Impact of understory vegetation on forest canopy reflectance and remotely sensed LAI estimates

Forest leaf area index (LAI), is an important variable in carbon balance models. However, understory vegetation is a recognized problem that limits the accuracy of satellite-estimated forest LAI. A canopy reflectance model was used to investigate the impact of the understory vegetation on LAI estimated from reflectance values estimated from satellite sensor data. Reflectance spectra were produced by the model using detailed field data as input, i.e. forest LAI, tree structural parameters, and the composition, distribution and reflectance of the forest floor. Common deciduous and coniferous forest types in southern Sweden were investigated. A negative linear relationship (r² = 0.6) was observed between field estimated LAI and the degree of understory vegetation, and the results indicated better agreement when coniferous and deciduous stands were analysed separately. The simulated spectra verified that the impact of the understory on the reflected signal from the top of the canopy is important; the reflectance values varying by up to ± 18% in the red and up to ± 10% in the near infra-red region of the spectra due to the understory. In order to predict the variation in LAI due to the understory vegetation, model inversions were performed where the input spectra were changed between the minimum, average and maximum reflectance values obtained from the forward runs. The resulting variation in LAI was found to be 1.6 units on average. The LAI of the understory could be predicted indirectly from simple stand data on forest characteristics, i.e. from allometric estimates, as an initial step in the process of estimating LAI. It is suggested here that compensation for the effect of the understory would improve the accuracy in the estimates of canopy LAI considerably.     

The influence of canopy strata on remotely sensed observations of savanna-woodlands

Upwelling radiance from savanna woodlands may originate from two separate layers: (1) the field layer, which is a mixture of soil, litter and herbs, and (2) the tree layer composed of woody parts and leaves. Unless detailed field data are available for a particular savanna location, it is unknown how the individual layers may influence the red and near-infrared signals and whether radiative interactions between layers are important. We employed an existing radiative transfer model (SAIL) in conjunction with a simple, single-scattering model to analyse the variation in Advanced Very High Resolution Radiometer (AVHRR) channel 1 and 2 response as well as NDVI for savanna-woodland vegetation in eastern Zambia. Linear fits between predicted and observed values of reflectance and NDVI were significant ( p 0.05) in the red and in NDVI, however, the model failed to explain a high proportion of the variation in near-infrared. Red and NDVI in sites where tree cover was high were also poorly modelled, which suggests that multiple interactions between canopy layers make a single-scattering model unreliable, particularly in the near-infrared. Modelled results were also compared to aircraft radiometer measurements provided by the integrated camera and radiometer instrument (ICAR). Simulations parameterized with field data suggest that the model may be used to infer tree and field layer influences at different points during the seasonal cycle. Results also suggest that the field layer dominated the signal in our savanna woodland sites throughout most points of the seasonal cycle, which is consistent with other canopy radiative-transfer studies. Simulations indicated that the tree layer was a relatively more important component of NDVI during the dry season when the field layer was largely senescent, accounting for 20-40 per cent of the satellite signal.     

Mapping understory vegetation using phenological characteristics derived from remotely sensed data

Understory vegetation is an important component in forest ecosystems not only because of its contributions to forest structure, function and species composition, but also due to its essential role in supporting wildlife species and ecosystem services. Therefore, understanding the spatio-temporal dynamics of understory vegetation is essential for management and conservation. Nevertheless, detailed information on the distribution of understory vegetation across large spatial extents is usually unavailable, due to the interference of overstory canopy on the remote detection of understory vegetation. While many efforts have been made to overcome this challenge, mapping understory vegetation across large spatial extents is still limited due to a lack of generality of the developed methods and limited availability of required remotely sensed data. In this study, we used understory bamboo in Wolong Nature Reserve, China as a case study to develop and test an effective and practical remote sensing approach for mapping understory vegetation. Using phenology metrics generated from a time series of Moderate Resolution Imaging Spectroradiometer data, we characterized the phenological features of forests with understory bamboo. Using maximum entropy modeling together with these phenology metrics, we successfully mapped the spatial distribution of understory bamboo (kappa: 0.59; AUC: 0.85). In addition, by incorporating elevation information we further mapped the distribution of two individual bamboo species, Bashania faberi and Fargesia robusta (kappa: 0.68 and 0.70; AUC: 0.91 and 0.92, respectively). Due to its generality, flexibility and extensibility, this approach constitutes an improvement to the remote detection of understory vegetation, making it suitable for mapping different understory species in different geographic settings. Both biodiversity conservation and wildlife habitat management may benefit from the detailed information on understory vegetation across large areas through the applications of this approach.     

