Little research has been conducted on how differing spatial resolutions or classification techniques affect image-driven identification and categorization of slum neighborhoods in developing nations. This study assesses the correlation between satellite-derived land cover and census-derived socioeconomic variables in Accra, Ghana to determine whether the relationship between these variables is altered with a change in spatial resolution or scale. ASTER and Landsat TM satellite images are each used to classify land cover using spectral mixture analysis (SMA), and land cover proportions are summarized across Enumeration Areas in Accra and compared to socioeconomic data for the same areas. Correlation and regression analyses compare the SMA results with a Slum Index created from various socio-economic data taken from the Census of Ghana, as well as to data derived from a "hard" per-pixel classification of a 2.4 m Quickbird image. Results show that the vegetation fraction is significantly correlated with the Slum Index (Pearson's r ranges from -0.33 to -0.51 depending on which image-derived product is compared), and the use of a spatial error model improves results (multivariate model pseudo-R (2) ranges from 0.37 to 0.40 by image product). We also find that SMA products derived from ASTER are a sufficient substitute for classification products derived from higher spatial resolution QB data when using land cover fractions as a proxy for slum presence, suggesting that SMA might be more cost-effective for deriving land cover fractions than the use of high-resolution imagery for this type of demographic analysis.
Impervious surface area (ISA) is an important parameter related to environmental change and socioeconomic conditions, and has been given increasing attention in the past two decades. However, mapping ISA using remote sensing data is still a challenge due to the variety and complexity of materials comprising ISA and the limitations of remote sensing data spectral and spatial resolution. This paper examines ISA mapping with Landsat Thematic Mapper (TM) images in urban and urban-rural landscapes in the Brazilian Amazon. A fractional-based method and a per-pixel based method were used to map ISA distribution, and their results were evaluated with QuickBird images based on the 2010 Brazilian census at the sector scale of analysis for examining the mapping performance. This research showed that the fraction-based method improved the ISA estimation, especially in urban-rural frontiers and in a landscape with a small urban extent. Large errors were mainly located at the sites having ISA proportions of 0.2-0.4 in a census sector. Calibration with high spatial resolution data is valuable for improving Landsat-based ISA estimates.
This study evaluated the utility of narrowband (EO-1 Hyperion) and broadband (Landsat ETM+) remote sensing data for the estimation of leaf area index (LAI) in a tropical environment in Sulawesi, Indonesia. LAI was inferred from canopy gap fraction measurements taken in natural tropical forest and cocoa plantations. Single and multiple spectral bands and spectral indices were used as predictor variables in reduced major axis (RMA) and ordinary least squares (OLS) regression models. The predictive power of most regression models was notably higher when employing narrowband data instead of broadband data. Highly significant relationships between LAI and spectral reflectance were observed near the red-edge region and in most shortwave infrared (SWIR) bands. In contrast to most near-infrared (NIR) narrow bands, the correlation between SWIR reflectance and LAI was not confounded when including both vegetation types and did not suffer from saturation. The results demonstrate that leaf area index of a challenging tropical environment can be estimated with satisfactory accuracy from hyperspectral remote sensing data.
The advancement of computer technology enables the integration of geographic information system (GIS) and multimedia technologies
that allow to incorporate not only spatial-temporal geographic information in image/vector format, but also multimedia geographic
information in descriptive text, scanned ground photographs, graphics, digital video and sound. The concept of hypermedia
GIS is defined in this chapter. Design issues on the development of hypermedia GIS for use on individual personal computers
(PC) (i.e., discrete system) and on the Internet (i.e., distributed system) are discussed. Software requirement, file format
and data structure used in each system are described. The discrete and distributed hypermedia GIS provide the essential concepts
and techniques for many new GIS applications such as visualization, spatial decision support systems and spatial database
management and exploration.
The non-stationarity of land-change patterns can potentially affect the accuracy of a spatially explicit land-change projection. Thus, methods for understanding this phenomenon are urgently needed. This paper presents a geospatial approach for detecting and characterizing the non-stationarity of land-change patterns and examining its potential effect on land-change modeling accuracy. It proposes two types of non-stationarity of land-change patterns, viz., non-stationarity+ and non-stationarity–. The former is characterized by an increase in the rate of land change, for example, non-built to built, across the calibration and simulation intervals along the gradient of an explanatory variable, for example, slope, while the latter is characterized by a decrease.
Given the economic importance of loblolly pine (Pinus taeda) in the southeastern US, there is a need to establish efficient methods of detecting potential nutrient deficiencies that may limit productivity. This study evaluated the use of remote sensing for macronutrient assessment in loblolly pine. Reflectance-based models were developed at two spatial scales: (1) a natural nutrient gradient across the species’ range, and (2) localized fertilization and genotype treatments in North Carolina and Virginia. Fascicles were collected regionally from 237 samples of 3 flushes at 18 sites, and locally from 72 trees with 2 fertilization treatments and 6 genotypes. Sample spectral reflectance was calculated using a spectroradiometer, and nutrient concentrations were measured with dry combustion and wet chemical digestion. Results were analyzed statistically using nutrient correlations with reflectance and common vegetation indices, and partial least squares regression (PLSR). PLSR performed well at the regional scale, with R 2 values for nitrogen, phosphorus, potassium, calcium, and magnesium of 0.81, 0.70, 0.68, 0.42, and 0.51, respectively. No model successfully predicted nutrients at local sites for any treatment or canopy stratum. This discrepancy implies that a large nutrient range and/or spatial scale may be necessary to model loblolly pine nutrients with spectral reflectance.
