This study introduces case adaptive detection models for detecting asbestos-containing building materials employing short-wave infrared hyperspectral images. The models are optimized for indoor and outdoor conditions, which could be applied to asbestos detection in interior and exterior building materials. Samples were collected from the most probable types and forms of building materials for both asbestos-containing materials (ACMs) and non-asbestos-containing materials (non-ACMs) to represent real-world conditions, utilizing the largest and most diverse dataset to date (11 types, 4 directions, and 4 forms). X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy dispersive spectroscopy (EDS) analyses demonstrated that chrysotile was the main asbestos mineral in the most of ACMs, as evidenced by characteristic chrysotile diffraction peaks, fibrous serpentine morphology, and coincident Mg-Si elemental distributions. The spectral characteristics of building materials are manifested by mineral components for ACMs and non-ACMs, where asbestos absorption (Mg-OH) identifies ACMs. The indoor RF model identified critical absorption features at 2299-2350nm, 1923-1967nm, and 2366-2426nm, corresponding to Mg-OH, OH, and CO₃²⁻ absorptions, achieving 95.3% accuracy. The outdoor RF model incorporated additional Fe-OH and Al-OH bands, achieving 96.5% accuracy. Real-world validation at three asbestos abatement sites and one large-scale site demonstrated practical applicability including scalability and computational efficiency with overall accuracies of 92.1% for indoor and 92.5% for outdoor conditions. Comparative analysis with existing benchmark methodologies confirmed superior performance, with our models significantly outperforming previous approaches. The models successfully detected ACMs across various material conditions, scales and effectively verified complete removal post-abatement with high computational speed. This approach delivers immediate practical value for compliance verification and quality assurance in real-world abatement scenarios and large-scale surveys, providing objective documentation for regulatory compliance while substantially reducing costs compared to conventional destructive sampling methods.

Low-cost, high-density water quality data in combination with new high-fidelity imaging satellites can enable high-accuracy simplified remote sensing of inland drinking water sources for sustainability and resilience at the local level. Remote Sensing for sustainability and resilience requires affordability and simplicity for implementation at the local level for drinking water monitoring and protection. United Nations Environment Program (UNEP) studies focused on implementing Integrated Water Resources Management (IWRM) within UN Sustainable Development Goal (SDG) 6.5 indicate that water pollution has worsened over the last three decades in the majority of rivers in Latin America, Africa and Asia [1] with all three areas having relatively limited information on water quality, especially for routine water management. UNEP has suggested the use of satellite remote sensing of several basic water parameters including temperature, chlorophyll a, Secchi Disk depth/transparency, turbidity and sediment dynamics with focused "water-truthing" to fill this information gap.

Previous studies have recognized that traditional empirical water quality remote sensing models are region-specific and lack spatial transferability and temporal repeatability, which prevents the mapping and monitoring inland water quality at a large regional or basin scale. This research evaluates the spatial transferability and temporal repeatability of our multi-predictor ensemble learning model in comparison with traditional empirical models. Our evaluations show that the multi-predictor ensemble model not only substantially improves the prediction accuracy in comparison with traditional individual empirical models, but also has strong spatial and temporal extensibility. The multi-predictor ensemble model calibrated at one specific place and at one certain time can be transferred and re-used in other places and in different time periods with reliable and accurate predictions. The strong spatial and temporal extensibility of the multi-predictor ensemble model is largely attributed to the selective strategy for combining component model results according to the spectral-space partition, which makes the ensemble model dynamically adaptable to a wide range of water conditions over space and time.

In this research, we derived indicator spectral bands and classification models for detecting diesel or gasoline pollution in soil using a near-and shortwave-infrared (NIR-SWIR) hyperspectral camera under a close range and laboratory condition. The soils samples were collected from temperate climate soil with spectral characteristics manifested by secondary minerals. The hyperspectral images show that the diesel and gasoline polluted soil samples have distinctive spectral differences from clean soil. Different from moisture soil, the spectral absorption features of petroleum hydrocarbons (PHCs) are preserved with an increase in gravimetric content. The more PHCs contents, the stronger the depths at the spectral absorption features. In diesel polluted soils, the absorption features were observed in various content levels. However, we found a detection limit for gasoline content in soil, because the absorption features by PHCs disappeared at 8 wt.%. To derive the indicator bands, the images were classified by the random forest algorithm (RF) with an accuracy and kappa coefficient of 94.3% and 0.92 using three groups of bands corresponding to ferric ion, C-H stretch/bending, and BTEX C-H absorptions. The detection models derived from a logistic regression achieved an overall accuracy of 91.82%. The field test of the models on unprocessed soils achieved an accuracy of 83.36%. Because of their simple forms, the logistic detection models can be transferred to remote sensing applications of soil PHCs pollution under a close-range condition such as drone-based projects.

