The importance of mapping the forest height (FH) is increasing due to the more frequent impacts of climate change in the society (wildfires, droughts, and extreme weather events). Remote Sensing is often used for mapping this variable; however, it usually relies in costly and extensive field or airborne campaigns. In addition, when using synthetic aperture radar (SAR), most approaches do not use freely available data. Considering this, in this work a model is proposed that resorts to Advanced Land Observing Satellite 2 (ALOS-2), Sentinel-1 (S1), and ancillary data. Airborne laser scanning (ALS) data are used for local calibration but, with the aim of developing a more scalable model, the latter is optimized to work with small calibration datasets (representative of just 25% of the study area to be mapped). With this purpose, the model combines a featuring generation and a features’ processing stage with a stacking regressor to produce estimates at the pixel level. Their impact was assessed, and an improvement of 8.11 and 2.01 pp in the relative root mean square error (rRMSE) was achieved by including the features’ generation and features’ processing stages, respectively. In addition, when the multifrequency dataset was used, the model achieved an rRMSE better than when using only a C-band dataset (S1) or only an L-band dataset (ALOS-2), respectively, by 4.21 and 3.05 pp. Finally, the model achieved an average${R} ^{2}$/rRMSE of 0.6240%/24.30% and 0.5901%/22.64% for the validation and test study areas, respectively. The proposed approach revealed to be effective on mapping the FH resorting to multifrequency SAR and small calibration datasets acquired by ALS.

The impacts of the climate change in the society make forest monitoring increasingly important. Consequently, there is a growing interest in mapping variables as the Forest Height (FH). The direct measurement of the FH through field campaigns is expensive and difficult to scale. Alternatively, Airborne Laser Scanning (ALS) campaigns can be used to map it, however they share the same disadvantages of the previous approach. Therefore, Remote Sensing (RS) data have been used for local and large-scale mapping of the FH. In this paper a Regression Methodology (RM) that combines GEDI, ALOS-2, Sentinel-1/2, and ancillary data is proposed for mapping the FH in Mediterranean forests. The proposed RM, tested for the 15 regions of interest, achieves a RMSE/rRMSE of 4.95m/33.93%, when evaluated with GEDI data, and 5.11m/41.70%, when evaluated with ALS data.

Canopy Height (CH) is an important variable in any forest inventory, not only by its own information, but also as a proxy variable to estimate other parameters as the above-ground biomass. The CH information can also be helpful to understand the climate change trends, for forest management, and in decision support systems related to wildfires. The growing availability of Remote Sensing observations acquired from different sensors, create an alternative for the CH mapping to field campaigns and Airborne Laser Scanning (ALS) missions. Here a comparison between using Multispectral and Synthetic Aperture Radar sensors for CH estimation is presented. Both used the same Regression Methodology, being achieved a R2/RMSE between 43.71%-72.85%/0.85-4.03m for Multispectral and 42.12%-62.62%/0.96m-4.49m for SAR, for a total of 17 regions of interest. It is concluded that Multispectral data revealed to be more suitable for the CH mapping.

The knowledge of the Forest Height (FH) is important for monitoring the forests, and it can be used as a proxy variable of other forest parameters as the aboveground biomass. It is also important for understanding the climate change and prepare the wildfire seasons. The most effective way to map the FH is through field campaigns or airborne laser scanning, but both are expensive and not scalable. Alternatively, spaceborne Synthetic Aperture Radar (SAR) data may be used. However, it often relies on the acquisition of large ground truth datasets. In this paper, a new Regression Methodology (RM) that makes use of SAR data and a Stacking Regressor that minimises the amount of data needed to map the FH of a region is presented. Tested on a total of 16 regions between Portugal and Spain, plus one in California, the RM achieved a R2between 42.12%-62.62%, and a RMSE between 0.96m-4.49m.

The climate change impacts can also be seen in the growing number of wildfires. Consequently, forest management and the updating of forest inventories become more important in the wildfires’ avoidance. Measuring the Forest Height (FH) is an important activity in forests monitoring, since the FH can serve as a proxy variable of other parameters, as the aboveground biomass. Normally, FH is mapped through field campaigns or airborne laser scanning missions. However, these approaches do not offer the scalability needed and they are expensive. Therefore, multispectral data from Remote Sensing can be used for producing regional maps of FH. Here it is proposed a regionally calibrated Regression Methodology that uses multispectral data from Sentinel-2 and a Stacking Regressor for mapping the FH in Mediterranean forests. For a total of 17 regions across Portugal, Spain, and California, a R2between 43.71% and 72.85% and a RMSE between 0.85m and 4.03m.

