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1.
InSAR to support sustainable urbanization over compacting aquifers: The case of Toluca Valley,Mexico
This paper illustrates how InSAR alone can be used to delineate potential ground fractures related to aquifer system compaction. An InSAR-derived ground fracturing map of the Toluca Valley, Mexico, is produced and validated through a field campaign. The results are of great interest to support sustainable urbanization and show that InSAR processing of open-access Synthetic Aperture Radar (SAR) data from the Sentinel-1 satellites can lead to reliable and cost-effective products directly usable by cities to help decision-making.The Toluca Valley Aquifer (TVA) sustains the water needs of two million inhabitants living within the valley, a growing industry, an intensively irrigated agricultural area, and 38% of the water needs of the megalopolis of Mexico City, located 40 km east of the valley. Ensuring water sustainability, infrastructure integrity, along with supporting the important economic and demographic growth of the region, is a major challenge for water managers and urban developers. This paper presents a long-term analysis of ground fracturing by interpreting 13 years of InSAR-derived ground displacement measurements. Small Baseline Subset (SBAS) and Persistent Scatterer Interferometry (PSI) techniques are applied over three SAR datasets totalling 93 acquisitions from Envisat, Radarsat-2, and Sentinel-1A satellites and covering the period from 2003 to 2016.From 2003 to 2016, groundwater level declines of up to 1.6 m/yr, land subsidence up to 77 mm/yr, and major infrastructure damages are observed. Groundwater level data show highly variable seasonal responses according to their connectivity to recharge areas. However, the trend of groundwater levels consistently range from −0.5 to −1.5 m/yr regardless of the well location and depth. By analysing the horizontal gradients of vertical land subsidence, we provide a potential ground fracture map to assist in future urban development planning in the Toluca Valley. 相似文献
2.
Kousik Biswas Debashish Chakravarty Pabitra Mitra Arundhati Misra 《Journal of the Indian Society of Remote Sensing》2017,45(6):913-926
Interferometric Synthetic Aperture Radar (InSAR), nowadays, is a precise technique for monitoring and detecting ground deformation at a millimetric level over large areas using multi-temporal SAR images. Persistent Scatterer Interferometric SAR (PSInSAR), an advanced version of InSAR, is an effective tool for measuring ground deformation using temporally stable reference points or persistent scatterers. We have applied both PSInSAR and Small Baseline Subset (SBAS) methods, based on the spatial correlation of interferometric phase, to estimate the ground deformation and time-series analysis. In this study, we select Las Vegas, Nevada, USA as our test area to detect the ground deformation along satellite line-of-sight (LOS) during November 1992–September 2000 using 44 C-band SAR images of the European Remote Sensing (ERS-1 and ERS-2) satellites. We observe the ground displacement rate of Las Vegas is in the range of ?19 to 8 mm/year in the same period. We also cross-compare PSInSAR and SBAS using mean LOS velocity and time-series. The comparison shows a correlation coefficient of 0.9467 in the case of mean LOS velocity. Along this study, we validate the ground deformation results from the satellite with the ground water depth of Las Vegas using time-series analysis, and the InSAR measurements show similar patterns with ground water data. 相似文献
3.
Determining the location and nature of hazardous ground motion resulting from natural and anthropogenic processes such as landslides, tectonic movement and mining is essential for hazard mitigation and sustainable resource use. Ground motion estimates from satellite ERS-1/2 persistent scatterer interferometry (PSI) were combined with geospatial data to identify areas of observed geohazards in Stoke-on-Trent, UK. This investigation was performed within the framework of the EC FP7-SPACE PanGeo project which aimed to provide free and open access to geohazard information for 52 urban areas across Europe. Geohazards identified within the city of Stoke-on-Trent and neighbouring rural areas are presented here alongside an examination of the PanGeo methodology.A total of 14 areas experiencing ground instability caused by natural and anthropogenic processes have been defined, covering 122.35 km2. These are attributed to a range of geohazards, including landslides, ground dissolution, made ground and mining activities. The dominant geohazard (by area) is ground movement caused by post-mining groundwater recharge and mining-related subsidence (93.19% of total geohazard area), followed by landsliding (5.81%). Observed ground motions along the satellite line-of-sight reach maxima of +35.23 mm/yr and −22.57 mm/yr. A combination of uplift, subsidence and downslope movement is displayed.‘Construction sites’ and ‘continuous urban fabric’ (European Urban Atlas land use types) form the land uses most affected (by area) by ground motion and ‘discontinuous very low density urban fabric’ the least. Areas of ‘continuous urban fabric’ also show the highest average velocity towards the satellite (5.08 mm/yr) and the highest PS densities (1262.92 points/km2) along with one of the lowest standard deviations. Rural land uses tend to result in lower PS densities and higher standard deviations, a consequence of fewer suitable reflectors in these regions. PSI is also limited in its ability to identify especially rapid ground motion. As a consequence the supporting geospatial data proved especially useful for the identification of landslides and some areas of ground dissolution. The mapped areas of instability are also compared with modelled potential geohazards (the BGS GeoSure dataset). 相似文献
4.