This study developed case-adaptive spectral indices for detecting heavy metal pollution in skarn soil, hydrothermal soil, and acid mine drainage (AMD) soil, and a universal index for general cases by merging data from all cases. Sequential analyses were conducted including heavy metal concentration, mineral identification, grain size, and spectral characteristics. A factor analysis was used to combine multiple heavy metal elements into representative factors and used for establishing the spectral indices using the stepwise multiple linear regression (SMLR), which is compared with random forest regression (RFR). The spectral indices of skarn soil predict Zn, As, and Pb pollution from moderate to ultrahigh levels using absorption bands of skarn and supergene minerals. The spectral index for hydrothermal soil predicts pollution of Cr and Ni from very low to very high using absorption bands of hydrothermal alteration or supergene minerals. The index for the AMD soil predicts Al, Fe, and As pollution from very low to very high levels by employing the absorption features of iron hydroxide. The universal index predicts Fe, Zn, and Pb pollution levels from very low to ultrahigh by utilizing the clay and iron oxide absorptions, commonly observed in general soils. All models showed statistical significance with R2= 0.66-0.92 for SMLR and R2= 0.66-0.95 for RFR. Because our models were derived from large number of samples (600s) for detecting the pollution levels of multiple heavy metal elements for the first time, the models could provide a decision-making tool for soil survey in heavy metal pollution.

The inevitable coarse resolutions (~25 km) of satellite passive microwave images introduce large uncertainty to surface melt estimation on Antarctic ice margins. Our test showed that the melt index (MI) of the Austral year 2012–2013 in the Antarctic Peninsula calculated from a high-resolution product of Calibrated Enhanced-Resolution Passive Microwave Daily EASE-Grid 2 Brightness Temperature (CETB) was 33% lower than the original low-resolution Special Sensor Microwave Imager/Sounder (SSMIS) images. Therefore, to allow for fractional melt estimation, a subpixel mapping method was adopted in this research to improve the accuracy and reliability of surface melt measurement from passive microwave images. This method uses the least-squares mixture analysis (LSMA) on the time series of daily passive microwave images by taking advantage of their high temporal resolution. The endmembers of the unmixing calculation were collected under the constraint of Voronoi polygons. The fractional MI was calculated by multiplying its area with melt fraction. Using the CETB images as reference, we found that the overestimation of surface melt using the Boolean classification was corrected by the LSMA method. The root-mean-square difference (RMSD) of the MI derived from SSMIS compared with the CETB data was reduced from 0.0104 to$0.0083\times 10 ^{{6}}$day$\cdot $km2. A log-linear relationship was found between melt fraction and elevation, which showed that melt fraction is inversely correlated with elevation, and topography is a dominant factor for its variation in low elevations. Such a subpixel unmixing analysis on the passive microwave images can improve the accuracy and reliability of surface melt mapping.

This article examined the spectral interference by heavy metal on the spectral signal of moisture content of heavy metal contaminated soils. Soil samples were collected from an abandoned mine area, and the chemical analysis revealed extremely high contamination amount of copper (Cu), zinc (Zn), arsenic (As), cadmium (Cd), and lead (Pb). The mineralogical analysis showed that the spectral signature of the heavy metal contaminated soils was manifested by secondary minerals. Water content suppressed the spectral reflectance of the soil samples but increased the absorption depths. Although a regression model can predict moisture content using the magnitude of the water absorption feature, the accuracy was much lower when the heavy metal concentration was extremely high. It indicates that geochemical reactions between the heavy metal cation and iron oxide/clay minerals may have affected the spectral responses of the contaminated soils at the water absorption bands. Our model also showed that there was a shift of the absorption features of moisture content if the heavy metal contamination level went up. Unlike normal soils, the absorption features of clay minerals and ferric iron were not able to accurately predict moisture in highly contaminated soils. Given the fact that the spectral bands selected in this article were associated with water absorption, the findings from this article may only be useful to a drone-based low-altitude remote sensing of soil moisture content.