Soil water and plant canopy effects on remotely measured surface temperatures

Abstract.

Thermal infrared remote sensing of diurnal crop canopy temperature variations represents a possible method for determining the availability of soil water to plants. This study was performed to assess the effects of soil water and crop canopy on apparent temperatures observed by means of remote sensors, and to determine the impact of these effects on remote soil water monitoring. Airborne thermal scanner and apparent reflectance data (one date) and ground PRT-5 data (three dates) were collected primarily over barley and other small grain canopies. Plant heights, cover, and available soil water for four layers in the top 20 cm were determined. Analysis of the data showed a close inverse linear relationship between the available water and the day minus night temperature difference δT, for thick barley canopies (plant cover above 90 per cent) only. The use of apparent reflectance values in the visible region did not improve available soil water regression equations substantially. These results suggest that the available water or plant stress could only be accurately determined for thick canopies, and that the reflectance data could probably be used to identify such canopies but would not improve regression estimates of soil water from remote sensing data.     

Applying neural networks in rare vegetation communities classification of remotely sensed images

Artificial neural networks (ANNs) are used for rare vegetation communities’ classification using remotely sensed data. Training of a neural network requires that the user specifies the network structure and sets the learning parameters. Heuristics proposed by a number of researchers to determine the optimum values of network parameters are compared using datasets. Training and test samples were collected for each class type (12 classes). After preliminary statistical tests for training samples, two modification algorithms of the classification scheme were defined: the first one led to creating a scheme which consisted of 7 classes, and the second one led us to creating of 5 class’s scheme. Testing results show that the use of ANNs on the based of 5 class’s scheme can produce higher classification accuracies than either alternative. The visual analysis of the results of the classification is described using Geoinformation Technologies in details. The text was submitted by the authors in English.     

Mapping the vegetation changes in giant panda habitat using Landsat remotely sensed data

Since the 1950s, with national policy changes and socio-economic development, the habitat of the giant pandas has altered accordingly. This can also be inferred from the population changes of the giant pandas as reported in three national surveys. Thus, monitoring the changes in giant panda habitat and then taking appropriate action would be a valuable contribution to giant panda protection. In this paper, using existing habitats and potential habitats of the giant pandas as the study area, multitemporal remotely sensed data from the three national surveys are used as the data source. The land cover of the study area is mapped by the maximum likelihood classification (MLC) method. The overall accuracy and kappa statistics for the resulting classification are 0.8 and 90%, respectively. The results reveal that the current status of the giant panda habitat is very good. Between 1974 and 1989, because of deforestation in the area, the giant panda habitat deteriorated considerably; the total area of broadleaved forests, coniferous forests, mixed coniferous and deciduous broadleaved forests and shrubs decreased from 62.03% to 57.40% in the study area. However, from 1989 to 2002, due to the conservation policies put into action, the giant panda habitat recovered to some extent; the total area of broadleaved forests, coniferous forests, mixed coniferous and deciduous broadleaved forests and shrubs increased from 57.40% to 60.68% in the study area. However, conditions are different among the mountain systems. Taking into account only the total of broadleaved forests, coniferous forests, mixed coniferous and deciduous broadleaved forests and shrubs, in the Minshan mountain system, the forest cover changed from 57.70% in 1974 to 56.74% in 1989 and to 56.30% in 2002, which can be regarded as stable. In the Qinling mountain system, forested areas changed from 70.05% in 1974 to 66.93% in 1989 and to 67.17% in 2002, which reveals little change in this area. In the Qionglai mountain system, forested areas changed from 72.84% in 1974 to 71.53% in 1989 and to 73.22% in 2002; therefore, an increase can be noted in this area. In the Xiangling mountain system, forested areas also increased from 50.81% in 1974 to 50.20% in 1989 and to 53.15% in 2002. In the Liangshan mountain system, forested areas changed from 68.43% in 1974 to 55.81% in 1989 and to 60.07% in 2002. These results are in accordance with the giant panda population changes in different mountain systems. Thus, the best way to improve the threatened status of the panda population is to protect the current habitat and the potential habitat.     