The last few decades have been marked by important changes in the Brazilian agriculture, especially with respect to crop-management practices. This study aimed to analyze the dynamics of cropping systems in a typical agricultural river basin in the state of Mato Grosso. Landsat satellite images and time series of Moderate Resolution Imaging Spectroradiometer vegetation index profiles were analyzed from 2000 to 2010. First, we assessed the horizontal expansion that occurred in the agricultural areas. Subsequently, using the product MOD13Q1, some metrics were established to identify the vertical intensification of soil use (single- or double-cropping systems). Results showed stagnation in the expansion of new deforested areas for agriculture in the 2003/2004 growing season, with simultaneous vertical intensification of agriculture. The adoption of the double-cropping system (e.g., soybean/corn and soybean/cotton) expanded by 266% during the studied period and reached 56% of the croplands in the 2009/2010 growing season.
Land use and land cover changes have as consequences several social, economic, and environmental impacts. The understanding of these changes allows a better planning of public policies in order to map and monitor areas more susceptible to environmental problems. This research presents an analysis of the land use and land cover changes of a watershed region located in the Brazilian Amazon, and an evaluation of their impacts on sediment yield. Land use/land cover maps for each of the analyzed time periods (1973, 1984, and 2005) were compiled using images obtained by MSS/Landsat-1, TM/Landsat-5, and the MODIS/Terra sensors. The sediment yield modeling was performed by dividing the watershed into homogeneous subregions. Each of the subregions received average attributes that were used as input parameters for the Universal Soil Loss Equation. The results revealed that up to 2005, around 40% of the study area was already deforested, replaced by agricultural activities. In some parts of the watershed these changes were responsible for an increase of up to 7 ton/ha in annual average sediment yield. This study was successful in providing an assessment of the magnitude and spatial distribution of the changes.
The iterative self-organizing data analysis technique algorithm (ISODATA) was implemented over supercomputers Kraken, Keeneland and Beacon to explore scalable and high-performance solutions for image processing and analytics using emerging advanced computer architectures. When 10 classes are extracted from one 18-GB image tile, the calculation can be reduced from several hours to no more than 90 seconds when 100 CPU, GPU or MIC processors are utilized. High-performance scalability tests were further implemented over Kraken using 10,800 processors to extract various number of classes from 12 image tiles totalling 216 gigabytes. As the first geospatial computations over GPU clusters (Keeneland) and MIC clusters (Beacon), the success of this research illustrates a solid foundation for exploring the potential of scalable and high-performance geospatial computation for the next generation cyber-enabled image analytics.
Estimating tree characteristics with field plots located in remote and inaccessible areas can be a costly and timely endeavor. Light Detection and Ranging (Lidar) remote sensing allowing for the estimation of the 3-dimensional structure of forest vegetation offers an alternative to traditional ground based forest measurements. This project assessed the utility of using Lidar data to estimate number of trees, tree height and crown width within Barksdale Air Force Base forest management area, Bossier City, Louisiana. Two programs, Lidar Data Filtering and Forest Studies (Tiffs) and Lidar Analyst were used to derive forest measurements, which were compared to field measurements. Based on Root Mean Square Error (RMSE), Lidar Analyst (3.81 trees) performed better than Tiffs (5.71 trees) at estimating average tree count per plot. Tiffs was better at deriving average tree height than Lidar Analyst with an RMSE of 19.08 feet to Lidar Analyst’s RMSE of 21.20 feet. Lidar Analyst, with a RMSE of 25.41 feet, was better in deriving average crown diameter over Tiffs RMSE of 30.54 feet. All linear correlation coefficients between average field measured tree height and Lidar derived average tree height were highly significant at the 0.01 probability level for both Tiffs and Lidar Analyst on hardwood, conifers and a combined hardwood-conifer comparison.
This article presents an integrated GIS tool for automatic forest inventory of Pinus radiata plantations from light detection and ranging (LiDAR) data. Built as a set of tools running in the desktop GIS software package ArcGIS, it integrates spatial analysis, LiDAR data analysis and image segmentation techniques as well as empirical tree models to support forest inventories of Pinus radiata on an individual-tree basis. The integrated GIS tool allows users to define or select plots to extract LiDAR data for forest inventory, build and process canopy height models (CHMs) from the extracted LiDAR data through surface modelling, delineate individual trees on the CHMs by applying the marker-controlled watershed segmentation technique, and to derive forest inventory estimates based on the CHMs and identified individual trees through spatial analysis and tree modelling using the empirical models. It takes advantage of combining GIS and LiDAR to automatically conduct a forest inventory, build and manage a forest inventory database and to spatially and statistically summarise and visualise the inventory data. Although developed for forest inventories of Pinus radiata plantations in Victoria, Australia, the integrated tool can be customised for forest inventories of Pinus radiata plantations in other regions and for other types of plantations by incorporating empirical tree models built and calibrated for those regions and those species of trees.
This study was conducted to assess fire susceptibility of Mediterranean vegetation by analyzing a time series of Moderate Resolution Imaging Spectroradiometer (MODIS) Terra images from 2000 to 2006. Synthetic indicators of vegetation status were defined based on analysis of annual variations of the Normalized Difference Vegetation Index (NDVI) and an understanding of phenological cycles. Spring and annual greenness indicators were calculated by combining NDVI values measured at different key phenological stages. The various fire susceptibility indicators were used to characterize fluctuations of vegetation activity related to changes in photosynthetic activity and fuel dryness. Susceptibility indicators were also mapped, and statistical relationships with meteorological conditions were identified.
Detecting road poles from mobile terrestrial laser scanner (MTLS) point clouds is important for many geographical information system (GIS) applications such as right-of-way asset inventory compilation. The aim of this research is to automatically detect the road poles from unorganized 3D point clouds captured by an MTLS system named TITAN. The proposed pole detection pipeline consists of a sequence of five steps: organizing the 3D point clouds and nearest neighbor search, 2D density-based segmentation, vertical region growing, segment merging, and pole classification. The obtained average detection rate and accuracy for the three data sets tested were 86% and 97%, respectively.
