京九铁路赣南段沿线地区水土流失特征遥感分析

京九铁路赣南段沿线地区是我国南方水土流失最严重地区之一,也是我国南方一个典型的水土流失区。本文根据遥感调查结果,较详细地论述了该区水土流失的类型、分布特征、发展趋势,并分析了水土流失产生的原因及其造成的危害,以便为京九铁路沿线地区的经济开发、国土整治和环境评价提供科学依据。     

Spatial Analysis of Urban Growth Impacts on Vegetative Greenness with Landsat TM Data

Abstract

Landsat Thematic Mapper (TM) data have been used to monitor land cover types and to estimate biophysical parameters. However, studies examining the spatial relationships between land cover change and biophysical parameters are generally lacking. With the integration of remote sensing and Geographic Information Systems (GIS), these relationships can be better explored. The research reported in this paper applies this integrated approach for detecting urban growth and assessing its impact on vegetative greenness in the Zhujiang Delta, China. Multi‐temporal Landsat TM data were utilized to map urban growth and to extract and identify changes in vegetative greenness. GIS analyses were conducted to examine the changing spatial patterns of urban growth and greenness change. Statistical analyses were then used to examine the impact of urban growth on vegetative greenness. The results revealed that there was a notably uneven urban growth pattern in the delta, and urban development had reduced the scaled Normalized Difference Vegetation Index (NDVI) value by 30% in the urbanized area.     

A comparison of ASCAT and SMOS soil moisture retrievals over Europe and Northern Africa from 2010 to 2013

A comparison between ASCAT/H-SAF and SMOS soil moisture products was performed in the frame of the EUMETSAT H-SAF project. The analysis was extended to the whole H-SAF region of interest, including Europe and North Africa, and the period between January 2010 and November 2013 was considered. Since SMOS and ASCAT soil moisture data are expressed in terms of absolute and relative values, respectively, different approaches were adopted to scale ASCAT data to use the same volumetric soil moisture unit. Effects of land cover, quality index filtering, season and geographical area on the matching between the two products were also analyzed. The two satellite retrievals were also compared with other independent datasets, namely the NCEP/NCAR volumetric soil moisture content reanalysis developed by NOAA and the ERA-Interim/Land soil moisture produced by ECMWF. In situ data, available through the International Soil Moisture Network, were also considered as benchmark. The results turned out to be influenced by the way ASCAT data was scaled. Correlation between the two products exceeded 0.6, while the root mean square difference did not decrease below 8%. ASCAT generally showed a fairly good degree of correlation with ERA, while, as expected considering the different kinds of measurement, the discrepancies with respect to local in situ data were large for both satellite products.     

森林地上生物量遥感估测研究进展

森林生物量是衡量生态系统生产力的重要指标,也是研究森林生态系统物质循环的重要基础,其估测方法可以分为传统地面实测法、遥感监测法和综合模型法.随着生物量估测从样地研究发展到区域应用,空间尺度的增大导致宏观资料和参数的获取存在很多困难.在深入分析目前应用遥感技术估算森林生物量的方法及原理基础上,系统评述了统计模型、物理模型...     

一种黄土区土壤侵蚀强度遥感调查新方法

通过对TM图像的线性纹理提取和密度统计,首先获得了黄土丘陵区沟谷密度图,然后由沟谷密度反演区域土壤侵蚀强度。这种方法避开了影响土壤侵蚀量的诸多复杂因子。可直接从水土流失的外在表现---沟谷密度来反推土壤侵蚀强度。研究结果证明,这种方法对快速调查大范围黄土丘陵区的土壤侵蚀强度是十分有效的。     

卫星遥感估测土壤水分的一种方法

对于植被影响土壤水分遥感的大小 ,提出了“光学植被盖度”新概念来衡量 ,并用遥感法进行估测。用分解象元法排除植被干扰来提取土壤水分光谱信息。采用土壤水分光谱法并借助回归分析建立土壤水分遥感的TM数据模型。以此为桥梁 ,建立了AVHRR数据模型     

Re-parameterization of a quasi-analytical algorithm for colored dissolved organic matter dominant inland waters

