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Night-Time light (NTL) data have been widely used for monitoring the dynamics of human activities and socioeconomics. As a new-generation satellite for acquiring NTL data, the National Oceanic and Atmospheric Administration-20 (NOAA-20) was successfully launched in November 2017. To support broad-scale environmental applications, it is necessary to generate monthly NTL composites of NOAA-20. Taking China as the study area, we produced NOAA-20 monthly NTL composites from April 2019 to December 2019. First, we performed a series of de-noising steps to eliminate NOAA-20 NTL pixels affected by sunlight, moonlight, high scan angles, clouds and further eliminated NTL outliers in time series by using a modified z-score method. Then, we aggregated daily NOAA-20 NTL data to produce monthly NTL composites to improve data coverage and stability. Subsequently, we examined the consistency of monthly composites between NOAA-20 and S-NPP at multiple scales, including provincial, prefectural, and pixel levels. Our results show that the monthly NTL composites of NOAA-20 are in good agreement with that of S-NPP with an R2 ranging from 0.81 to 0.99. Besides, we found that the spatial variation trends of the NOAA-20 monthly NTL composites are similar to that of S-NPP monthly NTL composites. We believe that the monthly NTL composites of NOAA-20 are very close to that of S-NPP and will open up more opportunities for relevant NTL studies. [ABSTRACT FROM AUTHOR]

Characterizing landscape patterns and revealing their underlying processes are critical for studying climate change and environmental problems. Previous methods for mapping land cover changes largely focused on the classification of remote sensing images. Therefore, they could not provide information about the evolutionary process of land cover changes. In this paper, we developed a spatiotemporal structural graph (STSG) technique for a comprehensive analysis of land cover changes. First, a land cover neighborhood graph was generated for each snapshot to quantify the spatial relationship between adjacent land cover objects. Then, an object-based temporal tracking algorithm was designed to monitor the temporal changes between land cover objects over time. Finally, land cover evolutionary trajectories, pixel-level land cover change trajectories, and node-wise connectivity changes over time were characterized. We applied the proposed method to analyze land cover changes in Suffolk County, New York from 1996 to 2010. The results demonstrated that STSG can not only characterize and visualize detailed land cover changes spatially but also maintain the temporal sequence and relations of land cover objects in an integrated space-time environment. The proposed STSG provides a useful framework for analyzing land cover changes and can be adapted to characterize and quantify other spatiotemporal phenomena. [ABSTRACT FROM AUTHOR]

Urban hierarchies are closely related to economic growth, urban planning and sustainable urban development. Due to the limited availability of reliable statistical data at fine scales, most existing studies on urban hierarchy characterization failed to capture the detailed urban spatial structure information. Previous studies have demonstrated that night time light data are correlated with many urban socio-economic indicators and hence can be used to characterize urban hierarchies. This paper presents a novel method for studying urban hierarchies from night time light data. Night time light data were first conceptualized as continuous mathematical surfaces, termed night time light surfaces. From the morphology of these surfaces the corresponding surface networks were derived. Hereafter, a night time light intensity (NTLI) graph was defined to describe the morphology of the surface network. Then, structural similarity between the night time light surfaces of any two different cities was calculated via a threshold-based maximum common induced graph searching algorithm. Finally, urban hierarchies were defined on the basis of the structural similarities between different cities. Using the 2015 annual NPP-VIIRS night time light data, the urban hierarchies of 32 major cities in China were successfully examined. The results are highly consistent with the reference urban hierarchies. [ABSTRACT FROM AUTHOR]

Detailed and precise information on urban building patterns is essential for urban design, landscape evaluation, social analyses and urban environmental studies. Although a broad range of studies on the extraction of urban building patterns has been conducted, few studies simultaneously considered the spatial proximity relations and morphological properties at a building-unit level. In this study, we present a simple and novel graph-theoretic approach, Extended Minimum Spanning Tree (EMST), to describe and characterize local building patterns at building-unit level for large urban areas. Building objects with abundant two-dimensional and three-dimensional building characteristics are first delineated and derived from building footprint data and high-resolution Light Detection and Ranging data. Then, we propose the EMST approach to represent and describe both the spatial proximity relations and building characteristics. Furthermore, the EMST groups the building objects into different locally connected subsets by applying the Gestalt theory-based graph partition method. Based on the graph partition results, our EMST method then assesses the characteristics of each building to discover local patterns by employing the spatial autocorrelation analysis and homogeneity index. We apply the proposed method to the Staten Island in New York City and successfully extracted and differentiated various local building patterns in the study area. The results demonstrate that the EMST is an effective data structure for understanding local building patterns from both geographic and perceptual perspectives. Our method holds great potential for identifying local urban patterns and provides comprehensive and essential information for urban planning and management. [ABSTRACT FROM PUBLISHER]

