Heritage buildings are valuable assets that represent national cultural identity. Proper building maintenance is a major issue for preservation, as building monitoring aspects and preventive measures are often only taken after physical damage happens. In the context of Indonesian heritage buildings, high levels of humidity which may cause condensation and soil dampness are often overlooked. Early detection methods are urgently required to effectively detect potential risks of condensation. This study aims to investigate condensation risk for heritage building surfaces by calculating thermal properties (i.e., emissivity, albedo) and Blinn-Phong BRDF values through the integration of thermal imaging and 3D scanning techniques. This approach supports architects and conservators in making informed decisions to protect and maintain cultural heritage structures. The study also highlights gaps in current Indonesian regulations regarding moisture presence and condensation risk detection in heritage buildings.

Monitoring surface albedo at a fine spatial resolution in forests can enrich process understanding and benefit ecosystem modeling and climate-oriented forest management. Direct estimation of surface albedo using 10 m reflectance imagery from Sentinel-2 is a promising research avenue to this extent, although questions remain regarding the representativeness of the underlying model of surface reflectance anisotropy originating from coarser-resolution imagery (e.g., MODIS). Here, using Fennoscandia (Norway, Sweden, Finland) as a case region, we test the hypothesis that systematic stratification of the forested landscape into similar species compositions and physical structures prior to the step of carrying out angular bin regressions can lead to improved albedo estimation accuracy of direct estimation algorithms. We find that such stratification does not lead to statistically meaningful improvement over stratification based on conventional land cover classification, suggesting that factors other than forest structure (e.g., soils, understory vegetation) may be equally important in explaining within-forest variations in surface reflectance anisotropy. Nevertheless, for Sentinel-2-based direct estimation based on conventional forest classification, we document total-sky surface albedo errors (RMSE) during snowfree and snow-covered conditions of 0.015 (15 %) and 0.037 (21 %), respectively, which align with those of the coarser spatial resolution products in current operation.

The non-Lambertian surface features varying particle size and discrete distribution, resulting in reflectance to be unevenly distributed in different directions. Mine soil with a high content of coarse particles and non-uniform particle distribution exhibits significant non-Lambertian properties on its surface. Consequently, not only vertical observation of the reflectance spectra but also multi-angle reflectance spectra are related to the physical and chemical properties (e.g. soil organic carbon, moisture content and particle size) of mine soil. Understanding the bidirectional reflectance distribution of mine soil with various particle sizes is essential for accurately estimating soil properties using spectroscopy. Current estimations of soil properties using spectroscopy mainly focus on vertical observations, overlooking the bidirectional reflectance characteristics. This study reports the bidirectional reflectance distribution of mine soil with various particle sizes. Furthermore, the performance of different bidirectional reflectance distribution function (BRDF) models in simulating the bidirectional reflectance of mine soil with various particle sizes was evaluated. Soil samples from three typical mine areas were collected and sieved into seven particle sizes ranging from 25 to 3500 mu m. The bidirectional reflectance in the Vis-NIR wavelength region was measured in a laboratory using the Northeastern University bidirectional reflectance measurement system. The performance of five BRDF models (isotropic multiple scattering approximation, anisotropic multiple scattering approximation, H2008, H2012 and SOILSPECT) in modelling the bidirectional reflectance distribution of mine soil with different particle sizes was compared. Sobol's sensitivity indices were used to quantify the contributions of the parameters in the BRDF models. The results showed that (1) small mine soil particles (25 mu m) exhibited greater reflectance than large particles (3500 mu m). Large particles (3500 mu m) exhibited backward scattering, whereas small particles (25 mu m) exhibited extremely forward scattering characteristics because of the high silicon dioxide content; (2) the SOILSPECT model outperformed the other BRDF models in simulating the bidirectional reflectance of mine soil and had the smallest root mean square error (0.004-0.04); (3) the single-scattering albedo (omega) parameter had the greatest contribution in the SOILSPECT model. Four parameters in the phase function (b, b ', c and c ') effectively indicated the scattering behaviour of mine soil with different particle sizes. These findings improve our understanding of the scattering characteristics of mine soil with various particle sizes and can be used to improve the accuracy of extracting particle size and other soil properties from mine soil.