Vegetation stress detection through chlorophyll a + b estimation and fluorescence effects on hyperspectral imagery

Physical principles applied to remote sensing data are key to successfully quantifying vegetation physiological condition from the study of the light interaction with the canopy under observation. We used the fluorescence-reflectance-transmittance (FRT) and PROSPECT leaf models to simulate reflectance as a function of leaf biochemical and fluorescence variables. A series of laboratory measurements of spectral reflectance at leaf and canopy levels and a modeling study were conducted, demonstrating that effects of chlorophyll fluorescence (CF) can be detected by remote sensing. The coupled FRT and PROSPECT model enabled CF and chlorophyll a + b (Ca + b) content to be estimated by inversion. Laboratory measurements of leaf reflectance (r) and transmittance (t) from leaves with constant Ca + b allowed the study of CF effects on specific fluorescence-sensitive indices calculated in the Photosystem I (PS-I) and Photosystem II (PS-II) optical region, such as the curvature index [CUR; (R675.R690)/R2(683)]. Dark-adapted and steady-state fluorescence measurements, such as the ratio of variable to maximal fluorescence (Fv/Fm), steady state maximal fluorescence (F'm), steady state fluorescence (Ft), and the effective quantum yield (delta F/F'm) are accurately estimated by inverting the FRT-PROSPECT model. A double peak in the derivative reflectance (DR) was related to increased CF and Ca + b concentration. These results were consistent with imagery collected with a compact airborne spectrographic imager (CASI) sensor from sites of sugar maple (Acer saccharum Marshall) of high and low stress conditions, showing a double peak on canopy derivative reflectance in the red-edge spectral region. We developed a derivative chlorophyll index (DCI; calculated as D705/D722), a function of the combined effects of CF and Ca + b content, and used it to detect vegetation stress.     

Hyperspectral reflectance response of freshwater macrophytes to salinity in a brackish subtropical marsh

Coastal freshwater wetlands are threatened by increased salinity due to relative sea level rise and reduced freshwater inputs. Remote radiometric measurement of freshwater marsh canopies to detect small shifts in water column salinity would be useful for assessing salinity encroachment. We measured leaf hyperspectral (300-1100 nm) reflectance of freshwater macrophytes (cattail, Typha latifolia and sea oxeye, Borrichia frutescens) in a field study in a subtropical brackish (2.5-4.5 parts per thousand salinity, per thousand) marsh to determine salinity effects on visible and near-infrared spectral band reflectance and to identify reflectance indices sensitive to small (1 per thousand) changes in wetland salinity. For sea oxeye, floating-position water band index [fWBI = R(900)/minimum(R(930) - R(980)), where R(lambda) = reflectance at band lambda], normalized difference vegetation index [NDVI = (R(774) - R(681))/(R(774) + R(681))], and a proposed wetland salinity reflectance ratio (WSRR = R(990)/R(933)) were sensitive to salinity with R2 of 40, 35, and 65%, respectively (p < 0.01). For cattail, NDVI and photochemical reflectance index [PRI = (R(531) - R(570))/(R(570) + R(531))] were sensitive to salinity with R2 of 29 and 33%, respectively (p 相似文献   

Monitoring effects of a controlled subsurface carbon dioxide release on vegetation using a hyperspectral imager

A hyperspectral imaging system was used to monitor vegetation during a subsurface controlled release of carbon dioxide (CO₂). From August 3 to 10, 2007, 0.3 tons CO₂/day were released through a 70 m horizontal pipe located at a nominal depth of 1.8 m below the surface. Hyperspectral images of alfalfa plants were collected during the controlled release and used along with classification tree analysis to study changes in the reflectance spectra as a function of perpendicular distance from the horizontal pipe. Changes in the reflectance spectra near the red edge (650–750 nm) were observed over the course of the controlled release experiment for plants within a perpendicular distance of 1 m of the release pipe. These results indicate monitoring vegetation over a carbon sequestration site has the potential to allow monitoring of the integrity of the CO₂ storage.     

