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International journal of applied earth observation and geoinformation
International Institute for Aerospace Survey and Earth Sciences
International journal of applied earth observation and geoinformation

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
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    Evaluation of subsidence induced by long-lasting buildings load using InSAR technique and geotechnical data: The case study of a Freight Terminal (Tuscany, Italy)

    Ciampalini, AndreaSolari, LorenzoGiannecchini, RobertoGalanti, Yuri...
    14页
    查看更多>>摘要：This paper shows the results of the comparison between Multi-temporal Synthetic Aperture Radar (MTInSAR) products derived from different sensors (C-band ERS 1/2, Envisat, Sentinel-1 and X-band COSMO-SkyMed) and geotechnical data to investigate the driving factors of subsidence which affect a freight terminal located along the a coastal plain of Tuscany (central Italy). MTInSAR data have been acquired in a very long period, between 1992 and 2018 and were analyzed in terms of subsidence rates and deformation time series at building scale. The obtained results show that the oldest buildings are still affected by a deformation rate close to - 5 mm/yr, whereas recent buildings register rates around -40 mm/yr. Time series of deformation suggest that the deformation rates decrease over time following time-dependent trend that approximates the typical consolidation curve for compressible soils. The geotechnical and stratigraphical analysis of the subsurface data (boreholes, cone penetration tests and dilatometer tests) highlights the presence of a 15 m thick layer formed of clay characterized by poor geotechnical characteristics. The comparison among InSAR data, subsurface geological framework and geotechnical reconstruction suggests a possible evaluation of the timing of the primary and secondary consolidation processes.
      原文链接:
    • NSTL
    • Elsevier

    Elevational and structural shifts in the treeline of an oceanic island (Tenerife, Canary Islands) in the context of global warming

    Bello-Rodriguez, VictorCubas, JonayDel Arco, Marcelino J.Martin, Jose L....
    8页
    查看更多>>摘要：Global warming is changing the structure and elevational limits of treelines around the world. This could become a threat particularly on islands, where usually high mountain ecosystems occupy small areas. Tenerife, with a maximum elevation of 3715 m, is an excellent example of this. In this subtropical island, the treeline composed by endemic pine forests is expected to go up in elevation due to global warming, invading the summit scrub ecosystem. However, there is a lack of knowledge about shifts and trends of the island treeline from a multitemporal perspective and how invasive herbivores are influencing these changes. In this study, we evaluated the past and current state of the Pinus canariensis treeline on the southern areas of Tenerife, where natural forests persist, and an increase in temperature due to global warming has been already detected in the last decades. For that purpose, we counted the number of pine trees in aerial photographs for the years 1963, 1987 and 2016 and performed generalized additive models to evaluate the role of the different macro-variables involved in the regeneration processes. Complementarily, we performed ten transects to evaluate current forest structure and the influence of invasive herbivores (rabbits and mouflons) from 1600 m to the upper limits (2400 m). Our results reveal an increase in tree density and slow but consistent advance of the treeline in this part of the island during the last 53 years. Interestingly, positive relationships were found between number of trees and temperature. On the contrary, negative correlation was detected between seedlings and saplings and herbivores, a factor that is influencing the forests structure at all elevation levels. Our results show the importance of the herbivore control to ensure a healthy forest structure that allow an adequate migratory capacity of the species with the global warming.
      原文链接:
    • NSTL
    • Elsevier

    Fallowing temporal patterns assessment in rainfed agricultural areas based on NDVI time series autocorrelation values

