODIS time series, provide access and easy-to-use tools for on demand change monitoring in remote Siberian forests.
... Использование методов дистанционного зондирования Земли (ДЗЗ) обеспечивает достаточный уровень достоверности данных о состоянии лесного покрова, в т. ч. -для измерения ключевых характеристик биомассы. Находят применение три основных метода ДЗЗ: наземное (TLiDAR) и воздушное лазерное сканирование (LiDAR), цифровая аэрофотосъемка и снимки со спутников [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]. ...
The three-dimensional modelling of forest landscape scenes based on laser (LiDAR) scanning data isn't a trivial task. It requires a getting some subtasks. Firstly starting point cloud clustered into several groups: the point of the Earth, plant mass and man-made objects. Based on the received subset of points it is restored forest vegetation cover in three dimensional space. There are several approaches modeling of complex natural objects of vegetation, which describe using graphical primitives is not possible. The first approach is based on the use of L-systems, the second approach used voxel modeling. Voxel is an extension of the pixel for three-dimensional space and a minimum three-dimensional unit, the smaller the dimension, the more accurate and realistic models of objects. Widespread use of voxel modeling obtained in medicine and computer games. Description of the proposed method is divided into four stages: preparation of the starting point clouds, including the standard data pre-filtering of noise and alignment of data in a single coordinate system; the construction of three-dimensional map of the heights of the original data; display of the results; optimization and visualization of the final model of the landscape. Keywords: laser scanning, the cloud of laser points, three dimensional modelling of forest landscapes. Citation: Tkacheva A.A., Danilin I.M. The use of laser scanning data for modelling forest landscape scenes,
Radar time series are powerful means to improve retrieval algorithms about land surface characteristics in the following ways: (i) as information for identification of land surface conditions, (ii) as source of multivariate statistics for mapping methodologies, (iii) to select the right scene(s) for dedicated retrieval procedures, or (iv) to train model parameters in physical retrievals. Albeit radar data from air- and spaceborne platforms have been investigated since 40 years, operational applications are limited - partly due to the non-intuitive handling of complex microwave backscatter signals, and partly due to restricted geometric and temporal resolutions or frequency and polarization constraints. This chapter gives an overview of 20 years of pilot projects performed by the authors and their collaborators with the goal of large-area radar data exploration. All studies lead to innovative pre-operational applications, several with promising discoveries that can now be realized with a new and expanding fleet of radar satellites. Four case studies for land cover, forest mapping, forest cover change and savannah monitoring conclude this chapter.
More than 50 percent of the Russian forest inventory was updated more than 25 years ago. The consequence is that most of the existing forest inventory is outdated. Human and environmental forest disturbances continuously affect changes of forest cover and biomass stocks. The magnitude and extent of ongoing environmental pressures (e.g., forest fragmentation and the impact of global climate change) and the loss rates of particular habitat types is unknown. The aim of the paper is to provide an integrated concept for the monitoring and assessment of forest resources, as demonstrated for Central Siberia in the framework of the ZAPÁS project and the Siberian Earth System Science Cluster (SIB-ESS-C).
Regional and local scale in-situ and multi-agency satellite data were analyzed to generate biomass and forest cover change maps at local scales which were validated with up-to-date forest inventory data. At regional scales a synergy map of land cover and biomass information was developed. Annual PALSAR backscatter data proved to be applicable for local scale forest biomass monitoring and reforestation assessment. The forest resource maps were integrated in a data middleware system to provide Web-based analyses interoperability, data access of multi-scale forest resource maps to local stakeholders, and decision support for regional and local forest management in Central Siberia.
Land monitoring is a key issue in Earth system sciences to study environmental changes. To generate knowledge about change, e.g., to decrease uncertaincy in the results and build confidence in land change monitoring, multiple information sources are needed. Earth observation (EO) satellites and in situ measurements are available for operational monitoring of the land surface. As the availability of well-prepared geospatial time-series data for environmental research is limited, user-dependent processing steps with respect to the data source and formats pose additional challenges. In most cases, it is possible to support science with spatial data infrastructures (SDI) and services to provide such data in a processed format. A data processing middleware is proposed as a technical solution to improve interdisciplinary research using multi-source time-series data and standardized data acquisition, pre-processing, updating and analyses. This solution is being implemented within the Siberian Earth System Science Cluster (SIB-ESS-C), which combines various sources of EO data, climate data and analytical tools. The development of this SDI is based on the definition of automated and on-demand tools for data searching, ordering and processing, implemented along with standard-compliant web services. These tools, consisting of a user-friendly download, analysis and interpretation infrastructure, are available within SIB-ESS-C for operational use.
