Estimation of methane emission from West Siberian wetland by scaling technique between NOAA AVHRR and SPOT HRV

Wetlands are one of the most important ecosystems in the world and at the same time they are presumed to be a source of methane gas, which is one of the most important greenhouse gases. The West Siberian wetlands is the largest in the world and remote sensing techniques can play an important role for monitoring the wetland.High spatial resolution satellite data are effective for monitoring land cover type changes, but can't cover a wide area because of a narrow swath width. On the other hand, global scale data are indispensable in covering a large area, but are too coarse to get the detailed information due to the low spatial resolution. It is necessary to devise a method for the fusion of the data with different spatial resolutions for monitoring the scale-differed phenomena.In this paper, firstly, a SPOT HRV image near Plotnikovo mire was used to map four wetland ecosystems (birch forest, conifer forest, forested bog and open bog) supplemented by field observation. Then, spectral mixture analysis was performed between NOAA AVHRR and SPOT HRV data acquired on the same day.Secondly, field observations were scaled up with these different spatial resolution satellite data. Each of the wetland ecosystem coverage fraction at the sub-pixel level was provided by spectral mixture analysis. Field observation shows that the mean rate of CH₄ emission from forested bog and open bog averaged 21.1 and 233.1 (mg CH₄/m²/day), respectively. The methane emission from the area was estimated by multiplying these average methane emission rates and the fraction coverage in each AVHRR pixel.Finally, the total methane emission over AVHRR coverage was estimated to be 9.46 (10⁹ g CH₄/day) and the mean methane emission over AVHRR coverage was calculated as 59.3 (mg CH₄/m²/day). We could conclude that this mean value is within the probabilistic variability as compared with the airborne measurement results.     

Statistical models of fragmented land cover and the effect of coarse spatial resolution on the estimation of area with satellite sensor imagery

The feasibility of correcting for errors in apparent extent of land cover types on coarse spatial resolution satellite imagery was analysed using a modelling approach. The size distributions for small burn scars mapped with two Landsat Multi-spectral Scanner (MSS) images and ponds mapped with an ERS-1 synthetic aperture radar (SAR) image were measured using geographical information system (GIS) software. Regression analysis showed that these size distributions could be modelled with two types of statistical distributions a power distribution and an exponential distribution. A comparison of the size distributions of small burn scars as observed with the Landsat MSS imagery to the distribution observed with National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) imagery indicated that distortions due to the coarse spatial resolution of AVHRR caused overestimation of the burn area. This bias was primarily caused by detection in two or three AVHRR pixels of burns whose actual size was on the order of a single AVHRR pixel. Knowledge of the type of the actual size distribution of small fragments in a scene and the causes of distortion may lead to methods for correcting area estimates involving models of the size distribution observed with coarse imagery and requiring little or no recourse to fine scale data.     

A geometric approach to a mixed pixel problem: Detecting subpixel woody vegetation

In digital satellite imagery, small fragments of woody vegetation are difficult to detect because they frequently are smaller than the pixel size and are mixed with other land cover classes. A method for detecting subpixel woody vegetation, which analyzes mixture phenomena at the individual pixel level, is presented. This method relies on a moving window to collect training sets for adjacent land cover. In order to locate pixels of interest and to decrease noise, image-derived masks are integrated with the original digital imagery in a geocoded information system. A rule-based scheme is employed to organize relative spatial and spectral information into classification decision procedures. Tests using simulated multispectral and panchromatic SPOT HRV imagery of lowland Britain have shown that the developed method discriminates significantly more woody vegetation than standard multispectral classification.     

Per-field classification: an example using SPOT HRV imagery

Abstract

The classification of land cover on remotely sensed imagery is usually undertaken in a per-pixel format within an image file or in a per-field format within a non-image file. The latter is more accurate but does not produce an image output and is not readily input to a vector-based geographical information system. We propose setting the pixels in each field to a representative statistic for that field and then using a per-pixel classifier to perform a per-field classification in an image file. This procedure was evaluated using SPOT high resolution visible (HRV) imagery. The highest classification accuracy of 62.1 per cent (12 class) was achieved using measures of prior probabilities and image texture within the proposed per-field format.     

