Inference of herder presence from GPS collar data of semi-free range cattle

Although global positioning system (GPS) location data have been used to derive animal movement parameters including step length, rarely have these parameters been used to predict animal responses to human interventions. In this study, we tested whether GPS-derived step length of semi-free range cattle is a function of herder presence. The derived step-length model was used to predict herder presence on independent cattle GPS collar data. We also tested whether cattle foraging behaviour is explained by herder activity and vegetation greenness. We used logistic regression to model herder presence as a function of step length and relate cattle behaviour with herder activity and vegetation greenness. The field-based step length model successfully predicted herder presence on GPS collar data. The average predicted frequency of herder presence for the GPS-collared herds was 31%, whilst the field-based GPS frequency was 27%. Herding activities and vegetation greenness also explained different cattle foraging behaviour.     

Spectroradiometric requirements for the reflective module of the airborne spectrometer ARES

The Airborne Reflective/Emissive Spectrometer is specified as a whisk-broom imaging spectrometer for remote sensing of land surfaces covering the wavelength regions 0.47-2.45 /spl mu/m and 8-12 /spl mu/m with 160 spectral bands. The instrument is being built by Integrated Spectronics, financed by the German Aerospace Agency (DLR) and the GeoResearch Centre Potsdam (GFZ) and will be available to the scientific community from end 2005 on. The spectroradiometric design is based on scientific requirements derived from three main application scenarios comprising vegetation, soil, and mineral sciences. Two of these are described in this letter. Measured or modeled reflectance spectra are input to a simulation model that calculates at-sensor radiance spectra, resamples them with the channel-specific response functions, adds different amounts of noise in the radiance domain, and performs a retrieval to get the corresponding noisy surface reflectance spectra. The retrieval results as a function of the sensor noise level are compared with the accuracy requirements imposed by the different application fields taking into account the technical boundary conditions. The final specifications account for the most demanding requirements of the three application fields: a spectral sampling distance of 13-14 nm in the 470-1800 nm region, and 12 nm in the 2000-2450-nm region. The required noise-equivalent radiances are 5, 3, and 2 nW/spl middot/cm/sup -2//spl middot/sr/sup -1//spl middot/nm/sup -1/ for the spectral regions 470-1000, 1000-1800, and 2000-2450 nm, respectively.     

Monitoring vegetation dynamics with medium resolution MODIS-EVI time series at sub-regional scale in southern Africa

Currently there is a lack of knowledge on spatio-temporal patterns of land surface dynamics at medium spatial scale in southern Africa, even though this information is essential for better understanding of ecosystem response to climatic variability and human-induced land transformations. In this study, we analysed vegetation dynamics across a large area in southern Africa using the 14-years (2000–2013) of medium spatial resolution (250 m) MODIS-EVI time-series data. Specifically, we investigated temporal changes in the time series of key phenometrics including overall greenness, peak and timing of annual greenness over the monitoring period and study region. In order to specifically capture spatial and per pixel vegetation changes over time, we calculated trends in these phenometrics using a robust trend analysis method. The results showed that interannual vegetation dynamics followed precipitation patterns with clearly differentiated seasonality. The earliest peak greenness during 2000–2013 occurred at the end of January in the year 2000 and the latest peak greenness was observed at the mid of March in 2012. Specifically spatial patterns of long-term vegetation trends allowed mapping areas of (i) decrease or increase in overall greenness, (ii) decrease or increase of peak greenness, and (iii) shifts in timing of occurrence of peak greenness over the 14-year monitoring period. The observed vegetation decline in the study area was mainly attributed to human-induced factors. The obtained information is useful to guide selection of field sites for detailed vegetation studies and land rehabilitation interventions and serve as an input for a range of land surface models.     