We introduce a spatially explicit statistical downscaling (SESD) method that fuses multiscale geospatial data with the soil moisture (SM) product from NASA's SM Active and Passive (SMAP) satellite. The multiscale data included the 9-km resolution SMAP SM image, 1-km resolution normalized difference vegetation index (NDVI), 1-km digital elevation model (DEM), 1-km resolution MODIS land surface temperature (LST), 500-m resolution gross primary productivity (GPP), 30-m resolution topographical water index (TWI), and West Texas Mesonet (WTM) station data. We used the random forest (RF) machine learning method to make a downscaled SM prediction at the 1-km resolution. Then, a regression kriging was applied to model the unpredicted variability at local scales to produce downscaled SM using the WTM station data. Due to the low number of ground truth samples, the validation was based on Monte -Carlo cross validation (CV) to calculate the unbiased root-mean-square deviation (ubRMSD), root-mean-square deviation (RMSD), and bias of the test set randomly separated from the training set from the WTM station data. Model validation showed that the downscaled SM data at the 1-km resolution can significantly improve the accuracy of the SM product as well as enhancing its spatial resolution. This article has its novelty in using the spatially explicit model to reconcile the scale difference from satellite data and ground observations.

We analyzed heavy metal contamination, mineral composition, and spectral characteristics of white aluminum precipitates from an acid mine drainage in Taebaek, South Korea. We introduced a single index for prediction of overall heavy metal contamination level in white precipitates using spectral variables. The white precipitates were severely contaminated with heavy metal elements and consisted of primary and secondary minerals. Due to the contamination in the environment, the precipitation pH values ranged from 4.76 to 7.80. The spectral characteristics of white precipitates are dominated by secondary minerals. The spectral reflectance of white precipitates decreased in all wavelengths, and the absorption depth related with OH, H2O, and Al-OH decreased with increase in heavy metal contamination. We found the distinctive differences at ferric iron and Al-OH absorptions between the white and reddish-brown precipitates. Based on these observations, we developed heavy metal contamination index and built prediction models using the index. The validation tests showed the index, and the regression model can accurately predict the amount of heavy metal from the spectral readings. Given the fact that the stream precipitates resulted by acid mine drainage is typically similar for each type, we expect that the heavy metal contamination index can be used to make reliable estimates of heavy metal contamination in the white precipitates by remote sensing applications.

This paper investigated the spectral characteristics of heavy metal contaminated soils of a hydrothermal ore deposit developed in carbonate host rock associated with heavy metal concentration and mineral composition. The results showed that spectral response of heavy metal contaminated soils was statistically correlated with zinc, cadmium, and lead concentrations. Empirical equations for predicting zinc, cadmium, and lead concentrations were derived. Spectral characteristics of the soils were expressed by smectite, chlorite, tremolite, and talc which resulted from hydrothermal alteration and weathering products of the parent rocks. The stepwise multiple linear regression (SMLR) model of zinc, cadmium, and lead was statistically satisfactory with R2greater than 0.7. The SMLR results indicated that the spectral response to cadmium and zinc concentration was sensitive to reflectance at 1850 nm and first derivative at ~950 and 2154 nm corresponding to the smectite absorption features. On the other hand, lead concentration is closely related to first derivatives at 1453, 2316, and 2337 nm, which are absorption features of chlorite, tremolite, and talc. These results revealed that the spectral bands sensitive to the heavy metal concentration varied with the geochemical absorption mechanism between specific minerals and heavy metal elements. Therefore, the geological setting of the soils is one of the major controlling factors associated with spectral response to heavy metal contamination. Given the fact that a hydrothermal ore deposit is one of the most widely distributed types, the laboratory result of this paper may be applied to the real-world cases with similar geological environments.

We present a localized neural network algorithm for water depth estimation from optical remote sensing images. Our new model is called a locally adaptive back-propagation neural network (LABPNN). In an LABPNN, the neural networks were trained at regularly distributed normative sites. For each unit of LABPNN, training data samples were identified by a specified search radius from the normative sites. Water depth was estimated by an ensemble of LABPNNs, with weights assigned inversely by their distances to the point of estimation. The water depth prediction accuracy from the LABPNN models doubled compared to the regular back-propagation network that uses all of the samples without considering nonstationarity. We also compared the LABPNN model with the regression-based inversion model with the localization feature. The prediction error of LABPNN is less by about 5% in the first case study, and 7% less in the second case study. It is because of the better performance of neural networks than that of the regression models when the sample data are relatively sparse. The experiments suggest that the LABPNN model is a viable solution to water depth retrieval from optical images.