Narrow-waveband reflectance ratios for remote estimation of nitrogen status in cotton

Tailoring nitrogen (N) fertilizer applications to cotton (Gossypium hirsutum L.) in response to leaf N status may optimize N use efficiency and reduce off-site effects of excessive fertilizer use. This study compared leaf and canopy reflectance within the 350 to 950 nm range in order to identify reflectance ratios sensitive to leaf chlorophyll (Chl), and hence N status, in cotton. Plants were grown outdoors in large pots using half-strength Hoagland's (control) solution until some three-row plots received a restricted supply of N. Treatments comprised control, 20% of control N at first flower bud (square) onward; 0 and 20% of control N at first flower onward; and 0% of control N at fruit-filling onward. Despite leaf N values ranging from 51 to 19 g kg-1 across treatments and sampling dates, a weak correlation was obtained between Chl and N (r2 = 0.32, df = 70). In general, N stress led to increased reflectance at 695 +/- 2.5 nm (R695) and decreased reflectance at R410, and changes in leaf N were best correlated with either R695 or R755 in leaves and either R410 or R700 in canopies. The strongest associations between leaf constituent and canopy reflectance ratio were Chl vs. R415/R695 (r2 = 0.72), carotenoids vs. R415/R685 (r2 = 0.79), and N vs. R415/R710 (r2 = 0.70). The R415 measure appears to be a more stable spectral feature under N stress, as compared with more pronounced changes along the reflectance red edge (690-730 nm). Multiple regression identified a three-waveband canopy reflectance model that explained 80% of the variability in leaf N. Results indicate that remote sensing of N status in cotton is feasible using narrow-waveband reflectance ratios that involve the violet or blue region of the spectrum (400 to 450 nm) and the more commonly featured red-edge region.     

Estimating yields of salt- and water-stressed forages with remote sensing in the visible and near infrared

In arid irrigated regions, the proportion of crop production under deficit irrigation with poorer quality water is increasing as demand for fresh water soars and efforts to prevent saline water table development occur. Remote sensing technology to quantify salinity and water stress effects on forage yield can be an important tool to address yield loss potential when deficit irrigating with poor water quality. Two important forages, alfalfa (Medicago sativa L.) and tall wheatgrass (Agropyron elongatum L.), were grown in a volumetric lysimeter facility where rootzone salinity and water content were varied and monitored. Ground-based hyperspectral canopy reflectance in the visible and near infrared (NIR) were related to forage yields from a broad range of salinity and water stress conditions. Canopy reflectance spectra were obtained in the 350- to 1000-nm region from two viewing angles (nadir view, 45 degrees from nadir). Nadir view vegetation indices (VI) were not as strongly correlated with leaf area index changes attributed to water and salinity stress treatments for both alfalfa and wheatgrass. From a list of 71 VIs, two were selected for a multiple linear-regression model that estimated yield under varying salinity and water stress conditions. With data obtained during the second harvest of a three-harvest 100-d growing period, regression coefficients for each crop were developed and then used with the model to estimate fresh weights for preceding and succeeding harvests during the same 100-d interval. The model accounted for 72% of the variation in yields in wheatgrass and 94% in yields of alfalfa within the same salinity and water stress treatment period. The model successfully predicted yield in three out of four cases when applied to the first and third harvest yields. Correlations between indices and yield increased as canopy development progressed. Growth reductions attributed to simultaneous salinity and water stress were well characterized, but the corrections for effects of varying tissue nitrogen (N) and very low leaf area index (LAI) are necessary.     

THE POTENTIAL FOR REMOTE SENSING OF LOESS SOILS SUSPENDED IN SURFACE WATERS1

ABSTRACT: The potential for detecting the concentration and type of soils suspended in surface water through remote sensing techniques was investigated by studying the spectral reflectance of two types of soils in suspension. In a large tank filled with 7510 liters of water, 20 levels of suspended sediment (soil) concentration (SSC), ranging from 50 to 1000 mg/l were prepared. A high resolution spectroradiometer was used to measure the reflectance at each SSC level. The reflectance spectra of two contrasting soils were distinct in the visible and near infrared (NIR) portions of the electromagnetic spectrum. The wavelength range between 580–690 nm (visible) was found to be optimal for indicating the type of soil, whereas, the wavelength range between 714–880 (NIR) was found to be appropriate for estimating the concentration of sediment suspended in surface waters.     

