Lorenzo Iannini

Remotely sensed monitoring of small reservoir dynamics: A Bayesian approach

Multipurpose small reservoirs are important for livelihoods in rural semi-arid regions. To manage and plan these reservoirs and to assess their hydrological impact at a river basin scale, it is important to monitor their water storage dynamics. This paper introduces a Bayesian approach for monitoring small reservoirs with radar satellite images. The newly developed growing Bayesian classifier has a high degree of automation, can readily be extended with auxiliary information and reduces the confusion error to the land-water boundary pixels. A case study has been performed in the Upper East Region of Ghana, based on Radarsat-2 data from November 2012 until April 2013. Results show that the growing Bayesian classifier can deal with the spatial and temporal variability in synthetic aperture radar (SAR) backscatter intensities from small reservoirs. Due to its ability to incorporate auxiliary information, the algorithm is able to delineate open water from SAR imagery with a low land-water contrast in the case of wind-induced Bragg scattering or limited vegetation on the land surrounding a small reservoir.

Calibration of SAR Polarimetric Images by Means of a Covariance Matching Approach

In this paper, a numerical method optimizer based on covariance matching is proposed for synthetic aperture radar (SAR) polarimetric calibration. The method makes use of the information provided by a distributed target and a corner reflector in order to jointly estimate the system polarimetric distortion parameters and the Faraday rotation. A preliminary analysis is conducted to show the expected accuracy values and to identify the intrinsic ambiguities of the problem. Results from simulations are shown to assess the accuracy and convergence of the method. Finally, tests have been conducted on stack of repeated full polarimetric ALOS PALSAR images to check the stability of the retrieved distortion parameters in a realistic case.

Integration of multispectral and C-band SAR data for crop classification

The paper debates the impact of sensor configuration diversity on the crop classification performance. More specifically, the analysis accounts for multi-temporal and polarimetric C-Band SAR information used individually and in synergy with Multispectral imagery. The dataset used for the investigation comprises several multi-angle Radarsat-2 (RS2) fullpol acquisitions and RapidEye (RE) images both at fine resolution collected over the Indian Head (Canada) agricultural site area and spanning the summer crop growth cycle from May to September. A quasi-Maximum Likelihood (ML) classification approach applied at per-field level has been adopted to integrate the different data sources. The analysis provided evidence on the overall accuracy enhancement with respect to the individual sensor performances, with 4%-8% increase over a single RE image, a 40%-10% increase over a single 1-pol/full-pol image and 15%-0% increase over multitemporal 1-pol/full-pol RS2 series respectively. A more detailed crop analysis revealed that in particular canola and the cereals benefit from the integration, whereas lentil and flax can experience similar or worse performance when compared to the RE-based classification. Comments and suggestions for further development are presented.

Ground reference data for sugarcane biomass estimation in São Paulo state, Brazil

In order to make effective decisions on sustainable development, it is essential for sugarcane-producing countries to take into account sugarcane acreage and sugarcane production dynamics. The availability of sugarcane biophysical data along the growth season is key to an effective mapping of such dynamics, especially to tune agronomic models and to cross-validate indirect satellite measurements. Here, we introduce a dataset comprising 3,500 sugarcane observations collected from October 2014 until October 2015 at four fields in the São Paulo state (Brazil). The campaign included both non-destructive measurements of plant biometrics and destructive biomass weighing procedures. The acquisition plan was designed to maximize cost-effectiveness and minimize field-invasiveness, hence the non-destructive measurements outnumber the destructive ones. To compensate for such imbalance, a method to convert the measured biometrics into biomass estimates, based on the empirical adjustment of allometric models, is proposed. In addition, the paper addresses the precisions associated to the ground measurements and derived metrics. The presented growth dynamics and associated precisions can be adopted when designing new sugarcane measurement campaigns. Design Type(s) observation design • time series design Measurement Type(s) plant structure • leaf area index • plant matter • water-based rainfall Technology Type(s) data collection method Factor Type(s) Sample Characteristic(s) Saccharum hybrid cultivar RB867515 • Saccharum hybrid cultivar SP80-3280 • Piracicaba Mesoregion • cropland biome Design Type(s) observation design • time series design Measurement Type(s) plant structure • leaf area index • plant matter • water-based rainfall Technology Type(s) data collection method Factor Type(s) Sample Characteristic(s) Saccharum hybrid cultivar RB867515 • Saccharum hybrid cultivar SP80-3280 • Piracicaba Mesoregion • cropland biome Machine-accessible metadata file describing the reported data (ISA-Tab format)