Many ecological and environmental applications require time-series data, which are collected with spatial extent from local, regional or continental levels and granularity ranging from fine to coarse spatial resolution. These types of data can be too difficult to collect using typical field surveys, but they can be derived using remote-sensing images and image-processing technologies. The common time-series normalized difference vegetation index (NDVI) data are AVHRR-derived at spatial resolutions of 1or 8km, MODIS-derived at 250m, 500m, 1km or 8km resolutions, and SPOT-VGT imagery at 1 km resolution. Landsat imagery-derived time-series NDVI data are often unavailable for many areas due to the difficulty of acquiring cloud-free images given the temporally infrequent coverage of this sensor. For this study, we used a closest-spectral-fit concept as the basis for a data mining model and developed a data assimilation model in order to derive 16-day time-series NDVI data. In a multidimensional spectral space, if pixel i has the closest reflectance values to pixel j, pixel i is called the closest-spectral-fit of pixel j, and pixels i and j are called closest-spectral-fit pixels. Fifteen Landsat TM images covering northern Michigan were acquired from 22 March 2010 to 1 November 2010, in a 16-day cycle. A cloud-free image is used as the reference image to predict NDVI images for the other 14 dates. The forecasted NDVI data explain 80% variation in the observed NDVI. TM band 4 is forecasted at the same performance level as NDVI, and forecasts of bands 3 and 2 are relatively highly correlated with the forecasts of observed bands. The closest-spectral-fit data assimilation method has the capability to produce historical NDVI data at finer spatial resolution as far back as 1972, which broaden and enhance potential applications to the modeling of environmental and ecological patterns and processes.
Increasing availability and advancements of aerial Light Detection and Ranging (LiDAR) data have radically been shifting the way archaeological surveys are performed. Unlike optical remote sensing imagery, LiDAR pulses travel through small gaps in dense tree canopies enabling archaeologists to discover ‘hidden’ past settlements and anthropogenic landscape features. While LiDAR has been increasingly adopted in archaeological studies world-wide, its full potential is still being explored in the United States. Furthermore, while hand-digitizing features in remote sensing datasets remains a valuable method for archaeological surveys, it is often time- and labor-intensive. The central objective of this research is to develop a geographic object based image analysis (GEOBIA) driven methodological framework linking low-level feature and domain knowledge to automatically extract targets of interest from LiDAR-based digital terrain models (DTMs) and to closely examine the degree of interoperability of knowledge-based rulesets across different study sites focusing on the same semantic class. We apply this framework in southern New England, a geographic region in the northeastern United States where numerous 17th-century to early 20th-century features such as relict charcoal hearths, stone walls, and building foundations lie abandoned in densely forested terrain. Focusing on relict charcoal hearths in this study, our results show promising agreement between manual and automated detection of these features. Overall, we show that the use of LiDAR data augmented with object-based classification workflows provides valuable baseline data for future archaeological study and reconstruction of land use land cover change over the past 300 years.
This study examined changes in urban expansion and land surface temperature in Beijing between 1990 and 2014 using multitemporal TM, ETM+, and OLI images, and evaluated the relationship between percent impervious surface area (%ISA) and relative mean annual surface temperature (RMAST). From 1990 to 2001, both internal land transformation and outward expansion were observed. In the central urban area, the high-density urban areas decreased by almost 7 km2, while the moderate- and high-density urban land areas increased by 250 and 90 km2, respectively, outside of the third ring road. From 2001 to 2014, high-density urban areas between the fifth and sixth ring roads experienced the greatest increase by more than 210 km2, and RMAST generally increased with %ISA. During 1990–2001 and 2001–2014, RMAST increased by more than 1.5 K between the south third and fifth ring roads, and %ISA increased by more than 50% outside of the fifth ring road. These trends in urban expansion and RMAST over the last two decades in Beijing can provide useful information for urban planning decisions.
Sentinel-1A C-SAR and Sentinel-2A MultiSpectral Instrument (MSI) provide data applicable to the remote identification of crop type. In this study, six crop types (beans, beetroot, grass, maize, potato, and winter wheat) were identified using five C-SAR images and one MSI image acquired during the 2016 growing season. To assess the potential for accurate crop classification with existing supervised learning models, the four different approaches namely kernel-based extreme learning machine (KELM), multilayer feedforward neural networks, random forests, and support vector machine were compared. Algorithm hyperparameters were tuned using Bayesian optimization. Overall, KELM yielded the highest performance, achieving an overall classification accuracy of 96.8%. Evaluation of the sensitivity of classification models and relative importance of data types using data-based sensitivity analysis showed that the set of VV polarization data acquired on 24 July (Sentinel-1A) and band 4 data (Sentinel-2A) had the greatest potential for use in crop classification.