水面实测遥感光谱数据的叶绿素a反演模型构建—以太湖为例

叶绿素a遥感反演的关键是建立遥感数据和叶绿素含量的定量关系。本文根据2007年11月在太湖实测的水体反射光谱及实验室分析得到的叶绿素浓度数据，对太湖水体反射光谱特征与叶绿素浓度之间的关系进行分析和建模。研究结果表明：687nm附近反射峰位置对叶绿素浓度有很好的指示作用，在太湖水体叶绿素浓度的估测模型中，反射峰位置的指数模型要优于线性模型；叶绿素a浓度与684nm和633nm附近的一阶微分有很好的相关性，利用R’684和R’633所建叶绿素a浓度反演模型的估算精度R2分别达到0.89和0.91。     

Seasonal change detection of riparian zones with remote sensing images and genetic programming in a semi-arid watershed

Riparian zones are deemed significant due to their interception capability of non-point source impacts and the maintenance of ecosystem integrity region wide. To improve classification and change detection of riparian buffers, this paper developed an evolutionary computational, supervised classification method--the RIparian Classification Algorithm (RICAL)--to conduct the seasonal change detection of riparian zones in a vast semi-arid watershed, South Texas. RICAL uniquely demonstrates an integrative effort to incorporate both vegetation indices and soil moisture images derived from LANDSAT 5 TM and RADARSAT-1 satellite images, respectively. First, an estimation of soil moisture based on RADARSAT-1 Synthetic Aperture Radar (SAR) images was conducted via the first-stage genetic programming (GP) practice. Second, for the statistical analyses and image classification, eight vegetation indices were prepared based on reflectance factors that were calculated as the response of the instrument on LANDSAT. These spectral vegetation indices were then independently used for discriminate analysis along with soil moisture images to classify the riparian zones via the second-stage GP practice. The practical implementation was assessed by a case study in the Choke Canyon Reservoir Watershed (CCRW), South Texas, which is mostly agricultural and range land in a semi-arid coastal environment. To enhance the application potential, a combination of Iterative Self-Organizing Data Analysis Techniques (ISODATA) and maximum likelihood supervised classification was also performed for spectral discrimination and classification of riparian varieties comparatively. Research findings show that the RICAL algorithm may yield around 90% accuracy based on the unseen ground data. But using different vegetation indices would not significantly improve the final quality of the spectral discrimination and classification. Such practices may lead to the formulation of more effective management strategies for the handling of non-point source pollution, bird habitat monitoring, and grazing and live stock management in the future.     

Aerospace wetland monitoring by hyperspectral imaging sensors: a case study in the coastal zone of San Rossore Natural Park

The San Rossore Natural Park, located on the Tuscany (Italy) coast, has been utilized over the last 10 years for many remote sensing campaigns devoted to coastal zone monitoring. A wet area is located in the south-west part of the Natural Park and it is characterized by a system of ponds and dunes formed by sediment deposition occurring at the Arno River estuary. The considerable amount of collected data has permitted us to investigate the evolution of wetland spreading and land coverage as well as to retrieve relevant biogeochemical parameters, e.g. green biomass, from remote sensing images and products. This analysis has proved that the monitoring of coastal wetlands, characterized by shallow waters, moor and dunes, demands dedicated aerospace sensors with high spatial and spectral resolution. The outcomes of the processing of images gathered during several remote sensing campaigns by airborne and spaceborne hyperspectral sensors are presented and discussed. A particular effort has been devoted to sensor response calibration and data validation due to the complex heterogeneity of the observed natural surfaces.     