Monitoring LULC dynamics in the Sao Paulo region through landsat and C-band SAR time series

The paper debates a novel approach for sugarcane identification and characterization based on multi-spectral and multi-temporal profile matching. A parametric model aimed at identifying sugarcane among pasture/grasses/shrubs, annual crops and forest is proposed. Differently from other supervised and unsupervised classification techniques, the discussed profile-based parametric model accounts for variability in growth date, that becomes valuable information to be extracted, rather than simply a nuisance parameter, and delivers an effective extrapolation of the cane vigor. The approach is then applied to Landsat 5 TM and ERS/ENVISAT SAR time-series over the Orindiuva area attaining preliminary promising although perfectible results.

Vegetation Characterization through the Use of Precipitation-Affected SAR Signals

Current space-based SAR offers unique opportunities to classify vegetation types and to monitor vegetation growth due to its frequent acquisitions and its sensitivity to vegetation geometry. However, SAR signals also experience frequent temporal fluctuations caused by precipitation events, complicating the mapping and monitoring of vegetation. In this paper, we show that the influence of a priori known precipitation events on the signals can be used advantageously for the classification of vegetation conditions. For this, we exploit the change in Sentinel-1 backscatter response between consecutive acquisitions under varying wetness conditions, which we show is dependent on the state of vegetation. The performance further improves when a priori information on the soil type is taken into account.

Integration of sar and optical dense time series for land cover monitoring

Multi-temporal and multi-sensor solutions are essential to increase timeliness and reliability of land monitoring systems. This paper advocates the exploitation of the temporal contextual information provided by temporally dense SAR and optical data series series through the use of a Hidden Markov model (HMM)-based approach. An efficient strategy to incorporate the C-Band SAR data into the HMM framework, relying so far on Landsat, will be debated and assessed over a dynamic agricultural scenario, i.e. characterized by high temporal and spatial diversity in cropping practices. The site is located in the state of São Paulo (Brazil), where recent ground surveying activities has been conducted.

Land deformation monitoring using PS-InSAR technique over Sahel-Doukkala (Morocco)

Even if land deformation in Sahel-Doukkala may not directly threaten human life, it could lead to serious economic losses. Therefore, the monitoring of this deformation becomes a priority. In this study, PS-InSAR technique was applied in order to extract information regarding land deformation. This method was successful in detecting a considerable amount of PS targets from which the land deformation was estimated. The deformation rate was between −2.4 mm/year and 1.9 mm/year showing an alternation between uplift and subsidence. The origin of this deformation is suggested to be related to tectonic and climatological origins.

Sugarcane growth monitoring through spatial cluster and temporal trend analysis of radar and optical remote sensing images

During the 2014-2015 sugarcane growth season in São Paulo, Brazil, a considerable dataset was acquired consisting of space-based remote sensing images from radar and optical sensors, together with intensive ground measurements. In this work, images from the Sentinel-1, Radarsat-2 and Landsat-8 satellites are used to test the effectiveness of satellite-based indicators in sugarcane growth monitoring. A two-fold hypothesis testing is applied, in order to find statistically significant emerging hot spots and cold spots, both in space and time. Especially the comparison of results from the radar and optical sensors gives an insight into the difference in capability of these sensors to detect spatial and temporal patterns and trends.

Assessment of the P- and L-band SAR tomography for the characterization of tropical forests

The objective of this paper is to provide a better understanding of tomographic capabilities in characterization of dense forested areas at P-and L-band. The analysis is carried out on airborne data acquired by ONERA over the site of Paracou, French Guyana, during the ESA campaign TropiSAR. The results shown support the idea that ground- and -volume interactions play a negligible role at L-band, whereas they are significant at P-band. For a dense forest of 30 m and more, there is very weak ground contribution at L-band. The L-band tomographic profile is quite disturbed as compared to the P-band profile in dense tropical forest areas. In this condition, the use of tomographic imaging at L-band in tropical forests appears limited. However, when the forest top height is roughly below 20 m (e.g., in forest regrowth), the tomographic results are expected to be the same as in boreal forests. Whereas P-band tomography allow us to retrieve the whole forest vertical structure, better characterizing of the ground and/or volume scatterings and providing an unique solution in high biomasss ranging from 150-600 t/ha.