The spatial prediction of tree species diversity in savanna woodlands of Southern Africa

In this study, we tested the utility of remotely sensed data in predicting tree species diversity in savanna woodlands. Specifically, we developed linear regression functions based on a combination of the coefficient of variation of near infrared (NIR) radiance and the soil-adjusted vegetation index (SAVI), both derived from advanced space-borne thermal emission and reflection radiometer satellite imagery. Using the regression functions in a Geographic Information System (GIS), we predicted the spatial variations in tree species diversity. Our results showed that tree species diversity can be predicted using a combination of the coefficient of variation of NIR radiance and SAVI. We conclude that remotely sensed data can be used to spatially predict tree species diversity in savanna woodlands.     

Predicting forest carbon stocks from high resolution satellite data in dry forests of Zimbabwe: exploring the effect of the red-edge band in forest carbon stocks estimation

In this study, we tested whether the inclusion of the red-edge band as a covariate to vegetation indices improves the predictive accuracy in forest carbon estimation and mapping in savanna dry forests of Zimbabwe. Initially, we tested whether and to what extent vegetation indices (simple ratio SR, soil-adjusted vegetation index and normalized difference vegetation index) derived from high spatial resolution satellite imagery (WorldView-2) predict forest carbon stocks. Next, we tested whether inclusion of reflectance in the red-edge band as a covariate to vegetation indices improve the model's accuracy in forest carbon prediction. We used simple regression analysis to determine the nature and the strength of the relationship between forest carbon stocks and remotely sensed vegetation indices. We then used multiple regression analysis to determine whether integrating vegetation indices and reflection in the red-edge band improve forest carbon prediction. Next, we mapped the spatial variation in forest carbon stocks using the best regression model relating forest carbon stocks to remotely sensed vegetation indices and reflection in the red-edge band. Our results showed that vegetation indices alone as an explanatory variable significantly (p < 0.05) predicted forest carbon stocks with R² ranging between 45 and 63% and RMSE ranging from 10.3 to 12.9%. However, when the reflectance in the red-edge band was included in the regression models the explained variance increased to between 68 and 70% with the RMSE ranging between 9.56 and 10.1%. A combination of SR and reflectance in the red edge produced the best predictor of forest carbon stocks. We concluded that integrating vegetation indices and reflectance in the red-edge band derived from high spatial resolution can be successfully used to estimate forest carbon in dry forests with minimal error.     

遥感地表温度降尺度方法比较————性能对比及适应性评价

在归纳现有遥感地表温度降尺度方法的基础上, 选取3种代表性方法:Normalized Difference Vegetation Index (NDVI)、Pixel Block Intensity Modulation (PBIM)和Linear Spectral Mixture Model (LSMM)方法进行实验比较, 并建立了一种纹理相似性度量指标CO-RMSE (Co-Occurrence Root Mean Square Error)。结果表明:(1)NDVI方法受季节影响最严重, 不适于春、冬季, 其次为PBIM方法;(2)LSMM方法受分辨率限制最大, 低分辨率时丢失大量纹理信息, NDVI方法在较高分辨率时优于PBIM方法, 较低分辨率时则相反;(3)3种方法的适用区域分别为植被与裸土像元并存区域, 山区和反照率变化较大区域, 以及类别间温差较大区域;(4)NDVI方法操作最简单, LSMM方法最复杂。分析认为, 尺度因子是决定方法性能的关键, 应根据季节、分辨率、地表覆盖、应用目的和操作性等综合选择。     

A comparative evaluation of IRS-1A LISS-II and landsat MSS images using computer-aided lineament data analysis

Lineament patterns detected from remotely sensed data provide useful information to geoscientists, specially in the study of basement tectonics, groundwater targetting and mineral exploration. Improvements in the spatial resolution of satellite images have resulted in the detection of short and faint lineaments which have hitherto gone unnoticed The IRS-1A LISS-II data offers a significant improvement in spatial resolution as compared to the Landsat MSS. A set of computer programmes developed for analysis of lineaments were used to study the parameters such as lineament frequency, length and density in order to quantify the added information derived using IRS-1A LISS-II images. The incremental contribution of LISS-II images are of the order of 100 per cent for lineament frequency and about 60 per cent for total line kilometers of lineaments detected.     