Quasi-Analytical Algorithms (QAAs) are based on radiative transfer equations and have been used to derive inherent optical properties (IOPs) from the above surface remote sensing reflectance (R_rs) in aquatic systems in which phytoplankton is the dominant optically active constituents (OACs). However, Colored Dissolved Organic Matter (CDOM) and Non Algal Particles (NAP) can also be dominant OACs in water bodies and till now a QAA has not been parametrized for these aquatic systems. In this study, we compared the performance of three widely used QAAs in two CDOM dominated aquatic systems which were unsuccessful in retrieving the spectral shape of IOPS and produced minimum errors of 350% for the total absorption coefficient (a), 39% for colored dissolved matter absorption coefficient (a_CDM) and 7566.33% for phytoplankton absorption coefficient (a_phy). We re-parameterized a QAA for CDOM dominated (hereafter QAA_CDOM) waters which was able to not only achieve the spectral shape of the OACs absorption coefficients but also brought the error magnitude to a reasonable level. The average errors found for the 400–750 nm range were 30.71 and 14.51 for a, 14.89 and 8.95 for a_CDM and 25.90 and 29.76 for a_phy in Funil and Itumbiara Reservoirs, Brazil respectively. Although QAA_CDOM showed significant promise for retrieving IOPs in CDOM dominated waters, results indicated further tuning is needed in the estimation of a(λ) and a_phy(λ). Successful retrieval of the absorption coefficients by QAA_CDOM would be very useful in monitoring the spatio-temporal variability of IOPS in CDOM dominated waters.     

Soil erosion modelled with USLE and PESERA using QuickBird derived vegetation parameters in an alpine catchment

The focus of soil erosion research in the Alps has been in two categories: (i) on-site measurements, which are rather small scale point measurements on selected plots often constrained to irrigation experiments or (ii) off-site quantification of sediment delivery at the outlet of the catchment. Results of both categories pointed towards the importance of an intact vegetation cover to prevent soil loss. With the recent availability of high-resolution satellites such as IKONOS and QuickBird options for detecting and monitoring vegetation parameters in heterogeneous terrain have increased. The aim of this study is to evaluate the usefulness of QuickBird derived vegetation parameters in soil erosion models for alpine sites by comparison to Cesium-137 (Cs-137) derived soil erosion estimates. The study site (67 km²) is located in the Central Swiss Alps (Urseren Valley) and is characterised by scarce forest cover and strong anthropogenic influences due to grassland farming for centuries. A fractional vegetation cover (FVC) map for grassland and detailed land-cover maps are available from linear spectral unmixing and supervised classification of QuickBird imagery. The maps were introduced to the Pan-European Soil Erosion Risk Assessment (PESERA) model as well as to the Universal Soil Loss Equation (USLE). Regarding the latter model, the FVC was indirectly incorporated by adapting the C factor. Both models show an increase in absolute soil erosion values when FVC is considered. In contrast to USLE and the Cs-137 soil erosion rates, PESERA estimates are low. For the USLE model also the spatial patterns improved and showed “hotspots” of high erosion of up to 16 t ha⁻¹ a⁻¹. In conclusion field measurements of Cs-137 confirmed the improvement of soil erosion estimates using the satellite-derived vegetation data.     

Assessing land cover change resulting from large surface mining development

A remote sensing based land cover change assessment methodology is presented and applied to a case study of the Oil Sands Mining Development in Athabasca, Alta., Canada. The primary impact was assessed using an information extraction method applied to two LANDSAT scenes. The analysis based on derived land cover maps shows a decrease of natural vegetation in the study area (715,094 ha) for 2001 approximately −8.64% relative to 1992. Secondary assessment based on a key resources indicator (KRI), calculated using normalized difference vegetation index (NDVI measurements acquired by NOAA–AVHRR satellites), air temperature and global radiation was performed for a time period from 1990 to 2002. KRI trend analysis indicates a slightly decreasing trend in vegetation greenness in close proximity to the mining development. A good agreement between the time series of inter-annual variations in NDVI and air temperature is observed increasing the confidence of NDVI as an indicator for assessing vegetation productivity and its sensitivity to changes in local conditions.     