Differential Interferometric Synthetic Aperture Radar (DInSAR) can be considered as an efficient and cost effective technique for monitoring land subsidence due to its large spatial coverage and high accuracy provided. The recent commissioning of the first Sentinel-1 satellite offers improved support to operational surveys using DInSAR due to regular observations from a wide-area product. In this paper we show the results of an intermittent small-baseline subset (ISBAS) time-series analysis of 18 Interferometric Wide swath (IW) products of a 39,000 km2 area of Mexico acquired between 3 October 2014 and 7 May 2015 using the Terrain Observation with Progressive Scans in azimuth (TOPS) imaging mode. The ISBAS processing was based upon the analysis of 143 small-baseline differential interferograms. After the debursting, merging and deramping steps necessary to process Sentinel-1 IW products, the method followed a standard approach to the DInSAR analysis. The Sentinel-1 ISBAS results confirm the magnitude and extent of the deformation that was observed in Mexico City, Chalco, Ciudad Nezahualcóyotl and Iztapalapa by other C-band and L-band DInSAR studies during the 1990s and 2000s. Subsidence velocities from the Sentinel-1 analysis are, in places, in excess of −24 cm/year along the satellite line-of-sight, equivalent to over ∼40 cm/year vertical rates. This paper demonstrates the potential of Sentinel-1 IW TOPS imagery to support wide-area DInSAR surveys over what is a very large and diverse area in terms of land cover and topography. 相似文献
5.
Fariba Mohammadimanesh Bahram Salehi Masoud Mahdianpari Jerry English Joseph Chamberland Pierre-Jean Alasset 《地理信息系统科学与遥感》2019,56(4):485-510
Permafrost-induced deformation of ground features is threating infrastructure in northern communities. An understanding of permafrost distribution is therefore critical for sustainable adaptation planning and infrastructure maintenance. Considering the large area underlain by permafrost in the Yukon Territory, there is a need for baseline information to characterize the permafrost in this region. In this study, the Differential Interferometric Synthetic Aperture Radar (DInSAR) technique was used to identify areas of ground movement likely caused by changes in permafrost. The DInSAR technique was applied to a series of repeat-pass C-band RADARSAT-2 observations collected in 2015 over the Village of Mayo, in central Yukon Territory, Canada. The conventional DInSAR technique demonstrated that ground deformation could be detected in this area, but the resulting deformation maps contained errors due to a loss of coherence from changes in vegetation and atmospheric phase delay. To address these limitations, the Small BAseline Subset (SBAS) InSAR technique was applied to reduce phase error, thus improving the deformation maps. To understand the relationship between the deformation maps and land cover types, an object-based Random Forest classification was developed to classify the study area into different land cover types. Integration of the InSAR results and the classification map revealed that the built-up class (e.g., airport) was affected by subsidence on the order of ?2 to ?4 cm. The spatial extent of the surface displacement map obtained using the SBAS InSAR technique was then correlated with the surficial geology map. This revealed that much of the main infrastructure in the Village of Mayo is underlain by interbedded glaciofluvial and glaciolacustrine sediments, the latter of which caused the most damage to human made structures. This study provides a method for permafrost monitoring that builds upon the synergistic use of the SBAS InSAR technique, object-based image analysis, and surficial geology data. 相似文献
6.
基于SBAS技术的金属矿山沉陷规律研究 总被引：1，自引：0，他引：1
D-InSAR技术已经发展成为一种重要的地表监测手段,在矿山监测方面已经有较多成功的案例。但是矿山地表沉陷是一个长周期的形变序列,D-InSAR技术由于失相关等因素影响无法获得长周期的时序形变,小基线技术削弱了失相关的影响,可以获得时序的地表形变。文中利用小基线技术分析金属矿山沉陷规律,以某大型锑金属矿山为研究背景,用过14期形变序列探索一定地质采矿条件下的金属矿山的开采沉陷规律。结果表明,金属矿山受重力作用较大,部分区域地表形变受复杂地质构造控制。 相似文献
7.
