Land cover discrimination potential of radar multitemporal series and optical multispectral images in a Mediterranean cultural landscape

The aim of the present study is (1) to evaluate the performances of two series of European Remote Sensing (ERS) Synthetic Aperture Radar (SAR) images for land cover classification of a Mediterranean landscape (Minorca, Spain), compared with multispectral information from Système Pour l'Observation de la Terre (SPOT) and Landsat Thematic Mapper (TM) sensors, and (2) to test the synergy of SAR and optical data with a fusion method based on the Demspter–Shafer evidence theory, which is designed to deal with imprecise information. We have evaluated as a first step the contribution of multitemporal ERS data and contextual methods of classification, with and without filtering, for the discrimination of vegetation types. The present study shows the importance of time series of the ERS sensor and of the vectorial MMSE (minimum mean square error) filter based on segmentation for land cover classification. Fifteen land cover classes were discriminated (eight concerning different vegetation types) with a mean producer's accuracy of 0.81 for a five-date time series within 1998, and of 0.71 for another four-date time series for 1994/1995. These results are comparable to those from SPOT XS images: 0.69 for July, 0.67 for October (0.85 for July plus October), and also from TM data (0.81). These results are corroborated by the kappa coefficient of agreement. The fusion between the 1994 series of ERS and XS (July), based on a derived method of the Dempster–Shafer evidence theory, shows a slight improvement on overall accuracies: +0.06 of mean producer's accuracy and +0.04 of kappa coefficient.     

Prospects for quantifying structure,floristic composition and species richness of tropical forests

Airborne spectral and light detection and ranging (lidar) sensors have been used to quantify biophysical characteristics of tropical forests. Lidar sensors have provided high-resolution data on forest height, canopy topography, volume, and gap size; and provided estimates on number of strata in a forest, successional status of forests, and above-ground biomass. Spectral sensors have provided data on vegetation types, foliar biochemistry content of forest canopies, tree and canopy phenology, and spectral signatures for selected tree species. A number of advances are theoretically possible with individual and combined spectral and lidar sensors for the study of forest structure, floristic composition and species richness. Delineating individual canopies of over-storey trees with small footprint lidar and discrimination of tree architectural types with waveform distributions is possible and would provide scientists with a new method to study tropical forest structure. Combined spectral and lidar data can be used to identify selected tree species and identify the successional status of tropical forest fragments in order to rank forest patches by levels of species richness. It should be possible in the near future to quantify selected patterns of tropical forests at a higher resolution than can currently be undertaken in the field or from space.     

运用SPOT数据进行河流水体悬浮固体浓度的研究——以台湾淡水河为例

根据含沙水体的光谱特征,通过对比分析认为ＳＰＯＴ影像是河流水质遥感的理想数据源,其中１ 波段和２ 波段对反映水体悬浮固体比较敏感。根据遥感影像灰度值与水中悬浮固体含量之间的相关关系,运用ＳＰＯＴ影像的１、２ 波段和实测数据将淡水河悬浮固体含量分为４ 级,并对结果进行了评价。通过对悬浮固体污染等级图的分析,得出淡水河１０％ 以下的水体悬浮固体含量较高。悬浮固体含量从上游向下游递增,流经城市的河段悬浮物含量高,说明水体悬浮物含量受植被覆盖和人为作用影响。     

Using remote sensing for monitoring of autumn tillage in Norway

Management systems on agricultural land in Norway are causing concern because of increasing area wide soil erosion, consequent increase in sediment loads and nutrient losses to waters, and their consequent pollution. Monitoring of areas in autumn is important because this is the start of a season when much of the erosion occurs. This is often caused by management systems in autumn, but also because climatic conditions in this season are important for erosion processes.

We have investigated several sources of remote sensing data, with a view to assembling the best overall monitoring system for this geoscience problem. The sources were digitised CIR (colour infrared pseudocolour) aerial photography, SPOT and ERS-1 SAR. CIR photography proved more accurate than SPOT data, especially with regard to exposed soils as result of CIR's continuous spectral sensitivity. We found that SPOT is clearly limited as a monitoring tool for soils and there is a need for better sensors in range 680 to 790 nm wavelengths.

ERS-1 SAR provides information when the optical sources cannot, but is dependant on good digital elevation models. We found that the most effective monitoring is done using a combination of microwave and optical sources.     

