A Multi-scenario prospection of urban change - the case of urban growth in the Algarve

The Algarve faces unprecedented challenges resulting from increase of urban sprawl and population density along its coastal perimeters. A growing loss of ecosystems and natural landscapes have led to major asymmetries between the interior of the Algarve and the littoral areas. The depletion of natural resources taken for granted during the sixties, are conveying to the degradation of landscape, while the formerly beautiful region of the Algarve is losing its tourist attractions, largely explored since the latter. Loss of agricultural land to urban areas, has not only been a reality in Portugal, but is a common problem in peri-urban Europe and is overshadowing sustainable development. This paper aims to analyze the land-use change tendencies for the Algarve region from the beginning of the nineties up to 2020. By using a multi-scenario perspective of weight drivers such as agriculture, coastal proximity, urban proximity, population density and road networks, an Analytical Hierarchy Process will be applied to form three growth patterns for urban propensity within the coming 10 years and expanding over a total time frame of 30 years. The novelty of this approach is shared by the usage of story-lines which generate three distinct scenarios: More Ecological, Business as Usual and Economic Reasoning (maximization of economic growth). While story-lines are naturally qualitative, this methodology proposes a quantitative validation of qualitative information, giving a much more accurate result of current trends of urban growth and environmental change in the Algarve. The projection of future land-use is managed through the CORINE Land Cover spatial databases and iterations of cellular automata with the storylines, which shall allow the projection of future urban growth. By understanding the different path-tendencies of urban growth for the region, better decision-making can be done, as to avoid unbalanced city growth, bringing forth more sustainable cities within natural landscapes

Influence of Sample Size in Land Cover Classification Accuracy Using Random Forest and Sentinel-2 Data in Portugal

Moraes, D., Benevides, P., Costa, H., Moreira, F. D., & Caetano, M. (2021). Influence of Sample Size in Land Cover Classification Accuracy Using Random Forest and Sentinel-2 Data in Portugal. In IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium: Proceedings (pp. 4232-4235). IEEE. https://doi.org/10.1109/IGARSS47720.2021.9553924Classification accuracy of remote sensing images with supervised learning depends on the quality and characteristics of training samples. Size is a key aspect of a sample and its impact on classification depends on several factors, including the classifier employed, dimension on the feature space and land cover characteristics. Random Forest classifier is considered to be of low sensitivity to variations in sample size. However, further investigation is required when feature spaces are large and training is performed with spectral subclasses of the land cover classes to be mapped. This paper proposes to assess the impact of sample size in the classification accuracy of Random Forest using multitemporal Sentinel-2 data and a detailed set of training subclasses to produce a map with general land cover classes. The results revealed similar classification accuracies after major reductions in sample size.authorsversionpublishe

Annual Crop Classification Experiments in Portugal Using Sentinel-2

Benevides, P., Costa, H., Moreira, F. D., Moraes, D., & Caetano, M. (2021). Annual Crop Classification Experiments in Portugal Using Sentinel-2. In IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium: Proceedings (pp. 5838-5841). IEEE. https://doi.org/10.1109/IGARSS47720.2021.9555009 --------------------------- This work has been supported by projects IPSTERS (DSAIPA/AI/0100/2018), foRESTER (PCIF/SSI/0102/2017), and SCAPEFIRE (PCIF/MOS/0046/2017), and by Centro de Investigação em Gestão de Informação (MagIC), all funded by the Portuguese Foundation for Science and Technology (FCT). Value-added data processed by CNES for the Theia data centre www.theia-land.fr using Copernicus products. The satellite image pre-processing uses algorithms developed by Theia's Scientific Expertise Centers. SIP validation data was kindly provided by Instituto de Financiamento da Agricultura e Pescas.This paper presents an experimental crop classification of the 10 most abundant annual crop types in Portugal, using a study area located in Alentejo region. This region has great diversity of land uses as well as multiple crop types. Sentinel-2 2018 intra-annual time-series imagery is considered in the experiment. The Portuguese Land Parcel Identification System (LPIS) is used to extract automatic training samples. LPIS information is automatically processed with the help of auxiliary datasets to filter out crop areas more likely to have been mislabeled. Classification is obtained using random forest. Validation is performed using an independent dataset also based on LPIS. A global accuracy of 76% is obtained. The novelty of the methodology here presented shows that LPIS can be used together with auxiliary data for crop type mapping, helping to characterize the agriculture land diversity in Portugal.authorsversionpublishe