Modeling soil parameters using hyperspectral image reflectance in subtropical coastal wetlands

Developing spectral models of soil properties is an important frontier in remote sensing and soil science. Several studies have focused on modeling soil properties such as total pools of soil organic matter and carbon in bare soils. We extended this effort to model soil parameters in areas densely covered with coastal vegetation. Moreover, we investigated soil properties indicative of soil functions such as nutrient and organic matter turnover and storage. These properties include the partitioning of mineral and organic soil between particulate (>53 μm) and fine size classes, and the partitioning of soil carbon and nitrogen pools between stable and labile fractions. Soil samples were obtained from Avicennia germinans mangrove forest and Juncus roemerianus salt marsh plots on the west coast of central Florida. Spectra corresponding to field plot locations from Hyperion hyperspectral image were extracted and analyzed. The spectral information was regressed against the soil variables to determine the best single bands and optimal band combinations for the simple ratio (SR) and normalized difference index (NDI) indices. The regression analysis yielded levels of correlation for soil variables with R² values ranging from 0.21 to 0.47 for best individual bands, 0.28 to 0.81 for two-band indices, and 0.53 to 0.96 for partial least-squares (PLS) regressions for the Hyperion image data. Spectral models using Hyperion data adequately (RPD > 1.4) predicted particulate organic matter (POM), silt + clay, labile carbon (C), and labile nitrogen (N) (where RPD = ratio of standard deviation to root mean square error of cross-validation [RMSECV]). The SR (0.53 μm, 2.11 μm) model of labile N with R² = 0.81, RMSECV= 0.28, and RPD = 1.94 produced the best results in this study. Our results provide optimism that remote-sensing spectral models can successfully predict soil properties indicative of ecosystem nutrient and organic matter turnover and storage, and do so in areas with dense canopy cover.     

Rainfall-runoff modelling by using SM2RAIN-derived and state-of-the-art satellite rainfall products over Italy

Satellite rainfall products (SRPs) are becoming more accurate with ever increasing spatial and temporal resolution. This evolution can be beneficial for hydrological applications, providing new sources of information and allowing to drive models in ungauged areas. Despite the large availability of rainfall satellite data, their use in rainfall-runoff modelling is still very scarce, most likely due to measurement issues (bias, accuracy) and the hydrological community acceptability of satellite products.In this study, the real-time version (3B42-RT) of Tropical Rainfall Measurement Mission Multi-satellite Precipitation Analysis, TMPA, and a new SRP based on the application of SM2RAIN algorithm (Brocca et al., 2014) to the ASCAT (Advanced SCATterometer) soil moisture product, SM2R_ASC, are used to drive a lumped hydrologic model over four basins in Italy during the 4-year period 2010–2013.The need of the recalibration of model parameter values for each SRP is highlighted, being an important precondition for their suitable use in flood modelling. Results shows that SRPs provided, in most of the cases, performance scores only slightly lower than those obtained by using observed data with a reduction of Nash–Sutcliffe efficiency (NS) less than 30% when using SM2R_ASC product while TMPA is characterized by a significant deterioration during the validation period 2012–2013. Moreover, the integration between observed and satellite rainfall data is investigated as well. Interestingly, the simple integration procedure here applied allows obtaining more accurate rainfall input datasets with respect to the use of ground observations only, for 3 out 4 basins. Indeed, discharge simulations improve when ground rainfall observations and SM2R_ASC product are integrated, with an increase of NS between 2 and 42% for the 3 basins in Central and Northern Italy. Overall, the study highlights the feasibility of using SRPs in hydrological applications over the Mediterranean region with benefits in discharge simulations also in well gauged areas.     

Use of multi-temporal Landsat images for analyzing forest transition in relation to socioeconomic factors and the environment

Recently there have been reports of forest regrowth occurring in different regions across the world. There is also a growing recognition of the potential beneficial impact that secondary forests may have on the global environment: providing crucial ecosystem services such as soil conservation, stabilization of hydrological cycles, carbon sequestration, and support for forest dependent communities. Consequently, there is a growing awareness of the need to recognize that landscapes are complex shifting mosaics wherein forest clearing and reforestation take place. In this study, the rates of reforestation, deforestation, forest regrowth and degradation were measured using multi-temporal Landsat images of Danjiangkou, China. Landsat data from 1990, 1999 and 2007 were (1) classified as dense forest, open forest and non-forest areas and (2) compared between years to identify forest cutting, regeneration and degradation. The results showed that there was a net gain of 29,315 ha of forest area (including dense and open forest) from 1990 to 2007, showing a clear trend of reforestation in the study area. Forest modification (degradation and regrowth) and change categories (deforestation and reforestation) occurred simultaneously during the observation time period. Socioeconomic data from public statistics and environmental attributes allowed the assessment of the socioeconomic factors and the environmental conditions that caused these changes using non-metric multidimensional scaling (NMDS). The research showed that the socioeconomic factors due to different policies were major driving forces of forest transition, whereas environmental attributes of the underlying landscape constrained forest cover changes. These findings have led to a better understanding of forest transition at a local scale in our study region. Comprehensive knowledge of these relationships may be useful to reconstruct past forest transitions and predict future changes, and may help to enhance sustainable management practices aimed at preserving essential ecological functions.     