Retrieval of temporal profiles of reflectances from simulated and real NOAA-AVHRR data over heterogeneous landscapes

To carry out functioning and dynamic vegetation studies, a temporal analysis is needed. So far, only data provided by the National Oceanic and Atmospheric Administration (NOAA) satellites with Advanced Very High Resolution Radiometer (AVHRR) sensors offer the required temporal resolution, but their spatial resolution is coarse (1.1 km). But, in many situations, the vegetation cover is heterogeneous and the 1.1 km AVHRR pixel contains several types of land use radiometrically different and is, in fact, a mixed pixel. Thus, the reflectance and consequently deduced parameters (NDVI, LAI, etc.) measured by AVHRR is actually average value and does not represent a value for each vegetation class present in the pixel. The objective is to extract the reflectance of each vegetation class from the mixed pixel using NOAA-AVHRR data and SPOT-HRV data simultaneously which give the proportions of each type of vegetation inside the mixed pixel through a classification map. The paper presents a method for radiometrically unmixing coarse resolution signals through the inversion of linear mixture modelling on heterogeneous regions of natural vegetation (Bidi-Bahn) in Burkina-Faso and in Niger (Hapex site). In a first step, simulated coarse resolution data (NOAA-AVHRR) obtained from the degradation of SPOT images are used to assess the method. In a second step, real NOAA-AVHRR data are used and some elements of validation are given by comparing the results to airborne reflectance measurements.     

遥感影像亚像元定位研究综述

遥感影像亚像元定位是在混合像元分解基础上,利用地物空间分布特征确定不同地物类型在混合像元中的具体位置,得到亚像元尺度的地物分类图,是一种有效解决混合像元空间不确定性的方法。首先介绍遥感影像亚像元定位的基本概念,分析亚像元定位的理论模型和求解算法;然后总结亚像元定位模型的误差来源、精度评价方法以及结果不确定性的表达手段,同时讨论利用辅助数据源提高亚像元定位精度的主要方法;最后对亚像元定位的研究趋势做了进一步展望。     

Development and validation of AVHRR-derived regional forest cover data for the north-eastern U.S.

As part of developing the geographic information system (GIS) to support a north-eastern U.S. regional forest change modelling effort, we investigated the utility of several sources of AVHRR data in regional forest cover mapping. Single-date classified Advanced Very High Resolution Radiometer (AVHRR) imagery in combination with existing USGS Land Use/Land Cover data was used to create a forest cover database that encompassed eastern New York state and all of New England. The USGS EROS Data Center Conterminous U.S. Land Cover Characteristics database was also evaluated for comparison. Statistical analysis showed that the AVHRR-derived regional land cover datasets provided estimates of total forest area that were comparable to U.S. Forest Service county level estimates. The AVHRR imagery recorded after leaf fall appeared to enhance the discrimination of coniferous vs. deciduous forests.     

Sub-pixel land cover mapping for per-field classification

A method was developed to transform a soft land cover classification into hard land cover classes at the sub-pixel scale for subsequent per-field classification. First, image pixels were segmented using vector boundaries. Second, the pixel segments (ranked by area) were labelled with a land cover class (ranked by class typicality). Third, a hard per-field classification was generated by examining each polygon (representing a land cover parcel, or field) in its entirety (by grouping the fragments of the polygon contained within different image pixels) and assigning to it the modal land cover class. The accuracy of this technique was considerably higher than that of both a corresponding hard per-pixel classification and a perfield classification based on hard per-pixel classified imagery.     

Land cover change detection at coarse spatial scales based on iterative estimation and previous state information

This study focuses on the use of coarse spatial resolution (CR, pixel size about 1 km²) remote sensing data for land cover change detection and qualification. Assuming the linear mixing model for CR pixels, the problem is that both the multitemporal class features and the pixel composition in terms of classes are unknown. The proposed algorithm is then based on the iterative alternate estimation of each unknown variable. At each iteration, the class features are estimated, thanks to the knowledge of the composition of some pixels, and then the pixel composition is re-estimated knowing the class features. The subset of known composition pixels is the subset of pixels where no change has occurred, i.e. the previous land cover map is still valid. It is derived automatically by removing at each iteration the pixels where the new composition estimation disagrees with the former one. Finally, for the final estimation of the pixel composition, a Markovian chain model is used to guide the solution, i.e. the previous land cover map is used as a ‘reminder’ or ‘memory’ term.This approach has been first validated using simulated data with different spatial resolution ratios. Then, the detection of forest change with SPOT/VGT-S10 has been considered as an actual application case. Finally, the method has been applied to change detection on the Val de Saône watershed between the 1980s and 2000. The results obtained from three coarse resolution series, NOAA/AVHRR, SPOT/VGT-S10 and SPOT/VGT-P, have been compared.     