Exploring Different Levels of Class Nomenclature in Random Forest Classification of Sentinel-2 Data

Moraes, D., Benevides, P., Costa, H., Moreira, F. D., & Caetano, M. (2022). Exploring Different Levels of Class Nomenclature in Random Forest Classification of Sentinel-2 Data. In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium: Proceedings (pp. 2279-2282). (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2022-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS46834.2022.9883798--------- Funding:The work has been supported by project foRESTER (PCIF ISSI/0102/20 17), SCAPEFIRE (PCIF IMOS/0046/ 2017) and by Centro de Investigçãao em Gestae de Informação (MagIC), all funded by the Portuguese Foundation for Science and Technology (FCT). Value-added data processed by CNES for the Theia data centre www.theia-land.fr using Copernicus products. The processing uses algorithms developed by Theia's Scientific Expertise Centres.The current land cover mapping paradigm relies on automatic classification of satellite images, with supervised methods being the most used, implying training data to have a crucial role. Aspects such as training sample size and quality should be carefully considered. This paper proposes assessing the use of a detailed class nomenclature to reinforce class diversity in the training sample. A Random Forest (RF) classification of Sentinel-2 multi-temporal data was conducted. Additionally, the effect of sample size and class distribution were evaluated. The results indicate that the use of a detailed nomenclature provided better results in terms of classification accuracy. With respect to sample distribution, adopting class sizes proportional to their occurrence in a reference land cover map exhibited superior performance in comparison to an equal size approach. The effect of sample size on classification performance was limited, as previous studies with RF suggested.authorsversionpublishe

Improving specific class mapping from remotely sensed data by cost-sensitive learning

In many remote-sensing projects, one is usually interested in a small number of land-cover classes present in a study area and not in all the land-cover classes that make-up the landscape. Previous studies in supervised classification of satellite images have tackled specific class mapping problem by isolating the classes of interest and combining all other classes into one large class, usually called others, and by developing a binary classifier to discriminate the class of interest from the others. Here, this approach is called focused approach. The strength of the focused approach is to decompose the original multi-class supervised classification problem into a binary classification problem, focusing the process on the discrimination of the class of interest. Previous studies have shown that this method is able to discriminate more accurately the classes of interest when compared with the standard multi-class supervised approach. However, it may be susceptible to data imbalance problems present in the training data set, since the classes of interest are often a small part of the training set. A result the classification may be biased towards the largest classes and, thus, be sub-optimal for the discrimination of the classes of interest. This study presents a way to minimize the effects of data imbalance problems in specific class mapping using cost-sensitive learning. In this approach errors committed in the minority class are treated as being costlier than errors committed in the majority class. Cost-sensitive approaches are typically implemented by weighting training data points accordingly to their importance to the analysis. By changing the weight of individual data points, it is possible to shift the weight from the larger classes to the smaller ones, balancing the data set. To illustrate the use of the cost-sensitive approach to map specific classes of interest, a series of experiments with weighted support vector machines classifier and Landsat Thematic Mapper data were conducted to discriminate two types of mangrove forest (high-mangrove and low-mangrove) in Saloum estuary, Senegal, a United Nations Educational, Scientific and Cultural Organisation World Heritage site. Results suggest an increase in overall classification accuracy with the use of cost-sensitive method (97.3%) over the standard multi-class (94.3%) and the focused approach (91.0%). In particular, cost-sensitive method yielded higher sensitivity and specificity values on the discrimination of the classes of interest when compared with the standard multi-class and focused approaches

Exploring the Potential of Sentinel-2 Data for Tree Crown Mapping in Oak Agro-Forestry Systems