Vegetation recovery monitoring over the waste dump in Haizhou opencast coalmine area,China, during 1975–2000 using NDVI and VF index

Vegetation condition monitoring has been done from 1975 to 2000 in the waste dump of Haizhou opencast coalmine area, China, using remote sensing techniques with the objective of improving our understanding of the temporal and spatial variation of vegetation recovery in the mining dump. Four historical vegetation indexes (NDVI, VF, soil brightness and vegetation greenness) from two Landsat 2 MSS images and two Landsat 5 TM images are extracted and analyzed. For the purpose of comparison and analysis two improved techniques such as normalization grading of change slope and image segmentation were used in this study. Based on the results obtained through the above analysis two conclusions are derived: (1) vegetation recovery in the study area is in an improved condition, (2) two remote sensing based vegetation indexes such as VF and NDVI are the optimal parameters to monitor vegetation condition, which could be used as the indicators of land reclamation progress in the mining area.     

Camera derived vegetation greenness index as proxy for gross primary production in a low Arctic wetland area

The Arctic is experiencing disproportionate warming relative to the global average, and the Arctic ecosystems are as a result undergoing considerable changes. Continued monitoring of ecosystem productivity and phenology across temporal and spatial scales is a central part of assessing the magnitude of these changes. This study investigates the ability to use automatic digital camera images (DCIs) as proxy data for gross primary production (GPP) in a complex low Arctic wetland site. Vegetation greenness computed from DCIs was found to correlate significantly (R² = 0.62, p < 0.001) with a normalized difference vegetation index (NDVI) product derived from the WorldView-2 satellite. An object-based classification based on a bi-temporal image composite was used to classify the study area into heath, copse, fen, and bedrock. Temporal evolution of vegetation greenness was evaluated and modeled with double sigmoid functions for each plant community. GPP at light saturation modeled from eddy covariance (EC) flux measurements were found to correlate significantly with vegetation greenness for all plant communities in the studied year (i.e., 2010), and the highest correlation was found between modeled fen greenness and GPP (R² = 0.85, p < 0.001). Finally, greenness computed within modeled EC footprints were used to evaluate the influence of individual plant communities on the flux measurements. The study concludes that digital cameras may be used as a cost-effective proxy for potential GPP in remote Arctic regions.     

Motivation,development and validation of a new spectral greenness index: A spectral dimension related to foliage projective cover

A method is presented for the development of a regional Landsat-5 Thematic Mapper (TM) and Landsat-7 Enhanced Thematic Mapper plus (ETM+) spectral greenness index, coherent with a six-dimensional index set, based on a single ETM+ spectral image of a reference landscape. The first three indices of the set are determined by a polar transformation of the first three principal components of the reference image and relate to scene brightness, percent foliage projective cover (FPC) and water related features. The remaining three principal components, of diminishing significance with respect to the reference image, complete the set.The reference landscape, a 2200 km² area containing a mix of cattle pasture, native woodland and forest, is located near Injune in South East Queensland, Australia. The indices developed from the reference image were tested using TM spectral images from 19 regionally dispersed areas in Queensland, representative of dissimilar landscapes containing woody vegetation ranging from tall closed forest to low open woodland. Examples of image transformations and two-dimensional feature space plots are used to demonstrate image interpretations related to the first three indices. Coherent, sensible, interpretations of landscape features in images composed of the first three indices can be made in terms of brightness (red), foliage cover (green) and water (blue). A limited comparison is made with similar existing indices. The proposed greenness index was found to be very strongly related to FPC and insensitive to smoke. A novel Bayesian, bounded space, modelling method, was used to validate the greenness index as a good predictor of FPC. Airborne LiDAR (Light Detection and Ranging) estimates of FPC along transects of the 19 sites provided the training and validation data. Other spectral indices from the set were found to be useful as model covariates that could improve FPC predictions. They act to adjust the greenness/FPC relationship to suit different spectral backgrounds. The inclusion of an external meteorological covariate showed that further improvements to regional-scale predictions of FPC could be gained over those based on spectral indices alone.     