Land surface temperature (LST) is a key parameter of great interest in many remote sensing applications. However, no single satellite system can produce thermal infrared (TIR) images at both high spatial and temporal resolution to retrieve LST. Various algorithms have been developed to enhance the spatial or temporal resolution of TIR data in the past decades. Among them, the Spatio-temporal Adaptive Data Fusion Algorithm for Temperature mapping (SADFAT) model is one of the most widely used algorithms for fusing Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. To our knowledge, Visible Infrared Imaging Radiometer Suite (VIIRS) TIR data have not yet been used in thermal downscaling with Landsat-8 TIR data. This study aims to generate daily LST images at Landsat-8 resolution (100 m) by fusing VIIRS and Landsat-8 TIR data for the first time with the SADFAT algorithm. The results indicate that the prediction accuracy for the study area ranged from 1.1 K to 1.4 K, which suggests that VIIRS data can be used as a good alternative for MODIS data for generating daily LST images by fusing Landsat TIR data. [ABSTRACT FROM PUBLISHER]

Hurricanes and tropical storms are severe threats to coastal properties, settlements, and infrastructure. Airborne light detection and ranging (lidar) surveys conducted before and after storm events allow detailed analysis of coastal geomorphologic and sediment volumetric changes and have been proved very useful in the study of coastal changes. Traditionally, most studies use the pixel-based differencing method to quantify the spatial extent and magnitude of coastal changes based on sequential lidar surveys. This research presents a graph theory-based approach and associated software tools for representing and quantifying storm-induced damages to buildings, beaches and sand dunes, coastal vegetation canopy, and infrastructure. Generation of elevation difference grids, construction of local contour trees, and derivation of semantic properties are key components of the new algorithm for change object detection and extraction. An ontology and taxonomy are proposed to classify change objects into different types of coastal damages in terms of their semantic properties. This method has been successfully applied to assess damages of Hurricane Ike to the Bolivar Peninsula on the Texas Gulf Coast based on pre- and post-storm airborne lidar data and colour infrared aerial photographs. [ABSTRACT FROM PUBLISHER]

Extracting ground surface from high-density point clouds collected by Mobile Laser Scanning (MLS) systems is of vital importance in urban planning and digital city mapping. This article proposes a novel approach for automated extraction of ground surface along urban roads from MLS point clouds. The approach, which was designed to handle both ordered and unordered MLS point clouds, consists of three key steps: constructing vertical profile from MLS point clouds along the vehicle trajectory; extracting candidate ground points using an adaptive alpha shapes algorithm; refining the candidate ground points with an elevation variance filter. To evaluate the performance of the proposed method, experiments were conducted using two types of urban street-scene point clouds. The results reveal that the ground points can be detected with an error rate of as low as 1.9%, proving that our proposed method offers a promising solution for automated extraction of ground surface from MLS point clouds. [ABSTRACT FROM PUBLISHER]

Surface depressions are abundant in topographically complex landscapes, and they exert significant influences on hydrological, ecological, and biogeochemical processes at local and regional scales. The increasing availability of high-resolution topographical data makes it possible to resolve small surface depressions. By analogy with the reasoning process of a human interpreter to visually recognize surface depressions from a topographic map, we developed a localized contour tree method that is able to fully exploit high-resolution topographical data for detecting, delineating, and characterizing surface depressions across scales with a multitude of geometric and topological properties. In this research, we introduce a new concept ‘pour contour’ and a graph theory-based contour tree representation for the first time to tackle the surface depression detection and delineation problem. Beyond the depression detection and filling addressed in the previous raster-based methods, our localized contour tree method derives the location, perimeter, surface area, depth, spill elevation, storage volume, shape index, and other geometric properties for all individual surface depressions, as well as the nested topological structures for complex surface depressions. The combination of various geometric properties and nested topological descriptions provides comprehensive and essential information about surface depressions across scales for various environmental applications, such as fine-scale ecohydrological modeling, limnological analyses, and wetland studies. Our application example demonstrated that our localized contour tree method is functionally effective and computationally efficient. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]