Growing stock volume is an important biophysical parameter describing the state and dynamics of the Boreal zone. Validation of growing stock volume (GSV) maps based on satellite remote sensing is challenging due to the lack of consistent ground reference data. The monitoring and assessment of the remote Russian forest resources of Siberia can only be done by integrating remote sensing techniques and interdisciplinary collaboration. In this paper, we assess the information content of GSV estimates in Central Siberian forests obtained at 25 m from ALOS-PALSAR and 1 km from ENVISAT-ASAR backscatter data. The estimates have been cross-compared with respect to forest inventory data showing 34% relative RMSE for the ASAR-based GSV retrievals and 39.4% for the PALSAR-based estimates of GSV. Fragmentation analyses using a MODIS-based land cover dataset revealed an increase of retrieval error with increasing fragmentation of the landscape. Cross-comparisons of multiple SAR-based GSV estimates helped to detect OPEN ACCESS Forests 2014, 5 1754 inconsistencies in the forest inventory data and can support an update of outdated forest inventory stands.
Changing trends in ecosystem productivity can be quantified using satellite observations of Normalized Difference Vegetation Index (NDVI). However, the estimation of trends from NDVI time series differs substantially depending on analyzed satellite dataset, the corresponding spatiotemporal resolution, and the applied statistical method. Here we compare the performance of a wide range of trend estimation methods and demonstrate that performance decreases with increasing inter-annual variability in the NDVI time series. Trend slope estimates based on annual aggregated time series or based on a seasonal-trend model show better performances than methods that remove the seasonal cycle of the time series. A breakpoint detection analysis reveals that an overestimation of breakpoints in NDVI trends can result in wrong or even opposite trend estimates. Based on our results, we give practical recommendations for the application of trend methods on long-term NDVI time series. Particularly, we apply and compare different methods on NDVI time series in Alaska, where both greening and browning trends have been previously observed. Here, the multi-method uncertainty of NDVI trends is quantified through the application of the different trend estimation methods. Our results indicate that greening NDVI trends in Alaska are more spatially and temporally prevalent than browning trends. We also show that detected breakpoints in NDVI trends tend to coincide with large fires. Overall, our analyses demonstrate that seasonal trend methods need to be improved against inter-annual variability to quantify changing trends in ecosystem productivity with higher accuracy.
Forests in Flux
Forests worldwide are in a state of flux, with accelerating losses in some regions and gains in others. Hansen et al. (p. 850 ) examined global Landsat data at a 30-meter spatial resolution to characterize forest extent, loss, and gain from 2000 to 2012. Globally, 2.3 million square kilometers of forest were lost during the 12-year study period and 0.8 million square kilometers of new forest were gained. The tropics exhibited both the greatest losses and the greatest gains (through regrowth and plantation), with losses outstripping gains.
Advanced Land Observing Satellite (ALOS) Phased Array L-band type Synthetic Aperture Radar (PALSAR) backscatter images with 50 m pixel size (strip images) at HV-polarization were used to map clear-cuts at a regional and national level in Sweden. For a set of 31 clear-cuts, on average 59.9% of the pixels within each clear-cut were correctly detected. When compared with a one-pixel edge-eroded version of the reference dataset, the accuracy increased to 88.9%. With respect to statistics from the Swedish Forest Agency, county-wise clear-felled areas were underestimated by the ALOS PALSAR dataset (between 25% and 60%) due to the coarse resolution. When compared with statistics from the Swedish National Forest Inventory, the discrepancies were larger, partly due to the estimation errors from the plot-wise forest inventory data. In Sweden, for the time frame of 2008–2010, the total area felled was estimated to be 140,618 ha, 172,532 ha and 194,586 ha using data from ALOS PALSAR, the Swedish Forest Agency and the Swedish National Forest Inventory, respectively. ALOS PALSAR strip images at HV-polarization appear suitable for detection of clear-felled areas at a national level; nonetheless, the pixel size of 50 m is a limiting factor for accurate delineation of clear-felled areas.
