首页期刊导航|International journal of applied earth observation and geoinformation
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International journal of applied earth observation and geoinformation
International Institute for Aerospace Survey and Earth Sciences
International Institute for Aerospace Survey and Earth Sciences
1569-8432
International journal of applied earth observation and geoinformation/Journal International journal of applied earth observation and geoinformationISTPSCIAHCI
查看更多>>摘要：This study describes a method to map shoreline indicators on a sandy beach. The hypothesis is that, on this beach, spectral albedo is predominantly determined by moisture content and water lines can, therefore, be detected as albedo contrasts. A laboratory experiment is performed to relate moisture content to image albedo, and supervised edge detection is subsequently used to map the shoreline indicators with remote sensing imagery. The algorithm is tested with data from visible, near-infrared and shortwave-infrared wavelength regions. These results are compared to shoreline indicators obtained by a field survey and a shoreline indicator derived from a digital elevation model. Both the water line present when the imagery was acquired, as well as the maximum extent of the last flood, can be detected as a single edge. Older high water lines are confused with the last high water line and appear dispersed, as there are multiple debris lines present on the beach. The low water line, usually in saturated sand, also appears dispersed due to the presence of channels and troughs. Shorelines are constant moving boundaries, which is why shoreline indicators are used as a proxy. Unlike a mathematical indicator that is based on an elevation model, our method is more sensitive to the dynamic nature of shorelines. Supervised edge-detection is a technique for generating reproducible measurements of shoreline indicator positions over time, and aids in the monitoring of coastline migration.
van der Werff, HaraldLievens, CarolinePurwadi, Imam
9页
查看更多>>摘要：Rare Earth Elements (REEs) are indispensable in the manufacturing of renewable energy and clean technologies. Due to the high demand for REEs, mine waste, called "tailings", are sought because they could be a possible resource for REEs. In this study, tailing samples were collected from two tin mine tailings in Bangka Island, Indonesia, and analyzed using inductively coupled plasma optical emission spectrometry, x-ray powder diffraction, and Fourier transform infrared - attenuated total reflectance. Results showed that the tailing samples were identified as quartz and contained a high amount of erbium between 111.6 and 3768.4 mu g/g. Absorption features in infrared reflectance spectra were related to REE concentrations. We found a positive correlation between erbium and two absorption features that have not been reported before: One feature is centered at 500 nm with a correlation of 0.685, and one is centered at 674 nm with a correlation of 0.829.
查看更多>>摘要：South-west of Tehran, the capital city of Iran, is subjected to a high deformation rate due to excessive groundwater extractions. Persistent Scatterrer SAR Interferometry (PS-InSAR) technique is used to monitor Tehran's deformation. Three time series data including two Sentinel-1A (S-1A) spanning from 2014 to 2017, and an ENVISAT-ASAR data stack spanning from 2004 to 2010, are analyzed. The PS-InSAR technique does not perform well on ENVISAT-ASAR due to poor selection of PS points induced by large perpendicular baselines and strong temporal decorrelation of the dataset. In this paper, a novel Iterative PSI method (IPSI) is proposed to increase the PS points which are lost in PS-InSAR technique because of the unsuccessful derivation of the absolute phase value due to an integer ambiguity. The method selects PS points based on simultaneous analysis of their amplitude and phase. Results demonstrate that the density of PSs has been increased by about 4.5 times. Line of Sight (LOS) velocities obtained from both S-1A and ENVISAT-ASAR data analysis are highly compatible with each other, indicating the reliability of the both applied methods. The maximum cumulative displacements are estimated as 39.6 cm and 88.4 cm for Sentinel-1A and ENVISAT-ASAR datasets respectively. Moreover, the subsidence area has grown in the period between the data acquisition time. The methods are successfully validated by subsidence rates obtained from precise leveling and GPS observations.