Forest conservation is considered an option for mitigating the effect of greenhouse gases on global climate, hence monitoring forest carbon pools at global and local levels is important. The present study explores the capability of remote-sensing variables (vegetation indices and textures derived from SPOT-5; backscattering coefficient and interferometric coherence of ALOS PALSAR images) for modeling the spatial distribution of above-ground biomass in the Environmental Conservation Zone of Mexico City. Correlation and spatial autocorrelation coefficients were used to select significant explanatory variables in fir and pine forests. The correlation for interferometric coherence in HV polarization was negative, with correlations coefficients r = −0.83 for the fir and r = −0.75 for the pine forests. Regression-kriging showed the least root mean square error among the spatial interpolation methods used, with 37.75 tC/ha for fir forests and 29.15 tC/ha for pine forests. The results showed that a hybrid geospatial method, based on interferometric coherence data and a regression-kriging interpolator, has good potential for estimating above-ground biomass carbon. 相似文献
8.
Urban geological hazards involving ground instability can be costly, dangerous, and affect many people, yet there is little information about the extent or distribution of geohazards within Europe’s urban areas. A reason for this is the impracticality of measuring ground instability associated with the many geohazard processes that are often hidden beneath buildings and are imperceptible to conventional geological survey detection techniques. Satellite radar interferometry, or InSAR, offers a remote sensing technique to map mm-scale ground deformation over wide areas given an archive of suitable multi-temporal data. The EC FP7 Space project named PanGeo (2011–2014), used InSAR to map areas of unstable ground in 52 of Europe’s cities, representing ∼15% of the EU population. In partnership with Europe’s national geological surveys, the PanGeo project developed a standardised geohazard-mapping methodology and recorded 1286 instances of 19 types of geohazard covering 18,000 km2. Presented here is an analysis of the results of the PanGeo-project output data, which provides insights into the distribution of European urban geohazards, their frequency and probability of occurrence. Merging PanGeo data with Eurostat’s GeoStat data provides a systematic estimate of population exposures. Satellite radar interferometry is shown to be as a valuable tool for the systematic detection and mapping of urban geohazard phenomena. 相似文献
9.
For the observation and monitoring of glacier surface velocity (GSV), remote sensing is an increasingly suitable tool thanks to the high temporal and spatial resolution of the data. Radar sensors have the specific advantage over optical sensors of being nearly weather and time-independent.Two image pairs separated by 11 days, acquired with the high-resolution spotlight (HS) and stripmap (SM) modes of the German sensor TerraSAR-X, were used to estimate GSV over Switzerland’s Aletsch Glacier. The SM mode covers larger ground swaths, making it more suitable for glacier-wide observations, while the HS images cover less area but offer the highest-possible spatial resolution, approximately 1 × 1 m on the ground. The images were acquired during the summer to maximise feature visibility by minimal snow cover.GSV estimation was performed using two methods, the comparison of which was a major goal of this study: traditional cross-correlation optimisation and a dense image matching algorithm based on complex wavelet decomposition. Each method was found to have unique advantages and disadvantages, but it was concluded that for GSV monitoring, cross-correlation is probably preferable to the wavelet-based approach. While it generates fewer estimates per unit area, this is not necessarily a critical requirement for all glaciological applications, and the method requires less initial “tuning” (calibration) than the wavelet algorithm, making it a slightly better tool in operational contexts. Also, the use of the highest-resolution spotlight datasets is recommended over stripmap mode images when large-area coverage is less critical. The comparative lack of visible features at the resolution of the stripmap images made reliable GSV estimation difficult, with the exception of several small areas dominated by large crevasses. 相似文献
10.
