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1.
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
2.
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
3.
The potential of the short-wave infrared (SWIR) bands to detect dry-season vegetation mass and cover fraction is investigated with ground radiometry and MODIS data, confronted to vegetation data collected in rangeland and cropland sites in the Sahel (Senegal, Niger, Mali). The ratio of the 1.6 and 2.1 μm bands (called STI) acquired with a ground radiometer proved well suited for grassland mass estimation up to 2500 kg/ha with a linear relation (r2 = 0.89). A curvilinear regression is accurate for masses ranging up to 3500 kg/ha. STI proved also well suited to retrieve vegetation cover fraction in crop fields, fallows and rangelands. Such dry-season monitoring, with either ground or satellite data, has important applications for forage, erosion risk and fire risk assessment in semi-arid areas. 相似文献
4.
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
5.
Satellite radar interferometry is a well-documented technique for the characterisation of ground motions over large spatial areas. However, the measurement density is often constrained by the land use, with best results obtained over urban and semi urban areas. We use an implementation of the Small Baseline Subset (SBAS) methodology, whereby areas exhibiting intermittent coherence are considered alongside those displaying full coherence, in the final result, to characterise the ground motion over the South Wales Coalfield, United Kingdom. 55 ERS-1/2 Synthetic Aperture Radar (SAR) C-band images for the period between 1992 and 1999 are processed using the ISBAS (Intermittent Small BAseline Subset) technique, which provides 3.4 times more targets, with associated measurements than a standard SBAS implementation. The dominant feature of the observed motions is a relatively large spatial area of uplift. Uplift rates are as much as 1 cm/yr. and are centred on the part of the coalfield which was most recently exploited. Geological interpretation reveals that this uplift is most likely a result of mine water rebound. Collieries in this part of the coalfield required a ground water to be pumped to enable safe coal extraction; following their closure pumping activity ceased allowing the water levels to return to equilibrium. The ISBAS technique offers significant improvements in measurement density ensuring an increase in detection of surface motions and enabling easier interpretation. 相似文献
6.
Average maize yield in eastern Africa is 2.03 t ha−1 as compared to global average of 6.06 t ha−1 due to biotic and abiotic constraints. Amongst the biotic production constraints in Africa, stem borers are the most injurious. In eastern Africa, maize yield losses due to stem borers are currently estimated between 12% and 21% of the total production. The objective of the present study was to explore the possibility of RapidEye spectral data to assess stem borer larva densities in maize fields in two study sites in Kenya. RapidEye images were acquired for the Bomet (western Kenya) test site on the 9th of December 2014 and on 27th of January 2015, and for Machakos (eastern Kenya) a RapidEye image was acquired on the 3rd of January 2015. Five RapidEye spectral bands as well as 30 spectral vegetation indices (SVIs) were utilized to predict per field maize stem borer larva densities using generalized linear models (GLMs), assuming Poisson (‘Po’) and negative binomial (‘NB’) distributions. Root mean square error (RMSE) and ratio prediction to deviation (RPD) statistics were used to assess the models performance using a leave-one-out cross-validation approach. The Zero-inflated NB (‘ZINB’) models outperformed the ‘NB’ models and stem borer larva densities could only be predicted during the mid growing season in December and early January in both study sites, respectively (RMSE = 0.69–1.06 and RPD = 8.25–19.57). Overall, all models performed similar when all the 30 SVIs (non-nested) and only the significant (nested) SVIs were used. The models developed could improve decision making regarding controlling maize stem borers within integrated pest management (IPM) interventions. 相似文献
7.
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
8.