Optical satellite images are prone to cloud contamination in the tropics and subtropics especially during the monsoon season which is contemporaneous with rice cultivation. Producing rice field maps in such areas using a single optical satellite source is therefore very challenging. To obtain an adequate number of usable optical images that captures the seasonal profile of rice fields for their discrimination, this study explored an operational approach to combining quad-source optical satellite data that include Landsat-8, Sentinel-2A, China’s environment and disaster monitoring and forecasting satellite constellation (HJ-1 A and B), and Gaofen-1 over a test site located in southeast China at two rice growing seasons (2016 and 2017). To minimise inter-sensor differences, a spectral index (SI) dataset containing cross-calibrated Enhanced Vegetation Index (EVI) images from all optical sensors and Modified Normalised Difference Water Index (MNDWI) images from Landsat-8 and Sentinel-2, was derived. As more accurate rice maps have been obtained with the synergistic use of optical and microwave data, the aforementioned datasets were combined with the vertical transmitted and horizontal received (VH) and vertical transmitted and vertical received (VV) polarisation images of Sentinel-1A. The resultant optical, microwave, and combinations of optical and microwave data were used as inputs to the Support Vector Machine (SVM) and Random Forest (RF) algorithms available in ENVI 5.3 and ENMAP Box 2.0, respectively to discriminate rice fields from other land-cover types. Results showed that Sentinel-1A data produced higher mapping accuracies than the quad-source optical datasets. This is attributed to the higher number of Sentinel-1A images at the early stages of rice growth (vegetative phase), a period where changes in rice satellite signals are most abrupt and therefore most diagnostic for discriminating the rice crop from other land-cover types. Additionally, combinations of optical and microwave data produced higher overall accuracies than when used separately, and the highest overall accuracies for both years were achieved with the application of RF on a combination of VH, VV and SI (VHVVSI), yielding 98.43% in 2016 and 96.73% in 2017. Moreover, combining VH with SI (VHSI) produced overall mapping accuracies that are more equivalent to the above (98.40% in 2016 and 96.53% in 2017) than with the combination of VV and SI (VVSI) which yielded 97.22% in 2016 and 93.92% in 2017. This indicates that VH is not only more complementary to optical satellite imagery in rice discrimination, but its combination with SI alone can produce rice distribution maps of the highest possible accuracies in cloud-prone environments.
The physical processes associated with the constituents of the troposphere, such as aerosols have an immediate impact on human health. This study employs a novel method to calibrate Aerosol Optical Depth (AOD) obtained from the MODerate resolution Imaging Spectrometer (MODIS – Terra satellite) for estimating surface PM2.5 concentration. The Combined Deep Blue Deep Target daily product from the MODIS AOD data acquired across the Indian Subcontinent was used as input, and the daily averaged PM2.5pollution level data obtained from 33 monitoring stations spread across the country was used for calibration. Mixed Effect Models (MEM) is a linear model to deal with non-independent data from multiple levels or hierarchy using fixed and random effects of dependent parameters. MEM was applied to the dataset obtained for the period from January to August 2017. The MEM considers a fixed and random component, where the random components model the daily variations of the AOD – PM2.5 relationships, site-specific adjustment parameters, temporal (meteorological) variables such as temperature, and spatial variables such as the percentage of agricultural area, forest cover, barren land and road density with the resolution of 10 km × 10 km. Estimation accuracy was improved from an R² value of 0.66 from our earlier study (when PM2.5 was modeled against only AOD and site-specific parameters) toR² value of 0.75 upon the inclusion of spatiotemporal (meteorological) variables with increased % within Expected Error from 18% to 35%, reduced Mean Bias Error from 3.22 to 0.11 and reduced RMSE from 29.11 to 20.09. We also found that spline interpolation performed better than IDW and Kriging inefficiently estimating the PM2.5 concentrations wherever there were missing AOD data. The estimated minimum PM2.5 is 93 ± 25μg/m³ which itself is in the upper limit of the hazardous level while the maximum is estimated as 170 ± 70μg/m³. The study has thus made it possible to determine the daily spatial variations of PM2.5 concentrations across the Indian subcontinent utilizing satellite-based AOD data.
To control for the defects found in remote sensing-derived estimates of population distributions, this study created a new method for estimating the population density of China at a 1 × 1 km spatial resolution by combining remote sensing-derived land use with China residence polygon data. As a result, we obtained three sets of land use data (i.e., remote sensing-derived, China residence polygon, and a combination of the two) to estimate population. On the basis of these data, we developed both urban and rural population distribution models. The results demonstrated that this new method could improve the accuracy of population estimation.
Monoculture rubber plantations have been replacing tropical rain forests substantially in Southern China and Southeast Asia over the past several decades, which have affected human wellbeing and ecosystem services. However, our knowledge on the extent of rubber plantation expansion and their stand ages is limited. We tracked the spatiotemporal dynamics of deciduous rubber plantations in Xishuangbanna, the second largest natural rubber production region in China, from 2000 to 2010 using time series data from the Phased Array type L-band Synthetic Aperture Radar (PALSAR), Landsat, and Moderate Resolution Imaging Spectroradiometer (MODIS). We found that rubber plantations have been expanding across a gradient from the low elevation plains to the high elevation mountains. The areas of deciduous rubber plantations with stand ages ≤5, 6-10, and ≥11 years old were ~1.2 × 10⁵ ha, ~0.8 × 10⁵ ha, and ~2.9 × 10⁵ ha, respectively. Older rubber plantations were mainly located in low-elevation and species-rich regions (500-900 m) and younger rubber trees were distributed in areas of relative high-elevation with fragile ecosystems. Economic and market factors have driven the expansion of rubber plantations, which is not only a threat to biodiversity and environmental sustainability, but also a trigger for climatic disasters. This study illustrates that the integration of microwave, optical, and thermal data is an effective method for mapping deciduous rubber plantations in tropical mountainous regions and determining their stand ages. Our results demonstrate the spatiotemporal pattern of rubber expansions over the first decade of this century.
Land subsidence has been occurring in Beijing since the 1970s. Five major land subsidence areas have been formed: Dongbalizhuang–Dajiaoting, Laiguangying, Changping Shahe–Ba Xianzhuang, Daxing Yufa–Lixian, and Shunyi–Ping Gezhuang. In this paper, we studied on land subsidence in Dongbalizhuang–Dajiaoting and Laiguangying using small baseline subset interferometry and interferometric point target methods of 47 ENVISAT ASAR and 29 RADARSAT-2 data. The results showed that the degree of land subsidence in these areas varied significantly. The mean land subsidence rate ranged from 143.43 to 8.2 mm/a and from 132.11 to 7.3 mm/a during 2005–2010 and 2011–2013, respectively. We correlated the observed settlement with the land use (agricultural, residential, and industrial). Displacement in the agricultural areas was greater than that in the other areas from 2005 to 2013. Moreover, we compared the observed deformation and the groundwater level in phreatic and confined aquifers. There was a strong correlation between ground subsidence and the groundwater level and the ground settlement increased with a decrease in the groundwater level and the maximum correlation coefficient can reach 0.525. Furthermore, subsidence appeared to be associated with compressible deposits, suggesting that for 90–210-m thick compressible deposits, ground settlement is more likely to occur as the thickness of the compressible layer increases.