Comparison of hydrologically simulated and remotely sensed actual evapotranspiration from some Mediterranean vegetation formations

Actual evapotranspiration (AET) estimates representing four Mediterranean vegetation formations (from shrubland to dense woodland) have been obtained using a physically-based hydrological model and a simplified residual energy budget method applied to HCMM satellite sensor data. A good agreement between simulated and remotely sensed AET values has been obtained. Results are discussed with reference to roughness scaling lengths for momentum and heat.     

Estimation of regional evapotranspiration and soil moisture conditions using remotely sensed crop surface temperatures

Crop surface temperature gives information on the evapotranspiration rate and on soil moisture conditions. The use of crop surface temperatures remotely sensed by infrared line scanning (IRLS) can provide this information over large hydrologically nonuniform areas. A composite method has been developed to calculate actual evapotranspiration rates from remotely sensed crop surface temperatures, with the aid of the energy budget equation and aerodynamic heat and water vapor transport equations, while incorporating soil-plant-water relations. This method has been tested for grassland in an area near Losser, in the eastern part of the Netherlands. In this area, drought damage occurs due to groundwater extraction for drinking-water supply. A way to analyze accuracy was established. This analysis did show small standard deviations for measurements of evapotranspiration near the potential level, and a doubling of standard deviations when evapotranspiration approaches zero.     

Information extraction from remotely sensed data.

Digital image processing is now widely available for users of remotely sensed data. Although such processing offers many new opportunities for the user (or analyst) it also makes heavy demands on the acquisition of new skills, if the data are to yield useful information efficiently. In deciding on the best approach for image classification the user faces a bewildering array of choices, many of which have been poorly evaluated. It is clear, however, that the use of both internal and external contextual information can be of great value in improving classification performance. The ultimate use of information extracted from remote sensing data is strongly affected by its compatability with other geographic data planes. Problems in achieving such compatibility in the framework of automated geographical information systems are discussed. The success of image analysis and classification methods is highly dependent on the relationships between the abilities of sensing systems themselves and the character of the phenomena being studied. This is illustrated by reference to the capabilities of future high resolution satellite systems.     

Emittance effect on the remotely sensed sea surface temperature

A systemtic underestimation of remotely sensed sea surface temperature occurs in calculations which assume a value of the surface emittance equal to unity in the atmospheric window, where the measurements are taken. The paper includes a detailed examination of the effect on the sea surface temperature estimation caused by assuming a wrong value of the emittance. Results show that this effect is a function of the atmospheric transmittance and the surface temperature. The angular dependence and the influence of the sea state on this effect are also investigated     