Two new soil moisture indices based on the NIR-red triangle space of Landsat-8 data

In this study, the NIR-red spectral space of Landsat-8 images, which is manifested by a triangle shape, is deployed for developing two new Soil Moisture (SM) indices. First, ten parameters consisting of six distances and four angles were extracted using the position of a random pixel in this triangle. Then, some correlation assessments were made to derive those parameters that were useful for SM estimation, which were five parameters. To build a soil moisture index, all combinations of these five parameters, which were in total 31 different regression equations, were considered, and the best model was named the Triangle Soil Moisture Index (TSMI). The TSMI consists of three parameters. It showed a RMSE of 0.08 and correlation coefficient (R) of 0.67. Since the TSMI does not consider vegetation interface in SM estimation, the Modified TSMI (MTSMI), which takes into account the fraction of soil cover in each pixel, beside those parameters which were used in the TSMI, was developed (MTSMI: RMSE = 0.07, R = 0.74). The results of the TSMI and MTSMI were compared with each other, and with another soil moisture index (SMMRS introduced by Zhan et al. (2007)). It was concluded that the TSMI and MTSMI provide similar results for bare soil or sparsely vegetated surfaces. However, the MTSMI demonstrated a much better performance in densely vegetated surfaces. The accuracy of both the TSMI and MTSMI were significantly higher than the SMMRS. Moreover, the TSMI and MTSMI were validated by comparison with field measured SM data at five different depths. The results showed that satellite estimated SM by these two indices was more correlated with in situ data at 5 cm soil depth compared to other depths. Also, to show the high applicability of the proposed approach for SM estimation, we selected another set of field SM data collected in Australia. The results proved the effectiveness of the method in different study areas.     

Relationship between oriental migratory locust plague and soil moisture extracted from MODIS data

Locust plagues have been the source of some of the most severe natural disasters in human history. Soil moisture content is among the most important of the numerous factors influencing plague onset and severity. This paper describes a study initiated in three pilot locust plague monitoring regions, i.e., Huangzao, Yangguanzhuang, and Tengnan in Huanghua county, Hebei province, China, to examine the impact of soil moisture status on oriental migratory locust [Locusta migratoria manilensis (L.) Meyen] plague breakout as related to the life cycle, oviposition in autumn, survival in winter, and incubation in summer. Thirty-nine temperature vegetation dryness index (TVDI) data sets, which represent soil moisture content, were extracted from MODIS remote sensing images for two representative time periods: a severe locust plague breakout year (2001–2002) and a slight plague year (2003–2004). TVDI values demonstrated distinctive soil moisture status differences between the 2 years concerned. Soil moisture conditions in the severe plague year were shown to be lower than those in slight plague year. In all three pilot regions, average TVDI value in the severe plague year was 0.07 higher than that in slight plague year, and monthly TVDI values in locust oviposition period (September and October) and incubation period (March, April and May) were higher than their corresponding monthly figures in slight plague year. No remarkable TVDI differences were found in other months during the locust life cycle between the 2 years. TVDI values for September and October (2001), March, April and May (2002) were 0.11, 0.08, 0.16, 0.11 and 0.16 higher than their corresponding monthly figures in 2003–2004 period, respectively.     