Giovanni, the Goddard Earth Sciences Data and Information Services Center (GES DISC) Interactive Online Visualization and Analysis Infrastructure, has provided researchers with advanced capabilities to perform data exploration and analysis with observational data from NASA Earth observation satellites. In the past 5-10 years, examining geophysical events and processes with remote-sensing data required a multistep process of data discovery, data acquisition, data management, and ultimately data analysis. Giovanni accelerates this process by enabling basic visualization and analysis directly on the World Wide Web. In the last two years, Giovanni has added new data acquisition functions and expanded analysis options to increase its usefulness to the Earth science research community.
We present results of a quantitative analysis of fire regimes and a verified assessment of fire carbon emissions in Northern Eurasia's ecosystems (limited to territories of Russia) for 1998-2010. Burnt areas were defined based on a consistent methodology (AVHRR data were modified to eliminate biases) over the period. A hybrid land cover (resolution 1 km 2) was used for identification of forest classes and spatial quantification of fuel by components. Consumption of fuel was assessed by land classes based on multiyear empirical data on distribution of fire type, time of burning, and bioclimatic zone. The average annual burnt area on forest land was estimated at 5.35 x 10 6 ha (65% of the area of all vegetation fires) with seasonal variation from 3.16 x 10 6 ha (1999) to 13.17 x 10 6 ha (2003). Average annual amount of consumed carbon by all vegetation fire is 121.0 Tg yr -1 , including an estimated 92.0 Tg yr -1 on forest land (76.0% of the total emissions), ranging from 35.8 Tg yr -1 (2004) to 201.5 Tg yr -1 (2003). Specific density of consumed carbon in forest fires (average 18.73 Mg C yr -1 ha -1) depends mostly on severity of seasonal fire regimes and regional distribution of burnt areas.
Techniques based on multi-temporal, multi-spectral, satellite-sensor- acquired data have demonstrated potential as a means to detect, identify, map and monitor ecosystem changes, irrespective of their causal agents. This review paper, which summarizes the methods and the results of digital change detection in the optical/infrared domain, has as its primary objective a synthesis of the state of the art today. It approaches,digital change,detection from,three angles. First, the different perspectives from which the variability in ecosystems and the change,events have been dealt with are summarized.,Change,detection between pairs of images,(bi-temporal) as well as between,time profiles of imagery,derived indicators (temporal trajectories), and, where relevant, the appropriate choices for digital imagery acquisition timing and change interval length definition, are discussed. Second, pre-processing routines either to establish a more direct linkage between remote sensing data and biophysical phenomena, or to temporally mosaic imagery and extract time profiles, are reviewed. Third, the actual change,detection,methods,themselves,are categorized,in an analytical framework and critically evaluated. Ultimately, the paper highlights how some of these methodological,aspects are being,fine-tuned as this review,is being written, and we summarize the new developments that can be expected in the near future. The review,highlights the high complementarity,between,different change,detection methods.
Questions related to the distribution and spatio-temporal dynamics of the terrestrial carbon fluxes are at the core of current scientific and policy debates. In recent years, the primary concern has been the increasing CO2 content in the atmosphere, its effect on climate, and the associated role of terrestrial ecosystems in mitigating the increase and impact of climate change. However, terrestrial carbon dynamics is also closely related to biodiversity land degradation, and other pressing policy and assessment questions. Yet at the global level, no system in place now can provide quantitative information about carbon sources and sinks systematically, reliably, and accurately.
This paper summarizes the results obtained from geometric and radiometric calibrations of the Phased-Array L-Band Synthetic Aperture Radar (PALSAR) on the Advanced Land Observing Satellite, which has been in space for three years. All of the imaging modes of the PALSAR, i.e., single, dual, and full polarimetric strip modes and scanning synthetic aperture radar (SCANSAR), were calibrated and validated using a total of 572 calibration points collected worldwide and distributed targets selected primarily from the Amazon forest. Through raw-data characterization, antenna-pattern estimation using the distributed target data, and polarimetric calibration using the Faraday rotation-free area in the Amazon, we performed the PALSAR radiometric and geometric calibrations and confirmed that the geometric accuracy of the strip mode is 9.7-m root mean square (rms), the geometric accuracy of SCANSAR is 70 m, and the radiometric accuracy is 0.76 dB from a corner-reflector analysis and 0.22 dB from the Amazon data analysis (standard deviation). Polarimetric calibration was successful, resulting in a VV/HH amplitude balance of 1.013 (0.0561 dB) with a standard deviation of 0.062 and a phase balance of 0.612deg with a standard deviation of 2.66deg .