Soil moisture retrievals from biangular L-band passive microwave observations

A simple approach for correcting the effect of vegetation in the estimation of soil moisture (w/sub S/) from L-band passive microwave observations is presented in this study. The approach is based on statistical relationships, calibrated from simulated datasets, which requires only two observations made at distinct incidence angles (/spl theta//sub 1/,/spl theta//sub 2/). A sensitivity study was carried out, and best retrieval remote sensing configurations, in terms of polarization and couple of incidence angles (/spl theta//sub 1/,/spl theta//sub 2/), were investigated. Best estimations of w/sub S/ could be made at H polarization, for /spl theta//sub 1/ varying between 15/spl deg/ and 30/spl deg/, and with a difference (/spl theta//sub 2/-/spl theta//sub 1/) larger than 30/spl deg/. The method was tested against two experimental datasets acquired over crop fields (soybean and wheat). The average accuracy in the soil moisture retrievals during the whole crop cycle was found to be about 0.05 m/sup 3//m/sup 3/ for both crops.     

An Assessment of Snow Avalanche Paths and Forest Dynamics Using Ikonos Satellite Data

Abstract

Ikonos panchromatic and multispectral satellite data were acquired in October 2000 and August 2002 for a test area along US Highway 2, the southern border of Glacier National Park (GNP), Montana, USA. The research goals were to map snow avalanche paths and to characterize vegetation patterns in selected paths for longitudinal (i.e., source, track, and runout) and transverse (i.e., inner, flanking, outer) zones as part of a study of forest dynamics and nutrient flux from paths into terrestrial and aquatic systems. In some valleys, as much as 50 percent of the area may be covered by snow avalanche paths, and as such, serve as an important carbon source servicing terrestrial and aquatic ecosystems. Snow avalanches move woody debris down‐slope by snapping, tipping, trimming, and excavating branches, limbs, and trees, and by injuring and scaring trees that remain in‐place. Further, snow avalanches alter the vegetation structure on paths through secondary plant succession of disturbed areas. Contrast and edge enhancements, Normalized Difference Vegetation Index (NDVI), and the Tasseled Cap greenness and wetness transformations were used to examine vegetation patterns in selected paths that were affected by high magnitude snow avalanches during the winter of 2001-2002. Using image transects organized in longitudinal patterns in paths and in forests, and transects arranged in transverse patterns across the sampled paths, the Tasseled Cap transforms (and NDVI values) were plotted and assessed. Preliminary results suggest that NDVI patterns are different for paths and forests, and Tasseled Cap greenness and wetness patterns are different for longitudinal and transverse zones that describe the morphology of snow avalanche paths. The differentiation of paths from the background forest and the characterization of paths by morphometric zones through remote sensing has implications for mapping forest disturbances and dynamics over time and for large geographic areas and for modeling nutrient flux in terrestrial and aquatic systems.     

青岛市生态环境变化遥感监测与分析

城市化进程不断加快带来一系列生态环境问题,本文利用植被指数、湿度指数、地表温度、建筑物—裸土指数定量表征绿度、湿度、热度、干度4个生态要素指标,通过主成分分析法,建立遥感生态指数模型,并从时间和空间两个维度对比分析2013、2019年两个时期遥感生态指数。结果表明,青岛市生态环境呈局部优化改进、整体下降趋势;同时,将青岛市遥感生态指数与人类活动相关的地表覆盖变化数据、路网交通数据、夜光数据进行耦合性分析,进一步分析探寻生态环境变化的影响因子。     

基于MODIS数据的火险潜在指数（FPI）及其应用研究

死、活可燃物含水率大小决定森林点燃的难易度,是判断林火能否发生、进行林火预报的重要因子。本文应用火险潜在指数（FPI,Fire Potential Index）模型,从这2个方面分析研究可燃物湿度对林火发生的影响。利用MODIS遥感数据提取FPI模型所需因素（气象数据： 相对湿度、温度; 植被数据： 10 h时滞可燃物湿度、归一化水分指数、植被绿度）,并将获得的2004年10月黑龙江省和2008年3月南方几省的气象、植被数据输入FPI模型,得到火险指数和火险等级划分。实践证明,应用该模型能够提高火险在时间和地理分布上的预报能力及预防技术。     