Factors affecting remotely sensed snow water equivalent uncertainty

State-of-the-art passive microwave remote sensing-based snow water equivalent (SWE) algorithms correct for factors believed to most significantly affect retrieved SWE bias and uncertainty. For example, a recently developed semi-empirical SWE retrieval algorithm accounts for systematic and random error caused by forest cover and snow morphology (crystal size — a function of location and time of year). However, we have found that climate and land surface complexities lead to significant systematic and random error uncertainties in remotely sensed SWE retrievals that are not included in current SWE estimation algorithms. Joint analysis of independent meteorological records, ground SWE measurements, remotely sensed SWE estimates, and land surface characteristics have provided a unique look at the error structure of these recently developed satellite SWE products. We considered satellite-derived SWE errors associated with the snow pack mass itself, the distance to significant open water bodies, liquid water in the snow pack and/or morphology change due to melt and refreeze, forest cover, snow class, and topographic factors such as large scale root mean square roughness and dominant aspect. Analysis of the nine-year Scanning Multichannel Microwave Radiometer (SMMR) SWE data set was undertaken for Canada where many in-situ measurements are available. It was found that for SMMR pixels with 5 or more ground stations available, the remote sensing product was generally unbiased with a seasonal maximum 20 mm average root mean square error for SWE values less than 100 mm. For snow packs above 100 mm, the SWE estimate bias was linearly related to the snow pack mass and the root mean square error increased to around 150 mm. Both the distance to open water and average monthly mean air temperature were found to significantly influence the retrieved SWE product uncertainty. Apart from maritime snow class, which had the greatest snow class affect on root mean square error and bias, all other factors showed little relation to observed uncertainties. Eliminating the drop-in-the-bucket averaged gridded remote sensing SWE data within 200 km of open water bodies, for monthly mean temperatures greater than − 2 °C, and for snow packs greater than 100 mm, has resulted in a remotely sensed SWE product that is useful for practical applications.     

适合高分辨率遥感影像处理的分布式环境研究

随着遥感卫星的发展及遥感图像分辨率的增加,遥感图像的分布式处理变得越来越重要,应用分布式环境处理高分辨率遥感图像变得越来越迫切。传统的分布式处理主要采用B／S或C／S结构,这种结构当多用户并发访问时往往由于服务器的访问量过大而容易造成网络堵塞。在本项研究中采用了当前比较先进的网格计算及Web Service中资源共享的思想,根据高分辨率遥感图像分布式处理方面的需要,对遥感图像分布式处理框架进行了设计,并结合实际应用中的遥感图像分割对系统进行了实现。结果表明,新模型支持下的系统在多用户并发访问支持及系统优化方面都有很大的改善。     

Image processing for boundary extraction of remotely sensed data

Image processing algorithms for analysing remotely sensed data are developed. The algorithms proposed in the paper provide means for autoregressive texture modelling and for boundary detection of uniform subimage areas. The boundary detection methods make use of a semicircular entropy operator and of the binary hypothesis testing of the Poisson counting process. The proposed algorithms are applied to the pattern analysis of the isothermal distribution in the oceanic environment.     

Evaluation of IMERG and MRMS remotely sensed snowfall products

In this study, snow accumulation (SA) estimates of the Integrated Multi-satellitE Retrievals for GPM (Global Precipitation Measurement) (IMERG) and Multi-Radar/Multi-Sensor (MRMS) products were evaluated against the SNOwpack TELemetry (SNOTEL) ground observations over a Basin in the western United States from October 2016 to February 2017. IMERG underestimated SA in three snowfall probability thresholds of 45%, 65% and 85%. With increasing the threshold from 45% to 85%, MRMS Bias index showed overestimation compared to that of the IMERG. Overall, MRMS presented more accurate results than the IMERG. In categorical analysis, IMERG had better probability of detection (POD) values than the MRMS although MRMS was generally more accurate than the IMERG in all thresholds. Moreover, with respect to Bias, mean absolute error (MAE) and root mean square error (RMSE) indices in various elevation classes, IMERG was more efficient in lower elevation classes while in categorical analysis, MRMS performed worse than the IMERG based on the POD values. This weakness increased in higher elevation classes. Also, in another comparison in different SA classes, the results showed that IMERG had better performance than the MRMS under moderate snowfall condition. However, MRMS estimates improved in heavy snowfall. In general, it was concluded that the IMERG performed better in snowfall detection than the MRMS; while in contrast, the opposite was true in estimating the SA.     