Wildfires are recurrent natural disasters in some regions of the globe, being Portugal one of these areas. The Portuguese Institute for Nature Conservation and Forests implemented a national fuel break (FB) network, with the goal of decreasing fire hazard. FBs are areas of reduced fuel load that slow down fire spread, creating firefighting opportunities. Its effectiveness relies on periodic treatments to maintain the fuel load in levels that can reduce the effects of wildfires. This paper proposes a methodology to assess the FB state, according to its fuel load, based on the analysis of the inter-annual variability of Sentinel-2 NDVI time series. Inter-annual comparison allows it to adapt to different regions. To assess the reliability of NDVI data to evaluate the FB state, a linear regression with the vegetation height acquired by the Global Ecosystem Dynamics Investigation (GEDI) mission was tested, achieving determination coefficients between 0.57 and 0.98.

Nowadays, the number of aerial unmanned vehicles (UAVs) is growing at a tremendous speed, as well as its technology. Therefore, it is essential to follow this growth with increasingly robust algorithms to be possible to exist cooperation between robots autonomously. One of the major events currently being developed in autonomous cooperation is relatively terrain classification where, this classification, is mainly important for emergency landings, mapping and decision making. This paper presents a robust computer vision system to sort terrain types using two main algorithms: Particle Swarm Optimization (PSO) and Gray-Level Co-Occurrence Matrix (GLCM). In addition to these two algorithms, a neural network was designed with the aim of increasing the probability of success of the proposed system. In order to evaluate this article, the system is validated using videos acquired onboard of a UAV with a RGB camera.

The information generated by a computer vision system capable of labelling a land surface as water, vegetation, soil or other type, can be used for mapping and decision making.For example, an unmanned aerial vehicle (UAV) can use it to find a suitable landing position or to cooperate with other robots to navigate across an unknown region.Previous works on terrain classification from RGB images taken onboard of UAVs shown that only static pixel-based features were tested with a considerable classification error.This paper proposes a robust and efficient computer vision algorithm capable of classifying the terrain from RGB images with improved accuracy.The algorithm complement the static image features with dynamic texture patterns produced by UAVs rotors downwash effect (visible at lower altitudes) and machine learning methods to classify the underlying terrain.The system is validated using videos acquired onboard of a UAV.

Several major advances in Cell and Molecular Biology have been made possible by recent advances in live-cell microscopy imaging. To support these efforts, automated image analysis methods such as cell segmentation and tracking during a time-series analysis are needed. To this aim, one important step is the validation of such image processing methods. Ideally, the “ground truth” should be known, which is possible only by manually labelling images or in artificially produced images. To simulate artificial images, we have developed a platform for simulating biologically inspired objects, which generates bodies with various morphologies and kinetics and, that can aggregate to form clusters. Using this platform, we tested and compared four tracking algorithms: Simple Nearest-Neighbour (NN), NN with Morphology and two DBSCAN-based methods. We show that Simple NN works well for small object velocities, while the others perform better on higher velocities and when clustering occurs. Our new platform for generating new benchmark images to test image analysis algorithms is openly available at (http://griduni.uninova.pt/Clustergen/ClusterGen_vl.0.zip).

In computer vision systems an unpredictable image corruption can have significant impact on its usability. Image recovery methods for partial image damage, in particular in moving scenarios, can be crucial for recovering corrupted images. In these situations, image fusion techniques can be successfully applied to congregate information taken at different instants and from different points-of-view to recover damaged parts. In this article we propose a technique for temporal and spatial image fusion, based on fuzzy classification, which allows partial image recovery upon unexpected defects without user intervention. The method uses image alignment techniques and duplicated information from previous images to create fuzzy confidence maps. These maps are then used to detect damaged pixels and recover them using information from previous frames.

MOTIVATION: Cell division in Escherichia coli is morphologically symmetric. However, as unwanted protein aggregates are segregated to the cell poles and, after divisions, accumulate at older poles, generate asymmetries in sister cells' vitality. Novel single-molecule detection techniques allow observing aging-related processes in vivo, over multiple generations, informing on the underlying mechanisms. RESULTS: CellAging is a tool to automatically extract information on polar segregation and partitioning in division of aggregates in E.coli, and on cellular vitality. From time-lapse, parallel brightfield and fluorescence microscopy images, it performs cell segmentation, alignment of brightfield and fluorescence images, lineage construction and pole age determination, and it computes aging-related features. We exemplify its use by analyzing spatial distributions of fluorescent protein aggregates from images of cells across generations. AVAILABILITY: CellAging, instructions and an example are available at http://www.cs.tut.fi/%7esanchesr/cellaging/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.