Combining remote sensing imagery and forest age inventory for biomass mapping

Aboveground biomass (AGB) of forests is an important component of the global carbon cycle. In this study, Landsat ETM(+) images and field forest inventory data were used to estimate AGB of forests in Liping County, Guizhou Province, China. Three different vegetation indices, including simple ratio (SR), reduced simple ratio (RSR), and normalized difference vegetation index (NDVI), were calculated from atmospherically corrected ETM(+) reflectance images. A leaf area index (LAI) map was produced from the RSR map using a regression model based on measured LAI and RSR. The LAI map was then used to develop an initial AGB map, from which forest stand age was deduced. Vegetation indices, LAI, and forest stand age were together used to develop AGB estimation models for different forest types through a stepwise regression analysis. Significant predictors of AGB changed with forest types. LAI and NDVI were significant predictors of AGB for Chinese fir (R(2)=0.93). The model using LAI and stand age as predictors explained 94% of the AGB variance for coniferous forests. Stand age captured 79% of the AGB variance for broadleaved forests (R(2)=0.792). AGB of mixed forests was predicted well by LAI and SR (R(2)=0.931). Without differentiating among forest types, the model with SR and LAI as predictors was able to explain 90% of AGB variances of all forests. In Liping County, AGB shows a strong gradient that increases from northeast to southwest. About 64% of the forests have AGB in the range from 90 to 180 t ha(-1).     

Monitoring the effect of restoration measures in Indonesian peatlands by radar satellite imagery

In the context of the ongoing climate change discussions the importance of peatlands as carbon stores is increasingly recognised in the public. Drainage, deforestation and peat fires are the main reasons for the release of huge amounts of carbon from peatlands. Successful restoration of degraded tropical peatlands is of high interest due to their huge carbon store and sequestration potential. The blocking of drainage canals by dam building has become one of the most important measures to restore the hydrology and the ecological function of the peat domes. This study investigates the capability of using multitemporal radar remote sensing imagery for monitoring the hydrological effects of these measures. The study area is the former Mega Rice Project area in Central Kalimantan, Indonesia, where peat drainage and forest degradation is especially intense. Restoration measures started in July 2004 by building 30 large dams until June 2008. We applied change detection analysis with more than 80 ENVISAT ASAR and ALOS PALSAR images, acquired between 2004 and 2009. Radar signal increases of up to 1.36 dB show that high frequency multitemporal radar satellite imagery can be used to detect an increase in peat soil moisture after dam construction, especially in deforested areas with a high density of dams. Furthermore, a strong correlation between cross-polarised radar backscatter coefficients and groundwater levels above -50 cm was found. Monitoring peatland rewetting and quantifying groundwater level variations is important information for vegetation re-establishment, fire hazard warning and making carbon emission mitigation tradable under the voluntary carbon market or REDD (Reducing Emissions from Deforestation and Degradation) mechanism.     

彩色合成法在新疆地表植被覆盖变化中的应用--以多时相NOAA-AVHRR影像为例

以NOAA—AVHRR遥感数据为基础，使用彩色合成法对新疆地表植被变化进行分析。结果证明，利用不同时相的NDVI影像进行彩色合成可以直观地反映地表植被的变化趋势，通过遥感软件对彩色图像处理，还可以对地表植被变化进行定量分析。     

Building spectral libraries for wetlands land cover classification and hyperspectral remote sensing

Recent advances in remote sensing provide opportunities to map plant species and vegetation within wetlands at management relevant scales and resolutions. Hyperspectral imagers, currently available on airborne platforms, provide increased spectral resolution over existing space-based sensors that can document detailed information on the distribution of vegetation community types, and sometimes species. Development of spectral libraries of wetland species is a key component needed to facilitate advanced analytical techniques to monitor wetlands. Canopy and leaf spectra at five sites in California, Texas, and Mississippi were sampled to create a common spectral library for mapping wetlands from remotely sensed data. An extensive library of spectra (n=1336) for coastal wetland communities, across a range of bioclimatic, edaphic, and disturbance conditions were measured. The wetland spectral libraries were used to classify and delineate vegetation at a separate location, the Pacheco Creek wetland in the Sacramento Delta, California, using a PROBE-1 airborne hyperspectral data set (5m pixel resolution, 128 bands). This study discusses sampling and collection methodologies for building libraries, and illustrates the potential of advanced sensors to map wetland composition. The importance of developing comprehensive wetland spectral libraries, across diverse ecosystems is highlighted. In tandem with improved analytical tools these libraries provide a physical basis for interpretation that is less subject to conditions of specific data sets. To facilitate a global approach to the application of hyperspectral imagers to mapping wetlands, we suggest that criteria for and compilation of wetland spectral libraries should proceed today in anticipation of the wider availability and eventual space-based deployment of advanced hyperspectral high spatial resolution sensors.     