Sub-pixel estimation of urban land cover components with linear mixture model analysis and Landsat Thematic Mapper imagery

We examine the utility of linear mixture modelling in the sub-pixel analysis of Landsat Enhanced Thematic Mapper (ETM) imagery to estimate the three key land cover components in an urban/suburban setting: impervious surface, managed/unmanaged lawn and tree cover. The relative effectiveness of two different endmember sets was also compared. The interior endmember set consisted of the median pixel value of the training pixels of each land cover and the exterior endmember set was the extreme pixel value. As a means of accuracy assessment, the resulting land cover estimates were compared with independent estimates obtained from the visual interpretation of digital orthophotography and classified IKONOS imagery. Impervious surface estimates from the Landsat ETM showed a high degree of similarity (RMS error (RMSE) within approximately ±10 to 15%) to that obtained using high spatial resolution digital orthophotography and IKONOS imagery. The partition of the vegetation component into tree vs grass cover was more problematic due to the greater spectral similarity between these land cover types with RMSE of approximately ±12 to 22%. The interior endmember set appeared to provide better differentiation between grass and urban tree cover than the exterior endmember set. The ability to separate the grass vs tree components in urban vegetation is of major importance to the study of the urban/suburban ecosystems as well as watershed assessment.     

Image masking for crop yield forecasting using AVHRR NDVI time series imagery

One obstacle to successful modeling and prediction of crop yields using remotely sensed imagery is the identification of image masks. Image masking involves restricting an analysis to a subset of a region's pixels rather than using all of the pixels in the scene. Cropland masking, where all sufficiently cropped pixels are included in the mask regardless of crop type, has been shown to generally improve crop yield forecasting ability, but it requires the availability of a land cover map depicting the location of cropland. The authors present an alternative image masking technique, called yield-correlation masking, which can be used for the development and implementation of regional crop yield forecasting models and eliminates the need for a land cover map. The procedure requires an adequate time series of imagery and a corresponding record of the region's crop yields, and involves correlating historical, pixel-level imagery values with historical regional yield values. Imagery used for this study consisted of 1-km, biweekly AVHRR NDVI composites from 1989 to 2000. Using a rigorous evaluation framework involving five performance measures and three typical forecasting opportunities, yield-correlation masking is shown to have comparable performance to cropland masking across eight major U.S. region-crop forecasting scenarios in a 12-year cross-validation study. Our results also suggest that 11 years of time series AVHRR NDVI data may not be enough to estimate reliable linear crop yield models using more than one NDVI-based variable. A robust, but sub-optimal, all-subsets regression modeling procedure is described and used for testing, and historical United States Department of Agriculture crop yield estimates and linear trend estimates are used to gauge model performance.     

The estimation of atmospheric effects for SPOT using AVHRR channel-1 data

The aim of this paper is to estimate the effect of atmospheric contamination on SPOT satellite data for stereoscopic modelling by using data from the AVHRR instrument carried on the NOAA satellite series. This paper includes the development of an atmospheric correction algorithm for the visible spectral channel data from the AVHRR instrument, and an analysis of the atmospherically corrected AVHRR data from many successive days, bearing in mind that the SPOT data for stereoscopic modelling will be from two orbits which will be separated by several days.     