The mountainous areas of the northwestern Iberian Peninsula have undergone intense land abandonment. In this work, we wanted to determine if the abandonment of the rural areas was the main driver of landscape dynamics in Gerês–Xurés Transboundary Biosphere Reserve (NW Iberian Peninsula), or if other factors, such as wildfires and the land management were also directly affecting these spatio-temporal dynamics. For this purpose, we used earth observation data acquired from Landsat TM and ETM + satellite sensors, complemented by ancillary data and prior field knowledge, to evaluate the land use/land cover changes in our study region over a 10-year period (2000–2010). The images were radiometrically calibrated using a digital elevation model to avoid cast- and self-shadows and different illumination effects caused by the intense topographic variations in the study area. We applied a maximum likelihood classifier, as well as other five approaches that provided insights into the comparison of thematic maps. To describe the land cover changes we addressed the analysis from a multilevel approach in three areas with different regimes of environmental protection. The possible impact of wildfires was assessed from statistical and spatially explicit fire data. Our findings suggest that land abandonment and forestry activities are the main factors causing the changes in landscape patterns. Specifically, we found a strong decrease of the ‘meadows and crops’ and ‘sparse vegetation areas’ in favor of woodlands and scrublands. In addition, the huge impact of wildfires on the Portuguese side have generated new ‘rocky areas’, while on the Spanish side its impact does not seem to have been a decisive factor on the landscape dynamics in recent years. We conclude rural exodus of the last century, differences in land management and fire suppression policies between the two countries and the different protection schemes could partly explain the different patterns of changes recorded in these covers. 相似文献
9.
Defining critical source areas (CSAs) of diffuse pollution in agricultural catchments depends upon the accurate delineation of hydrologically sensitive areas (HSAs) at highest risk of generating surface runoff pathways. In topographically complex landscapes, this delineation is constrained by digital elevation model (DEM) resolution and the influence of microtopographic features. To address this, optimal DEM resolutions and point densities for spatially modelling HSAs were investigated, for onward use in delineating CSAs. The surface runoff framework was modelled using the Topographic Wetness Index (TWI) and maps were derived from 0.25 m LiDAR DEMs (40 bare-earth points m−2), resampled 1 m and 2 m LiDAR DEMs, and a radar generated 5 m DEM. Furthermore, the resampled 1 m and 2 m LiDAR DEMs were regenerated with reduced bare-earth point densities (5, 2, 1, 0.5, 0.25 and 0.125 points m−2) to analyse effects on elevation accuracy and important microtopographic features. Results were compared to surface runoff field observations in two 10 km2 agricultural catchments for evaluation. Analysis showed that the accuracy of modelled HSAs using different thresholds (5%, 10% and 15% of the catchment area with the highest TWI values) was much higher using LiDAR data compared to the 5 m DEM (70–100% and 10–84%, respectively). This was attributed to the DEM capturing microtopographic features such as hedgerow banks, roads, tramlines and open agricultural drains, which acted as topographic barriers or channels that diverted runoff away from the hillslope scale flow direction. Furthermore, the identification of ‘breakthrough’ and ‘delivery’ points along runoff pathways where runoff and mobilised pollutants could be potentially transported between fields or delivered to the drainage channel network was much higher using LiDAR data compared to the 5 m DEM (75–100% and 0–100%, respectively). Optimal DEM resolutions of 1–2 m were identified for modelling HSAs, which balanced the need for microtopographic detail as well as surface generalisations required to model the natural hillslope scale movement of flow. Little loss of vertical accuracy was observed in 1–2 m LiDAR DEMs with reduced bare-earth point densities of 2–5 points m−2, even at hedgerows. Further improvements in HSA models could be achieved if soil hydrological properties and the effects of flow sinks (filtered out in TWI models) on hydrological connectivity are also considered. 相似文献
10.
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
11.