REMOTE SENSING OF RIPARIAN BUFFERS: PAST PROGRESS AND FUTURE PROSPECTS1

Riparian buffer zone management is an area of increasing relevance as human modification of the landscape continues unabated. Land and water resource managers are continually challenged to maintain stream ecosystem integrity and water quality in the context of rapidly changing land use, which often offsets management gains. Approaches are needed not only to map vegetation cover in riparian zones, but also to monitor the changes taking place, target restoration activities, and assess the success of previous management actions. To date, these objectives have been difficult to meet using traditional techniques based on aerial photos and field visits, particularly over large areas. Recent advances in remote sensing have the potential to substantially aid buffer zone management. Very high resolution imagery is now available that allows detailed mapping and monitoring of buffer zone vegetation and provides a basis for consistent assessments using moderately high resolution remote sensing (e.g., Landsat). Laser‐based remote sensing is another advance that permits even more detailed information on buffer zone properties, such as refined topographic derivatives and multidimensional vegetation structure. These sources of image data and map information are reviewed in this paper, examples of their application to riparian buffer mapping and stream health assessment are provided, and future prospects for improved buffer monitoring are discussed.     

Multi‐Decadal Remote‐Sensing Analysis of Irrigated Areas in the Lower Rio Grande Valley,New Mexico

A time series of estimates of irrigated area was developed for the Lower Rio Grande valley (LRG) in New Mexico from the 1970s to present day. The objective of the project was to develop an independent, accurate, and scientifically justifiable evaluation of irrigated area in the region for the period spanning from the mid‐1970s to the present. These area estimates were used in support of groundwater modeling of the LRG region, as well as for other analyses. This study used a remote‐sensing‐based methodology to evaluate overall irrigated area within the LRG. We applied a methodology that involved the normalization of vegetation indices derived from satellite imagery to get a more accurate estimation of irrigated area across multiple time periods and multiple Landsat platforms. The normalization allows more accurate evaluation of vegetation index data that span several decades. An accuracy assessment of the methodology and results from this study was performed using field‐collected crop data from the 2008 growing season. The comparisons with field data indicate that the accuracy of the remote‐sensing‐based estimates of historical irrigated area is very good, with rates of false positives (areas identified as irrigated that are not truly irrigated) of only about 4%, and rates of false negatives (areas identified as not irrigated that are truly irrigated) in the range of 0.6‐2.0%.     

Optical properties of intact leaves for estimating chlorophyll concentration

Changes in leaf chlorophyll content can serve as relative indicators of plant vigor and environmental quality. This study identified reflectance, transmittance, and absorptance wavebands and band ratios within the 400- to 850-nm range for intact leaves that could be used to estimate extracted leaf chlorophyll per unit leaf area (areal concentration) with minimal error. Leaf optical properties along with chlorophyll a, b, and a + b concentrations were measured for the planar-leaved sweetgum (Liquidambar styraciflua L.), red maple (Acer rubrum L.), wild grape (Vitis rotundifolia Michx.), and switchcane [Arundinaria gigantea (Walter) Muhl.], and for needles of longleaf pine (Pinus palustris Miller). Generally, reflectance, transmittance, and absorptance corresponded most precisely with chlorophyll concentrations at wavelengths near 700 nm, although regressions were also strong in the 550- to 625-nm range. A power function was superior to a simple linear function in yielding low standard deviations of the estimate (s). When data were combined among the planar-leaved species, s values were low at approximately 50 mumol/m2 out of a 940 mumol/m2 range in chlorophyll a + b at best-fit wavelengths of 707 to 709 nm. Minimal s values for chlorophyll a + b ranged from 32 to 62 mumol/m2 across species when band ratios having numerator wavelengths of 693 to 720 nm were used with the application of a power function. Optimal denominator wavelengths for the band ratios were 850 nm for reflectance and transmittance and 400 nm for absorptance. This information can be applied in designing field portable chlorophyll meters and in the landscape-scale remote sensing of plant responses to the environment.     