基于Terra/MODIS的沙尘暴业务化遥感监测研究

探讨了以MODIS为数据源进行沙尘暴监测的业务化技术流程及方法。以2003年4月9~11日连续发生在我国西北部特大沙尘暴为例，进行了沙尘信息提取及等级划分的示例研究，并与已经业务化运行的气象卫星（NOAA-16、风云1C气象卫星）的结果进行了相关比较。结果表明，该研究为卫星遥感监测沙尘提供了新的数据源和监测途径。     

赣中红壤区TM图像的居民地信息自动提取专家模式研究

从遥感信息分析入手,分析了抚州地区居民地在Landsat TM1 TM2 TM3 TM4 TM5 TM6 TM7各个波段上与其它背景地物的可分性。通过对各地物光谱曲线特征进行分析,利用居民地光谱特征与其它地物的差异性,建立条件表达式,即可把该区的居民地提取出来。通过验证,其点位精度达到81%,面积精度达到75%。该研究表明,通过简单模型的建立,可将以水泥顶房屋和瓦顶房屋为主的居民地提取出来。     

Soil respiration mapped by exclusively use of MODIS data for forest landscapes of Saskatchewan,Canada

Soil respiration (R_s) is of great importance to the global carbon balance. Remote sensing of R_s is challenging because of (1) the lack of long-term R_s data for model development and (2) limited knowledge of using satellite-based products to estimate R_s. Using 8-years (2002–2009) of continuous R_s measurements with nonsteady-state automated chamber systems at a Canadian boreal black spruce stand (SK-OBS), we found that R_s was strongly correlated with the product of the normalized difference vegetation index (NDVI) and the nighttime land surface temperature (LSTn) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. The coefficients of the linear regression equation of this correlation between R_s and NDVI × LSTn could be further calibrated using the MODIS leaf area index (LAI) product, resulting in an algorithm that is driven solely by remote sensing observations. Modeled R_s closely tracked the seasonal patterns of measured R_s and explained 74–92% of the variance in R_s with a root mean square error (RMSE) less than 1.0 g C/m²/d. Further validation of the model from SK-OBS site at another two independent sites (SK-OA and SK-OJP, old aspen and old jack pine, respectively) showed that the algorithm can produce good estimates of R_s with an overall R² of 0.78 (p < 0.001) for data of these two sites. Consequently, we mapped R_s of forest landscapes of Saskatchewan using entirely MODIS observations for 2003 and spatial and temporal patterns of R_s were well modeled. These results point to a strong relationship between the soil respiratory process and canopy photosynthesis as indicated from the greenness index (i.e., NDVI), thereby implying the potential of remote sensing data for detecting variations in R_s. A combination of both biological and environmental variables estimated from remote sensing in this analysis may be valuable in future investigations of spatial and temporal characteristics of R_s.     

遥感找矿模型的研究进展与评述

在遥感找矿模型研究现状分析的基础上,对当前研究中存在的一些问题进行了探讨,指出遥感找矿模型的研究应注重矿床形成和保存两方面的信息,尤其是矿床保存方面的信息。遥感找矿模型是在当前技术条件下,描述一类矿床形成和保存的一系列遥感找矿标志的组合,应包括遥感地质找矿标志、遥感蚀变信息标志和矿床改造信息标志等几个方面的内容。     

Estimating vegetation parameter for soil erosion assessment in an alpine catchment by means of QuickBird imagery

Soil erosion rates in alpine regions are related to high spatial variability complicating assessment of risk and damages. A crucial parameter triggering soil erosion that can be derived from satellite imagery is fractional vegetation cover (FVC). The objective of this study is to assess the applicability of normalized differenced vegetation index (NDVI), linear spectral unmixing (LSU) and mixture tuned matched filtering (MTMF) in estimating abundance of vegetation cover in alpine terrain. To account for the small scale heterogeneity of the alpine landscape we used high resolved multispectral QuickBird imagery (pixel resolution = 2.4 m) of a site in the Urseren Valley, Central Swiss Alps (67 km²). A supervised land-cover classification was applied (total accuracy 93.3%) prior to the analysis in order to stratify the image. The regression between ground truth FVC assessment and NDVI as well as MTMF-derived vegetation abundance was significant (r² = 0.64, r² = 0.71, respectively). Best results were achieved for LSU (r² = 0.85). For both spectral unmixing approaches failed to estimate bare soil abundance (r² = 0.39 for LSU, r² = 0.28 for MTMF) due to the high spectral variability of bare soil at the study site and the low spectral resolution of the QuickBird imagery. The LSU-derived FVC map successfully identified erosion features (e.g. landslides) and areas prone to soil erosion. FVC represents an important but often neglected parameter for soil erosion risk assessment in alpine grasslands.