Filling invalid values in a lidar-derived canopy height model with morphological crown control

The light detection and ranging (lidar) technique has rapidly developed worldwide in numerous fields. The canopy height model (CHM), which can be generated from lidar data, is a useful model in forestry research. The CHM shows the canopy height above ground, and it indicates vertical elevation changes and the horizontal distribution of the canopy’s upper surface. Many vegetation parameters, which are important in forest inventory, can be extracted from the CHM. However, some abnormal or sudden changes of the height values (i.e. invalid values), which appear as unnatural holes in an image, exist in CHMs. This article proposes an approach to fill the invalid values in lidar-derived CHMs with morphological crown control. First, the Laplacian operator is applied to an original CHM to determine possible invalid values. Then, the morphological closing operator is applied to recover the crown coverage. By combining the two results, the possible invalid values in the CHM can be confirmed and replaced by corresponding values in the median-filtered CHM. The filling results from this new method are compared with those from other methods and with charge-coupled device images for evaluation. Finally, a CHM with random noise is used to test the filling correctness of the algorithm. The experiments show that this approach can fill the most invalid values well while refraining from overfilling.     

A comparison of TerraSAR-X Quadpol backscattering with RapidEye multispectral vegetation indices over rice fields in the Mekong Delta,Vietnam

Satellite-based multispectral imagery and/or synthetic aperture radar (SAR) data have been widely used for vegetation characterization, plant physiological parameter estimation, crop monitoring or even yield prediction. However, the potential use of satellite-based X-band SAR data for these purposes is not fully understood. A new generation of X-band radar satellite sensors offers high spatial resolution images with different polarizations and, therefore, constitutes a valuable information source. In this study, we utilized a TerraSAR-X satellite scene recorded during a short experimental phase when the sensor was running in full polarimetric ‘Quadpol’ mode. The radar backscatter signals were compared with a RapidEye reference data set to investigate the potential relationship of TerraSAR-X backscatter signals to multispectral vegetation indices and to quantify the benefits of TerraSAR-X Quadpol data over standard dual- or single-polarization modes. The satellite scenes used cover parts of the Mekong Delta, the rice bowl of Vietnam, one of the major rice exporters in the world and one of the regions most vulnerable to climate change. The use of radar imagery is especially advantageous over optical data in tropical regions because the availability of cloudless optical data sets may be limited to only a few days per year. We found no significant correlations between radar backscatter and optical vegetation indices in pixel-based comparisons. VV and cross-polarized images showed significant correlations with combined spectral indices, the modified chlorophyll absorption ratio index/second modified triangular vegetation index (MCARI/MTVI2) and transformed chlorophyll absorption in reflectance index/optimized soil-adjusted vegetation index (TCARI/OSAVI), when compared on an object basis. No correlations between radar backscattering at any polarization and the normalized difference vegetation index (NDVI) were observed.     

Estimating life-form cover fractions in California sage scrub communities using multispectral remote sensing

The California sage scrub (CSS) community type in California's Mediterranean-type ecosystems is known for its high biodiversity and is home to a large number of rare, threatened, and endangered species. Because of extensive urban development in the past fifty years, this ecologically significant community type is highly degraded and fragmented. To conserve endangered CSS communities, monitoring internal conditions of communities is as crucial as monitoring distributions of the community type in the region. Vegetation type mapping and field sampling of individual plants provide ecologically meaningful information about CSS communities such as spatial distribution and species compositions, respectively. However, both approaches only provide spatially comprehensive information but no information about internal conditions or vice versa. Therefore, there is a need for monitoring variables which fill the information gap between vegetation type maps and field-based data. A number of field-based studies indicate that life-form fractional cover is an effective indicator of CSS community health and habitat quality for CSS-obligated species. This study investigates the effectiveness of remote sensing approaches for estimating fractional cover of true shrub, subshrub, herb, and bare ground in CSS communities of southern California. Combinations of four types of multispectral imagery ranging from 0.15 m resolution scanned color infrared aerial photography to 10 m resolution SPOT 5 multispectral imagery and three image processing models - per-pixel, object-based, and spectral mixture models - were tested.An object-based image analysis (OBIA) routine consistently yielded higher accuracy than other image processing methods for estimating all cover types. Life-form cover was reliably predicted, with error magnitudes as low as 2%. Subshrub and herb cover types required finer spatial resolution imagery for more accurate predictions than true shrub and bare ground types. Positioning of sampling grids had a substantial impact on the reliability of accuracy assessment, particularly for cover estimates predicted using multiple endmember spectral mixture analysis (MESMA) applied to SPOT imagery. Of the approaches tested in this study, OBIA using pansharpened QuickBird imagery is one of the most promising approaches because of its high accuracy and processing efficiency and should be tested for more heterogeneous CSS landscapes. MESMA applied to SPOT imagery should also be examined for effectiveness in estimating factional cover over more extensive habitat areas because of its low data cost and potential for conducting retrospective studies of vegetation community conditions.     

