Urban areas are facing increasing challenges from extreme weather events caused by climate change. The Intergovernmental Panel on Climate Change (IPCC) emphasizes the alteration of weather patterns, resulting in higher frequency and intensity of weather extremes. This phenomenon is evident in the Netherlands, as observed in the summers of 2021 and 2022, characterized by prolonged periods of drought and severe rainfall. Given these changes, there is a pressing need to adapt urban landscapes to improve resilience against such extremes. In the past , the Netherlands has largely focused on flood prevention and water drainage systems. However, little attention has been given to the implementation of drought-resilient water management systems that are becoming increasingly important for our societal resource management and infrastructure. We propose a mathematical optimization framework to evaluate mitigation strategies to address drought in urban green infrastructure. Our approach integrates local soil and vegetation characteristics with future climate scenarios obtained from the Royal Netherlands Meteorological Institute (KNMI). The model considers key physical processes such as soil moisture balance and evapotranspiration, as well as different vegetation types and soil parameters, including leaf area, rooting depth and soil water retention capacity. The model allows for a comprehensive analysis of the complex relationships among these factors and their dynamics under future climate conditions. As such, it shows their relation and impact on the system resilience against drought. The model is validated through a case study in the Bajeskwartier in Amsterdam Venserpolder. The findings reveal the model efficacy in determining the minimum irrigation requirements and the necessary water storage capacity to prevent plant stress. The model can be used by urban planners and policymakers seeking effective strategies to enhance urban drought resilience in the face of changing climate. Through analysis, the study demonstrates that the Bajeskwartier possesses adequate water storage capacity to meet the demand during a 1-in-30-year drought scenario projected for the year 2085. In the future we will refine the proposed mathematical optimization framework by performing additional analyses and validation of different soil parameters, vegetation types and associated physical processes. Further, we will enhance the temporal and spatial resolution of the model. Finally, we will examine its applicability by testing it against a broader range of urban environments and climate conditions.

According to the latest IPCC reports heat waves will increase in both intensity and frequency. Due to the physical nature of cities, they are particularly vulnerable to this increase in heat. Urban heat island (UHI) effect analysis often relies on satellite imagery, which gives a planar representation of often three-dimensional features. In this study, we propose to integrate street view data to create a large-scale approximation of local street-level micro-climates in urban environments, using Amsterdam as a case study. We present a method that incorporates street view images with a semantic segmentation model to capture finer urban elements from the panoramic images, such as sky, buildings, trees, pervious and impervious surfaces. Furthermore, our approach also involves the calculation of view factors derived from panoramic street view images, employing a hemispherical azimuthal projection technique to accurately capture the 3D element of the urban environment. This allows us to assess the impact of various environmental features on LST, considering elements such as tree view factor (TVF), sky view factor (SVF) and building view factor (BVF). We then use the extracted features to model the relationship between these features and LST using machine learning algorithms such as Support Vector Regression, Gradient Boosting Tree, and a Random Forest.

The City of Amsterdam is facing specific problems relating to climate change. One of these problems is the increase in Harmful Algae Blooms (HABs) in the extensive canal network of the city. HABs are the spontaneous and excessive growth of algae capable of producing toxins harmful to humans as well as marine animals and birds. The occurrence of HABs has a detrimental effect on the water quality and biodiversity of the canals and also prevents the recreational use of water bodies affected by them. Currently, many urban phenomena are measured by using stationary sensors spread around the city. However, it has been shown that the use of drive by sensing, a new paradigm in urban sensing, can increase the spatiotemporal coverage of sensor networks. For instance, MIT’s City Scanner project aims to transform everyday vehicles such as buses and taxis into sensing nodes for measuring air quality, showcasing the potential of drive-by sensing in expanding the spatiotemporal coverage of sensor networks. When it comes to the monitoring of algae growth in Amsterdam. The current approach of Waternet (the water authority of Amsterdam) is the use of several fixed sampling locations across the city. While this is a good start at monitoring HABs, the fact that the sensors are fixed makes the spatial coverage quite low. This is a problem because the excessive growth of algae colonies is a hyper-local phenomenon with hyper-local implications. The ambition is to use drive-by sensing similar to that in the City Scanner project to capture high-resolution data of algal colonies. Researchers at the Senseable City Lab have therefore designed a mobile LED-induced fluorescence spectrometer that can be placed on boats owned by the municipality. This new device, called REMORA, can make measurements autonomously, without the need for human interference. The REMORA sensor can be used to detect, quantify, and identify algae species. The sensor consists of an LED light source emitting light at certain wavelengths. This light excites the pigments in the algae which then emit light at a certain wavelength as well. These wavelengths are then measured and collected in the output as an Excitation Emission Matrix (EEM). This high dimensional datatype is used in several different contexts. Recently, researchers have detailed the use of machine learning in analyzing EEM data. This research aims to find the model best suited for the classification of algae species, using EEM data from the REMORA sensor. Therefore making it possible to analyze the real time growth of algae in Amsterdam. The following research question has been devised: ”Could the performance of the REMORA sensor in identifying and quantifying algae species be improved by using machine learning techniques?”

Urban greenery plays a crucial role in developing sustainable cities. Commonly used metrics to assess urban greenery are the Normalized Difference Vegetation Index (NDVI) and Green View Index (GVI), derived from satellite and street view imagery. Although deemed objective metrics, they can yield inconsistent results and introduce biases. To attain a comprehensive understanding and precise mapping of urban greenery, it is imperative to scrutinize the inherent biases associated with these measurement methods. In this study, we calculate satellite-based and street view-based urban greenery based on these two approaches and examine the characteristics of the two datasets across ten cities globally. Through a systematic analysis of the statistical and spatial disparities between NDVI and GVI, our findings demonstrate significant differences in urban greenery measurements derived from these two sources. We identify eight critical factors contributing to these disparities: distance-perspective limitation, single-profile constraint, access limitation, temporal data discrepancy, proximity amplification, vegetative wall effect, multi-layer greenery concealment, and noise. We analyze the implications of these measurement disparities in two urban domains: housing price estimation and air pollution correlation. The findings underscore that the choice of measurement method significantly influences research outcomes, necessitating a nuanced approach that combines both satellite and street view measurements for comprehensive urban greenery analysis. In conclusion, our study advances the understanding of biases and disparities in urban greenery measurement, advocating for a nuanced approach that integrates both satellite and street view data as there is no “true” greenery. This research contributes to the ongoing discourse on urban greenery and inspires future studies aimed at refining measurement methodologies and enhancing the quality of urban life.