Fusing MODIS with Landsat 8 data to downscale weekly normalized difference vegetation index estimates for central Great Basin rangelands,USA

Data fused from distinct but complementary satellite sensors mitigate tradeoffs that researchers make when selecting between spatial and temporal resolutions of remotely sensed data. We integrated data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the Terra satellite and the Operational Land Imager sensor aboard the Landsat 8 satellite into four regression-tree models and applied those data to a mapping application. This application produced downscaled maps that utilize the 30-m spatial resolution of Landsat in conjunction with daily acquisitions of MODIS normalized difference vegetation index (NDVI) that are composited and temporally smoothed. We produced four weekly, atmospherically corrected, and nearly cloud-free, downscaled 30-m synthetic MODIS NDVI predictions (maps) built from these models. Model results were strong with R² values ranging from 0.74 to 0.85. The correlation coefficients (r ≥ 0.89) were strong for all predictions when compared to corresponding original MODIS NDVI data. Downscaled products incorporated into independently developed sagebrush ecosystem models yielded mixed results. The visual quality of the downscaled 30-m synthetic MODIS NDVI predictions were remarkable when compared to the original 250-m MODIS NDVI. These 30-m maps improve knowledge of dynamic rangeland seasonal processes in the central Great Basin, United States, and provide land managers improved resource maps.     

数据驱动的蒸散发遥感反演方法及产品研究进展

蒸散发是水圈、大气圈和生物圈中水分循环和能量交换的纽带。在全球尺度上,蒸散发约占陆地降水总量的60%;作为其能量表达形式,潜热通量约占地表净辐射的80%。随着通量观测技术的发展,全球长期持续的观测数据得以获取和共享,近年来基于数据驱动的蒸散发遥感反演方法取得了较好的研究进展。本文针对数据驱动的蒸散发遥感反演方法和产品,从经验回归、机器学习和数据融合3个方面展开,对现有的研究进展进行了梳理、归纳和总结,并从驱动数据、反演方法、已有产品等方面指出目前仍存在的问题和不足。未来仍需开展数据驱动的高时空分辨率的蒸散发遥感反演方法的研究,有效考虑地表温度和土壤水分等可以指示地表蒸散发短期变化的重要信息,同时加强基于过程驱动的物理模型与数据驱动的模型的结合,使两类模型能互为补充、各自发挥所长,共同推动蒸散发遥感反演研究水平的进步。     

Evaluation of SPOT imagery for the estimation of grassland biomass

In many regions, a decrease in grasslands and change in their management, which are associated with agricultural intensification, have been observed in the last half-century. Such changes in agricultural practices have caused negative environmental effects that include water pollution, soil degradation and biodiversity loss. Moreover, climate-driven changes in grassland productivity could have serious consequences for the profitability of agriculture. The aim of this study was to assess the ability of remotely sensed data with high spatial resolution to estimate grassland biomass in agricultural areas. A vegetation index, namely the Normalized Difference Vegetation Index (NDVI), and two biophysical variables, the Leaf Area Index (LAI) and the fraction of Vegetation Cover (fCOVER) were computed using five SPOT images acquired during the growing season. In parallel, ground-based information on grassland growth was collected to calculate biomass values. The analysis of the relationship between the variables derived from the remotely sensed data and the biomass observed in the field shows that LAI outperforms NDVI and fCOVER to estimate biomass (R² values of 0.68 against 0.30 and 0.50, respectively). The squared Pearson correlation coefficient between observed and estimated biomass using LAI derived from SPOT images reached 0.73. Biomass maps generated from remotely sensed data were then used to estimate grass reserves at the farm scale in the perspective of operational monitoring and forecasting.     