The use of airborne lidar to assess avian species diversity, density, and occurrence in a pine/aspen forest

Vegetation structure is an important factor that influences wildlife-habitat selection, reproduction, and survival. However, field-based measurements of vegetation structure can be time consuming, costly, and difficult to undertake in areas that are remote and/or contain rough terrain. Light detection and ranging (lidar) is an active remote sensing technology that can quantify three-dimensional vegetation structure over large areas and thus holds promise for examining wildlife-habitat relationships. We used discrete-return airborne lidar data acquired over the Black Hills Experimental Forest in South Dakota, USA in combination with field-collected vegetation and bird data to assess the utility of lidar data in quantifying vegetation structural characteristics that relate to avian diversity, density, and occurrence. Indices of foliage height diversity calculated from lidar data were positively and significantly correlated with indices of bird species diversity, with the highest correlations observed when foliage height diversity categories contained proportionally more foliage layers near the forest floor (< 5 m). In addition, lidar-derived indices of vegetation volume were significantly correlated with bird density. Using lidar-derived vegetation height data in combination with multispectral IKONOS data, we delineated five general habitat types within the study area according to the presence of prominent vegetation layers at lower levels of the forest and predominant tree type (deciduous or conifer). Habitat type delineations were tested by examining the occurrence and relative density of two bird species common to the study area that prefer lower level vegetation for foraging and nesting. Dark-eyed Juncos were significantly associated with the 0.5–2.0 m high vegetation layer in pine-dominated stands, and Warbling Vireos were significantly associated with this same layer in aspen-dominated stands. These results demonstrate that discrete-return lidar can be an effective tool to remotely quantify vegetation structural attributes important to birds, and may be enhanced when used in combination with spectral data.     

Forest fuel treatment detection using multi-temporal airborne lidar data and high-resolution aerial imagery: a case study in the Sierra Nevada Mountains,California

Treatments to reduce forest fuels are often performed in forests to enhance forest health, regulate stand density, and reduce the risk of wildfires. Although commonly employed, there are concerns that these forest fuel treatments (FTs) may have negative impacts on certain wildlife species. Often FTs are planned across large landscapes, but the actual treatment extents can differ from the planned extents due to operational constraints and protection of resources (e.g. perennial streams, cultural resources, wildlife habitats). Identifying the actual extent of the treated areas is of primary importance to understand the environmental influence of FTs. Light detection and ranging (lidar) is a powerful remote-sensing tool that can provide accurate measurements of forest structures and has great potential for monitoring forest changes. This study used the canopy height model (CHM) and canopy cover (CC) products derived from multi-temporal airborne laser scanning (ALS) data to monitor forest changes following the implementation of landscape-scale FT projects. Our approach involved the combination of a pixel-wise thresholding method and an object-of-interest (OBI) segmentation method. We also investigated forest change using normalized difference vegetation index (NDVI) and standardized principal component analysis from multi-temporal high-resolution aerial imagery. The same FT detection routine was then applied to compare the capability of ALS data and aerial imagery for FT detection. Our results demonstrate that the FT detection using ALS-derived CC products produced both the highest total accuracy (93.5%) and kappa coefficient (κ) (0.70), and was more robust in identifying areas with light FTs. The accuracy using ALS-derived CHM products (the total accuracy was 91.6%, and the κ was 0.59) was significantly lower than that using ALS-derived CC, but was still higher than using aerial imagery. Moreover, we also developed and tested a method to recognize the intensity of FTs directly from pre- and post-treatment ALS point clouds.     