查看更多>>摘要：The objective of this study is to investigate spatial structures of error in the assessment of continuous raster data. The use of conventional diagnostics of error often overlooks the possible spatial variation in error because such diagnostics report only average error or deviation between predicted and reference values. In this respect, this work uses a moving window (kernel) approach to generate geographically weighted (GW) versions of the mean signed deviation, the mean absolute error and the root mean squared error and to quantify their spatial variations. Such approach computes local error diagnostics from data weighted by its distance to the centre of a moving kernel and allows to map spatial surfaces of each type of error. In addition, a GW correlation analysis between predicted and reference values provides an alternative view of local error. These diagnostics are applied to two earth observation case studies. The results reveal important spatial structures of error and unusual clusters of error can be identified through Monte Carlo permutation tests. The first case study demonstrates the use of GW diagnostics to fractional impervious surface area datasets generated by four different models for the Jakarta metropolitan area, Indonesia. The GW diagnostics reveal where the models perform differently and similarly, and found areas of under-prediction in the urban core, with larger errors in peri-urban areas. The second case study uses the GW diagnostics to four remotely sensed aboveground biomass datasets for the Yucatan Peninsula, Mexico. The mapping of GW diagnostics provides a means to compare the accuracy of these four continuous raster datasets locally. The discussion considers the relative nature of diagnostics of error, determining moving window size and issues around the interpretation of different error diagnostic measures. Investigating spatial structures of error hidden in conventional diagnostics of error provides informative descriptions of error in continuous raster data.
查看更多>>摘要：We develop a new algorithm, the simplified urban-extent (SUE) algorithm, to estimate the surface urban heat island (UHI) intensity at a global scale. We implement the SUE algorithm on the Google Earth Engine platform using Moderate Resolution Imaging Spectroradiometer (MODIS) images to calculate the UHI intensity for over 9500 urban clusters using over 15 years of data, making this one of the most comprehensive characterizations of the surface UHI to date. The results from this algorithm are validated against previous multi-city studies to demonstrate the suitability of the method. The dataset created is then filtered for elevation differentials and percentage of urban area and used to estimate the diurnal, monthly, and long-term variability in the surface UHI in different climate zones. The global mean surface UHI intensity is 0.85 degrees C during daytime and 0.55 degrees C at night. Cities in arid climate show distinct diurnal and seasonal patterns, with higher surface UHI during nighttime (compared to daytime) and two peaks throughout the year. The diurnal variability in surface UHI is highest for equatorial climate zone (0.88 degrees C) and lowest for arid zone (0.53 degrees C). The seasonality is highest in the snow climate zone and lowest for equatorial climate zone. While investigating the change in the surface UHI over a decade and a half, we find a consistent increase in the daytime surface UHI in the urban clusters of the warm temperate climate zone (0.04 degrees C/decade) and snow climate zone (0.05 degrees C/decade). Only arid climate zones show a statistically significant increase in the nighttime surface UHI intensity (0.03 degrees C/decade). Globally, the change is mainly seen during the daytime (0.03 degrees C/decade). Finally, the importance of vegetation differential between urban and rural areas on the spatiotemporal variability is examined. Vegetation has a strong control on the seasonal variability of the surface UHI and may also partly control the long-term variability. The complete UHI data are available through this website (https://yceo.yale.edu/research/global-surface-uhi-explorer) and allows the user to query the UHI of urban clusters using a simple interface.
Shrestha, ShantiMiranda, IsabelKumar, AbhishekPardo, Maria Luisa Escobar...
14页
查看更多>>摘要：Mangroves are one of the most productive ecosystems known for provisioning of various ecosystem goods and services. They help in sequestering large amounts of carbon, protecting coastline against erosion, and reducing impacts of natural disasters such as hurricanes. Bhitarkanika Wildlife Sanctuary in Odisha harbors the second largest mangrove ecosystem in India. This study used Terra, Landsat and Sentinel-1 satellite data for spatio-temporal monitoring of mangrove forest within Bhitarkanika Wildlife Sanctuary between 2000 and 2016. Three biophysical parameters were used to assess mangrove ecosystem health: leaf chlorophyll (CHL), Leaf Area Index (LAI), and Gross Primary Productivity (GPP). A long-term analysis of meteorological data such as precipitation and temperature was performed to determine an association between these parameters and mangrove biophysical characteristics. The correlation between meteorological parameters and mangrove biophysical characteristics enabled forecasting of mangrove health and productivity for year 2050 by incorporating IPCC projected climate data. A historical analysis of land cover maps was also performed using Landsat 5 and 8 data to determine changes in mangrove area estimates in years 1995, 2004 and 2017. There was a decrease in dense mangrove extent with an increase in open mangroves and agricultural area. Despite conservation efforts, the current extent of dense mangrove is projected to decrease up to 10% by the year 2050. All three biophysical characteristics including GPP, LAI and CHL, are projected to experience a net decrease of 7.7%, 20.83% and 25.96% respectively by 2050 compared to the mean annual value in 2016. This study will help the Forest Department, Government of Odisha in managing and taking appropriate decisions for conserving and sustaining the remaining mangrove forest under the changing climate and developmental activities.