Large footprint waveform LiDAR sensors have been widely used for numerous airborne studies. Ground peak identification in a large footprint waveform is a significant bottleneck in exploring full usage of the waveform datasets. In the current study, an accurate and computationally efficient algorithm was developed for ground peak identification, called Filtering and Clustering Algorithm (FICA). The method was evaluated on Land, Vegetation, and Ice Sensor (LVIS) waveform datasets acquired over Central NY. FICA incorporates a set of multi-scale second derivative filters and a k-means clustering algorithm in order to avoid detecting false ground peaks. FICA was tested in five different land cover types (deciduous trees, coniferous trees, shrub, grass and developed area) and showed more accurate results when compared to existing algorithms. More specifically, compared with Gaussian decomposition, the RMSE ground peak identification by FICA was 2.82 m (5.29 m for GD) in deciduous plots, 3.25 m (4.57 m for GD) in coniferous plots, 2.63 m (2.83 m for GD) in shrub plots, 0.82 m (0.93 m for GD) in grass plots, and 0.70 m (0.51 m for GD) in plots of developed areas. FICA performance was also relatively consistent under various slope and canopy coverage (CC) conditions. In addition, FICA showed better computational efficiency compared to existing methods. FICA’s major computational and accuracy advantage is a result of the adopted multi-scale signal processing procedures that concentrate on local portions of the signal as opposed to the Gaussian decomposition that uses a curve-fitting strategy applied in the entire signal. The FICA algorithm is a good candidate for large-scale implementation on future space-borne waveform LiDAR sensors. 相似文献
11.
Research on surface water temperature (SWT) variations in large lakes over the Qinghai–Tibet Plateau (QTP) has been limited by lack of in situ measurements. By taking advantage of the increased availability of remotely sensed observations, this study investigated SWT variation of Siling Co in central QTP by processing complete MODIS Land surface temperature (LST) images over the lake covering from 2001 to 2013. The temporal (diurnal, intra-annul and inter-annul) variations of Siling Co SWT as well as the spatial patterns were analyzed. The results show that on average from late December to mid-April the lake is in a mixing state of water and ice and drastic diurnal temperature differences occur, especially along the shallow shoreline areas. The extent of spatial variations in monthly SWT ranges from 1.25 °C to 3.5 °C, and particularly large at nighttime and in winter months. The spatial patterns of annual average SWT were likely impacted by the cooling effect of river inflow from the west and east side of the lake. The annual cycle of spatial pattern of SWT is characterized by seasonal reversions between the shallow littoral regions and deep parts due to different heat capacity. Compared to the deep regions, the littoral shallow shoreline areas warms up quickly in spring and summer, and cool down drastically in autumn and winter, showing large diurnal and seasonal variation amplitudes of SWT. Two cold belt zones in the western and eastern side of the lake and warm patches along the southwestern and northeastern shorelines are shaped by the combined effects of the lakebed topography and river runoff. Overall, the lake-averaged SWT increased at a rate of 0.26 °C/decade during 2001–2013. Faster increase of temperature was found at nighttime (0.34 °C/decade) and in winter and spring, consistent with the asymmetric warming pattern over land areas reported in prior studies. The rate of temperature increase over Siling Co is remarkably lower than that over Bangoin station, which is probably attributable to the large heat capacity of water and partly reflects the sensitive of alpine saltwater lake to climate change. 相似文献
12.
Significant structural damages to urban infrastructures caused by compaction of over-exploited aquifers are an important problem in Central Mexico. While the case of Mexico City has been well-documented, insight into land subsidence problems in other cities of Central Mexico is still limited. Among the cities concerned, we present and discuss the cases of five of them, located within the Trans-Mexican Volcanic Belt (TMVB): Toluca, Celaya, Aguascalientes, Morelia, and Queretaro. Applying the SBAS-InSAR method to C-Band RADARSAT-2 data, five high resolution ground motion time-series were produced to monitor the spatio-temporal variations of displacements and fracturing from 2012 to 2014. The study presents recent changes of land subsidence rates along with concordant geological and water data. It aims to provide suggestions to mitigate future damages to infrastructure and to assist in groundwater resources management.Aguascalientes, Celaya, Morelia and Queretaro (respectively in order of decreasing subsidence rates) are typical cases of fault-limited land subsidence of Central Mexico. It occurs as a result of groundwater over-exploitation in lacustrine and alluvial deposits covering highly variable bedrock topography, typical of horst-graben geological settings. Aguascalientes and Toluca show high rates of land subsidence (up to 10 cm/yr), while Celaya and Morelia show lower rates (from 2 to 5 cm/yr). Comparing these results with previous studies, it is inferred that the spatial patterns of land subsidence have changed in the city of Toluca. This change appears to be mainly controlled by the spatial heterogeneity of compressible sediments since no noticeable change occurred in groundwater extraction and related drawdown rates. While land subsidence of up to 8 cm/yr has been reported in the Queretaro Valley before 2011, rates inferior to 1 cm/yr are measured in 2013–2014. The subsidence has been almost entirely mitigated by major changes in the water management practices of the city, i.e., a 122 km long pipeline bringing surface water from an adjacent state allowed to cease pumping in half of the wells. 相似文献
13.
