Extraction of Urban Areas Using Spectral Indices Combination and Google Earth Engine in Algerian Highlands (Case Study: Cities of Djelfa, Messaad, Ain Oussera)

The fundamental difficulty in mapping urban areas, especially in semi-arid and arid environments, is the separation of built-up areas from bare lands, owing to their similar spectral characteristics. Accordingly, this study aims to identify the suitable spectral index that can provide high differentiation, between urban areas and bare lands, in semi-arid areas of three cities of the province of Djelfa, namely, Djelfa, Messaad, and Ain Oussera (Algerian central highlands), through a selection of four spectral indices including Urban Index (BUI), Band ratio for built-up area (BRBA), Normalized Difference Tillage Index (NDTI) and Dry Bare-soil Index (DBSI). In order to increase the mapping accuracy of the built-up in studied areas, a multi-index approach has been applied focusing on identifying an adequate combination of spectral indices of remote sensing that provides the highest performance compared to the images of sentinel 2A. The multi-index approach was developed using three spectral indices combinations and was created using a layer stack process. For forming bare land layer stacking data, both NDTI and DBSI indices were used, while the built-up area layer stacking data was made with both BUI and BRBA indices. The main process was carried out on the Cloud Computing Platform based on geospatial data of Google Earth Engine (GEE) and using machine learning classification by the Support Vector Machine (SVM) algorithm, based on imagery from sentinel 2A acquired during the dry season. The results indicated that the thresholds of the built-up areas are difficult to delineate and distinguish from bare land efficiently with a single index. The obtained results also revealed that the use of multi-index including BUI index provided the best results as they showed the highest effects with NDTI index and DBSI index compared to BRBA index, where the overall accuracies of the multi-index (DBSI/ NDTI/ BUI) were 98.7% in Djelfa, 96.5% in Messaad, and 97.87 % in Ain Oussera, and the kappa coefficients were 97.3%, 85.4%, and 95.3% respectively. These results show that this multi-index is effective and reliable and can be considered for use in other areas with similar characteristics.

Revealing Kunming’s (China) historical urban planning policies through Local Climate Zones

Over the last decade, Kunming has been subject to a strong urbanisation driven by rapid economic growth and socio-economic, topographical and proximity factors. As this urbanisation is expected to continue in the future, it is important to understand its environmental impacts and the role that spatial planning strategies and urbanisation regulations can play herein. This is addressed by (1) quantifying the cities' expansion and intra-urban restructuring using Local Climate Zones (LCZs) for three periods in time (2005, 2011 and 2017) based on the World Urban Database and Access Portal Tool (WUDAPT) protocol, and (2) cross-referencing observed land-use and land-cover changes with existing planning regulations. The results of the surveys on urban development show that, between 2005 and 2011, the city showed spatial expansion, whereas between 2011 and 2017, densification mainly occurred within the existing urban extent. Between 2005 and 2017, the fraction of open LCZs increased, with the largest increase taking place between 2011 and 2017. The largest decrease was seen for low the plants (LCZ D) and agricultural greenhouse (LCZ H) categories. As the potential of LCZs as, for example, a heat stress assessment tool has been shown elsewhere, understanding the relation between policy strategies and LCZ changes is important to take rational urban planning strategies toward sustainable city development

Feature Importance Analysis for Local Climate Zone Classification Using a Residual Convolutional Neural Network with Multi-Source Datasets