Seagrass habitats in subtidal coastal waters provide a variety of ecosystem functions and services and there is an increasing need to acquire information on spatial and temporal dynamics of this resource. Here, we explored the capability of IKONOS (IKO) data of high resolution (4 m) for mapping seagrass cover [submerged aquatic vegetation (%SAV) cover] along the mid-western coast of Florida, USA. We also compared seagrass maps produced with IKO data with that obtained using the Landsat TM sensor with lower resolution (30 m). Both IKO and TM data, collected in October 2009, were preprocessed to calculate water depth invariant bands to normalize the effect of varying depth on bottom spectra recorded by the two satellite sensors and further the textural information was extracted from IKO data. Our results demonstrate that the high resolution IKO sensor produced a higher accuracy than the TM sensor in a three-class % SAV cover classification. Of note is that the OA of %SAV cover mapping at our study area created with IKO data was 5–20% higher than that from other studies published. We also examined the spatial distribution of seagrass over a spatial range of 4–240 m using the Ripley’s K function [L(d)] and IKO data that represented four different grain sizes [4 m (one IKO pixel), 8 m (2 × 2 IKO pixels), 12 m (3 × 3 IKO pixels), and 16 m (4 × 4 IKO pixels)] from moderate-dense seagrass cover along a set of six transects. The Ripley’s K metric repeatedly indicated that seagrass cover representing 4 m × 4 m pixels displayed a dispersed (or slightly dispersed) pattern over distances of <4–8 m, and a random or slightly clustered pattern of cover over 9–240 m. The spatial pattern of seagrass cover created with the three additional grain sizes (i.e., 2 × 24 m IKO pixels, 3 × 34 m IKO pixels, and 4 × 4 m IKO pixels) show a dispersed (or slightly dispersed) pattern across 4–32 m and a random or slightly clustered pattern across 33–240 m. Given the first report on using satellite observations to quantify seagrass spatial patterns at a spatial scale from 4 m to 240 m, our novel analyses of moderate-dense SAV cover utilizing Ripley’s K function illustrate how data obtained from the IKO sensor revealed seagrass spatial information that would be undetected by the TM sensor with a 30 m pixel size. Use of the seagrass classification scheme here, along with data from the IKO sensor with enhanced resolution, offers an opportunity to synoptically record seagrass cover dynamics at both small and large spatial scales. 相似文献
12.
Recent advances in image-matching techniques and VHR satellite imaging at submeter resolution theoretically offer the possibility to measure Earth surface displacements with decimetric precision. However, this possibility has yet not been explored and requirements of ground control and external topographic datasets are considered as important bottlenecks that hinder a more common application of optical image correlation for displacement measurements. This article describes an approach combining spaceborne stereo-photogrammetry, orthorectification and sub-pixel image correlation to measure the horizontal surface displacement of landslides from Pléiades satellite images. The influence of the number of ground-control points on the accuracy of the image orientation, the extracted surface models and the estimated displacement rates is quantified through comparisons with airborne laser scan and in situ global navigation satellite measurements at permanent stations. The comparison shows a maximum error of 0.13 m which is one order of magnitude more accurate than what has been previously reported with spaceborne optical images from other sensors. The obtained results indicate that the approach can be applied without significant loss in accuracy when no ground control points are available. It could, therefore, greatly facilitate displacement measurements for a broad range of applications. 相似文献
13.
High-resolution digital elevation models (DEMs) generated by airborne remote sensing are frequently used to analyze landform structures (monotemporal) and geomorphological processes (multitemporal) in remote areas or areas of extreme terrain. In order to assess and quantify such structures and processes it is necessary to know the absolute accuracy of the available DEMs. This study assesses the absolute vertical accuracy of DEMs generated by the High Resolution Stereo Camera-Airborne (HRSC-A), the Leica Airborne Digital Sensors 40/80 (ADS40 and ADS80) and the analogue camera system RC30. The study area is located in the Turtmann valley, Valais, Switzerland, a glacially and periglacially formed hanging valley stretching from 2400 m to 3300 m a.s.l. The photogrammetrically derived DEMs are evaluated against geodetic field measurements and an airborne laser scan (ALS). Traditional and robust global and local accuracy measurements are used to describe the vertical quality of the DEMs, which show a non Gaussian distribution of errors. The results show that all four sensor systems produce DEMs with similar accuracy despite their different setups and generations. The ADS40 and ADS80 (both with a ground sampling distance of 0.50 m) generate the most accurate DEMs in complex high mountain areas with a RMSE of 0.8 m and NMAD of 0.6 m They also show the highest accuracy relating to flying height (0.14‰). The pushbroom scanning system HRSC-A produces a RMSE of 1.03 m and a NMAD of 0.83 m (0.21‰ accuracy of the flying height and 10 times the ground sampling distance). The analogue camera system RC30 produces DEMs with a vertical accuracy of 1.30 m RMSE and 0.83 m NMAD (0.17‰ accuracy of the flying height and two times the ground sampling distance). It is also shown that the performance of the DEMs strongly depends on the inclination of the terrain. The RMSE of areas up to an inclination <40° is better than 1 m. In more inclined areas the error and outlier occurrence increase for all DEMs. This study shows the level of detail to which airborne stereoscopically derived DEMs can reliably be used in high mountain environments. All four sensor systems perform similarly in flat terrain. 相似文献
14.