An extensive dataset of images acquired by the Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) is investigated for clear-cut detection in the county of Västerbotten, Sweden. Strong forest/non-forest contrast and temporal consistency were found for the Fine Beam Dual HV-polarized backscatter in summer/fall. In consequence of a clear-cut between image acquisitions, the HV-backscatter dropped in most cases between 2 and 3 dB. Thus, a simple thresholding algorithm that exploits the temporal consistency of time series of HV-backscatter measurements has been developed for clear-cut detection. The detection algorithm was applied at pixel level to ALOS PALSAR strip images with a pixel size of 50 m. The performance of the detection algorithm was tested with three different threshold values (2.0, 2.5 and 3.0 dB). The classification accuracy increased from 57.4% to 78.2% for decreasing value of the threshold. Conversely, the classification error increased from 3.0% to 9.7%. For about 90% of the clear-felled polygons used for accuracy assessment the proportion of pixels correctly detected as clear-felled was above 50% when using a threshold value of 2.0 dB. For the threshold values of 2.5 and 3.0 dB the corresponding figures were 80% and 65%, respectively. The total area classified as clear-felled during the time frame of the ALOS PALSAR data differed by 5% compared to an estimate of notified fellings for the same period of time when using a detection threshold of 2.5 dB. The performance of the simple detection algorithm is reasonable when aiming at detecting clear-cuts, whereas there are shortcomings in terms of delineation.
The Phased Array type L-band Synthetic Aperture Radar onboard the Advanced Land Observing Satellite has, since
its launch, been acquiring an extensive data set of images over two forest test sites in Sweden. The sites of Remningstorp (Lat.58◦30 N, Long. 13◦40 E) in the south and Krycklan (Lat.64◦14 N, Long. 19◦50 E) in the north of Sweden are characterized by hemiboreal and boreal forests, respectively. In this paper, we have investigated the signatures of standwise backscatter measurements from forests with different growth stages in relation to polarization, environmental conditions, image viewing geometry, and spatial resolution. The HV backscatter presented stronger sensitivity to the forest growth stage than the HH and VV backscatter. Under unfrozen conditions, the dynamic range of fine-beam data acquired at 34.3◦ was 8–9 dB for the HV polarization and 6–7 dB for the HH polarization. At 21.5◦, in the polarimetric mode, the dynamic range was 6, 7, and 9 dB at VV, HH, and HV polarizations, respectively. Regardless of the specific polarization, the backscatter was temporally consistent
under unfrozen conditions, with a small increase of backscatter in regrowing young forest for wet conditions. Under thawing and frozen conditions, repeated measurements were available only for the HH backscatter at 34.3◦. For thawing conditions, the backscatter level was similar to the unfrozen conditions even though the signatures differed depending on temperature dynamics, snow-cover properties, and precipitation. Under frozen conditions, the signatures varied depending on temperature. For images acquired when the temperature was well below the freezing point, the backscatter was low, and the dynamic range was small(2–4 dB); nonetheless, the measurements were consistent. Images
acquired when temperature was close to the freezing point presented a behavior similar to unfrozen conditions. The sensitivity of the backscatter to the image viewing geometry for different growth stages was studied for data acquired under dry unfrozen conditions. The backscatter difference increased for increasing look angle because of the increase in volume scattering and the decrease of ground-surface backscatter. The largest difference was
observed at 41.5◦ with 2.5–4-dB difference for the HH and 4–5-dB difference for the HV case. Loss of spatial resolution (20–50 m)did not have any effect on the backscatter signatures in Krycklan, whereas in Remningstorp, the smallest stands were affected.