Unmixing-based content retrieval system for remotely sensed hyperspectral imagery on GPUs

This paper presents a new unmixing-based retrieval system for remotely sensed hyperspectral imagery. The need for this kind of system is justified by the exponential growth in the volume and number of remotely sensed data sets from the surface of the Earth. This is particularly the case for hyperspectral images, which comprise hundreds of spectral bands at different (almost contiguous) wavelength channels. To deal with the high computational cost of extracting the spectral information needed to catalog new hyperspectral images in our system, we resort to efficient implementations of spectral unmixing algorithms on commodity graphics processing units (GPUs). Spectral unmixing is a very popular approach for interpreting hyperspectral data with sub-pixel precision. This paper particularly focuses on the design of the proposed framework as a web service, as well as on the efficient implementation of the system on GPUs. In addition, we present a comparison of spectral unmixing algorithms available in the system on both CPU and GPU architectures.     

Estimating live fuel moisture content from remotely sensed reflectance

Fuel moisture content (FMC) is used in forest fire danger models to characterise the moisture status of the foliage. FMC expresses the amount of water in a leaf relative to the amount of dry matter and differs from measures of leaf water content which express the amount of water in a leaf relative to its area. FMC is related to both leaf water content and leaf dry matter content, and the relationships between FMC and remotely sensed reflectance will therefore be affected by variation in both leaf biophysical properties. This paper uses spectral reflectance data from the Leaf Optical Properties EXperiment (LOPEX) and modelled data from the Prospect leaf reflectance model to examine the relationships between FMC, leaf equivalent water thickness (EWT) and a range of spectral vegetation indices (VI) designed to estimate leaf and canopy water content. Significant correlations were found between FMC and all of the selected vegetation indices for both modelled and measured data, but statistically stronger relationships were found with leaf EWT; overall, the water index (WI) was found to be most strongly correlated with FMC. The accuracy of FMC estimation was very low when the global range of FMC was examined, but for a restricted range of 0-100%, FMC was estimated with a root-mean-square error (RMSE) of 15% in the model simulations and 51% with the measured data. The paper shows that the estimation of live FMC from remotely sensed vegetation indices is likely to be problematic when there is variability in both leaf water content and leaf dry matter content in the target leaves. Estimating FMC from remotely sensed data at the canopy level is likely to be further complicated by spatial and temporal variations in leaf area index (LAI). Further research is required to assess the potential of canopy reflectance model inversion to estimate live fuel moisture content where a priori information on vegetation properties may be used to constrain the inversion process.     

Grid-enabled high-performance quantitative aerosol retrieval from remotely sensed data

As the quality and accuracy of remote-sensing instruments improve, the ability to quickly process remotely sensed data is in increasing demand. Quantitative remote-sensing retrieval is a complex computing process because of the terabytes or petabytes of data processed and the tight-coupling remote-sensing algorithms. In this paper, we intend to demonstrate the use of grid computing for quantitative remote-sensing retrieval applications with a workload estimation and task partition algorithm. Using a grid workflow for the quantitative remote-sensing retrieval service is an intuitive way to use the grid service for users without grid expertise. A case study showed that significant improvement in the system performance could be achieved with this implementation. The results of the case study also give a perspective on the potential of applying grid computing practices to remote-sensing problems.     

Estimation of total above-ground phytomass production using remotely sensed data

Remote sensing potentially offers a quick and nondestructive method for monitoring plant canopy condition and development. In this study, multispectral reflectance and thermal emittance data were used in conjunction with micrometeorological data in a simple model to estimate above-ground total dry phytomass production of several spring wheat canopies. The fraction of absorbed photosynthetic radiation (PAR) by plants was estimated from measurements of visible and near-infrared canopy reflectance. Canopy radiation temperature was used as a crop stress indicator in the model. Estimated above-ground phytomass values based on this model were strongly correlated with the measured phytomass values for a wide range of climate and plant-canopy conditions.     

Supervised classification of remotely sensed data with ongoing learning capability

A methodology to implement an automatic system for classifying remotely sensed data with an ongoing learning capability is introduced. The Nearest Neighbour (NN) rule is employed as the central classifier and several techniques are added to cope with the increase in computational load and with the risk of incorporating noisy data into the training sample. Experimental results confirm the enhancement in classification accuracy.