This study employed a rapid assessment method for post-hurricane forest damage estimation following Hurricane Felix (4 September 2007) in Nicaragua, using Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m, enhanced vegetation index (EVI) imagery. Post-hurricane forest damage was characterized using pre- and post-hurricane EVI data, assisted by post-hurricane aerial photographs. The agreement between the obtained EVI damage map and the reference data set ranged from 91% in high-damage locations to 85% in low-damage locations, with 86% overall agreement. Broadleaf forests comprised more than half of the study area and experienced larger damage (>75% trees blown down) than Pine forests. Results indicate that MODIS 250 m EVI products are adequate for post-hurricane damage assessment of tropical forests.
With the proliferation of satellite-derived data, it is now possible to link land cover with socioeconomic and environmental indicators. This manuscript focuses on the 2011 forest and cropland estimations for the European Union’s 28 countries (EU28) derived from moderate resolution imaging spectoradiometer observations and identifies links to socioeconomic indices such as the gross domestic product (GDP) per capita, urbanization degree (UD), Human Development Index (HDI), ecological footprint, and total biocapacity. Results show that GDP per capita and UD are related to forest decrease, as countries with high GDP per capita have a 10% higher urban population and on average 40% less forest share than the EU28 average. The negative relation between the urban population and forest share is even stronger within the moderate GDP countries. Linear fit models reveal that for these countries, a 5.0% increase in urban population leads to a 7.4% decrease of forest share and a 3.9 % increase of cropland share, something contradictory to the general belief that urban population increase is mostly at the expense of agricultural land. In addition, affluent countries with higher HDI tend to have smaller forest per capita values, which is less than half of the EU28 average. Such statistics can become an important indicator for policy advisors for assessing sustainable development.
Keywords: forest; cropland; Europe; MODIS; socioeconomic
The landslide, which occurred at Umyeon mountain (Mt. Umyeon) in Seoul, Korea in 2011, was a prime example that raised awareness about the landslide in the highly urbanized area. Although many studies have been done on Umyeon landslide, there is a lack of research that detects the area where the landslide occurred and quantifies the elevation changes through remote sensing data. In this regard, this paper aims to detect and assess topographic changes quantitatively over Mt. Umyeon by using digital elevation models (DEMs) derived from airborne laser scanning (ALS) data. Since Mt. Umyeon was hilly and covered with dense trees during summer, traces of the landslide were detected by estimating the spatially distributed uncertainty of ALS-derived DEMs. The probabilistic analysis with Bayes'™ theorem considering the spatially distributed DEM of difference (DoD) uncertainty enabled to detect the landslide traces efficiently and was less affected by the influence of ALS errors. The results indicated that ALS-derived DEMs have the potential to detect landslides with their uncertainty estimation, although the ALS data were acquired in hilly and densely vegetated areas. Moreover, quantifying topographic changes due to landslides with high reliability is considered to be beneficial and practically helpful for disaster recovery.
Land subsidence in densely urbanized areas is a global problem that is primarily caused by excessive groundwater withdrawal. The Kathmandu basin is one such area where subsidence due to groundwater depletion has been a major problem in recent years. Moreover, on 25th April 2015, this basin experienced large crustal movements caused by the Gorkha earthquake (Mw 7.8). Consequently, the effects of earthquake-induced deformation could affect the temporal and spatial nature of anthropogenic subsidence in the basin. However, this effect has not yet been fully studied. In this paper, we applied the SBAS-DInSAR technique to estimate the spatio-temporal displacement of land subsidence in the Kathmandu Basin before and after the Gorkha earthquake, using 16 ALOS-1 Phased Array L-band Synthetic Aperture Radar (PALSAR) images during the pre-seismic period and 26 Sentinel-1 A/B SAR images during the pre- and post-seismic periods. The results showed that the mean subsidence rate in the central part of the basin was about -8.2 cm/yr before the earthquake. The spatial extents of the subsiding areas were well-correlated with the spatial distributions of the compressible clay layers in the basin. We infer from time-series InSAR analysis that subsidence in the Kathmandu basin could be associated with fluvio-lacustrine (clay) deposits and local hydrogeological conditions. However, after the mainshock, the subsidence rate significantly increased to -15 cm/yr and -12 cm/yr during early post-seismic (108 days) and post-seismic (2015–2016) period, respectively. Based on a spatial analysis of the subsidence rate map, the entire basin uplifted during the co-seismic period has started to subside and become stable during the early-post-seismic period. This is because of the elastic rebound of co-seismic deformation. However, interestingly, the localized areas show increased subsidence rates during both the early-post- and post-seismic periods. Therefore, we believe that the large co-seismic deformation experienced in this basin might induce the local subsidence to increase in rate, caused by oscillations of the water table level in the clay layer.