The terrestrial carbon sink has been large in recent decades, but its size and location remain uncertain. Using forest inventory
data and long-term ecosystem carbon studies, we estimate a total forest sink of 2.4 ± 0.4 petagrams of carbon per year (Pg
C year–1) globally for 1990 to 2007. We also estimate a source of 1.3 ± 0.7 Pg C year–1 from tropical land-use change, consisting of a gross tropical deforestation emission of 2.9 ± 0.5 Pg C year–1 partially compensated by a carbon sink in tropical forest regrowth of 1.6 ± 0.5 Pg C year–1. Together, the fluxes comprise a net global forest sink of 1.1 ± 0.8 Pg C year–1, with tropical estimates having the largest uncertainties. Our total forest sink estimate is equivalent in magnitude to the
terrestrial sink deduced from fossil fuel emissions and land-use change sources minus ocean and atmospheric sinks.
An explicit form of the linear multichannel synthetic aperture
radar (SAR) intensity filter, which preserves radiometry while optimally
reducing speckle is derived, together with a compact expression for the
theoretical gain in equivalent numbers of looks (ENLs). The filter can
be applied to mixed data types, which is demonstrated using a
combination of ERS and JERS satellite data, and confirms the filter
performance predicted by the theory. Tests indicate that a simplified
form of the filter, which neglects correlation between images, gives an
ENL only slightly less than optimal, while being much easier to
implement. Exact analysis of the effect of estimating filter weights
shows that the linear increase in ENL with the number of images
predicted for the ideal filter does not occur. In practice, the ENL is
affected by the window size used to estimate the weights and saturates
as the number of images increases. An efficient recursive form of the
filter is described, which is most naturally applied to multitemporal
data for the practically important case where the current image is
uncorrelated with previous images in a data sequence
Examination of the physical background underlying the ERS response
of forest and analysis of time series of ERS data indicates that the
greater temporal stability of forest compared with many other types of
land cover presents a means of mapping forest area. The processing chain
necessary to make such area estimations involves reconstruction of an
optimal estimate of the backscattering coefficient at each pixel using
temporal and spatial filtering so that classification rules derived from
large scale averaging are applicable. The rationale behind the filtering
strategy and the level of averaging needed is explained in terms of the
observed multitemporal behavior of forest and nonforest areas, much of
this analysis is generic and applicable to a wide range of situation in
which significant information is carried by multitemporal features of
the data. The choice of decision rules is based on the forest
observations, with the added requirement for robustness. The performance
of a classifier based only on change is assessed on a range of test
sites in the UK, Finland, and Poland. Error sources in this classifier
are identified, and the possibility of improving performance by
including radiometric information in the mapping strategy is discussed.
Brief discussions of how the classification is affected by the addition
of coherence and how the processing chain would need to be modified for
other forms of satellite data are included
This study intended to demonstrate the effectiveness of phased array type L-band synthetic aperture radar (PALSAR) time series mosaics for automatic detection of tropical deforestation in Riau province, Indonesia. Using six time series PALSAR mosaics, the characteristics of HH and HV gamma nought (γ0) in natural forests and deforested areas from 2007 to 2010 and the accuracy of deforestation detection by using a threshold were investigated. We obtained the following results: (1) Applying a simple thresholding method to time series differences of γ0HV was effective for fully automatic detection of deforestation areas. When we used a fixed threshold, the accuracy ranged from 72% to 96% (average of 87%). (2) γ0HH did not always show systematic changes after deforestation. (3) The temporal variation of γ0 for deforested areas was larger than that for natural forests. These variations in γ0 were correlated with 10-day accumulated precipitation. High accumulated precipitation decreased the γ0 difference between deforested areas and natural forests, causing decreased accuracy of deforestation detection. (4) Integration of the results from two different dates in a given year can reduce the detection error due to time variations and provide highly accurate results (average accuracy of 91%, minimum of 82%). The accuracy values quoted except hilly areas, shadow, and lay-over. The proposed method is effective for detection of deforestation larger than 1 ha. The deforestation mapping method proposed in the study can be utilized for assessment of yearly changes in forested areas and is useful for tracking changes in forest cover on large scales.