    Recuero, L.Wiese, K.Huesca, M.Cicuendez, V...
    11页
    查看更多>>摘要：Fallowing is a common practice in Mediterranean areas where water scarcity becomes a limiting factor, affecting soil productivity, crop yield and biodiversity. In mainland Spain, fallow lands expand across three million hectares every year, constituting around 30% of rainfed arable lands and 6% of the national surface. There is a need of monitoring fallow lands to better map land use intensity and therefore achieve a sustainable expansion and intensification of agriculture. However, most of current land use classification systems do not include lands under fallowing practices as a specific class. In this research, a new and highly operative methodology based on NDVI time series autocorrelation values to assess fallowing temporal patterns across rainfed agricultural areas is proposed. This approach was tested in mainland Spain, using the autocorrelation function of MODIS NDVI time series from 2001 to 2012 at 250 m spatial resolution. The field observational database from the Spanish Ministry of Agriculture, Fisheries and Food was used for validation purposes. The dataset used includes 338 pixels with annual information about the cultivated and fallowed surface within the entire study period. It was demonstrated that specific autocorrelation values at lags corresponding to one, two, and three years contained relevant information to identify lands under fallowing practices and assess their temporal pattern. Integrating autocorrelation variables in a random forest model made it possible to improve the assessment. The classification results were in agreement with the field dataset with an overall accuracy higher than 80%. Results revealed that approximately half of rainfed agricultural areas were regularly cultivated and distributed mainly in the northwestern Spain. The other half mainly located across northeast, center and south of Spain, showed crop-fallow rotation patterns. This methodology is a promising technique to map land management intensity using the entire time series in a highly operative manner. It is expected that in the near future the availability of remote sensing time series with better spatial resolution will make it possible to improve the assessment of agricultural intensification.
      原文链接:
    • NSTL
    • Elsevier

    Rapid mapping of winter wheat yield, protein, and nitrogen uptake using remote and proximal sensing

    Wang, KuHuggins, David R.Tao, Haiying
    10页
    查看更多>>摘要：Farmers' interest in precision nitrogen (N) fertilization has grown in the inland Pacific Northwest due to significant within-field spatial variability in yield and protein content. Mapping the spatial variability of yield, protein content, and total grain N uptake (N-g) can be a useful tool for post-harvest evaluation of N sufficiency that, in turn, can guide precision N fertilization. We evaluated the applicability of combining RapidEye satellite imagery-derived vegetation indices with topographic variables derived from high-resolution data obtained from proximal sensors to estimate winter wheat yield, protein content, and N-g. Results indicate that the normalized difference vegetation index (NDVI), the normalized difference red edge index (NDRE), terrain curvature, slope, aspect, topographic wetness index, and solar radiation are the most relevant co-variables that contribute to spatial variability of yield and N-g. Yield and N-g exhibited strong relationships with NDVI and NDRE from late June through early July during grain filling stage, with nearly all R-2 > 0.6. We found greater yield and N-g values in concave-shaped terrain; 0-5 degrees and 10-20 degrees slopes; and flat, eastern, or northern aspects. In contrast, protein content exhibited weak relationships with vegetation indices and nearly all terrain factors. Prediction models demonstrated that these variables can provide good estimations of yield and N-g, with R-2 of 0.848 and 0.864, respectively, and RMSE of 48.47 and 0.0136, respectively. Combining RapidEye-based NDRE with proximal sensor-based topographical factors shows great potential for accurately and efficiently estimating winter wheat yield and N-g, which can help guide N fertilizer management.
      原文链接:
    • NSTL
    • Elsevier

    An investigation of inversion methodologies to retrieve the leaf area index of corn from C-band SAR data

    Rao, Y. S.Mitchell, ScottRobertson, Laura DingleDavidson, Andrew...
    11页
    查看更多>>摘要：Studies on the sensitivity of microwave scattering to vegetation canopies have led the researchers to conclude that crop biophysical parameters can be modeled from Synthetic Aperture Radar (SAR) backscatter. In this study, we assess different methods of modeling the Leaf Area Index (LAI), an important biophysical indicator of crop productivity, from dual-polarized SAR. Particularly, we evaluate the performance of the Water Cloud Model (WCM) to estimate the LAI of corn using VV and VH backscatter derived from RADARSAT-2 and Sentinel-1 satellites over two test sites (Canada and Poland). We tested the performance of four different approaches to invert the WCM. These are: (a) iterative optimization (IO), (b) Look-up table (LUT) search, (c) Support Vector Regression (SVR) and (d) Random Forest Regression (RFR). The accuracy of each inversion was measured by comparing the estimates from the WCM to the LAI of corn measured in-situ. Our results indicated that the inversion of the WCM using the SVR method delivered the best performance, yielding a correlation (r-value) between estimated and measured LAI of 0.92 and a root mean square error (RMSE) of 0.677 m(2) m(-2). The other approaches produced higher errors, with the LUT search resulting in the greatest error (RMSE of 0.977 m(2) m(-2)). This study will be of interest to the agricultural sector as this community works towards developing robust methods for tracking crop productivity from SAR technologies across multiple sites and using data from multiple satellite platforms.
      原文链接:
    • NSTL
    • Elsevier