THE DEPARTMENT OF PHOTOGRAMMETRY AND REMOTE SENSING AT WUHAN TECHNICAL UNIVERSITY,PEOPLE'S REPUBLIC OF CHINA

Many remote sensing applications are predicated on the fact that there is a known relationship between climate and vegetation dynamics as monitored from space. However, few studies investigate vegetation index variation on individual homogeneous land cover units as they relate to specific climate and environmental influences at the local scale. This study focuses on the relationship between the Palmer Drought Severity Index (PDSI) and different vegetation types through the derivation of vegetation indices from Landsat 7 ETM+ data (NDVI, Tasseled Cap, and SAVI). A series of closely spaced through time images from 1999 to 2002 were selected, classified, and analyzed for an area in northeastern Ohio. Supervised classification of the images allowed us to monitor the response in individual land cover classes to changing climate conditions, and compare these individual changes to those over the entire larger areas. Specifically, the images were compared using linear regression techniques at various time lags to PDSI values for these areas collected by NOAA. Although NDVI is a robust indicator of vegetation greenness and vigor, it may not be the best index to use, depending on the type of vegetation studied and the scale of analysis used. A combination of NDVI and other prominent vegetation indices can be used to detect subtle drought conditions by specifically identifying various time lags between climate condition and vegetation response.     

空间遥感图像的植被信息的提取与表达

在本文中讨论了气象卫星NOAA的AVHRR数字图像及陆地卫星TM数字图像植被信息表达的问题。对于AVHRR数字图像可以通过定义绿度的方法,找到方便而简洁的植被类型空间分布的彩色表达子集。TM数字图像的各波段植被信息丰富,缨帽变换可以有效地集中这些植被信息,使用该变换的前三个组份能重构出植被的空间分布。对于有严重阴影的山区,可考虑先行比值变换,再做缨帽变换来提取植被分布的信息。     

Monitoring interannual dynamics of desertification in Minqin County,China, using dense Landsat time series

ABSTRACT

Minqin County in northwestern China is highly affected by desertification. Campaigns have been initiated in recent decades to combat desertification in Minqin. To assess the effectiveness of these campaigns, this study used a dense Landsat time series from 1987 to 2017 to investigate the interannual dynamics of vegetation coverage and greenness over the past 31 years. First, this study applied an advanced technology to reconstruct a high-quality Landsat annual time series. Specifically, one image in the vegetation-peak season was selected as the base image in each year, and then problematic pixels were interpolated by the neighborhood similar pixel interpolator using ancillary images in the same year. Second, the land cover map and the enhanced vegetation index (EVI) were derived from all reconstructed images. Third, the change of vegetation coverage and EVI values over the 31 years were analyzed. The results show that the total vegetation coverage and greenness increased during the 31 years. Linking this change trend to other factors suggests that vegetation in Minqin County is highly affected by agriculture and groundwater supply rather than by climate. To mitigate desertification in a sustainable way, agriculture should be well managed to avoid overconsumption of natural resources such as underground water.     

我国土壤光谱线之研究

通过对我国各地的土壤光谱反射率的分析表明,无论在二维或多维空间中,确有土壤光谱线存在,但不是严格的一条直线,而是有适当宽度的带。不宜用全国统一的一条土壤光谱线来作植被分析。讨论了不同土壤光谱线对绿度及植被覆盖度估计的误差。还论述了土壤光谱反射率主成分分析的结果及其物理意义。     

The grey-green divide: multi-temporal analysis of greenness across 10,000 urban centres derived from the Global Human Settlement Layer (GHSL)

ABSTRACT

The presence of green spaces within city centres has been recognized as a valuable component of the city landscape. Vegetation provides a variety of benefits including energy saving, improved air quality, reduced noise pollution, decreased ambient temperature and psychological restoration. Evidence also shows that the amount of vegetation, known as ‘greenness’, in densely populated areas, can also be an indicator of the relative wealth of a neighbourhood. The ‘grey-green divide’, the contrast between built-up areas with a dominant grey colour and green spaces, is taken as a proxy indicator of sustainable management of cities and planning of urban growth. Consistent and continuous assessment of greenness in cities is therefore essential for monitoring progress towards the United Nations Sustainable Development Goal 11. The availability of multi-temporal greenness information from Landsat data archives together with data derived from the city centres database of the Global Human Settlement Layer (GHSL) initiative, offers a unique perspective to quantify and analyse changes in greenness across 10,323 urban centres all around the globe. In this research, we assess differences between greenness within and outside the built-up area for all the urban centres described by the city centres database of the GHSL. We also analyse changes in the amount of green space over time considering changes in the built-up areas in the periods 1990, 2000 and 2014. The results show an overall trend of increased greenness between 1990 and 2014 in most cities. The effect of greening is observed also for most of the 32 world megacities. We conclude that using simple yet effective approaches exploiting open and free global data it is possible to provide quantitative information on the greenness of cities and its changes over time. This information is of direct interest for urban planners and decision-makers to mitigate urban related environmental and social impacts.     