Cropland products are of great importance in water and food security assessments, especially in South Asia, which is home to nearly 2 billion people and 230 million hectares of net cropland area. In South Asia, croplands account for about 90% of all human water use. Cropland extent, cropping intensity, crop watering methods, and crop types are important factors that have a bearing on the quantity, quality, and location of production. Currently, cropland products are produced using mainly coarse-resolution (250–1000 m) remote sensing data. As multiple cropland products are needed to address food and water security challenges, our study was aimed at producing three distinct products that would be useful overall in South Asia. The first of these, Product 1, was meant to assess irrigated versus rainfed croplands in South Asia using Landsat 30 m data on the Google Earth Engine (GEE) platform. The second, Product 2, was tailored for major crop types using Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m data. The third, Product 3, was designed for cropping intensity (single, double, and triple cropping) using MODIS 250 m data. For the kharif season (the main cropping season in South Asia, Jun–Oct), 10 major crops (5 irrigated crops: rice, soybean, maize, sugarcane, cotton; and 5 rainfed crops: pulses, rice, sorghum, millet, groundnut) were mapped. For the rabi season (post-rainy season, Nov–Feb), five major crops (three irrigated crops: rice, wheat, maize; and two rainfed crops: chickpea, pulses) were mapped. The irrigated versus rainfed 30 m product showed an overall accuracy of 79.8% with the irrigated cropland class providing a producer’s accuracy of 79% and the rainfed cropland class 74%. The overall accuracy demonstrated by the cropping intensity product was 85.3% with the producer’s accuracies of 88%, 85%, and 67% for single, double, and triple cropping, respectively. Crop types were mapped to accuracy levels ranging from 72% to 97%. A comparison of the crop-type area statistics with national statistics explained 63–98% variability. The study produced multiple-cropland products that are crucial for food and water security assessments, modeling, mapping, and monitoring using multiple-satellite sensor big-data, and Random Forest (RF) machine learning algorithms by coding, processing, and computing on the GEE cloud.
Overshooting tops (OTs) play a crucial role in carrying tropospheric water vapor to the lower stratosphere. They are closely related to climate change as well as local severe weather conditions, such as lightning, hail, and air turbulence, which implies the importance of their detection and monitoring. While many studies have proposed threshold-based detection models using the spatial characteristics of OTs, they have shown varied performance depending on the seasonality and study areas. In this study, we propose a pre-trained feature-aggregated convolutional neural network approach for OT detection and monitoring. The proposed approach was evaluated using multi-channel data from Geo-Kompsat-2A Advanced Meteorological Imager (GK2A AMI) over East Asia. The fusion of a visible channel and multi-infrared channels enabled the proposed model to consider both physical and spatial characteristics of OTs. Six schemes were evaluated according to two types of data pre-processing methods and three types of deep learning model architectures. The best-performed scheme yielded a probability of detection (POD) of 92.1%, a false alarm ratio (FAR) of 21.5%, and a critical success index (CSI) of 0.7. The results were significantly improved when compared to those of the existing CNN-based OT detection model (POD increase by 4.8% and FAR decrease by 29.4%).
In this paper, we propose a method to regenerate Rational Polynomial Coefficients (RPCs) using KOMPSAT-3A imagery and to reduce the geolocation error using minimum ground control points (GCPs). To estimate the new RPCs, the physical sensor model fitted to KOMPSAT-3A imagery was utilized and virtual GCPs over the study area were created. The size of the virtual grid used was 20x20x20. To remove the sensor-related errors in physical sensor model, three different image correction models (image coordinate translation model, shift and drift model, and affine transformation model) were additionally applied. We evaluated our proposed method in two areas within Korea, one in urban (Seoul) and one in rural (Goheung) areas. The results showed that there was a significant improvement after applying the suggested approach in the two areas. The image coordinate translation model is suggested in terms of GCP requirement and expected errors estimated from the error propagation analysis using Gauss–Markov Model (GMM).
Information on geometries and kinematics of landslides are necessary to establish geological slope deformation models. We present two complementary geospatial methods to analyze landslide surface changes even in areas affected by strong surface pattern changes, making use of airborne laser scanning (ALS) data. An image correlation method based on shaded relief images with a uniformly diffuse lighting and a feature tracking based on terrain breaklines are applied on a data set of eight ALS flight campaigns analyzing an active deep-seated rockslide in the Eastern Alps (Austria). Both tracking methods are described in detail, including parameter assessment. Additionally, an accuracy assessment of the input data sets has been conducted. 3D vector displacement maps derived from image correlation are well suited for the study of landslides if only slight surface pattern changes occur. The smallest detectable displacements strongly depend on the accuracy of the ALS data and for image correlation results lie within the range of 0.24 and 0.75 m for this study. Displacement vectors derived by breakline tracking only allow to detect displacements greater than 2 m. However, in comparison to image correlation, breakline tracking is not limited to areas with slight surface pattern changes and allows us to detect displacements even in areas with strong surface pattern changes. For a comprehensive interpretation of landslide activity a combination of both methods, with consideration of additional supportive data such as elevation change images and orthoimages, is recommended.
Visibility determination is a key requirement in a wide range of national and urban applications, such as national security, landscape management, and urban design. Mobile LiDAR point clouds can depict the urban built environment with a high level of details and accuracy. However, few three-dimensional visibility approaches have been developed for the street-level point-cloud data. Accordingly, an approach based on mobile LiDAR point clouds has been developed to map the three-dimensional visibility at the street level. The method consists of five steps: voxelization of point-cloud data, construction of lines-of-sight, construction of sectors of sight, construction of three-dimensional visible space, and calculation of volume index. The proposed approach is able to automatically measure the volume of visible space and openness at any viewpoint along a street. This approach has been applied to three study areas. The results indicated that the proposed approach enables accurate simulation of visible space as well as high-resolution (1 m × 1 m) mapping of the visible volume index. The proposed approach can make a contribution to the improvement of urban planning and design processes that aim at developing more sustainable built environments.
