植被对潮沟发育影响的遥感研究——以崇明东滩为例

根据现场测量,通过高分辨率航空图像提取崇明东滩潮沟的空间形态、植被类型及植被盖度指数。结合多时相卫星图像及海洋数值模式,利用水边线法及宽深比法反演潮沟沟底高程并构建了研究区潮沟三维地形,分析了植被对潮沟发育的影响。结果表明:1潮沟反演的均方根误差为0.545 m,且高潮滩反演精度高于低潮滩地区;2从低潮滩到高潮滩的潮沟深度先增大再减小,主要原因是高潮滩水动力条件较弱,加之植被根系的固滩作用,下蚀作用较弱,潮沟深度较浅,低潮滩由于水域开阔,水动力条件较弱,无植被覆盖侧蚀作用增强,潮沟变宽变浅;3潮沟深度空间分布表现出南深北浅的特征,崇明东滩潮沟密度与植被盖度呈现明显的负相关(r=0.560 4,p<0.02),在高盖度植被覆盖的潮滩地区潮沟大多不发育,潮沟密度较低。潮沟长度与植被类型表现出较强的相关关系,在互花米草-芦苇群落为优势的区域潮沟长度普遍比以海三棱藨草群落为优势的区域长。

A Novel Dynamic Stretching Solution to Eliminate Saturation Effect in NDVI and Its Application in Drought Monitoring

The normalized difference vegetation index(NDVI) is one of the key input variables for developing drought indices.However,the NDVI quickly saturates in high vegetation surfaces,and thus,the generalization of a drought index over different ecosystems becomes a challenge.This paper presents a novel,dynamic stretching algorithm to overcome the saturation effect in NDVI.A scaling transformation function to eliminate saturation effects when the vegetation fraction(VF) is large is proposed.Dynamic range adjustment is conducted using three coefficients,namely,the normalization factor(a),the stretching range controlling factor(m),and the stretching size controlling factor(e).The results show that the stretched NDVI(S-NDVI) is more sensitive to vegetation fraction than NDVI when the VF is large,ranging from 0.75 to 1.00.Moreover,the saturation effect in NDVI is effectively removed by using the S-NDVI.Further analysis suggests that there is a good linear correlation between the S-NDVI and the leaf area index(LAI).At the same time,the proposed S-NDVI significantly reduces or even eliminates the saturation effect over high biomass.A comparative analysis is performed between drought indices derived from NDVI and S-NDVI,respectively.In the experiment,reflectance data from the moderate resolution imaging spectroradiometer(MODIS) products and in-situ observation data from the meteorological sites at a regional scale are used.In this study,the coefficient of determination(R2) of the stretched drought index(S-DI) is above 0.5,indicating the reliability of the proposed algorithm with surface soil moisture content.Thus,the S-DI is suggested to be used as a drought index in extended regions,thus regional heterogeneity should be taken into account when applying stretching method.

Effects of Normalized Difference Vegetation Index and Related Wavebands′ Characteristics on Detecting Spatial Heterogeneity Using Variogram-based Analysis

Spatial heterogeneity is widely used in diverse applications, such as recognizing ecological process, guiding ecological restoration, managing land use, etc. Many researches have focused on the inherent scale multiplicity of spatial heterogeneity by using various environmental variables. How these variables affect their corresponding spatial heterogeneities, however, have received little attention. In this paper, we examined the effects of characteristics of normalized difference vegetation index （NDVI） and its related bands variable images, namely red and near infrared （NIR）, on their corresponding spatial heterogeneity detection based on variogram models. In a coastal wetland region, two groups of study sites with distinct fractal vegetation cover were tested and analyzed. The results show that： l） in high fractal vegetation cover （H-FVC） area, NDV! and NIR variables display a similar ability in detecting the spatial he- terogeneity caused by vegetation growing status structure; 2） in low fractal vegetation cover （L-FVC） area, the NIR and red variables outperform NDVI in the survey of soil spatial heterogeneity; and 3） generally, NIR variable is ubiquitously applicable for vegetation spatial heterogeneity investigation in different fractal vegetation covers. Moreover, as variable selection for remote sensing applications should fully take the characteristics of variables and the study object into account, the proposed variogram analysis method can make the variable selection objectively and scientifically, especially in studies related to spatial heterogeneity using remotely sensed data.

The Relationship between Meteorological Variables and Clearness Index for Four Urban/Suburban Areas of Brazilian Cities

This study aimed to examine the relationship between meteorological variables and the clearness index for three sites in Cuiaba city and one site in Chapada dos Guimaraes city, Brazil during 2007. It described the microclimate of each site on the basis of constructive elements and their surroundings, considering sky coverage using a daily clearness index. The results were that micrometeorological values were influenced by the natural elements and construction within the surrounding site, with higher air temperatures in more urbanized areas and sites with high diffuse radiation. When determining the sky coverage, on average, the days were partly cloudy or cloudy due to two reasons： （a） during the wet season, rainfall created cloudy conditions and （b） during the dry season, increases of particulates in the atmosphere as a result of anthropogenic emissions of gases and aerosols in this region of the state resulted in sky conditions classified as partly cloudy and cloudy. Future research should aim to better quantify the measurements taken inside an urban area, considering the topography and vegetation cover. This will improve the models that support urban planning, therefore favoring the thermal comfort of areas already occupied or to be urbanized.

Vegetation dynamics in Qinling-Daba Mountains in relation to climate factors between 2000 and 2014

Using the Moderate Resolution Imaging Spectroradiometer-normalized difference vegetation index(NDVI) dataset,we investigated the patterns of spatiotemporal variation in vegetation coverage and its associated driving forces in the Qinling-Daba(Qinba) Mountains in 2000–2014.The Sen and Mann–Kendall models and partial correlation analysis were used to analyze the data,followed by calculation of the Hurst index to analyze future trends in vegetation coverage.The results of the study showed that(1) NDVI of the study area exhibited a significant increase in 2000–2014(linear tendency,2.8%/10a).During this period,a stable increase was detected before 2010(linear tendency,4.32%/10a),followed by a sharp decline after 2010(linear tendency,–6.59%/10a).(2) Spatially,vegetation cover showed a "high in the middle and a low in the surroundings" pattern.High values of vegetation coverage were mainly found in the Qinba Mountains of Shaanxi Province.(3) The area with improved vegetation coverage was larger than the degraded area,being 81.32% and 18.68%,respectively,during the study period.Piecewise analysis revealed that 71.61% of the total study area showed a decreasing trend in vegetation coverage in 2010–2014.(4) Reverse characteristics of vegetation coverage change were stronger than the same characteristics on the Qinba Mountains.About 46.89% of the entire study area is predicted to decrease in the future,while 34.44% of the total area will follow a continuously increasing trend.(5) The change of vegetation coverage was mainly attributed to the deficit in precipitation.Moreover,vegetation coverage during La Nina years was higher than that during El Nino years.(6) Human activities can induce ambiguous effects on vegetation coverage: both positive effects(through implementation of ecological restoration projects) and negative effects(through urbanization) were observed.