Use of principal components of UAV-acquired narrow-band multispectral imagery to map the diverse low stature vegetation fAPAR

The fraction of absorbed photosynthetically active radiation (fAPAR) is an important plant physiological index that is used to assess the ability of vegetation to absorb PAR, which is utilized to sequester carbon in the atmosphere. This index is also important for monitoring plant health and productivity, which has been widely used to monitor low stature crops and is a crucial metric for food security assessment. The fAPAR has been commonly correlated with a greenness index derived from spaceborne optical imagery, but the relatively coarse spatial or temporal resolution may prohibit its application on complex land surfaces. In addition, the relationships between fAPAR and remotely sensed greenness data may be influenced by the heterogeneity of canopies. Multispectral and hyperspectral unmanned aerial vehicle (UAV) imaging systems, conversely, can provide several spectral bands at sub-meter resolutions, permitting precise estimation of fAPAR using chemometrics. However, the data pre-processing procedures are cumbersome, which makes large-scale mapping challenging. In this study, we applied a set of well-verified image processing protocols and a chemometric model to a lightweight, frame-based and narrow-band (10 nm) UAV imaging system to estimate the fAPAR over a relatively large cultivated land area with a variety of low stature vegetation of tropical crops along with native and non-native grasses. A principal component regression was applied to 12 bands of spectral reflectance data to minimize the collinearity issue and compress the data variation. Stepwise regression was employed to reduce the data dimensionality, and the first, third and fifth components were selected to estimate the fAPAR. Our results indicate that 77% of the fAPAR variation was explained by the model. All bands that are sensitive to foliar pigment concentrations, canopy structure and/or leaf water content may contribute to the estimation, especially those located close to (720 nm) or within (750 nm and 780 nm) the near-infrared spectral region. This study demonstrates that this narrow-band frame-based UAV system would be useful for vegetation monitoring. With proper pre-flight planning and hardware improvement, the mapping of a narrow-band multispectral UAV system could be comparable to that of a manned aircraft system.     

The Vegetation Outlook (VegOut): A New Method for Predicting Vegetation Seasonal Greenness

The vegetation outlook (VegOut) is a geospatial tool for predicting general vegetation condition patterns across large areas. VegOut predicts a standardized seasonal greenness (SSG) measure, which represents a general indicator of relative vegetation health. VegOut predicts SSG values at multiple time steps (two to six weeks into the future) based on the analysis of "historical patterns" (i.e., patterns at each 1 km grid cell and time of the year) of satellite, climate, and oceanic data over an 18-year period (1989 to 2006). The model underlying VegOut capitalizes on historical climate-vegetation interactions and ocean-climate teleconnections (such as El Niño and the Southern Oscillation, ENSO) expressed over the 18-year data record and also considers several environmental characteristics (e.g., land use/cover type and soils) that influence vegetation's response to weather conditions to produce 1 km maps that depict future general vegetation conditions. VegOut provides regionallevel vegetation monitoring capabilities with local-scale information (e.g., county to sub-county level) that can complement more traditional remote sensing-based approaches that monitor "current" vegetation conditions. In this paper, the VegOut approach is discussed and a case study over the central United States for selected periods of the 2008 growing season is presented to demonstrate the potential of this new tool for assessing and predicting vegetation conditions.     