Global Local Climate Zone (LCZ) maps, indicating urban structures and land use, are crucial for Urban Heat Island (UHI) studies and also as starting points to better understand the spatio-temporal dynamics of cities worldwide. However, reliable LCZ maps are not available on a global scale, hindering scientific progress across a range of disciplines that study the functionality of sustainable cities. As a first step towards large-scale LCZ mapping, this paper tries to provide guidance about data/feature choice. To this end, we evaluate the spectral reflectance and spectral indices of the globally available Sentinel-2 and Landsat-8 imagery, as well as the Global Urban Footprint (GUF) dataset, the OpenStreetMap layers buildings and land use and the Visible Infrared Imager Radiometer Suite (VIIRS)-based Nighttime Light (NTL) data, regarding their relevance for discriminating different Local Climate Zones (LCZs). Using a Residual convolutional neural Network (ResNet), a systematic analysis of feature importance is performed with a manually-labeled dataset containing nine cities located in Europe. Based on the investigation of the data and feature choice, we propose a framework to fully exploit the available datasets. The results show that GUF, OSM and NTL can contribute to the classification accuracy of some LCZs with relatively few samples, and it is suggested that Landsat-8 and Sentinel-2 spectral reflectances should be jointly used, for example in a majority voting manner, as proven by the improvement from the proposed framework, for large-scale LCZ mapping

Using OpenStreetMap (OSM) to enhance the classification of local climate zones in the framework of WUDAPT

The World Urban Database and Access Portal Tools (WUDAPT) project has adopted the LocalClimate Zone (LCZ) scheme as a basic and consistent description of form and function of cities atneighbourhood scale. LCZs are classified using crowdsourced training samples, open data andopen source software but the quality of the maps still needs improvement. The aim of this paperis to investigate the use of data from OpenStreetMap (OSM) to enhance the development of LCZs,complement the existing data sources, and improve the accuracy of the maps. Various featureswere derived from the OSM database and combined with seasonal LCZ maps. Therefore amethodology was developed and tested for Hamburg, Germany, using a fuzzy approach and thena weighted combination method was applied to combine the inputs from OSM with each of theseasonal LCZ maps. The results showed that improvements can be achieved for certain classes,either in terms of accuracy, e.g. rectifying the misclassification of agricultural areas as heavyindustry, or representation on the map, e.g. a more detailed water network. The approach developedis flexible and allows for knowledge about which data sources are more reliable as inputsto the combination and weighting process

Spatial-Temporal Analysis of the Urban Heat Island Using Satellite Images: Capitals of Andalusia

La búsqueda de nuevas técnicas que permitan determinar de forma económica y precisa el fenómeno de alteración de clima urbano denominado Isla de Calor Urbana (ICU) se ha convertido en uno de los grandes retos de la sociedad. Su conocimiento sobre las urbes permitiría la implantación de medidas de mitigación y resiliencia que tiendan a minimizar sus efectos y el coste económico que conlleva. En esta investigación, se ha determinado la Temperatura de la Superficie Terrestre (TST) y la ICU mediante imágenes satelitales Séntinel 3 de las ocho capitales de Andalucía (España) durante el año 2020. Estas se ubican en una zona calificada como de alta vulnerabilidad a los efectos del cambio climático lo que unido al empleo de zonas climáticas locales (ZCL) permite que los resultados puedan ser extrapolados a otras ciudades con iguales tipologías de zonas climáticas. Los resultados obtenidos indican que durante la mañana se produce en las ciudades estudiadas una isla de enfriamiento urbano de temperatura media -0,76 ºC y durante la noche una ICU de temperatura media 1,29 ºC. Ambas presentan mayores intensidades en las ZCL compactas de media y baja densidad en contraposición con las ZCL abiertas e industriales. La variabilidad estacional de la ICU diurna se intensifica durante el verano y el invierno y la nocturna durante el invierno y el otoño. Se comprueba la existencia de relaciones diurnas negativas significativas al 99% (p<0,01) entre la ICU y la contaminación ambiental y de relaciones nocturnas, en iguales condiciones, entre la ICU y la TST, fracción vegetal (Pv) y la contaminación.The search for new techniques that make it possible to determine economically and precisely the phenomenon of urban climate alteration called Urban Heat Island (ICU) has become one of the great challenges of society. Their knowledge of cities would allow the implementation of mitigation and resilience measures that tend to minimize their effects and the economic cost that they entail. In this research, the Terrestrial Surface Temperature (TST) and the ICU have been determined through Sentinel 3 satellite images of the eight capitals of Andalusia (Spain) during the year 2020. These are located in an area classified as highly vulnerable to the effects of climate change, which, together with the use of local climate zones (ZCL), allows the results to be extrapolated to other cities with the same types of climate zones. The results obtained indicate that during the morning there is an urban cooling island with an average temperature of -0.76 ºC and during the night an ICU with an average temperature of 1.29 ºC. Both present higher intensities in compact ZCL of medium and low density in contrast to open and industrial ZCL. The seasonal variability of the diurnal ICU is intensified during the summer and winter and the nocturnal one during the winter and autumn. The existence of negative diurnal relationships significant at 99% (p <0.01) between the ICU and environmental contamination and of nocturnal relationships in the same conditions between the ICU and the TST, plant fraction (Pv) and contamination are verified