    Tidal-driven variation of suspended sediment in Hangzhou Bay based on GOCI data

    Hu, YuekaiYu, ZhifengZhou, BinLi, Yuan...
    13页
    查看更多>>摘要：The variation of suspended sediment concentration (SSC) in coastal waters plays a key role in the marine physical and chemical processes. Hangzhou Bay is a macrotidal estuary in eastern China. Under the influence of trapped tides, the SSC of Hangzhou Bay responds significantly commensurate with tidal frequencies, which leads to considerable changes in the ocean physical and chemical regimes, such as the underwater light field and turbidity front. However, polar-orbiting imaging satellites are unable to monitor the rapid variation of the SSC due to insufficient temporal resolution. In this study, we used the Geostatlonary Ocean Colour Imager (GOCI) data and in situ data of Hangzhou Bay to develop an appropriate remote sensing model to quantify the SSC of Hangzhou Bay and analyzed the impact of tides on the SSC. The research revealed the following findings: (1) The exponential model based on B8/B6 is best suited to remotely estimating the SSC in Hangzhou Bay, and the Mean Relative Error (MRE) is only 15.21%, (2) the variation of SSC in Hangzhou Bay is related to tidal forcing, especially during the ebb tide and the middle of the rising tide, (3) The effect of tidal driven SSC changes is less than the effect of diurnal tidal level variation of the middle tide day and the spring tide day tidal type, and (4) The maximum variation of SSC under tidal forcing is in the coastal ocean near the south bank of Hangzhou Bay and Nanhui Spit, while the origin of the variation of the Zhapu deep trough and the northern estuary is not obvious, which is speculated to be mainly related to the terrain and runoff of Hangzhou Bay. Furthermore, this study established a new model to map SSC in turbid seas and contributes to our understanding of SSC variation in the Hangzhou Bay, driven by strong tidal dynamics.
      原文链接:
    • NSTL
    • Elsevier

    Monitoring sugarcane growth response to varying nitrogen application rates: A comparison of UAV SLAM LiDAR and photogrammetry