加入空间-时相纹理的高分辨率图像城市建筑物物倒塌提取

将两种基于地统计学的纹理特征加入到高分辨率遥感影像的城市建筑物倒塌探测中,考察了多尺度纹理对探测结果的影响.采用基于单类支持向量机的多时相直接分类方法提取建筑物倒塌信息.以伊朗巴姆地区2003 年12 月地震前后的Quickbird 遥感影像为数据源,评价和验证了本文方法的有效性.研究表明,将多尺度的空间和时相纹理信息加入到高分辨率遥感影像的倒塌建筑物探测中,可以有效提高分类精度,该方法得到的结果可应用于灾害救援及评估.     

北京1号小卫星图像噪声评估

从理论和试验方面对图像的噪声评估方法进行了分析。结合北京1号小卫星特性, 进行了该类方法应用效能的评价, 讨论了分块评估噪声方法的最佳参数设置。选取满足噪声评估环境的图像, 实现了综合不同地表覆盖条件的北京1号小卫星图像噪声的评估。噪声评估结果与在轨测试情况的对比表明, 北京1号小卫星经过近3年的运行, 仍保持了较好的性能。     

改进的P-SVM支持向量机与遥感数据分类

本文介绍了将P-SVM算法引入多光谱/高分辨率遥感数据的分类, 并且展示了卫星ASTER和航空ADS40数字影像分类的技术过程和结果验证。结果表明：P-SVM方法的分类精度不低于SVM, 并减少了时耗。     

龙羊峡库区环境动态监测信息系统的建立与应用

以黄河上游最大的水利工程－－龙羊峡水库为实验区,围绕库区存在的主要环境问题的研究,应用遥感与ＧＩＳ一体化的方法,研究设计了系统结构、功能及数据库的组成,并建立了可运行的龙羊峡库区环境动态监测信息系统,在系统支持下有效的实现了库区环境动态监测与应用,由于该系统较强的可操作性和适应性,其成果在高寒干旱与半干旱地区环境研究中具有推广和应用价值。     

Predicting the spatial distribution of hunted and non-hunted Sable antelope (Hippotragus niger niger) using remotely sensed woody cover in a Southern African savanna

From remotely sensed woody cover, we tested whether sables under hunting pressure preferred closed woodland habitats and whether those not under hunting preferred more open woodland habitats. We applied a two factorial logistic regression analysis to model the probability of occurrence of sable antelope in hunted and non-hunted areas of northwest Zimbabwe as a function of vegetation cover density (estimated by a normalized difference vegetation index (NDVI)). We validated the results by high-spatial resolution imagery derived tree canopy area. We subsequently compared the predictions from the two models in order to compare sable cover selection between hunted and non-hunted areas. Our results suggest that hunted sables are likely to select closed woodland, while non-hunted ones would prefer more open woodland habitats. We also established a significant positive relationship between NDVI and tree canopy cover, thus emphasizing the importance of remote sensing in studies that measure the impact of hunting on habitat selection of targeted species.     

Wavelet-based detection of bush encroachment in a savanna using multi-temporal aerial photographs and satellite imagery