南方丘陵地区水稻种植面积遥感信息提取的可行性分析*

以浙江省为试验区,在地理信息系统支持下综合利用多种地理信息,探讨丘陵地区大面积提取水稻种植面积信息的可行性。开展了分类识别方法的比较试验及训练样点相对稳定性试验。针对丘陵地区的复杂地形,在数字化地形图的基础上,建立数字地形模型(DTM),并衍生出地面坡度等地貌因子的数字化图像,结合NOAA/AVHRR数据,进行分类。试验结果表明,传统的分类识别方法中,最大似然法的分类精度可满足业务化运行的要求;建立在混合像元分解基础上的模糊监督分类,有较高的分类精度和较好的稳定性,具有较强的适应性;地貌因子参与遥感影像的分类,不仅可以有效地提高丘陵地区水稻种植面积信息的提取精度,而且还可以使面积信息提取精度保持一定的稳定性,提高空间精度;为探讨丘陵地区水稻种植面积信息遥感提取的可靠性和客观性,在训练样点保持相对稳定的前提下,对1996年和1997年浙江省水稻种植面积进行测算,两年的数量精度均在92%以上。     

Accuracy of land cover area estimated from coarse spatial resolution images using an unmixing method

Many techniques intended to estimate land coverage of multiple categories occupied within each pixel from such coarse resolution data have been proposed. However, in traditional unmixing studies with coarse resolution imagery such as Advanced Very High Resolution Radiometer (AVHRR) data, it is assumed that only a few endmembers exist throughout an entire image. Therefore, it is essential to evaluate how well an unmixing method would work for various categories within pixels of coarse resolution images. In this study, the land coverage of eight classes in National Oceanic and Atmospheric Administration (NOAA) AVHRR imagery by using finer resolution Landsat Thematic Mapper (TM) imagery was estimated, and the accuracy of these estimated classes was evaluated. The results suggest that this method may be generally useful for comparing multi-spectral images in space and time.     

南方丘陵地区水稻种植面积遥感信息提取的可行性分析

以浙江省为试验区，在地理信息系统支持下综合利用多种地理信息，探讨丘陵地区大面积提取水稻种植面积信息的可行性。开展了分类识别方法的比较试验及训练样点相对稳定性试验。针对丘陵地区的复杂地形，在数字化地形图的基础上，建立数字地形模型（ＤＴＭ），并衍生出地面坡度等地貌因子的数字化图像，结合ＮＯＡＡ／ＡＶＨＲＲ数据，进行分类。试验结果表明，传统的分类识别方法中，最大似然法的分类精度可满足业务化运行的要求；建立在混合像元分解基础上的模糊监督分类，有较高的分类精度和较好的稳定性，具有较强的适应性；地貌因子参与遥感影像的分类，不仅可以有效地提高丘陵地区水稻种植面积信息的提取精度，而且还可以使面积信息提取精度保持一定的稳定性，提高空间精度；为探讨丘陵地区水稻种植面积信息遥感提取的可靠性和客观性，在训练样点保持相对稳定的前提下，对１９９６年和１９９７年浙江省水稻种植面积进行测算，两年的数量精度均在９２％以上。     

A linearised pixel-swapping method for mapping rural linear land cover features from fine spatial resolution remotely sensed imagery

Accurate maps of rural linear land cover features, such as paths and hedgerows, would be useful to ecologists, conservation managers and land planning agencies. Such information might be used in a variety of applications (e.g., ecological, conservation and land management applications). Based on the phenomenon of spatial dependence, sub-pixel mapping techniques can be used to increase the spatial resolution of land cover maps produced from satellite sensor imagery and map such features with increased accuracy. Aerial photography with a spatial resolution of 0.25 m was acquired of the Christchurch area of Dorset, UK. The imagery was hard classified using a simple Mahalanobis distance classifier and the classification degraded to simulate land cover proportion images with spatial resolutions of 2.5 and 5 m. A simple pixel-swapping algorithm was then applied to each of the proportion images. Sub-pixels within pixels were swapped iteratively until the spatial correlation between neighbouring sub-pixels for the entire image was maximised. Visual inspection of the super-resolved output showed that prediction of the position and dimensions of hedgerows was comparable with the original imagery. The maps displayed an accuracy of 87%. To enhance the prediction of linear features within the super-resolved output, an anisotropic modelling component was added. The direction of the largest sums of proportions was calculated within a moving window at the pixel level. The orthogonal sum of proportions was used in estimating the anisotropy ratio. The direction and anisotropy ratio were then used to modify the pixel-swapping algorithm so as to increase the likelihood of creating linear features in the output map. The new linear pixel-swapping method led to an increase in the accuracy of mapping fine linear features of approximately 5% compared with the conventional pixel-swapping method.     