Forest biomass mapping from lidar and radar synergies

The use of lidar and radar instruments to measure forest structure attributes such as height and biomass at global scales is being considered for a future Earth Observation satellite mission, DESDynI (Deformation, Ecosystem Structure, and Dynamics of Ice). Large footprint lidar makes a direct measurement of the heights of scatterers in the illuminated footprint and can yield accurate information about the vertical profile of the canopy within lidar footprint samples. Synthetic Aperture Radar (SAR) is known to sense the canopy volume, especially at longer wavelengths and provides image data. Methods for biomass mapping by a combination of lidar sampling and radar mapping need to be developed.In this study, several issues in this respect were investigated using aircraft borne lidar and SAR data in Howland, Maine, USA. The stepwise regression selected the height indices rh50 and rh75 of the Laser Vegetation Imaging Sensor (LVIS) data for predicting field measured biomass with a R² of 0.71 and RMSE of 31.33 Mg/ha. The above-ground biomass map generated from this regression model was considered to represent the true biomass of the area and was used as a reference map since no better biomass map exists for the area. Random samples were taken from the biomass map and the correlation between the sampled biomass and co-located SAR signature was studied. The best models were used to extend the biomass from lidar samples into all forested areas in the study area, which mimics a procedure that could be used for the future DESDYnI mission. It was found that depending on the data types used (quad-pol or dual-pol) the SAR data can predict the lidar biomass samples with R² of 0.63-0.71, RMSE of 32.0-28.2 Mg/ha up to biomass levels of 200-250 Mg/ha. The mean biomass of the study area calculated from the biomass maps generated by lidar-SAR synergy was within 10% of the reference biomass map derived from LVIS data. The results from this study are preliminary, but do show the potential of the combined use of lidar samples and radar imagery for forest biomass mapping. Various issues regarding lidar/radar data synergies for biomass mapping are discussed in the paper.     

Temporal and spatial soil moisture change pattern detection in an agricultural area using multi‐temporal Radarsat ScanSAR data

Monitoring the characteristics of spatially and temporally distributed soil moisture is important to the study of hydrology and climatology for understanding and calculating the surface water balance. The major difficulties in retrieving soil moisture with Synthetic Aperture Radar (SAR) measurements are due to the effects of surface roughness and vegetation cover. In this study we demonstrate a technique to estimate the relative soil moisture change by using multi‐temporal C band HH polarized Radarsat ScanSAR data. This technique includes two components. The first is to minimize the effects of surface roughness by using two microwave radar measurements with different incidence angles for estimation of the relative soil moisture change defined as the ratio between two soil volumetric moistures. This was done by the development of a semi‐empirical backscattering model using a database that simulated the Advanced Integral Equation Model for a wide range of soil moisture and surface roughness conditions to characterize the surface roughness effects at different incidence angles. The second is to reduce the effects of vegetation cover on radar measurements by using a semi‐empirical vegetation model and the measurements obtained from the optical sensors (Landsat TM and AVHRR). The vegetation correction was performed based on a first‐order semi‐empirical backscattering vegetation model with the vegetation water content information obtained from the optical sensors as the input. For the validation of this newly developed technique, we compared experimental data obtained from the Southern Great Plain Soil Moisture Experiment in 1997 (SGP97) with our estimations. Comparison with the ground soil moisture measurements showed a good agreement for predication of the relative soil moisture change, in terms of ratio, with a Root Mean Square Error (RMSE) of 1.14. The spatially distributed maps of the relative soil moisture change derived from Radarsat data were also compared with those derived from the airborne passive microwave radiometer ESTAR. The maps of the spatial characteristics of the relative soil moisture change showed comparable results.     