    Kendoul, FaridSkocaj, DanielleSofonia, JeremyShendryk, Yuri...
    15页
    查看更多>>摘要：The capabilities and utility of UAV LiDAR and surface from motion photogrammetry have been of wide discussion in the remote sensing community and assumptions made, often speculative, about the potential strengths and limitations of these systems. Here, we employ a side-by-side test of the CSIRO Hovermap LiDAR and Micasense RedEdge multispectral camera simultaneously mounted to a single UAV platform to acquire a time-series data set from both sensors over the growth cycle of a sugarcane crop in northeast Queensland, Australia. The primary aim was to compare the ability of each system to accurately measure crop height, over a single growing cycle. A secondary aim examined the correlation between these measures and the sugarcane biophysical parameters of stalk population (stalks.m(-2)), total fresh biomass (TFB, t.ha(-1)) and cane yield (Yield, t cane.ha(-1)). The experimental design included a randomised complete block design of four nitrogen fertiliser treatments (0, 70, 110, 150 and 190 Nkg.ha(-1)) with four replications to assess if either optical measure could detect significant effects of nitrogen application on crop growth. Both systems demonstrated similar capabilities for accurately measuring crop heights throughout the growth period with statistically significant coefficients of determination observed when comparing the maximum (R-Adj(2) = .885, F(1,118) = 910.806, p <= .001) and mean (R-Adj(2) = .929, F(1,118) = 1548.404, p <= .001) crop height estimations of both instruments. In addition, both systems responded similarly in the detection of differences in crop structural properties response to nitrogen treatments. Only Hovermap, however, demonstrated the capacity to obtain sufficient ground returns over the course of the growth period to enable comparisons to the biophysical samples using a ground-to-non-ground return ratio (Stalk Population: R-Adj(2) = .788, F(1,18) = 70.688, p <= .001, TFB: R-Adj(2) = .713, F(1,18) = 48.198, p <= .001, Yield: R-Adj(2) = .707, F(1,18) = 46.921, p <= .001) with the best results of RedEdge obtained when comparing mean height measures (Stalk Population R-Adj(2) = .502, F(1,18) = 20.172, p <= .001, TFB: R-Adj(2) = .309, F(1,18) = 9.481, p = 0.006, Yield: R-Adj(2) = .322, F(1,18) = 10.028, p <= .005). The results suggest that although both systems are comparable for accurate crop height measurements, and as such, provide early detection of potential problems, UAV LiDAR provided more consistent and significant correlations with optical remotely sensed data to the biophysical parameters of sugarcane.
      原文链接:
    • NSTL
    • Elsevier

    A metabolic scaling theory-driven remote sensing approach to map spatiotemporal dynamics of litterfall in a tropical montane cloud forest

    Hu, Kai-TingHuang, Cho-ying
    10页
    查看更多>>摘要：Litter production, or litterfall, is a predominant part of the carbon and nutrient cycles of forest ecosystems. As a major and relatively invariable aspect of primary production, litterfall could be a predictor of total plant production. However, the conventional field approach for its estimation is laborious and costly. High temporal resolution optical satellite imagery may strengthen our ability to estimate litterfall over a vast region, but this remote sensing method is limited in some tropical and near-tropical mountainous regions, mainly due to coarse spatial resolution and frequent cloud coverage. The metabolic scaling theory (MST) states that forest productivity (including litterfall) is directly proportional to the mass of the photosynthesis tissue (leaves), and spatially, it may be isometrically scaled using the size of the largest individual in the system. In this study, we investigated the temporal dimension of the MST and hypothesized that the maximum leaf abundance over a year could also be a key determinant of annual litter production. The relationship between the leaf mass during the peak growing season and annual litterfall could facilitate large-scale litterfall mapping in mountainous terrains, since vegetation indices such as the Enhanced Vegetation Index (EVI) or the Normalized Difference Vegetation Index (NDVI), commonly utilized as a surrogate for leaf abundance, can be derived from relatively high spatial resolution satellite imagery. We correlated the summer growing season Landsat Enhanced Thematic Mapper plus (ETM+) EVI and NDVI for 2016 and 2017 and the temporally corresponding annual accumulated field litterfall data acquired from hinoki (Chamaecyparis spp.) dominant tropical montane cloud forests in northeastern Taiwan (23.98 N, 120.97 E). We found that the summer growing season EVI and NDVI may be salient variables to explain the spatial variation of annual litter production (r(2) = 0.39-0.67, p <= 0.01). The results have profound implications for applying MST to the regional mapping of ecosystem production across space and time in humid tropical regions, and these implications may facilitate the remote assessment of the terrestrial carbon budget.
      原文链接:
    • NSTL
    • Elsevier