Synthetic Aperture Radar (SAR) has been get broad recognition and application in many domains such as surveying and mapping. But the SAR speckles influence the interpretation of the image badly. In this paper, we discuss the second-order and the fourth-order partial differential equations(PDE), and we unite the two equations to reduce the speckles. Finally we use SAR image to do some experiment, the result shows that this equation combine the strongpoint of the second-order and the fourth-order partial differential equation, it can keep the edge of the SAR image and reduce the SAR speckle better. It avoids the block effect when we use second- order PDE to progress image and makes the image smoothing naturally.
a b s t r a c t Conversion of tropical forests to oil palm plantations in Malaysia and Indonesia has resulted in large-scale environmental degradation, loss of biodiversity and significant carbon emissions. For both countries to participate in the United Nation's REDD (Reduced Emission from Deforestation and Degradation) mech-anism, assessment of forest carbon stocks, including the estimated loss in carbon from conversion to plantation, is needed. In this study, we use a combination of field and remote sensing data to quantify both the magnitude and the geographical distribution of carbon stock in forests and timber plantations, in Sabah, Malaysia, which has been the site of significant expansion of oil palm cultivation over the last two decades. Forest structure data from 129 ha of research and inventory plots were used at different spatial scales to discriminate forest biomass across degradation levels. Field data was integrated with ALOS PALSAR (Advanced Land-Observing Satellite Phased Array L-band Synthetic Aperture Radar) imagery to both discriminate oil palm plantation from forest stands, with an accuracy of 97.0% (j = 0.64) and predict AGB using regression analysis of HV-polarized PALSAR data (R 2 = 0.63, p < .001). Direct estimation of AGB from simple regression models was sensitive to both environmental conditions and forest structure. Precipitation effect on the backscatter data changed the HV prediction of AGB significantly (R 2 = 0.21, p < .001), and scattering from large leaves of mature palm trees significantly impeded the use of a single HV-based model for predicting AGB in palm oil plantations. Multi-temporal SAR data and algorithms based on forest types are suggested to improve the ability of a sensor similar to ALOS PALSAR for accurately mapping and monitoring forest biomass, now that the ALOS PALSAR sensor is no longer operational.
Area-perimeter ratios are often used to quantify 2D shape compactness. Shape compactness is of main importance to evaluate the effect of external disturbance on natural habitats. Reference values (Amin(p), Amax(p), Pmin(a), Pmax(a)) for area (A) and perimeter (P) are defined and proven. The calculation of these reference values is based on the characteristics of the object studied and on pixel geometry. The cases for 4- and 8-connectivity are discussed. Using these references, alternative area-perimeter ratios are composed. Examples are elaborated to illustrate the use of the indices and their performance.
A controlled experiment has been performed to quantify the ability to detect clear-cuts using ALOS PALSAR data. The experiment consisted of 8 old spruce dominated stands, each with a size of about 1.5 ha, located at a test site in southern Sweden. Four of the stands were clear-felled and the remaining stands were left untreated for reference. A time series of PALSAR images was acquired prior to, during, and after treatment, including 7 fine beam single polarization (FBS, look angle 34.3deg, HH-polarization) SAR images. The results clearly show that the clear-felled stands could be separated from the reference stands. The drop in backscattering coefficient between the reference and the clear-felled stands was on average 2.1 dB. This implies that ALOS PALSAR data potentially can be used for large-scale mapping of changes in forest cover.
A novel system for the classification of multitemporal synthetic aperture radar (SAR) images is presented. It has been developed by integrating an analysis of the multitemporal SAR signal physics with a pattern recognition approach. The system is made up of a feature-extraction module and a neural-network classifier, as well as a set of standard preprocessing procedures. The feature-extraction module derives a set of features from a series of multitemporal SAR images. These features are based on the concepts of long-term coherence and backscattering temporal variability and have been defined according to an analysis of the multitemporal SAR signal behavior in the presence of different land-cover classes. The neural-network classifier (which is based on a radial basis function neural architecture) properly exploits the multitemporal features for producing accurate land-cover maps. Thanks to the effectiveness of the extracted features, the number of measures that can be provided as input to the classifier is significantly smaller than the number of available multitemporal images. This reduces the complexity of the neural architecture (and consequently increases the generalization capabilities of the classifier) and relaxes the requirements relating to the number of training patterns to be used for classifier learning. Experimental results (obtained on a multitemporal series of European Remote Sensing 1 satellite SAR images) confirm the effectiveness of the proposed system, which exhibits both high classification accuracy and good stability versus parameter settings. These results also point out that properly integrating a pattern recognition procedure (based on machine learning) with an accurate feature extraction phase (based on the SAR sensor physics understanding) represents an effective approach to SAR data analysis.
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