Causal Evaluation of Contributing Factors to Urban Heat Island

Urban heat islands (UHI) are formed due to complex interactions between various factors. UHI, its contributing factors, and their interaction vary over time and location. Accordingly, understanding the causal relation between UHI and its contributing factors is essential to minimizing its adverse effects on the environment and human health. Here, we proposed a statistical method based on Hotelling's T-square test to analyze this association. The proposed test estimates the UHI trends across different urban districts and compares the UHI contributing factors between the districts with increasing and non-increasing UHI trends. This comparison, if significantly different, can be interpreted as evidence of a causal association between the factor and UHI. This research used the proposed test to analyze the UHI and its contributing factors across 22 municipal districts of Tehran between 2003 and 2021. We examined the time series of weather conditions (measured by precipitation, NDSI, and NDWI), vegetation cover (measured by NDVI and EVI), and urban density (measured by NDBI) as factors contributing to the UHI, which was measured through nighttime LST. The results showed that all districts in Tehran exhibited stable or increasing trends in LST, leading to UHI effects. The proposed test indicated that the temporal changes in NDWI and NDBI did not have a causal relationship with UHIs. Meanwhile, variations in other factors were identified as contributing to the intensification of UHIs.

Cluster-based Random Radial Basis Kernel Function for Hyperspectral Data Classification

Kernel-based classification methods, particularly the support vector machine (SVM), are among the most common algorithms for hyperspectral data classification. The Radial Basis function (RBF) kernel has earned great popularity in hyperspectral data classification due to its superior performance among other available kernel functions. Nonetheless, the cross-validation technique usually used for tunning the RBF parameter can be time-consuming and may result in sub-optimal values for the parameter. This paper proposed the cluster-based random radial basis function (CRRBF) kernel function as an alternative to the RBF kernel to achieve similar performance with a more manageable parameter, which is the number of clusters. The CRRBF kernel initially clusters the hyperspectral bands and then constructs an RBF kernel with a randomly assigned value as the kernel parameter from each cluster of bands. The final CRRBF kernel is constructed by adding up these basis RBF kernels. We have designed several experiments to evaluate the SVM performance trained with the CRRBF kernel considering a different number of clusters and training samples, using three hyperspectral data sets. The obtained results showed that the CRRBF kernel could provide comparable or better results than the RBF. The results also showed that the classification performance is pretty robust to the number of clusters, as the only open parameter of the CRRBF kernel.

Segment-based fusion of multi-sensor multi-scale satellite soil moisture retrievals

Synergetic use of sensors for soil moisture retrieval is attracting considerable interest due to the different advantages of different sensors. Active, passive, and optic data integration could be a comprehensive solution for exploiting the advantages of different sensors aimed at preparing soil moisture maps. Typically, pixel-based methods are used for multi-sensor fusion. Since, different applications need different scales of soil moisture maps, pixel-based approaches are limited for this purpose. Object-based image analysis employing an image object instead of a pixel could help us to meet this need. This paper proposes a segment-based image fusion framework to evaluate the possibility of preparing a multi-scale soil moisture map through integrated Sentinel-1, Sentinel-2, and Soil Moisture Active Passive (SMAP) data. The results confirmed that the proposed methodology was able to improve soil moisture estimation in different scales up to 20% better compared to pixel-based fusion approach.