Matching environmental data produced from remote sensing images to demographic data in Sub-Saharan Africa

In a globalized context increasingly impacted by climate change, demographic studies would gain from taking environmental data into account and be carried out at the transnational level. However, this is not always possible in Sub-Saharan Africa, as matching harmonized demographic and environmental data are seldom available. The large amount of data regularly acquired since 2015 (in 2019 only, Sentinel satellites from the European Space Agency produced 7.54 PiB of open-access data) are an opportunity to produce relevant standardized indicators at the global scale. Several indicators have been developed to help understanding geographical realities in a consistent (i.e., not location dependent) manner. Among them, local climate zones (LCZ) have been proposed by WUDAPT (World Urban Database and Access Portal Tools) to systematically label urban areas [2]. Their goal is to provide a map of the world following this legend, in open access, that can later be used by researchers for a wide range of studies. This data has been used to understand energy usage [1], climate [3] or geoscience modeling [10] or land consumption [5]. An important amount of work has been dedicated in the recent years to the automatic generation of such data, from sensors such as Landsat 8 or Sentinel 2. In a research competition organized by the IEEE IADF, several methods have been proposed to map LCZ from Landsat, Sentinel 2 and OpenStreetMap data [11]. Another recent study focused on the usage of Convolutional Neural Networks (CNNs) to tackle the task of automatically mapping LCZ using deep learning [7] and a large-scale benchmark dataset was proposed in [12], with a baseline of an attention-based CNN. However, these works mostly focused on developed urban areas. For instance, the challenge of [11] targeted Berlin, Hong Kong, Paris, Rome, São Paulo, Amsterdam, Chicago, Madrid, and Xi’An. This is problematic, as developed cities are generally well mapped through governmental censuses, and that spatial generalization of machine learning based methods is a challenge [6]. It is therefore necessary to develop adapted methods for developing areas [9]. In this work, we explore different methods to predict LCZ from Sentinel-2 data. The originality of the approach is to train a convolutional network-based model (ResNet34 [4]) on clusters of data representing similar morphological features as our target city (Ouagadougou, Burkina Faso). To select relevant cities as training data, we use the classification proposed in [8] and intersect relevant cities with those represented in the large-scale LCZ dataset [12]. As such, our dataset is composed of areas covering Karachi an Islamabad, Pakistan, Cairo, Egypt and Hong-Kong, China. Preliminary results show that ResNet34 [4] achieves good performance when training it on images representing similar morphological features (Overall accuracy: 94%). We perform LCZ classification on Ouagadougou with this model. It exhibits two main findings: • Resnet34 [4] can be generalized to an unseen area which has similar morphological features to the training areas. • Some of LCZ classifications are dependent to seasonal variations. In particular, we observed that some classes which do not contain vegetation (i.e., expected to be invariant to seasons) have not been similarly predicted when looking at several seasons. We attribute this phenomenon to the lack of seasonal changes within the training dataset, which does not consider weather fluctuations. Figure 1 shows LCZ classifications of Ouagadougou according to the seasons. Results are globally consistent, and