Discrete return lidar-based prediction of leaf area index in two conifer forests

Leaf area index (LAI) is a key forest structural characteristic that serves as a primary control for exchanges of mass and energy within a vegetated ecosystem. Most previous attempts to estimate LAI from remotely sensed data have relied on empirical relationships between field-measured observations and various spectral vegetation indices (SVIs) derived from optical imagery or the inversion of canopy radiative transfer models. However, as biomass within an ecosystem increases, accurate LAI estimates are difficult to quantify. Here we use lidar data in conjunction with SPOT5-derived spectral vegetation indices (SVIs) to examine the extent to which integration of both lidar and spectral datasets can estimate specific LAI quantities over a broad range of conifer forest stands in the northern Rocky Mountains. Our results show that SPOT5-derived SVIs performed poorly across our study areas, explaining less than 50% of variation in observed LAI, while lidar-only models account for a significant amount of variation across the two study areas located in northern Idaho; the St. Joe Woodlands (R² = 0.86; RMSE = 0.76) and the Nez Perce Reservation (R² = 0.69; RMSE = 0.61). Further, we found that LAI models derived from lidar metrics were only incrementally improved with the inclusion of SPOT 5-derived SVIs; increases in R² ranged from 0.02–0.04, though model RMSE values decreased for most models (0–11.76% decrease). Significant lidar-only models tended to utilize a common set of predictor variables such as canopy percentile heights and percentile height differences, percent canopy cover metrics, and covariates that described lidar height distributional parameters. All integrated lidar-SPOT 5 models included textural measures of the visible wavelengths (e.g. green and red reflectance). Due to the limited amount of LAI model improvement when adding SPOT 5 metrics to lidar data, we conclude that lidar data alone can provide superior estimates of LAI for our study areas.     

HJ-1A/B CCD与Landsat TM/ETM+植被指数的交互比较

利用5对同日过境的HJ-1A/B CCD和Landsat TM/ETM+影像对,研究了二者植被指数(NDVI,SAVI,EVI)之间的定量关系。选用其中的3对影像对作为实验影像,通过对均匀同质实验区对应的植被指数进行回归分析求出二者之间的转换方程,用未参与实验的2对影像对来验证所求转换方程的有效性,并对二者植被指数之间的差异进行了分析。结果表明：两种传感器对应的植被指数之间存在极显著的线性正相关关系,所求的转换方程具有较高的精度,可以利用转换方程将两种传感器的植被指数进行互为转换,有利于二者植被监测结果的互为补充,而两种传感器在光谱响应函数上的不同造成了二者植被指数间存在差异。     

山区地形星载SAR影像的几何纠正

随着搭载合成孔径雷达的各种卫星不断发射,SAR的研究越来越受到重视。由于SAR数据独特的成像方式,山区地形的星载SAR图像几何形变十分复杂。通常应用控制点,采用多项式拟合的方法已经无法将其改正。依据SAR的几何成像模型,利用有关卫星轨道参数和数字高程模型,进行山区地形SAR影像的几何纠正研究。研究利用少量轨道参数和DEM数据,通过坐标变换和投影成像误差纠正建立正确的坐标位置,并采用邻近元采样法完成几何纠正。以上方法应用于山区ERS-1/SAR影像的处理试验结果表明,该方法能够用于山区复杂地形的几何纠正,其误差小于2个像元。     

TanDEM-X elevation model data for canopy height and aboveground biomass retrieval in a tropical peat swamp forest

It was demonstrated in the past that radar data is useful to estimate aboveground biomass due to their interferometric capability. Therefore, the potential of a globally available TanDEM-X digital elevation model (DEM) was investigated for aboveground biomass estimation via canopy height models (CHMs) in a tropical peat swamp forest. However, CHMs based on X-band interferometers usually require external terrain models. High accurate terrain models are not available on global scale. Therefore, an approach exclusively based on TanDEM-X and the decrease of accuracy compared to an approach utilizing a high accurate terrain model is assessed. In addition, the potential of X-band interferometric heights in tropical forests needs to be evaluated. Therefore, two CHMs were derived from an intermediate TanDEM-X DEM (iDEM; as a precursor for WorldDEM^TM) alone and in combination with lidar measurements used as terrain model. The analysis showed high accuracies (root mean square error [RMSE] = 5 m) for CHMs based on iDEM and reliable estimation of aboveground biomass. The iDEM CHM, exclusively based on TanDEM-X, achieved a poor R² of 0.2, nonetheless resulted in a cross-validated RMSE of 54 t ha^?1 (16%). The low R² suggested that the X-band height alone was not sufficient to estimate an accurate CHM, and thus the need for external terrain models was confirmed. A CHM retrieved from the difference of iDEM and an accurate lidar terrain model achieved a considerably higher correlation with aboveground biomass (R² = 0.68) and low cross-validated RMSE of 24.5 t ha^?1 (7.5%). This was higher or comparable to other aboveground biomass estimations in tropical peat swamp forests. The potential of X-band interferometric heights for CHM and biomass estimation was thus confirmed in tropical forest in addition to existing knowledge in boreal forests.     