Remote sensing-based timber volume estimation is key for modelling the regional potential, accessibility and price of lignocellulosic raw material for an emerging bioeconomy. We used a unique wall-to-wall airborne LiDAR dataset and Landsat 7 satellite images in combination with terrestrial inventory data derived from the National Forest Inventory (NFI), and applied generalized additive models (GAM) to estimate spatially explicit timber distribution and volume in forested areas. Since the NFI data showed an underlying structure regarding size and ownership, we additionally constructed a socio-economic predictor to enhance the accuracy of the analysis. Furthermore, we balanced the training dataset with a bootstrap method to achieve unbiased regression weights for interpolating timber volume. Finally, we compared and discussed the model performance of the original approach (r2 = 0.56, NRMSE = 9.65%), the approach with balanced training data (r2 = 0.69, NRMSE = 12.43%) and the final approach with balanced training data and the additional socio-economic predictor (r2 = 0.72, NRMSE = 12.17%). The results demonstrate the usefulness of remote sensing techniques for mapping timber volume for a future lignocellulose-based bioeconomy. 相似文献
15.
In this study, sensible heat (H) calculation using remote sensing data over an alpine grass landscape is conducted from May to September 2010, and the calculation is validated using LAS (large aperture scintillometers) measurements. Data from two remote sensing sensors (FY3A-VIRR and TERRA-MODIS) are analysed. Remote sensing data, combined with the ground meteorological observations (pressure, temperature, wind speed, humidity) are fed into the SEBS (Surface Energy Balance System) model. Then the VIRR-derived sensible heat (VIRR_SEBS_H) and MODIS-derived sensible heat (MODIS_SEBS_H) are compared with the LAS-estimated H, which are obtained at the respective satellite overpass time. Furthermore, the similarities and differences between the VIRR_SEBS_H and MODIS_SEBS_H values are investigated. The results indicate that VIRR data quality is as good as MODIS data for the purpose of H estimation. The root mean square errors (rmse) of the VIRR_SEBS_H and MODIS_SEBS_H values are 45.1098 W/m2 (n = 64) and 58.4654 W/m2 (n = 71), respectively. The monthly means of the MODIS_SEBS_H are marginally higher than those of VIRR_SEBS_H because the satellite overpass time of the TERRA satellite lags by 25 min to that of the FT3A satellite. Relative evaporation (EFr), which is more time-independent, shows a higher agreement between MODIS and VIRR. Many common features are shared by the VIRR_SEBS_H and the MODIS_SEBS_H, which can be attributed to the SEBS model performance. In May–June, H is over-estimated with more fluctuations and larger rmse, whereas in July–September, H is under-estimated with fewer fluctuations and smaller rmse. Sensitivity analysis shows that potential temperature gradient (delta_T) plays a dominant role in determining the magnitude and fluctuation of H. The largest rmse and over-estimation in H occur in June, which could most likely be attributed to high delta_T, high wind speed, and the complicated thermodynamic state during the transitional period when bare land transforms to dense vegetation cover. 相似文献
16.
Local climate zone (LCZ) mapping is an emerging field in urban climate research. LCZs potentially provide an objective framework to assess urban form and function worldwide. The scheme is currently being used to globally map LCZs as a part of the World Urban Database and Access Portal Tools (WUDAPT) initiative. So far, most of the LCZ maps lack proper quantitative assessment, challenging the generic character of the WUDAPT workflow. Using the standard method introduced by the WUDAPT community difficulties arose concerning the built zones due to high levels of heterogeneity. To overcome this problem a contextual classifier is adopted in the mapping process. This paper quantitatively assesses the influence of neighbourhood information on the LCZ mapping result of three cities in Belgium: Antwerp, Brussels and Ghent. Overall accuracies for the maps were respectively 85.7 ± 0.5, 79.6 ± 0.9, 90.2 ± 0.4%. The approach presented here results in overall accuracies of 93.6 ± 0.2, 92.6 ± 0.3 and 95.6 ± 0.3% for Antwerp, Brussels and Ghent. The results thus indicate a positive influence of neighbourhood information for all study areas with an increase in overall accuracies of 7.9, 13.0 and 5.4%. This paper reaches two main conclusions. Firstly, evidence was introduced on the relevance of a quantitative accuracy assessment in LCZ mapping, showing that the accuracies reported in previous papers are not easily achieved. Secondly, the method presented in this paper proves to be highly effective in Belgian cities, and given its open character shows promise for application in other heterogeneous cities worldwide. 相似文献
17.