查看更多>>摘要：Temperature regime is one of the main controlling factors of greenhouse gas (GHG) emissions from peat bogs. Remotely sensed land surface temperature (LST) has a potential to become an efficient instrument in environmental monitoring of carbon dioxide and methane emissions from peat bogs. This paper examines the relationships between field-measured hydrometeorological variables and MODIS LST data in a hemiboreal raised bog for a period from May to September (2008-2016). The Pearson product-moment correlation was used to reveal the relationship between the field-measured parameters and LST over years and months. A multiple linear regression was chosen to model relationships between the hydrometeorological variables and LST by month. It was found that the relationships between the studied parameters and LST were year- and month-dependent. The main factor of LST was air temperature, and the correlation between LST and air temperature was the strongest during the entire period of study. This study has shown that the hydrometeorological factors of LST can explain 67%-81 % of the variance in LST in a hemiboreal raised bog. The relationships between the hydrometeorological variables and LST may be implemented in more accurate GHG emissions estimation from bogs.
查看更多>>摘要：Among the most productive ecosystems around the world, wetlands support a wide range of biodiversity such as waterfowl, fish, amphibians, plants and many other species. They also provide ecosystem services that play important roles in relation to nutrient cycling, climate mitigation and adaptation, as well as food security. In this research, we examined and projected the spatiotemporal trends of change in open wetlands by coupling logistic regression, Markov chain methods and a multi-objective land allocation model into a hybrid geosimulation model. To study the changes in open wetlands we used multi-temporal land cover information interpreted from LANDSAT images (1985, 1995, and 2005). We predicted future spatial distributions of open wetlands in the administrative region of Abitibi-Temiscamingue, Quebec, Canada for 2015, 2025, 2035, 2045 and 2055. A comparison and assessment of the model's outcomes were performed using map-comparison techniques as well as landscape metrics. Change analysis between 1985 and 2005 showed an increase of about 63% in open wetlands, while simulation results indicated that this tendency would persist into 2055 with a continuous augmentation of open wetlands in the region. The spatial distribution of predicted trends in open wetlands could provide support to local biodiversity assessments, management and conservation planning of the open wetlands in Quebec, Canada.
查看更多>>摘要：Remotely sensed observations of seasonal greenness dynamics represent a valuable tool for studying vegetation phenology at regional and ecosystem-level scales. We investigated the seasonal variability of forests in Italy, examining the different mechanisms of phenological response to biophysical drivers. For each point of the Italian National Forests Inventory, we processed a multitemporal profile of the MODIS Enhanced Vegetation Index. Then we applied a multivariate approach for the purpose of (i) classifying the Italian forests into phonological clusters (i.e. pheno-clusters), (ii) identifying the main phonological characteristics and the forest compositions of each pheno-cluster and (iii) exploring the role of climate and physiographic variables in the phenological timing of each cluster. Results identified four pheno-clusters, following a clear elevation gradient and a distinct separation along the Mediterranean-to-temperate climatic transition of Italy. The "High-elevation coniferous" and the "High elevation deciduous" resulted mainly affected by elevation, with the former characterized by low annual productivity and the latter by high seasonality. To the contrary, the "Low elevation deciduous" showed to be mostly associated to moderate climate conditions and a prolonged growing season. Finally, summer drought was the main driving variable for the "Mediterranean evergreen", characterized by low seasonality. The discrimination of vegetation phenology types can provide valuable information useful as a baseline framework for further studies on forests ecosystem and for management strategies.
NSTL
Elsevier