seasonal misclassifications are more frequent when looking at the outskirts of the city. Red classes are buildings, so are expected to be invariant to the seasons. As well as for seasons, this result highlights the necessity to generate data for rural areas, as training on urban areas does not appear to generalize well when inferring on rural areas. To study the correlation between population data and LCZ, we cross-referenced Ouagadougou’s population density data with our classification results in figure 2 and investigate the population density per LCZ class. As expected, class with compact mid-rise buildings are correlated with a high population density. Compact low-rise buildings are associated to a lower population density, and natural areas are predicted as not populated areas, which validate the results of our model. References: [1] Paul John Alexander, Gerald Mills, and Rowan Fealy. Using lcz data to run an urban energy balance model. Urban Climate, 13:14–37, 2015. [2] Benjamin Bechtel, Paul J Alexander, Jürgen Böhner, Jason Ching, Olaf Conrad, Johannes Feddema, Gerald Mills, Linda See, and Iain Stewart. Mapping local climate zones for a worldwide database of the form and function of cities. ISPRS International Journal of Geo-Information, 4(1):199–219, 2015.2 [3] Jan Geletič, Michal Lehnert, Petr Dobrovoln`y, and Maja Žuvela-Aloise. Spatial modelling of summerclimate indices based on local climate zones: expected changes in the future climate of brno, czech republic. Climatic Change, 152(3-4):487–502, 2019. [4] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016. [5] Jingliang Hu, Yuanyuan Wang, Hannes Taubenböck, and Xiao Xiang Zhu. Land consumption in cities: A comparative study across the globe. Cities, 113:103163, 2021. [6] Emmanuel Maggiori, Yuliya Tarabalka, Guillaume Charpiat, and Pierre Alliez. Can semantic labeling methods generalize to any city? the inria aerial image labeling benchmark. In2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pages 3226–3229. IEEE, 2017. [7] Chunping Qiu, Michael Schmitt, Lichao Mou, Pedram Ghamisi, and Xiao Xiang Zhu. Feature importance analysis for local climate zone classification using a residual convolutional neural network with multi-source datasets. Remote Sensing, 10(10):1572, 2018. [8] Hannes Taubenböck, Henri Debray, Chunping Qiu, Michael Schmitt, Yuanyuan Wang, and Xiao Xiang Zhu.Seven city types representing morphologic configurations of cities across the globe. Cities, 105:102814, 2020. [9] John E Vargas-Muñoz, Sylvain Lobry, Alexandre X Falcão, and Devis Tuia. Correcting rural building annotations in openstreetmap using convolutional neural networks. ISPRS journal of photogrammetry and remote sensing, 147:283–293, 2019. [10] Hendrik Wouters, Matthias Demuzere, Ulrich Blahak, Krzysztof Fortuniak, Bino Maiheu, Johan Camps,Daniël Tielemans, and Nicole Van Lipzig. The efficient urban canopy dependency parametrization (sury)v1. 0 for atmospheric modelling: description and application with the cosmo-clm model for a Belgian summer. Geoscientific Model Development, 9(9):3027–3054, 2016. [11] Naoto Yokoya, Pedram Ghamisi, Junshi Xia, Sergey Sukhanov, Roel Heremans, Ivan Tankoyeu, BenjaminBechtel, Bertrand Le Saux, Gabriele Moser, and Devis Tuia. Open data for global multimodal land use classification: Outcome of the 2017 IEEE GRSS data fusion contest. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(5):1363–1377, 2018. [12] Xiao Xiang Zhu, Jingliang Hu, Chunping Qiu, Yilei Shi, Jian Kang, Lichao Mou, Hossein Bagheri, Matthias Häberle, Yuan sheng Hua, Rong Huang, et al. So2sat lcz42: A benchmark dataset for global local climate zones classification. arXiv preprint arXiv:1912.12171, 201