基于SPOT5遥感影像丰宁县植被地上生物量估测研究

利用SPOT5遥感影像数据和同期获得的野外调查样地数据,基于按植被类型分类估测的方法,研究了河北省丰宁满族自治县植被地上生物量的遥感估测技术。研究结果显示,SPOT5影像的4个波段反射率和中红外植被指数(VI3)结合建立的多元回归模型,可用于森林生物量的遥感估测,估测的R2值达0.540,说明中红外波段信息提高森林生物量的估测精度有一定作用;通过分析样地生物量与多种植被指数的相关性发现,基于比值植被指数(RVI)的指数回归模型是灌丛生物量估测的最佳模型,估测的R2值达0.711,基于归一化植被指数(NDVI)的简单线性回归模型为估测草地生物量的最佳模型,R2值达0.790。利用2008年的全覆盖SPOT5影像,获得了丰宁县2008年植被地上生物量分布图,除农田植被外,全县地上生物总量为3.706×107 t,单位面积生物量平均为51.223t/hm2,其中,森林植被总生物量为3.578×107 t,灌丛植被总生物量为1.048×106 t,草地植被总生物量为2.277×105 t。     

Developing a spectral pipeline using open source software and low-cost hardware for material identification

ABSTRACT

The ability to access, design and create low cost sensors capable of returning scientifically useful data has led to an exponential increase in citizen science, education and environmental monitoring groups. Low-cost spectroscopy is one such application and mobile phone camera-based instruments have been used in pollution monitoring, medical applications in developing countries and vegetation analysis. Can such an instrument be developed and tested to assist with automated detection of materials, possibly from space? We tested two spectrometer designs inside a two unit (2U) cubesat frame against a series of materials exhibiting phenomenology in the visible/near infrared (Vis/NIR) portion of the spectrum and vegetation groups. This was conducted in order to determine whether open source designs were capable of discriminating against similar materials, such as types of vegetation or types of iron-rich minerals. A spectral pipeline was created using open source programming software that was capable of converting raw sensor data into spectra, comparing samples of interest against a spectral library and returning an identification result with a confidence interval. We found that low-cost hardware sensitive to NIR and freely available software were able to identify types of materials in the study set, enabling applications in citizen science, education and outreach or even low-cost near-space research.     

Spectral,spatial and temporal characteristics of Arctic tundra reflectance

Abstract

The objective was to quantify and analyse the spectral, spatial and temporal variability of solar radiation reflected from arctic tundra vegetation at a study site in the Brooks Range foothills of northern Alaska. Spectral radiance data from hand-held radiometers and the SPOT HRV sensor were sampled along hillslope transects (toposequences) and within four vegetation community types. The spatial trend of normalised difference vegetation index (NDVI) along the toposequences corresponded to variations in the abundance of green vegetation matter and in vegetation composition. A marked temporal increase in the NDVI occurred along the toposequences from the beginning of the growing season (mid-June) to peak green up (end of July). The spectral signatures of three tundra dominant vegetation communities, dry heath, moist tussock and wet sedge, were moderately separable, with dry heath being most separable. The overall separability of the major community types was similar at all times during the growing season, with the most divergent signatures occurring in late July during maximum greenness. Some of the important ecological features of the arctic tundra landscape are not resolved by the SPOT HRV sensor in multi-spectral mode, in spite of its high (20 m) spatial resolution.     

Extraction rice-planted areas by RADARSAT data using neural networks

A classification technique using the neural networks has recently been developed. We apply a neural network of learning vector quantization (LVQ) to classify remote-sensing data, including microwave and optical sensors, for the estimation of a rice-planted area. The method has the capability of nonlinear discrimination, and the classification function is determined by learning. The satellite data were observed before and after planting rice in 1999. Three sets of RADARSAT and one set of SPOT/HRV data were used in Higashi–Hiroshima, Japan. Three RADARSAT images from April to June were used for this study. The LVQ classification was applied the RADARSAT and SPOT to evaluate the estimate of the area of planted-rice. The results show that the true production rate of the rice-planted area estimation of RADASAT by LVQ was approximately 60% compared with that of SPOT by LVQ. It is shown that the present method is much better than the SAR image classification by the maximum likelihood method.