Globally, countries have experienced substantial increases in farmland abandonment. Although vegetation phenology is a key factor for the classification of land use, understanding of the phenological change of abandoned farmland is lacking. Using harmonic analysis of NDVI and NDWI extracted from Landsat imagery, this study investigates the distinctive phenological characteristics of abandoned farmland, which contrasts with that of three other agricultural types (paddy, agricultural field, orchard) in the study site of Gwangyang City in Jeollanam Province, South Korea. The results suggest that abandoned farmland has higher overall greenness coverage and overall water content in vegetation than the other uses. In terms of both indices, abandoned farmlands changed with relatively less fluctuation than those of other uses, suggesting the existence of constant and unmanaged vegetation from ecological succession, which differs from crop fields that undergo cultivation procedures. The significant harmonic components differed among agricultural types and vegetation indices. In paddy, NDVI was explained with multiple, higher-order harmonic components, while in other types only first-order components met the 5% statistical significance level. With NDWI, land types were more clearly discernible, because of the different cultivation procedures involving water: wet-field method (paddy), dryland farming (orchard, agricultural field), and no cultivation (abandoned farmland). The analysis confirms that harmonic analysis could be useful in discerning abandoned farmland among areas of active agricultural use and shows that the statistical significance of harmonic terms can be employed as indicators of different agricultural types. The observed pattern of the geographic distribution of abandoned farmland has policy implications for the promotion of sustainable reuse of marginal farmland.
Canopy height is an excellent indicator of forest productivity, biodiversity and other ecosystem functions. Yet, we know little about how elevation drives canopy height in mountain areas. Here we take advantage of an ambitious airborne LiDAR flight plan to assess the relationship between elevation and maximum forest canopy height, and discuss its implications for the monitoring of mountain forests’ responses to climate change. We characterized vegetation structure using Airborne Laser Scanning (ALS) data provided by the Spanish Geographic Institute. For each ALS return within forested areas, we calculated the maximum canopy height in a 20 × 20 m grid, and
then added information on potential drivers of maximum canopy height, including ground elevation, terrain slope and aspect, soil characteristics, and continentality. We observed a strong, negative, piece-wise response of maximum canopy height to increasing elevation, with a welldefined breakpoint (at 1623 ± 5 m) that sets the beginning of the relationship between both variables. Above this point, the maximum canopy height decreased at a rate of 1.7 m per each
100 m gain in elevation. Elevation alone explained 63% of the variance in maximum canopy height, much more than any other tested variable. We observed species- and aspect-specific effects of elevation on maximum canopy height that match previous local studies, suggesting common patterns across mountain ranges. Our study is the first regional analysis of the relationship between elevation and maximum canopy height at such spatial resolution. The tree-height decline breakpoint holds an intrinsic potential to monitor mountain forests, and can thus serve as a robust indicator to appraise the effects of climate change, and address fundamental questions about how tree development varies along elevation gradients at regional or global scales.
An evaluation of ALOS PALSAR (Phased Array type L-band Synthetic Aperture Radar) data for shrub height and aboveground biomass (AGB) estimation has been performed in scrub-dominated ecosystems of central coastal California. Comparison between AGB field measurements and SAR estimations showed a correlation coefficient of R-2 = 0.53 for the HV polarization. Post-fire AGB regeneration was examined two years after the Big Sur Basin Complex fire of 2008. In 2009, the average patch size of AGB density classes between 20-40 Mg ha(-1) and 50-70 Mg ha(-1) was 0.49 ha and 0.09 ha, respectively. In 2010, patch sizes in these same two AGB density classes increased to averages of 0.8 ha to 0.15 ha, respectively. No strong saturation in the SAR-AGB relationship was found, which indicated the potential for more advanced applications of L-band SAR data for coastal ecosystem AGB mapping.
Grassland biomass on the Qinghai-Tibet Plateau is of great significance for the study of wildlife habitat and climate change. Based on Systéme Pour lObservation de la Terre Vegetation normalized difference vegetation index (NDVI) data from 1998 to 2012 and a field survey investigation in 2013, we assessed aboveground biomass (AGB) dynamics in the Altun Mountain Nature Reserve. The results demonstrated that annual NDVI values varied greatly with an increasing trend. Areas of high- and medium-coverage grassland showed similar increasing trends. The NDVI-biomass relationship could be quantified by an exponential model and previous models overestimated the amount of biomass in the alpine desert grassland. The total biomass was estimated to be 62 × 106 kg per year with large annual variations. No significant relationship was found between AGB and soil organic carbon contents, and the total grassland area (NDVI > 0.1) was positively correlated with the annual average temperature.
This study combined 30-m spatial resolution Landsat Enhanced Thematic Mapper Plus (ETM+) data and 500-m spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) data, together with field data, to up-scale aboveground biomass (AGB) of tropical rainforests in Sulawesi, Indonesia. Contrasting to usual up-scaling approaches, this study uses in the intermediate step an estimation method based on application of geostatistics in the Landsat ETM+ spectral feature space. To connect ground-based measurements with spectral reflectance in Landsat ETM+ bands, this method employs ordinary Kriging in the three-dimesional (3-D) space where spectral features of satellite data are used instead of geographic coordinates. The most appropriate combination of the 3-D space spectral bands (bands 4 and 5) was then assigned with corresponding MODIS bands. In the final step, AGB could be mapped over the large area covered by the MODIS data. The results of the study indicate the effectiveness of the developed up-scaling method with respect to dealing with the problem of resolution mismatch between the ground sampling plots and the MODIS data.