Although increased woody plant abundance has been reported in tropical savannas worldwide, techniques for detecting the direction and magnitude of change are mostly based on visual interpretation of historical aerial photography or textural analysis of multi-temporal satellite images. These techniques are prone to human error and do not permit integration of remotely sensed data from diverse sources. Here, we integrate aerial photographs with high spatial resolution satellite imagery and use a discrete wavelet transform to objectively detect the dynamics in bush encroachment at two protected Zimbabwean savanna sites. Based on the recently introduced intensity-dominant scale approach, we test the hypotheses that: (1) the encroachment of woody patches into the surrounding grassland matrix causes a shift in the dominant scale. This shift in the dominant scale can be detected using a discrete wavelet transform regardless of whether aerial photography and satellite data are used; and (2) as the woody patch size stabilises, woody cover tends to increase thereby triggering changes in intensity. The results show that at the first site where tree patches were already established (Lake Chivero Game Reserve), between 1972 and 1984 the dominant scale of woody patches initially increased from 8 m before stabilising at 16 m and 32 m between 1984 and 2012 while the intensity fluctuated during the same period. In contrast, at the second site, which was formely grass-dominated site (Kyle Game Reserve), we observed an unclear dominant scale (1972) which later becomes distinct in 1985, 1996 and 2012. Over the same period, the intensity increased. Our results imply that using our approach we can detect and quantify woody/bush patch dynamics in savanna landscapes.     

Mapping impervious surface distribution in China using multi-source remotely sensed data

ABSTRACT

Impervious surface area (ISA) data are required for such studies as urban environmental modeling, hydrological modeling, and socioeconomic analysis, but updating these datasets in a large area remains a challenge due to the complex urban landscapes consisting of different materials and colors with various spatial patterns. This research explores the integration of multi-source remotely sensed data for mapping China’s ISA distribution at 30-m spatial resolution. The integration of Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS DNB) and Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data were used to extract initial ISA with spatial resolution of 250 m using a thresholding approach. The Landsat-derived NDVI and Modified Normalized Difference Water Index (MNDWI) were used to remove vegetation and water areas from the mixed pixels that existed in the initial ISA data. The spectral signatures of these ISA data were further extracted from Landsat multispectral images and used to refine the ISA data using expert knowledge. The results indicate that the integration of multi-source data can successfully map ISA distribution with 30-m spatial resolution in China with producer’s and user’s accuracies of 83.1 and 91.9%, respectively. These ISA data are valuable for better management of urban landscapes and for use as an input in other studies such as socioeconomic and environmental modeling.     

Landsat8和MODIS融合构建高时空分辨率数据识别秋粮作物

本文利用Wu等人提出的遥感数据时空融合方法 STDFA(Spatial Temporal Data Fusion Approach)以Landsat 8和MODIS为数据源构建高时间、空间分辨率的遥感影像数据。以此为基础,构建15种30 m分辨率分类数据集,然后利用支持向量机SVM(Support Vector Machine)进行秋粮作物识别,验证不同维度分类数据集进行秋粮作物识别的适用性。实验结果显示,不同分类数据集的秋粮作物分类结果均达到了较高的识别精度。综合各项精度指标分析,Red+Phenology数据组合对秋粮识别效果最好,水稻识别的制图精度和用户精度分别达到91.76%和82.49%,玉米识别的制图精度和用户精度分别达到85.80%和74.97%,水稻和玉米识别的总体精度达到86.90%。     

Local-scale agricultural drought monitoring with satellite-based multi-sensor time-series

ABSTRACT

Globally, drought constitutes a serious threat to food and water security. The complexity and multivariate nature of drought challenges its assessment, especially at local scales. The study aimed to assess spatiotemporal patterns of crop condition and drought impact at the spatial scale of field management units with a combined use of time-series from optical (Landsat, MODIS, Sentinel-2) and Synthetic Aperture Radar (SAR) (Sentinel 1) data. Several indicators were derived such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Land Surface Temperature (LST), Tasseled cap indices and Sentinel-1 based backscattering intensity and relative surface moisture. We used logistic regression to evaluate the drought-induced variability of remotely sensed parameters estimated for different phases of crop growth. The parameters with the highest prediction rate were further used to estimate thresholds for drought/non-drought classification. The models were evaluated using the area under the receiver operating characteristic curve and validated with in-situ data. The results revealed that not all remotely sensed variables respond in the same manner to drought conditions. Growing season maximum NDVI and NDMI (70–75%) and SAR derived metrics (60%) reflect specifically the impact of agricultural drought. These metrics also depict stress affected areas with a larger spatial extent. LST was a useful indicator of crop condition especially for maize and sunflower with prediction rates of 86% and 71%, respectively. The developed approach can be further used to assess crop condition and to support decision-making in areas which are more susceptible and vulnerable to drought.     