Costa, H., Machado, I., Moreira, F. D., Benevides, P., Moraes, D., & Caetano, M. (2021). Exploring the Potential of Sentinel-2 Data for Tree Crown Mapping in Oak Agro-Forestry Systems. In IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium: Proceedings (pp. 5807-5810). IEEE. https://doi.org/10.1109/IGARSS47720.2021.9553780 ----------- The work has been supported by projects IPSTERS (DSAIPA/AI/0100/2018), foRESTER (PCIF/SSI/0102/2017), and SCAPE FIRE (PCIF/MOS/0046/2017), and by Centro de Investigação em Gestão de Informação (MagIC), all funded by the Portuguese Foundation for Science and Technology (FCT). Value-added data processed by CNES for the Theia data centre www.theia-land.fr using Copernicus products. The Fig. 3: Tree crown map of Cork and Holm oaks with three levels of crown cover. Levels 100, 80 and 20 correspond to the classes of the same oak crown cover (e.g. level 100 are classes 1, 3 and 5 together in Table 1). Insets show contrasting examples of classification success with ortophotomaps of 2018 as background.Southern Portugal is characterized by disperse tree cover of Cork and Holm oaks in an agro-forestry system known as montado. Mapping these trees has been historically very difficult as they occur in isolation or in groups with different understory vegetation, including grass and shrubland. Automatic classification for binary tree/non-tree map production has been used elsewhere, but with limited success in the context of montado. Here, the potential of Sentinel-2 data was explored to map oaks using pure and mixed pixels to train a random forest. The output depicts a gradient of tree cover that can be transformed into a crisp map. The accuracy assessment of the latter shows commission and omission errors of 17% and 18%.authorsversionpublishe

A method to incorporate uncertainty in the classification of remote sensing images

The aim of this paper is to investigate if the incorporation of the uncertainty associated with the classification of surface elements into the classification of landscape units (LUs) increases the results accuracy. To this end, a hybrid classification method is developed, including uncertainty information in the classification of very high spatial resolution multi-spectral satellite images, to obtain a map of LUs. The developed classification methodology includes the following steps: (1) a pixel-based hard classification with a probabilistic Bayesian classifier; (2) computation of the posterior probabilities and quantification of the classification uncertainty using an uncertainty measure; (3) image segmentation and (4) object classification based on decision rules. The classification of the resulting objects into LUs is performed considering a set of decision rules that incorporate the pixelbased classification uncertainty. The proposed methodology was tested on the classification of an IKONOS satellite image. The accuracy of the classification was computed using an error matrix. The comparison between the results obtained with the proposed approach and those obtained without considering the classification uncertainty revealed a 12% increase in the overall accuracy. This shows that the information about uncertainty can be valuable when making decisions and can actually increase the accuracy of the classification results.info:eu-repo/semantics/publishedVersio

Assessment of the state of conservation of buildings through roof mapping using very high spatial resolution images

The assessment of the state of conservation of buildings is extremely important in urban rehabilitation. In the case of historical towns or city centres, the pathological characterization using traditional methods is a laborious and time consuming procedure. This study aims to show that Very High Spatial Resolution (VHSR) multispectral images can be used to obtain information regarding the state of conservation of roofs where, usually, building degradation starts. The study was performed with multispectral aerial images with a spatial resolution of 0.5 m. To extract the required information, a hybrid classification method was developed, that integrates pixel and object based classification methods, as well as information regarding the classification uncertainty. The proposed method was tested on the classification of the historical city centre of Coimbra, in Portugal, that includes over than 800 buildings. The results were then validated with the data obtained from a study conducted during 2 years by a nine element team from the University of Coimbra, using traditional methods. The study performed achieved a global classification accuracy of 78%, which proves that the state of conservation of roofs can be obtained from VHSR multispectralimages using the described methodology with a fairly good accuracy.info:eu-repo/semantics/publishedVersio

ρ-MtreeRing: A graphical user interface for X-ray microdensity analysis

Producción CientíficaWood microdensitometry provides an integrated measurement of inter and intra-annual changes in wood anatomy and lignification. Although it can be acquired through a wide array of techniques, X-ray-based techniques are still the standard. Conversion of a grayscale X-ray image to density and annual ring boundaries delimitation is performed through image analysis software. Proprietary software has dominated these applications, albeit Free Open Source Software (FOSS) has been developed recently. We present ρ-MtreeRing, a user-friendly FOSS that streamlines the entire microdensitometry analysis process through a graphical user interface based on Shiny R Software without any programming knowledge. We compared the results of this program with the most widely used commercial software (WinDendro), showing the validity of the results. ρ-MtreeRing can be personalized and developed by the microdensitometry research community.Junta de Castilla y León (VA171P20)Ministerio de Ciencia e Innovación y Ministerio de Universidades grant number CGL2017-87309-P (MGH PRE2018-084106) and project PROWARM (PID2020-118444GA-I00)Universidad Politécnica de Madrid (through Project RP200060107)Centro de Recursos Hídricos para la Agricultura y la Minería (ANID/FONDAP/15130015