Over the years, the vast majority of curvature-based simplification algorithms for vector data have employed pseudo-curvatures, rather than real curvatures. This is because the vector data in the field of Geographical Information Science (GIScience) is usually represented in the form of polylines, but polylines do not meet the requirements of traditional curvature calculation. This situation has been improved since the multi-scale visual curvature (MVC) was proposed. However, due to the high complexity and huge computation needed for the algorithm, it is difficult to make effective use of MVC in GIScience. In this paper, the MVC algorithm is used to simplify big vector data in a Hadoop-based cloud. The main challenge is that both the data and computation are simultaneously intensive. An accelerated MVC algorithm for simplification is proposed in this paper. This algorithm is performed by adopting a two-level acceleration approach: (1) a simplified calculation method of MVC for the vector data in GIScience, and (2) a parallelization strategy for the MVC algorithm in the Hadoop-based cloud. When simplifying big vector data in gigabyte (GB) size, the execution time is reduced to less than 2.2% of the original time. The proposed accelerated MVC algorithm has great potential in many GIScience applications, including map generalization, DEM simplification, and spatial-temporal data compression.
Thwaites Ice Shelf in the Amundsen Sea is one of the biggest ice shelves in West Antarctica and is well known for significant mass changes. In the shear zone between Thwaites Glacier Tongue and its eastern ice shelf, shear stress forced by different flow rates of the ice shelves is causing the ice to break apart. A time series analysis of remote sensing data obtained by Landsat 7 Enhanced Thematic Mapper Plus (ETM+), TerraSAR-X, and airborne synthetic aperture radar (SAR) revealed that the shear zone has extended since 2006 and eventually disintegrated in 2008. We quantified the acceleration of Thwaites Ice Shelf with time by using the feature tracking method. The buttressing loss induced by the extension of the shear zone and progressive disintegration accelerated the flow of Thwaites Glacier Tongue, which in turn increased the shear stress on its eastern ice shelf. We determine causes of disintegration in the newly formed shear zone to be oceanic basal melting and structural weakening induced by Circumpolar Deep Water intrusion beneath the eastern ice shelf since 2000. The structural weakening was examined by using the density distribution of rifts and crevasses on the ice shelf, which were well identified from high-resolution SAR and optical satellite images.
Community pharmacies selling potentially harmful products may contradict their role in health promotion. From a spatial analysis perspective, this study investigated the sale of alcohol, tobacco, and lottery tickets by community pharmacies in Passaic County, New Jersey, and assessed the relationship between sociodemographic factors of community residents and their potential accessibility to those community pharmacies. A mixed geographically weighted regression analysis revealed that census block groups with higher median household income tend to have less accessibility to pharmacies that sell addictive products. Relationships between Latino population and those pharmacies are mixed. No significant relationship was found for African American population.
Quantification and assessment of nation-wide population access to health care services is a critical undertaking for improving population health and optimizing the performance of national health systems. Rural-urban unbalance of population access to health care services is widely involved in most of nations. This unbalance is also potentially affected by varied weather and road conditions. This study investigates the rural and urban performances of public health system by quantifying the spatio-temporal variations of accessibility and assessing the impacts of potential factors. Australian health care system is used as a case study for the rural-urban comparison of population accessibility. A nation-wide travel time based modified kernel density two-step floating catchment area (MKD2SFCA) model is utilized to compute accessibility of travel time within 30, 60, 120 and 240 minutes to all public hospitals, hospitals that provide emergency care and hospitals that provide surgery service respectively. Results show that accessibility is varied both temporally and spatially, and the rural-urban unbalance is distinct for different types of hospitals. In Australia, from the perspective of spatial distributions of health care resources, spatial accessibility to all public hospitals in remote and very remote areas is not lower (and may even higher) than that in major cities, but the accessibility to hospitals that provide emergency and surgery services is much higher in major cities than other areas. From the angle of temporal variation of accessibility to public hospitals, reduction of traffic speed is 1.00% - 3.57% due to precipitation and heavy rain, but it leads to 18% - 23% and 31% - 50% of reduction of accessibility in hot-spot and cold-spot regions respectively, and the impact is severe in NSW, QLD and NT during wet seasons. Spatio-temporal analysis for the variations of accessibility can provide quantitative and accurate evidence for geographically local and dynamic strategies of allocation decision making of medical resources and optimizing health care systems both locally and nationally.
Uses of high spatial resolution data obtained from satellite-based sensors include creating land cover maps, deriving large-scale quantitative assessments such as vegetation indices, and visually assessing an area for qualitative information only assessable from large-scale digital data. One of the more popular uses of high spatial resolution data is to use the image as a base map for on-screen digitizing spatially dependent vector products. Since most geographic information system (GIS) databases store a variety of current and historical data, the accuracy of any on-screen digitized product is dependent on the spatial accuracy of the reference data. Therefore, it is important to understand and validate the accuracy of data used to create spatially referenced product, even though the data come with high spatial resolution. One of the more popular and historical high spatial resolution data within most GIS labs is QuickBird's multispectral data at 2.44x2.44 m(2). Although there are current sensors available with a higher spatial resolution, the sometimes prohibitive expense of obtaining high spatial resolution data necessitates the need to utilize and assess historic data. Since the QuickBird has been the mainstay of high spatial resolution data since 2001, understanding the geometric accuracy of the DigitalGlobe's QuickBird user-defined panchromatic and multispectral image bundle product remains relevant. In this study, we assessed the positional accuracy of this product for its utility as an off the shelf base map for creating other spatially referenced products. The average Euclidean distance, RMSE (root mean square error), and RSME (root square mean error) between QuickBird-identified Universal Transverse Mercator (UTM) coordinates and coincident in situ GPS-collected UTM coordinates were calculated at 33 systematically selected locations throughout the city of Nacogdoches, Texas, USA. The average Euclidean distance, RMSE, and RSME between QuickBird's projected UTM coordinates and its corresponding GPS-collected UTM coordinates measured at 5.34 meters, 5.79 meters, and 4.05 meters, respectively. They were well within DigitalGlobe's stated RMSE positional accuracy of 14.0 meters for a panchromatic and multispectral QuickBird image bundle.