Developing soil indices based on brightness,darkness, and greenness to improve land surface mapping accuracy

Soil, as one of the three basic biophysical components, has been understudied using remote sensing techniques compared to vegetation and impervious surface areas (ISA). This study characterized land surfaces based on the brightness–darkness–greenness model. These three dimensions, brightness, darkness, and greenness, were represented by the first Tasseled Cap Transformation (TC1), Normalize Difference Snow Index (NDSI), and Normalized Difference Vegetation Index (NDVI), respectively. The Ratio Index for Bright Soil (RIBS) was developed based on TC1 and NDSI, and the Product Index for Dark Soil (PIDS) was established by TC1 and NDVI. Their applications to the Landsat 8 Operational Land Imager images and 500 m 8-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) in China revealed the efficiency. The two soil indices proficiently highlighted soil covers with consistently the smallest values, due to larger TC1 and smaller NDSI values in bright soil, and smaller NDVI and TC1 values in dark soil. The RIBS is capable of distinguishing bright soil from ISA without masking vegetation and water body. The spectral separability bright soil and ISA were perfect, with a Jeffries–Matusita distance of 1.916. And the PIDS was the only soil index that could discriminate dark soil from other land covers including ISA. The soil areas in China were classified using a simple threshold method based on MODIS images. An overall accuracy of 94.00% was obtained, with the kappa index of 0.8789. This study provided valuable insights into developing indices for characterizing land surfaces from different perspectives.     

Automatic Feature Extraction Module for Change Detection in Al Ain,UAE: Analysis by Means of Multi-temporal Remote Sensing Data

Monitoring new changes in cities adjacent to dynamic sand dunes requires precise classifier technique. Unlike traditional techniques of supervised classification which use training sites, the integration of image transformation tasseled cap and automatic feature extraction module based on spectral signatures has provided to be sensitive and realistic techniques with time and cost effective. The proposed module was applied to Al Ain district, United Arab Emirates (UAE). The module consists of four steps in terms of segmentation, thresholding and clustering and computing attributes. The obtained greenness and classified maps were then enhanced by applying a 3?×?3 Sobel filter. The new changes were detected by combining the multi-temporal greenness and classification maps. Accuracy assessment and quantitative analysis were performed using confusion matrix and ground truthing. The results showed significant increasing in urban and agricultural areas from the year from 1990 to 2000 compared with the period of time from the year 2000 to 2006. The image difference showed that the vegetation and building classes had increased 7.58 and 20.28 km² respectively. This study showed that image difference and fuzzy logic approach are the most sensitive techniques for detecting new changes in areas adjacent to dynamic sand dunes.     

High-resolution airborne infrared measurements of ocean skin temperature

Airborne measurements of ocean skin temperature T/sub s/ are presented from the Coupled Boundary Layers, Air-Sea Transfer in Low Winds (CBLAST-Low) Pilot Experiment in August 2001 off Martha's Vineyard, MA. We used an infrared (IR) camera with a spatial resolution of 1 m or less and temperature resolution of roughly 0.02/spl deg/C. Using subframe sampling of the IR imagery, we achieve lower noise and higher spatial resolution than reported by previous investigators using IR radiometers. Fine-scale maps of T/sub s/ exhibit horizontal variability over spatial scales ranging from O(10 km) down to O(1 m) that are related to atmospheric and subsurface phenomena under low to moderate wind conditions. Based on supporting measurements of wind and waves, we identify coherent ramp-like structures in T/sub s/ with stratification breakdown and meandering streaky features with internal waves. Regional maps of T/sub s/ show the standard deviation for the region is /spl plusmn/1.04/spl deg/C, while the meridional and zonal variability is 0.23/spl deg/C /spl middot/ km/sup -1/ and 0.27/spl deg/C /spl middot/ km/sup -1/, respectively. This temperature variability results in meridional and zonal scalar heat flux variability of 7.0 W /spl middot/ m/sup -2/ /spl middot/ km/sup -1/ and 7.6 W /spl middot/ m/sup -2/ /spl middot/ km/sup -1/, respectively. Our results demonstrate the potential for airborne IR imagery accompanied by high-quality ocean data to identify T/sub s/ features produced by subsurface circulation.