Previous studies have demonstrated urban built-up areas can be derived from nighttime light satellite (DMSP-OLS) images at the national or continent scale. This paper presents a novel object-based method for detecting and characterizing urban spatial clusters from nighttime light satellite images automatically. First, urban built-up areas, derived from the regionally adaptive thresholding of DMSP-OLS nighttime light data, are represented as discrete urban objects. These urban objects are treated as basic spatial units and quantified in terms of geometric and shape attributes and their spatial relationships. Next, a spatial cluster analysis is applied to these basic urban objects to form a higher level of spatial units – urban spatial clusters. The Minimum Spanning Tree (MST) is used to represent spatial proximity relationships among urban objects. An algorithm based on competing propagation of objects is proposed to construct the MST of urban objects. Unlike previous studies, the distance between urban objects (i.e., the boundaries of urban built-up areas) is adopted to quantify the edge weight in MST. A Gestalt Theory-based method is employed to partition the MST of urban objects into urban spatial clusters. The derived urban spatial clusters are geographically delineated through mathematical morphology operation and construction of minimum convex hull. A series of landscape ecologic and statistical attributes are defined and calculated to characterize these clusters. Our method has been successfully applied to the analysis of urban landscape of China at the national level, and a series of urban clusters have been delimited and quantified.

Optical remote sensing imagery offers a cost-effective alternative to echo sounding and bathymetric light detection and ranging surveys for deriving high density bottom depth estimates for coastal and inland water bodies. The common practice of previous studies has been to calibrate a single global bathymetric inversion model for an entire image scene. The performance of conventional global models is limited when the bottom type and water quality vary spatially within the scene. To address the inadequacy of the conventional global models, this paper presents a geographically adaptive inversion model to better estimate bottom depth. Although the general mathematical form of the geographically adaptive model is the same, model parameters are optimally determined within a geographical region or a local area, in contrast to the entire scene in the global inversion model. By using high-resolution IKONOS and moderate-resolution Landsat satellite images, we demonstrated that regionally and locally calibrated inversion models can effectively address the problems introduced by spatial heterogeneity in water quality and bottom type, and provide significantly improved bathymetric estimates for more complex coastal waters.

Highly repetitive global-scale remote sensing systems, such as the Special Sensor Microwave/Imager (SSM/I), provide essential tools for monitoring changes on the Earth's surface. This letter presents a time-series segmentation and classification method to identify surface changes and to estimate the duration (days) for the changes using daily SSM/I observations. The method was developed based on a bottom-up segmentation algorithm for time-series data. The attributes of the linear segments provide the basis for understanding and classifying the surface changes. In the application examples, we calculated the number of surface snowmelt days at various locations on the Antarctic Ice Sheet by classifying the segmented time series of SSM/I brightness temperature observations. It is demonstrated that this novel method is robust to the data noise and efficient for processing large volume of spatially and temporally continuous remote sensing data for environmental monitoring.

This article presents an object-based conceptual framework and numerical algorithms for representing and analyzing coastal morphological and volumetric changes based on repeat airborne light detection and ranging (LiDAR) surveys. This method identifies and delineates individual zones of erosion and deposition as discrete objects. The explicit object representation of erosion and deposition zones is consistent with the perception and cognition of human analysts and geomorphologists. The extracted objects provide ontological and epistemological foundation to localize, represent, and interpret erosion and deposition patches for better coastal resource management and erosion control. The discrete objects are much better information carriers than the grid cells in the field-based representation of source data. A set of spatial and volumetric attributes are derived to characterize and quantify location, area, shape, orientation, depth, volume, and other properties of erosion and deposition objects. Compared with the conventional cell-by-cell differencing approaches, our object-based method gives a concise and high-level representation of information and knowledge about coastal morphological dynamics. The derived attributes enable the discrimination of true morphological changes from artifacts caused by data noise and processing errors. Furthermore, the concise object representation of erosion and deposition zones facilitates overlay analysis in conjunction with other GIS data layers for understanding the causes and impacts of morphological and volumetric changes. We have implemented a software tool for our object-based morphological analysis, which will be freely available for the public. An example is used to demonstrate the utility and effectiveness of this new method.

This paper presents the algorithms and analysis results for delineating snow zones using active and passive microwave satellite remote sensing data. With a high-resolution Radarsat synthetic aperture radar (SAR) image mosaic, dry snow zones, percolation zones, wet snow zones, and blue ice patches for the Antarctic continent have been successfully delineated. A competing region growing and merging algorithm is used to initially segment the SAR images into a series of homogeneous regions. Based on the backscatter characteristics and texture property, these image regions are classified into different snow zones. The higher level of knowledge about the areal size of and adjacency relationship between snow zones is incorporated into the algorithms to correct classification errors caused by the SAR image noise and relief-induced radiometric distortions. Mathematical morphology operations and a line-tracing algorithm are designed to extract a vector line representation of snow-zone boundaries. With the daily passive microwave Special Sensor Microwave/Imager (SSM/I) data, dry and melt snow zones were derived using a multiscale wavelet-transform-based method. The analysis results respectively derived from Radarsat SAR and SSM/I data were compared and correlated. The complementary nature and comparative advantages of frequently repeated passive microwave data and spatially detailed radar imagery for detecting and characterizing snow zones were demonstrated