LAI inversion algorithm based on directional reflectance kernels

Leaf area index (LAI) is an important ecological and environmental parameter. A new LAI algorithm is developed using the principles of ground LAI measurements based on canopy gap fraction. First, the relationship between LAI and gap fraction at various zenith angles is derived from the definition of LAI. Then, the directional gap fraction is acquired from a remote sensing bidirectional reflectance distribution function (BRDF) product. This acquisition is obtained by using a kernel driven model and a large-scale directional gap fraction algorithm. The algorithm has been applied to estimate a LAI distribution in China in mid-July 2002. The ground data acquired from two field experiments in Changbai Mountain and Qilian Mountain were used to validate the algorithm. To resolve the scale discrepancy between high resolution ground observations and low resolution remote sensing data, two TM images with a resolution approaching the size of ground plots were used to relate the coarse resolution LAI map to ground measurements. First, an empirical relationship between the measured LAI and a vegetation index was established. Next, a high resolution LAI map was generated using the relationship. The LAI value of a low resolution pixel was calculated from the area-weighted sum of high resolution LAIs composing the low resolution pixel. The results of this comparison showed that the inversion algorithm has an accuracy of 82%. Factors that may influence the accuracy are also discussed in this paper.     

高分辨率遥感影像在城市绿地信息提取中的应用研究

高分辨率卫星遥感图像数据量大、空间分辨率高、结构信息复杂、地物同物异谱现象更为突出等特征给专题信息提取技术带来了新的挑战。基于植被的光谱特征,利用监督分类、植被指数分类和目视解译等方法对QuickBird高分辨率卫星遥感影像的绿地信息进行提取,并对分类精度作了比对分析。研究结果表明,监督分类方法不能得到令人满意的结果,运用植被指数分类方法则有明显改善,其中归一化植被指数(NDVI)精度最高,因此NDVI能有效地对植被进行分类与识别。     

Emergence of Indigenous Vegetation Classifications Through Integration of Traditional Ecological Knowledge and Remote Sensing Analyses

Traditional ecological knowledge (TEK) can play an important role in the understanding of ecological systems. Although TEK has complemented scientific and managerial programs in a variety of contexts, its formal incorporation into remote sensing exercises has to date been limited. Here, we show that the vegetation classifications of the Ache, an indigenous hunter-gatherer tribe of the Mbaracayu Forest Reserve in Paraguay, are reflected in a supervised classification of satellite imagery of the reserve. Accuracy of classification was toward the low end of the range of published values, but was reasonable given the difficult nature of separating forest classes from satellite images. Comparison of the resultant map with a more traditionally elaborated vegetation map highlights differences between the two approaches and the gain in information obtained by considering TEK classifications. We suggest that integration of TEK and remote sensing may provide alternative insights into the ecology of vegetation communities and land cover, particularly in remote and densely forested areas where ecological field research is often limited by roads and/or trail systems.     

Effects of Light Zenith Angle Variation on Spectral Responses of a Water Body’s Environmental Quality

The effect of light source zenith angle (θ_z) variation on the spectral reflectance measurement from sediment-laden water was studied in the laboratory using a spectroradiometer. Light source zenith angle in addition to sediment characteristics and concentration play a dominant role in affecting the reflectance characteristics of a suspension. With an increase in θ_z values, an increase in spectral reflectance is observed. The spectral reflectance characteristics of a water body also increase with the sediment concentration for all soil types and at all wavelengths. Empirical relationships between the spectral reflectance, suspended sediment concentration, and the light source zenith angle have been developed. Such a remote sensing technique for monitoring the concentration of the suspended sediment and their characteristics is a useful tool for economical estimation of a large water body’s quality related parameters vis-a-vis its environmental quality.Retd. Professor of Environmental Engineering & Pollution Control, Presently Residing at Makkhan Kutir, Bhargava Lane, Devpura, Haridwar-249401, India.Former Ph.D. student.