抽取地下水进行农业灌溉是导致地下水位快速下降的重要因素，而长期过度开采地下水往往会引发地面沉降灾害，这种现象在干旱和半干旱地区非常普遍。为了研究农业灌溉超采引发的地表形变特征和演化规律，本文以准噶尔盆地南缘、天山北麓地带为研究区域，利用SBAS-InSAR技术对2003年—2009年覆盖呼图壁县的ENVISAT/ASAR升、降轨数据进行处理，获取了该地区的地表形变场，并结合研究区的农业灌溉方式、水资源补给和季节变化等资料对地面沉降的时空变化特征进行分析，为水资源和农业可持续发展提供参考意义。实验表明，研究区内主要有两个沉降幅度较大的漏斗，且都位于农田区域。2007年以前，研究区地表没有显著形变，之后发生了较大量级的沉降。采用传统灌溉方式和时针式灌溉系统的农业区平均沉降速率最高分别可达50 mm/a和30 mm/a，前者在时间上呈线性变化，而后者具有显著的周期性变化特征。在冬季时，采用时针式灌溉系统的地区地面抬升量可达40 mm，远大于传统灌溉方式的农田区域，而夏季地面沉降速率可达200 mm/a。对研究区农业灌溉活动进行分析后发现，农业灌溉造成的地下水超采是该地区地面沉降的主要影响因素，其形变机制与季节变化具有较高的相关性，在灌溉活动休止期内地表形变取决于地下水的补给量。研究区内的形变特征和影响因素分析将为地下水资源的充分利用和农业的可持续发展提供有效的信息。 相似文献
14.
Co-seismic deformation associated with the Lushan (China) earthquake that occurred along the south-western segment of the Longmenshan Fault Zone (LFZ) on the 20th April 2013 has been estimated by differential interferometric SAR (DInSAR) technique using Radarsat-2 data. The Lushan earthquake resulted in the deformation of the Sichuan basin and the Longmenshan ranges in proximity to the LFZ. The line of sight (LOS) displacement values obtained from DInSAR technique mainly range between −4.0 cm to +3.0 cm. The western Sichuan basin shows oblique westward movement with predominant downward component in areas farther from LFZ and predominant westward component over the downward movement in areas closer to the source fault. Inversion modelling has been used to derive the seismic source characteristics from DInSAR derived deformation values using elastic dislocation source type. The linear inversion model converged at a double-fault source solution consisting of a deeper, steep, NW dipping fault plane-1 of 60 km × 16 km dimension and a shallower, gentle, NW dipping fault plane-2 of 60 km × 15 km dimension, with distributed slip values varying between 0 to 2.26 m. These fault planes (fault planes-1 and -2) coincide with the Dachuan-Shuangshi fault and the buried Range Front Fault, respectively. The inversion model gives a moment magnitude of 6.81 and the geodetic moment of 2.07 × 1019 Nm, comparable to those given in literature, derived using teleseismic body wave data. Thus DInSAR technique helped to quantify the co-seismic deformation and to retrieve the source characteristics from the estimated deformation values. The study also evaluated the distribution pattern of earthquake induced landslides (EIL) triggered fresh or re-activated during the Lushan earthquake and found that they show spatial association with the seismic source zone and also with various pre-conditioning factors of slope instability. 相似文献
15.
Tracking water level fluctuations in small lakes and reservoirs is important in order to better understand and manage these ecosystems. A geographic object-based image analysis (GEOBIA) method using very high spatial and temporal resolution optical (Pléiades) and radar (COSMO-SkyMed and TerraSAR-X) remote sensing imagery is presented here which (1) tracks water level fluctuations via variations in water surface area and (2) avoids common difficulties found in using single-band radar images for water-land image classification. Results are robust, with over 98% of image surface area correctly classified into land or water, R2 = 0.963 and RMSE = 0.42 m for a total water level fluctuation range of 5.94 m. Multispectral optical imagery is found to be more straightforward in producing results than single-band radar imagery, but the latter crucially increase temporal resolution to the point where fluctuations can be satisfactorily tracked in time. Moreover, an analysis suggests that high and medium spatial resolution imagery is sufficient, in at least some cases, in tracking the water level fluctuations of small inland reservoirs. Finally, limitations of the methodology presented here are briefly discussed along with potential solutions to overcome them. 相似文献
16.