    Multi-scale mapping of oil-sands in Anhembi (Brazil) using imaging spectroscopy

    de Souza Filho, Carlos RobertoNanni, Marcos R.Batezelli, AlessandroAsadzadeh, Saeid...
    13页
    查看更多>>摘要：In this work, oil-sand outcrops of the Anhembi deposit located in the Parana Basin, Brazil, were investigated using multi-scale imaging spectroscopy. The study incorporated VNIR-SWIR (400-2500 nm) spectroscopic data from imaging (i.e. sisuCHEMA) and nonimaging (i.e. FieldSpec-4) instruments in the lab, an AisaFENIX hyperspectral system on the ground and from the air, and WorldView-3 multispectral instrument. The aim was to assess the usefulness of emerging remote sensing technologies in characterizing hydrocarbon-bearing targets and understand the spatial variability of oil-sands at different scales using multi-source spectroscopic data. The bitumen content of the sands, estimated to be as high as 12 wt. %, was revealed to be unevenly distributed at all scales. Its distribution was shown to be controlled mainly by the clay proportion and permo-porosity of the strata, with the sand sheet facies corresponding to the highest bitumen contents. The ubiquitous clays, identified to be dominantly montmorillonite, were found to be intimately mixed with bitumen at all studied scales. The mean bitumen content was estimated to decrease from (similar to) 6 to 4.5% while moving from small-, to deposit-scale as a consequence of the pixel aggregation effect and incremental clay contribution. The study showed that bitumen determination on the ground requires an imaging system with a high signal-to-noise ratio and good illumination conditions. Spectral denoising is also a crucial prerequisite for the extraction of coherent spectral information from the data. WV-3 data was proved capable of resolving HC's feature at 1700 nm wavelength by its SWIR band-4 over targets encompassing > 30% of the 7.5 m SWIR pixel; albeit it was not successful in determining the total bitumen content of the sands. This work indicated that HC signatures, particularly the one centered at 2300 nm, is consistent and comparable among scales, and upon employing properly calibrated data, could be used to confidently map the bitumen content of oil-sands at all imaging scales. A multi-scale spectroscopic approach can provide a complete picture of the variations in geologic targets and is able to fill the gap in scale differences across scales that when integrated, would add synergy and help reduce the uncertainties associated with ore grade estimation.
      原文链接:
    • NSTL
    • Elsevier

    Comparative analysis of the accuracy of surface soil moisture estimation from the C- and L-bands

    El Hajj, MohammadBaghdadi, NicolasZribi, Mehrez
    13页
    查看更多>>摘要：Surface soil moisture (SSM) estimation is of great importance in several areas, such as hydrology, agriculture and risk assessment. C-band SAR (synthetic aperture radar) data have been widely used to estimate SSM, whereas few studies have been performed using L-band SAR due to the low availability of L-band SAR data. In this context, the objective of the present paper is to compare the SSM estimation potentials of the C- (Sentinel-1) and L-bands (PALSAR) for wheat and grassland plots. The inversion approach developed in this study uses neural networks to invert the SAR signal and estimate the SSM. For each radar frequency, the developed neural networks were trained using the following as an input vector: SAR incidence angle, SAR polarization (VV for the C-band and HH for the L-band), and NDVI from optical images. Artificial Neural networks (ANNs) were developed and validated using synthetic and real databases. The results showed that the L-band provided slightly less accurate SSM estimates than the C-band. Moreover, the results showed that the accuracies of the SSM estimates for both frequencies strongly depended on the soil roughness (Hrms) and SSM values. From the synthetic database at SSM values less than 25 vol.%, the ANNs underestimated the SSM for Hrms values less than 1.5 cm and overestimated the SSM for Hrms values greater than 1.5 cm. In addition, the ANNs underestimated the SSM value regardless of the Hrms value when the SSM value was greater than 25 vol.%. An RMSE analysis of the SSM estimates showed that the highest RMSE values were observed for the L-band regardless of the SSM value, and high RMSE values were observed for the C-band only in very wet soil conditions (SSM > 25 vol.%). From the real database at NDVI values less than 0.7, the RMSE (root mean square error) of the SSM estimates was 4.6 vol.% for the C-band and 5.3 vol.% for the L-band. Most importantly, the L-band enabled the estimation of the SSM under a well-developed vegetation cover (NDVI > 0.7) with an RMSE of 6.7 vol.%, whereas the C-band SAR signal became completely attenuated for some crops when the NDVI value was greater than 0.7, and thus the estimation of SSM was impossible using the C-band.
      原文链接:
    • NSTL
    • Elsevier