Comparing terrain and vegetation-based visibility for explaining sable flight behaviour in a Southern African savanna

In this study, we tested whether terrain-based visibility modelled from a remotely sensed ASTER Digital Elevation Model (DEM) explains sable flight initiation distance (FID) better than vegetation-based visibility measured in the field. We also tested whether the effect of hunting on sable FID varies with spatial scale. We first performed a linear regression analysis relating FID to standardized coefficients of both vegetation- and terrain-based visibility where the variable with the larger coefficient was the better predictor of FID. We latter performed an analysis of covariance (ANCOVA) comparing the slopes relating FID to both measures of visibility, first at the large scale and later at the small scale within the hunting area. Our results suggest that remotely sensed terrain-based visibility predicts the FID of sable better than vegetation-based visibility. We also found that the effect of hunting on sable FID varies with spatial scale.     

Remotely sensed biomass over steep slopes: An evaluation among successional stands of the Atlantic Forest,Brazil

Remotely sensed images have been widely used to model biomass and carbon content on large spatial scales. Nevertheless, modeling biomass using remotely sensed data from steep slopes is still poorly understood. We investigated how topographical features affect biomass estimation using remotely sensed data and how such estimates can be used in the characterization of successional stands in the Atlantic Rainforest in southeastern Brazil. We estimated forest biomass using a modeling approach that included the use of both satellite data (LANDSAT) and topographic features derived from a digital elevation model (TOPODATA). Biomass estimations exhibited low error predictions (Adj. R² = 0.67 and RMSE = 35 Mg/ha) when combining satellite data with a secondary geomorphometric variable, the illumination factor, which is based on hill shading patterns. This improved biomass prediction helped us to determine carbon stock in different forest successional stands. Our results provide an important source of modeling information about large-scale biomass in remaining forests over steep slopes.     

基于频域滤波的高分辨率遥感图像城市河道信息提取

提出一种基于频域滤波的城市河道信息提取方法。首先对高分辨率遥感图像进行傅里叶变换得到频谱图, 并利用径向和角向分布图分析城市河道的频谱特征。其次, 基于城市河道的双线型特点, 将其分为边缘特征和低频信息两个部分, 并根据周期性纹理的频谱模型和地物频谱能量分布规律确定两个部分的频域识别标志。然后设计相应的扇环形带通log Butterworth滤波器和低通Butterworth滤波器分别对城市河道的边缘特征和低频信息进行提取, 并根据该两部分信息实现城市河道信息提取。最后对城市河道信息提取结果进行定量评价, 结果表明, 本文方法可以有效地实现城市河道的信息提取。     

单样本对卷积神经网络遥感图像时空融合

遥感图像时空融合是一种生成兼具高时空分辨率的合成遥感数据的技术。近年来,产生了一些基于卷积神经网络的时空融合方法。这些方法效果良好,但需要较多的图像样本对训练模型,限制了它们的应用。针对此问题,本文提出了一种单样本对卷积神经网络时空融合方法（SS-CNN）。该方法以高空间分辨率图像的波段平均图像提供的空间信息激励卷积神经网络建立高、低空间分辨率图像间的超分关系,进而利用该超分关系映射求解目标高空间分辨率图像。在实验中使用两个模拟数据集和一个真实数据集对该方法进行了测试,并与两种常用的时空融合方法做了比较。实验结果表明,SS-CNN在单样本对训练的情况下,可以较好地预测地物的物候变化和类型的变化,且在异质性高、地块破碎的区域表现良好。其不足之处在于会在地物边界上会造成轻微的模糊,将来需针对此问题做进一步改进。