Satellite remote sensing has been used successfully to map leaf area index (LAI) across landscapes, but advances are still needed to exploit multi-scale data streams for producing LAI at both high spatial and temporal resolution. A multi-scale Spatio-Temporal Enhancement Method for medium resolution LAI (STEM-LAI) has been developed to generate 4-day time-series of Landsat-scale LAI from existing medium resolution LAI products. STEM-LAI has been designed to meet the demands of applications requiring frequent and spatially explicit information, such as effectively resolving rapidly evolving vegetation dynamics at sub-field (30 m) scales. In this study, STEM-LAI is applied to Moderate Resolution Imaging Spectroradiometer (MODIS) based LAI data and utilizes a reference-based regression tree approach for producing MODIS-consistent, but Landsat-based, LAI. The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) is used to interpolate the downscaled LAI between Landsat acquisition dates, providing a high spatial and temporal resolution improvement over existing LAI products. STARFM predicts high resolution LAI by blending MODIS and Landsat based information from a common acquisition date, with MODIS data from a prediction date. To demonstrate its capacity to reproduce fine-scale spatial features observed in actual Landsat LAI, the STEM-LAI approach is tested over an agricultural region in Nebraska. The implementation of a 250 m resolution LAI product, derived from MODIS 1 km data and using a scale consistent approach based on the Normalized Difference Vegetation Index (NDVI), is found to significantly improve accuracies of spatial pattern prediction, with the coefficient of efficiency (E) ranging from 0.77–0.94 compared to 0.01–0.85 when using 1 km LAI inputs alone. Comparisons against an 11-year record of in-situ measured LAI over maize and soybean highlight the utility of STEM-LAI in reproducing observed LAI dynamics (both characterized by r2 = 0.86) over a range of plant development stages. Overall, STEM-LAI represents an effective downscaling and temporal enhancement mechanism that predicts in-situ measured LAI better than estimates derived through linear interpolation between Landsat acquisitions. This is particularly true when the in-situ measurement date is greater than 10 days from the nearest Landsat acquisition, with prediction errors reduced by up to 50%. With a streamlined and completely automated processing interface, STEM-LAI represents a flexible tool for LAI disaggregation in space and time that is adaptable to different land cover types, landscape heterogeneities, and cloud cover conditions. 相似文献
17.
This study investigates urbanization and its potential environmental consequences in Shanghai and Stockholm metropolitan areas over two decades. Changes in land use/land cover are estimated from support vector machine classifications of Landsat mosaics with grey-level co-occurrence matrix features. Landscape metrics are used to investigate changes in landscape composition and configuration and to draw preliminary conclusions about environmental impacts. Speed and magnitude of urbanization is calculated by urbanization indices and the resulting impacts on the environment are quantified by ecosystem services. Growth of urban areas and urban green spaces occurred at the expense of cropland in both regions. Alongside a decrease in natural land cover, urban areas increased by approximately 120% in Shanghai, nearly ten times as much as in Stockholm, where the most significant land cover change was a 12% urban expansion that mostly replaced agricultural areas. From the landscape metrics results, it appears that fragmentation in both study regions occurred mainly due to the growth of high density built-up areas in previously more natural/agricultural environments, while the expansion of low density built-up areas was for the most part in conjunction with pre-existing patches. Urban growth resulted in ecosystem service value losses of approximately 445 million US dollars in Shanghai, mostly due to the decrease in natural coastal wetlands while in Stockholm the value of ecosystem services changed very little. Total urban growth in Shanghai was 1768 km2 and 100 km2 in Stockholm. The developed methodology is considered a straight-forward low-cost globally applicable approach to quantitatively and qualitatively evaluate urban growth patterns that could help to address spatial, economic and ecological questions in urban and regional planning. 相似文献
18.