Scene selection and the use of NASA's global orthorectified Landsat dataset for land cover and land use change monitoring

This study examines the utility of NASA's circa 1990 and circa 2000 global orthorectified Landsat dataset for land cover and land use change mapping and monitoring across Africa. This is achieved by comparing the temporal and spatial variation of NDVI, measured independently by the NOAA‐AVHRR at the time of Landsat scene acquisition, against the seasonal mean for each Landsat scene extent. Decadal sequences of drift‐corrected NOAA‐AVHRR imagery were used to calculate NDVI means and standard deviations for the periods covered by the scenes composing the c.1990 and c.2000 Landsat datasets. The specific NOAA‐AVHRR NDVI values at the acquisition date of each individual Landsat scene were also calculated and the differences, both from the mean and scaled by standard deviation, were mapped for the Landsat scene footprints in the c.1990 and c.2000 datasets. The resulting maps show the temporal position of each Landsat scene within the seasonal NDVI cycle, and provide a valuable guide to assist in quantifying uncertainty and interpreting land cover and land use changes inferred from these Landsat data.     

Approaches for the production and evaluation of fuzzy land cover classifications from remotely-sensed data

Remote sensing is an attractive source of data for land cover mapping applications. Mapping is generally achieved through the application of a conventional statistical classification, which allocates each image pixel to a land cover class. Such approaches are inappropriate for mixed pixels, which contain two or more land cover classes, and a fuzzy classification approach is required. When pixels may have multiple and partial class membership measures of the strength of class membership may be output and, if strongly related to the land cover composition, mapped to represent such fuzzy land cover. This type of representation can be derived by softening the output of a conventional ‘hard’ classification or using a fuzzy classification. The accuracy of the representation provided by a fuzzy classification is, however, difficult to evaluate. Conventional measures of classification accuracy cannot be used as they are appropriate only for ‘hard’ classifications. The accuracy of a classification may, however, be indicated by the way in which the strength of class membership is partitioned between the classes and how closely this represents the partitioning of class membership on the ground. In this paper two measures of the closeness of the land cover representation derived from a classification to that on the ground were used to evaluate a set of fuzzy classifications. The latter were based on measures of the strength of class membership output from classifications by a discriminant analysis, artificial neural network and fuzzy c-means classifiers. The results show the importance of recognising and accommodating for the fuzziness of the land cover on the ground. The accuracy assessment methods used were applicable to pure and mixed pixels and enabled the identification of the most accurate land cover representation derived. The results showed that the fuzzy representations were more accurate than the ‘hard’ classifications. Moreover, the outputs derived from the artificial neural network and the fuzzy c-means algorithm in particular were strongly related to the land cover on the ground and provided the most accurate land cover representations. The ability to appropriately represent fuzzy land cover and evaluate the accuracy of the representation should facilitate the use of remote sensing as a source of land cover data.     

Trajectory analysis of land cover change in arid environment of China

Remotely sensed data have been utilized for environmental change study over the past 30 years. Large collections of remote sensing imagery have made it possible for spatio‐temporal analyses of the environment and the impact of human activities. This research attempts to develop both conceptual framework and methodological implementation for land cover change detection based on medium and high spatial resolution imagery and temporal trajectory analysis. Multi‐temporal and multi‐scale remotely sensed data have been integrated from various sources with a monitoring time frame of 30 years, including historical and state‐of‐the‐art high‐resolution satellite imagery. Based on this, spatio‐temporal patterns of environmental change, which is largely represented by changes in land cover (e.g., vegetation and water), were analysed for the given timeframe. Multi‐scale and multi‐temporal remotely sensed data, including Landsat MSS, TM, ETM and SPOT HRV, were used to detect changes in land cover in the past 30 years in Tarim River, Xinjiang, China. The study shows that by using the auto‐classification approach an overall accuracy of 85–90% with a Kappa coefficient of 0.66–0.78 was achieved for the classification of individual images. The temporal trajectory of land‐use change was established and its spatial pattern was analysed to gain a better understanding of the human impact on the fragile ecosystem of China's arid environment.