Understanding spatial and temporal patterns of burned areas at regional scales, provides a long-term perspective of fire processes and its effects on ecosystems and vegetation recovery patterns, and it is a key factor to design prevention and post-fire restoration plans and strategies. Remote sensing has become the most widely used tool to detect fire affected areas over large tracts of land (e.g., ecosystem, regional and global levels). Standard satellite burned area and active fire products derived from the 500-m Moderate Resolution Imaging Spectroradiometer (MODIS) and the Satellite Pour l’Observation de la Terre (SPOT) are available to this end. However, prior research caution on the use of these global-scale products for regional and sub-regional applications. Consequently, we propose a novel semi-automated algorithm for identification and mapping of burned areas at regional scale. The semi-arid Monte shrublands, a biome covering 240,000 km2 in the western part of Argentina, and exposed to seasonal bushfires was selected as the test area. The algorithm uses a set of the normalized burned ratio index products derived from MODIS time series; using a two-phased cycle, it firstly detects potentially burned pixels while keeping a low commission error (false detection of burned areas), and subsequently labels them as seed patches. Region growing image segmentation algorithms are applied to the seed patches in the second-phase, to define the perimeter of fire affected areas while decreasing omission errors (missing real burned areas). Independently-derived Landsat ETM+ burned-area reference data was used for validation purposes. Additionally, the performance of the adaptive algorithm was assessed against standard global fire products derived from MODIS Aqua and Terra satellites, total burned area (MCD45A1), the active fire algorithm (MOD14); and the L3JRC SPOT VEGETATION 1 km GLOBCARBON products. The correlation between the size of burned areas detected by the global fire products and independently-derived Landsat reference data ranged from R2 = 0.01–0.28, while our algorithm performed showed a stronger correlation coefficient (R2 = 0.96). Our findings confirm prior research calling for caution when using the global fire products locally or regionally. 相似文献
18.
Soil respiration (Rs) data from 45 plots were used to estimate the spatial patterns of Rs during the peak growing seasons of winter wheat and summer maize in Julu County, North China, by combining satellite remote sensing data, field-measured data, and a support vector regression (SVR) model. The observed Rs values were well reproduced by the model at the plot scale, with a root-mean-square error (RMSE) of 0.31 μmol CO2 m−2 s−1 and a coefficient of determination (R2) of 0.73. No significant difference was detected between the prediction accuracy of the SVR model for winter wheat and summer maize. With forcing from satellite remote sensing data and gridded soil property data, we used the SVR model to predict the spatial distributions of Rs during the peak growing seasons of winter wheat and summer maize rotation croplands in Julu County. The SVR model captured the spatial variations of Rs at the county scale. The satellite-derived enhanced vegetation index was found to be the most important input used to predict Rs. Removal of this variable caused an RMSE increase from 0.31 μmol CO2 m−2 s−1 to 0.42 μmol CO2 m−2 s−1. Soil properties such as soil organic carbon (SOC) content and soil bulk density (SBD) were the second most important factors. Their removal led to an RMSE increase from 0.31 μmol CO2 m−2 s−1 to 0.37 μmol CO2 m−2 s−1. The SVR model performed better than multiple regression in predicting spatial variations of Rs in winter wheat and summer maize rotation croplands, as shown by the comparison of the R2 and RMSE values of the two algorithms. The spatial patterns of Rs are better captured using the SVR model than performing multiple regression, particularly for the relatively high and relatively low Rs values at the center and northeast study areas. Therefore, SVR shows promise for predicting spatial variations of Rs values on the basis of remotely sensed data and gridded soil property data at the county scale. 相似文献
19.
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