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841.
O.R. van Lier J.E. Luther D.G. Leckie W.W. Bowers 《International Journal of Applied Earth Observation and Geoinformation》2011
Monitoring ecological indicators is important for assessing impacts of human activities on ecosystems. A means of identifying and applying appropriate indicators is a prerequisite for: environmental assessment; better assessment and understanding of ecosystem health; elucidation of biogeochemical trends; and more accurate predictions of future responses to global change, particularly those due to anthropogenic disturbance. The challenge is to derive meaningful indicators of change that capture the complexities of ecosystems yet can be monitored consistently over large areas and across time. In this study, methods for monitoring indicators of land cover (LC) and forest change were developed using multi-sensor Landsat imagery. Mapping and updating procedures were applied to the Humber River Basin (HRB) in Newfoundland and Labrador, one of four test sites in Canada selected for testing the development of national-scale methods. Procedures involved unsupervised clustering and labeling of baseline imagery, followed by image-to-image spectral clustering to derive binary change masks within which new LC types were classified for non-baseline imagery. Updated maps were compatible with the baseline map and reflected change in LC for three time periods: 1976–1990, 1990–2001, and 2001–2007. From the LC products, several change indicators were quantified including: forest depletion, forest regeneration, forest change, net forest change, and annual rates of change. The procedures were validated using field plots to assess the accuracy of the 2007 LC product (74.2% for 10 LC classes) and change classes observed from 2001 to 2007 (87.8% for four change classes: depletion, regeneration, non-treed class no change, and treed class no change). Methods were considered to be highly efficient and operationally feasible over large areas spanning multiple Landsat scenes. Specific results for the test site provided trend information supporting land and resource management in the HRB region. 相似文献
842.
Pre-processing of a sample of multi-scene and multi-date Landsat imagery used to monitor forest cover changes over the tropics 总被引:1,自引:0,他引:1
Catherine BodartHugh Eva René Beuchle Rastislav RašiDario Simonetti Hans-Jürgen StibigAndreas Brink Erik LindquistFrédéric Achard 《ISPRS Journal of Photogrammetry and Remote Sensing》2011,66(5):555-563
In support to the Remote Sensing Survey of the global Forest Resource Assessment 2010, the TREES-3 project has processed more than 12,000 Landsat TM and ETM+ data subsets systematically distributed over the tropics. The project aims at deriving area estimates of tropical forest cover change for the periods 1990-2000-2005. The paper presents the pre-processing steps applied in an operational and robust manner to this large amount of multi-date and multi-scene imagery: conversion to top-of-atmosphere reflectance, cloud and cloud shadow detection, haze correction and image radiometric normalization. The results show that the haze correction algorithm has improved the visual appearance of the image and significantly corrected the digital numbers for Landsat visible bands, especially the red band. The impact of the normalization procedures (forest normalization and relative normalization) was assessed on 210 image pairs: in all cases the correlation between the spectral values of the same land cover in both images was improved. The developed automatic pre-processing chain provided a consistent multi-temporal data set across the tropics that will constitute the basis for an automatic object-based supervised classification. 相似文献
843.
S.H. Chen H.B. SuJ. Tian R.H. ZhangJ. Xia 《International Journal of Applied Earth Observation and Geoinformation》2011
To date, there is little work concerning the application of fusing images with significantly different spectral and spatial resolutions. In this paper, a novel method based on support vector machine (SVM) is proposed to quickly estimate soil erosion using the fused results produced from fusing such multisensor images by à trous wavelet transform (AWT). In the proposed method, the AWT is used to derive the high-resolution vegetation coverage image (HVCI) while the SVM overlays the HVCI and the slope image to derive the soil erosion map. By taking MODIS and TM images as an example, the potential of the proposed method is evaluated both quantitatively and qualitatively. The results show that it is feasible to perform the fusion of MODIS and TM images and the soil erosion map produced from the fused images by the proposed method can be achieved with an accuracy level comparable to that solely from the TM images. The merging of MODIS and TM images partly solves the constrains associated with the TM data availability which is caused by the lower revisit frequency and narrower spatial coverage. 相似文献
844.
Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) has been used for the blending of Landsat and MODIS data. Specifically, the 30 m Landsat-7 ETM+ (Enhanced Thematic Mapper plus) surface reflectance was predicted for a period of 10 years (2000–2009) as the product of observed ETM+ and MODIS surface reflectance (MOD09A1) on the predicted and observed ETM+ dates. A pixel based analysis for six observed ETM+ dates covering winter and summer crops showed that the prediction method was more accurate for NIR band (mean r2 = 0.71, p ≤ 0.01) compared to green band (mean r2 = 0.53; p ≤ 0.01). A recently proposed chlorophyll index (CI), which involves NIR and green spectral bands, was used to retrieve gross primary productivity (GPP) as the product of CI and photosynthetic active radiation (PAR). The regression analysis of GPP derived from closet observed and synthetic ETM+ showed a good agreement (r2 = 0.85, p ≤ 0.01 and r2 = 0.86, p ≤ 0.01) for wheat and sugarcane crops, respectively. The difference between the GPP derived from synthetic and observed ETM+ (prediction residual) was compared with the difference in GPP values from observed ETM+ on the two dates (temporal residual). The prediction residuals (mean value of 1.97 g C/m2 in 8 days) was found to be significantly lower than the temporal residuals (mean value of 4.46 g C/m2 in 8 days) that correspondence to 12% and 27%, respectively, of GPP values (mean value of 16.53 g C/m2 in 8 days) from observed ETM+ data, implying that the prediction method was better than temporal pixel substitution. Investigating the trend in synthetic ETM+ GPP values over a growing season revealed that phenological patterns were well captured for wheat and sugarcane crops. A direct comparison between the GPP values derived from MODIS and synthetic ETM+ data showed a good consistency of the temporal dynamics but a systematic error that can be read as bias (MODIS GPP over estimation). Further, the regression analysis between observed evapotranspiration and synthetic ETM+ GPP showed good agreement (r2 = 0.66, p ≤ 0.01). 相似文献
845.
B.B. Maruthi Sridhar Robert K. Vincent Sheila J. Roberts Kevin Czajkowski 《International Journal of Applied Earth Observation and Geoinformation》2011
The accumulation of heavy metals in the biosolid amended soils and the risk of their uptake into different plant parts is a topic of great concern. This study examines the accumulation of several heavy metals and nutrients in soybeans grown on biosolid applied soils and the use of remote sensing to monitor the metal uptake and plant stress. Field and greenhouse studies were conducted with soybeans grown on soils applied with biosolids at varying rates. The plant growth was monitored using Landsat TM imagery and handheld spectroradiometer in field and greenhouse studies, respectively. Soil and plant samples were collected and then analyzed for several elemental concentrations. The chemical concentrations in soils and roots increased significantly with increase in applied biosolid concentrations. Copper (Cu) and Molybdenum (Mo) accumulated significantly in the shoots of the metal-treated plants. Our spectral and Landsat TM image analysis revealed that the Normalized Difference Vegetative Index (NDVI) can be used to distinguish the metal stressed plants. The NDVI showed significant negative correlation with increase in soil Cu concentrations followed by other elements. This study suggests the use of remote sensing to monitor soybean stress patterns and thus indirectly assess soil chemical characteristics. 相似文献
846.
S. Veraverbeke S. Lhermitte W.W. Verstraeten R. Goossens 《International Journal of Applied Earth Observation and Geoinformation》2011
Burn severity is an important parameter in post-fire management. It incorporates both the direct fire impact (vegetation depletion) and ecosystem responses (vegetation regeneration). From a remote sensing perspective, burn severity is traditionally estimated using Landsat's differenced normalized burn ratio (dNBR). In this case study of the large 2007 Peloponnese (Greece) wildfires, Landsat dNBR estimates correlated reasonably well with Geo composite burn index (GeoCBI) field data of severity (R2 = 0.56). The usage of Landsat imagery is, however, restricted by cloud cover and image-to-image normalization constraints. Therefore a multi-temporal burn severity approach based on coarse spatial, high temporal resolution moderate resolution imaging spectroradiometer (MODIS) imagery is presented in this study. The multi-temporal dNBR (dNBRMT) is defined as the 1-year integrated difference between burned pixels and their unique control pixels. These control pixels were selected based on time series similarity and spatial context and reflect how burned pixels would have behaved in the case no fire had occurred. Linear regression between downsampled Landsat dNBR and dNBRMT estimates resulted in a moderate-high coefficient of determination R2 = 0.54. dNBRMT estimates are indicative for the change in vegetation productivity due to the fire. This change is considerably higher for forests than for more sparsely vegetated areas like shrub lands. Although Landsat dNBR is superior for spatial detail, MODIS-derived dNBRMT estimates present a valuable alternative for burn severity mapping at continental to global scale without image availability constraints. This is beneficial to compare trends in burn severity across regions and time. Moreover, thanks to MODIS's repeated temporal sampling, the dNBRMT accounts for both first- and second-order fire effects. 相似文献
847.
ETWatch中不同尺度蒸散融合方法 总被引:3,自引:2,他引:1
高分辨率遥感蒸散数据集的构建受到数据源的限制和云的影响,单一传感器无法达到高时空分辨率覆盖。本文分
析了ETWatch不同尺度遥感蒸散结果的空间特征,通过几种融合方法的比较,分析数据融合前后的数据特征和信息量,将
时空适应性反射率融合模型(STARFM)集成到ETWatch,用于不同尺度遥感蒸散数据的融合,该方法可以很好的结合高
低分辨率数据的空间分布和时间分布信息,在时间上保留了高时间分辨率数据的时间变化趋势,空间上又反映了高空间分
辨率数据的空间细节差异,STARFM融合后的日ET数据与融合前1 km 日ET数据的平均相对误差为1.75%,融合后的月ET
数据与融合前1 km 月ET数据的平均相对误差为0.2%,STARFM适合于不同尺度下遥感ET数据的融合。 相似文献
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849.
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