Recent changes in rice crop management within Northern Italy rice district led to a reduction of seeding in flooding condition, which may have an impact on reservoir water management and on the animal and plant communities that depend on the flooded paddies. Therefore, monitoring and quantifying the spatial and temporal variability of water presence in paddy fields is becoming important. In this study we present a method to estimate dynamics of presence of standing water (i.e. fraction of flooded area) in rice fields using MODIS data. First, we produced high resolution water presence maps from Landsat by thresholding the Normalised Difference Flood Index (NDFI) made: we made it by comparing five Landsat 8 images with field-obtained information about rice field status and water presence. Using these data we developed an empirical model to estimate the flooding fraction of each MODIS cell. Finally we validated the MODIS-based flooding maps with both Landsat and ground information. Results showed a good predictability of water surface from Landsat (OA = 92%) and a robust usability of MODIS data to predict water fraction (R2 = 0.73, EF = 0.57, RMSE = 0.13 at 1 × 1 km resolution). Analysis showed that the predictive ability of the model decreases with the greening up of rice, so we used NDVI to automatically discriminate estimations for inaccurate cells in order to provide the water maps with a reliability flag. Results demonstrate that it is possible to monitor water dynamics in rice paddies using moderate resolution multispectral satellite data. The achievement is a proof of concept for the analysis of MODIS archives to investigate irrigation dynamics in the last 15 years to retrieve information for ecological and hydrological studies. 相似文献
19.
Pest risk maps for agricultural use are usually constructed from data obtained from in-situ meteorological weather stations, which are relatively sparsely distributed and are often quite expensive to install and difficult to maintain. This leads to the creation of maps with relatively low spatial resolution, which are very much dependent on interpolation methodologies. Considering that agricultural applications typically require a more detailed scale analysis than has traditionally been available, remote sensing technology can offer better monitoring at increasing spatial and temporal resolutions, thereby, improving pest management results and reducing costs. This article uses ground temperature, or land surface temperature (LST), data distributed by EUMETSAT/LSASAF (with a spatial resolution of 3 × 3 km (nadir resolution) and a revisiting time of 15 min) to generate one of the most commonly used parameters in pest modeling and monitoring: “thermal integral over air temperature (accumulated degree-days)”. The results show a clear association between the accumulated LST values over a threshold and the accumulated values computed from meteorological stations over the same threshold (specific to a particular tomato pest). The results are very promising and enable the production of risk maps for agricultural pests with a degree of spatial and temporal detail that is difficult to achieve using in-situ meteorological stations. 相似文献
20.
The validation study of leaf area index (LAI) products over rugged surfaces not only gives additional insights into data quality of LAI products, but deepens understanding of uncertainties regarding land surface process models depended on LAI data over complex terrain. This study evaluated the performance of MODIS and GLASS LAI products using the intercomparison and direct validation methods over southwestern China. The spatio-temporal consistencies, such as the spatial distributions of LAI products and their statistical relationship as a function of topographic indices, time, and vegetation types, respectively, were investigated through intercomparison between MODIS and GLASS products during the period 2011–2013. The accuracies and change ranges of these two products were evaluated against available LAI reference maps over 10 sampling regions which standed for typical vegetation types and topographic gradients in southwestern China.The results show that GLASS LAI exhibits higher percentage of good quality data (i.e. successful retrievals) and smoother temporal profiles than MODIS LAI. The percentage of successful retrievals for MODIS and GLASS is vulnerable to topographic indices, especially to relief amplitude. Besides, the two products do not capture seasonal dynamics of crop, especially in spring over heterogeneously hilly regions. The yearly mean LAI differences between MODIS and GLASS are within ±0.5 for 64.70% of the total retrieval pixels over southwestern China. The spatial distribution of mean differences and temporal profiles of these two products are inclined to be dominated by vegetation types other than topographic indices. The spatial and temporal consistency of these two products is good over most area of grasses/cereal crops; however, it is poor for evergreen broadleaf forest. MODIS presents more reliable change range of LAI than GLASS through comparison with fine resolution reference maps over most of sampling regions. The accuracies of direct validation are obtained for GLASS LAI (r = 0.35, RMSE = 1.72, mean bias = −0.71) and MODIS LAI (r = 0.49, RMSE = 1.75, mean bias = −0.67). GLASS performs similarly to MODIS, but may be marginally inferior to MODIS based on our direct validation results. The validation experience demonstrates the necessity and importance of topographic consideration for LAI estimation over mountain areas. Considerable attention will be paid to the improvements of surface reflectance, retrieval algorithm and land cover types so as to enhance the quality of LAI products in topographically complex terrain. 相似文献