Conventional urbanization transforms natural into paved landscapes, posing a significant environmental challenge. Detecting the changes in (im)pervious surfaces in cities, where patches are small and intermingled, is particularly challenging. This study introduces a novel approach to these changes by integrating Coupled Non-negative Matrix Factorization (CNMF) image fusion with an automatic pixel purification algorithm. By fusing low-resolution hyperspectral (30m) with high-resolution panchromatic (5m) PRISMA imagery, we achieved enhanced spatial resolution, crucial for accurate land use and land cover (LULC) classification. We introduced automatic pixel purification as a key innovation method to improve LULC mapping accuracy, sensitive to training pixel selection and mixed pixels. This method, which is tested in Dublin City area, enhanced/ refined spectral signatures and clarity across major LULC classes including bare soil, industrial roofs, grasslands, trees, residential roofs/asphalts, and water bodies, significantly improving classification accuracy by removing outliers and ensuring spectral consistency. The Random Forest (RF) algorithm, applied before and after pixel purification, showed substantial increases in overall accuracy (from 94.04% to 96.69%,) and Kappa coefficient (from 92.60% to 95.91%) for 2021, with similar improvements in 2022. This method enabled accurate differential analysis of (im)pervious surfaces, revealing a 4.08% decrease in pervious (from 33.29 km2 to 28.08 km2) and a 4.09% increase in impervious surfaces (from 79.96 km2 to 82.92 km2) over one year, highlighting the rapid urbanization’s impact on Dublin’s landscape permeability. This study significantly advances LULC classification and urban monitoring, offering valuable insights for sustainable urban development and advocating for its integration into future remote sensing and urban planning initiatives.

Green roofs are essential for urban greening and climate adaptation, especially in densely populated areas. Analyzing runoff reduction parameters is crucial for effectively designing and implementing these systems. This study enhances traditional assessments using advanced sensors to gather meteorological and hydrological data from four green roof installations at University College Dublin (UCD) in Dublin, Ireland. The comprehensive dataset enabled detailed modeling of runoff hydrograph parameters using rainfall hyetographs, which were subsequently analyzed through sophisticated machine learning algorithms. This research introduces an innovative approach by identifying the optimal combination of variables for modeling key runoff characteristics, including Water Retention Amount (WRA), Total RUnoff Volume (TRUV), Peak Runoff Discharge (PRD), and Peak Flow Reduction (PFR). The findings are compelling, with Support Vector Regression (SVR) achieving R² values ranging from 0.67 to 0.82 and RMSE values ranging from 0.37 to 1.51 millimeters for WRA, TRUV, PRD, and PFR. XGBoost (XGB) demonstrated superior performance, with R² values ranging from 0.77 to 0.84 and RMSE values ranging from 0.28 to 1.26 millimeters for the same parameters. Random Forest Regression (RF) also showed robust results, with R² values ranging from 0.76 to 0.84 and RMSE values ranging from 0.31 to 1.29 millimeters. Overall, the green roof system demonstrated a water retention rate of 55.69% for the studied events. The study identifies Cumulative Rainfall Volume (CRV) and Peak Rainfall Intensity (PRI) as crucial for modeling runoff, highlighting green roofs’ potential as sustainable urban infrastructure and offering key insights for their design and optimization.

Hyperspectral imageries are often degraded by systematic sensor-based errors known as “striping noises”. This study implements a spectral-based regression algorithm from highly correlated consecutive bands, i.e. left band, right band or both, to model and reconstruct the abnormal pixel values, stripe noises, in various bands of PRISMA (PRecursore IperSpettrale della Missione Applicativa) imagery. The modeling performance was evaluated based on the statistical difference between the reconstructed images’ pixel values (reflectance) and their corresponding original pixel values. Results referred to the model’s high accuracy in R², RMSE, rRMSE and skewness in most bands $(0.9492<{\mathrm{R}}^2<0.9995;0.0008<RMSE<0.0254;0.0116\text{break}<rRMSE<0.5713-0.9280<skewness<0.2708$). Furthermore, the results indicated that the combination of both bands had higher accuracy and pixels’ homogeneity preservation compared to single-band modeling. Our findings suggested that the algorithm significantly depends on the spectral similarities between neighboring bands so that the higher spectral similarities lead to the higher model performance and vice versa. Subsequently, the minimum model performance was observed in band 143 due to lower spectral similarity, lower spectral correlation and higher wavelength differences with its adjacent right band. Finally, the study suggests that alongside other methods, our algorithm may be used as a reliable, straightforward and accurate alternative for destriping different Earth observation satellite imageries. Limitations of the proposed approach are also discussed.

132Integration of morphometric and morphotectonic analysis with remote sensing and geographic information system (GIS) techniques is useful in quantifying the possible tectonic activity within a specific area. Such an integrated analysis was conducted in this study for the Qorveh–Dehgolan basin using 11 parameters. While morphometric parameters included stream order (U), stream number (N_u ), stream length (L_u ), and drainage density (D_d ), morphotectonic indices include stream length-gradient index (Sl), drainage basin asymmetry (Af), hypsometric curve (HC), hypsometric integral (HI), elongation ratio (R_e ), stream sinuosity (K), and basin slope (S_b ). The study area is part of the Sanandaj–Sirjan zone belonging to the Zagros orogenic belt. Because the study area is mainly a low relief basin, relevant indices like sinuosity (K), basin slope (S_b ), and elongation ratio (R_e ) were primarily used to account appropriately for such a physiographic condition. The whole area was divided into eight microwatersheds to have comparative results. Analysis of all 11 parameters revealed that basins that are located at a lower altitude are actually old where depositional and geomorphic processes control the area, whereas tectonic activity plays an important role in watershed hydrology of mountainous parts of the study area.

Resource-based cities (RBCs) in China are at a historic juncture in their transformative development. Observing and assessing the role of the resource curse in urban expansion and greening is crucial for the sustainable development of these cities. This study proposes a new framework to extract urban boundary data from 2000 to 2020 in China. Utilizing these data, we analyzed differences in urban expansion intensity and urban vegetation cover between 125 RBCs and 223 non-RBCs. We found the following: (1) While urban areas in China experienced steady growth from 2000 to 2020, the urban area expansion rates of RBCs lagged behind those non-RBCs located in the same geographical areas except in South China, with the lowest annual expansion rate of 1.18% occurring in the Northeast. (2) Although the existing urban areas in some cities show a greening trend, both existing and new urban areas in China are predominantly characterized overall by browning. Surprisingly, RBCs exhibit a stronger greening trend than non-RBCs, particularly in Northwestern China. (3) There is a nuanced interplay and coexistence between resource dependency and urban expansion, with a specific negative correlation when resource dependency is very high or very low. This study provides a novel method and approach for urban boundary delineation. It offers new insights into the developmental characteristics of RBCs, enriching the theoretical framework of resource curse research and supporting the green development of RBCs with robust data.