Remote sensing and the measurement of geographical entities in a forested environment. 1. The scale and spatial aggregation problem

Marceau, Danielle J.; Howarth, Philip J.; Gratton, Denis

doi:10.1016/0034-4257(94)90046-9

Cited by 135 publications

(81 citation statements)

References 15 publications

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“…(2) Aggregating the spatial resolution of the subset imagery In this section, each band of 2 m pan-sharpened multi-spectral subset imagery was aggregated progressively using the nearest neighboring algorithm to obtain a series of imagery with varying spatial resolutions. Many studies [10,28,29] support the utility of the nearest neighboring approach to resample the remote sensing imagery. The aggregating way includes arithmetic progression and geometric progression.…”

Section: The Average Local Variance Function (Alv)mentioning

confidence: 99%

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Selecting Appropriate Spatial Scale for Mapping Plastic-Mulched Farmland with Satellite Remote Sensing Imagery

et al. 2017

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Abstract:In recent years, the area of plastic-mulched farmland (PMF) has undergone rapid growth and raised remarkable environmental problems. Therefore, mapping the PMF plays a crucial role in agricultural production, environmental protection and resource management. However, appropriate data selection criteria are currently lacking. Thus, this study was carried out in two main plastic-mulching practice regions, Jizhou and Guyuan, to look for an appropriate spatial scale for mapping PMF with remote sensing. The average local variance (ALV) function was used to obtain the appropriate spatial scale for mapping PMF based on the GaoFen-1 (GF-1) satellite imagery. Afterwards, in order to validate the effectiveness of the selected method and to interpret the relationship between the appropriate spatial scale derived from the ALV and the spatial scale with the highest classification accuracy, we classified the imagery with varying spatial resolution by the Support Vector Machine (SVM) algorithm using the spectral features, textural features and the combined spectral and textural features respectively. The results indicated that the appropriate spatial scales from the ALV lie between 8 m and 20 m for mapping the PMF both in Jizhou and Guyuan. However, there is a proportional relation: the spatial scale with the highest classification accuracy is at the 1/2 location of the appropriate spatial scale generated from the ALV in Jizhou and at the 2/3 location of the appropriate spatial scale generated from the ALV in Guyuan. Therefore, the ALV method for quantitatively selecting the appropriate spatial scale for mapping PMF with remote sensing imagery has theoretical and practical significance.

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Section: The Average Local Variance Function (Alv)mentioning

confidence: 99%

“…Many studies [10,28,29] support the utility of the nearest neighboring approach to resample the remote sensing imagery. The aggregating way includes arithmetic progression and geometric progression.…”

Section: The Average Local Variance Function (Alv)mentioning

confidence: 99%

Selecting Appropriate Spatial Scale for Mapping Plastic-Mulched Farmland with Satellite Remote Sensing Imagery

et al. 2017

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“…Numerous studies, examining the effect of spatial scale on remote sensing applications (e.g., [62,63]), conclude that the impact of spatial scale originates from the relation between the size of the objects under observation and the size of the pixels (e.g., [40]). The pixel size offering the highest information content for the various applications is often determined by means of statistical analysis of the local variance of the pixel neighborhood.…”

Section: Spectrally Simulated Enmap and Sentinel-2 Data Setsmentioning

confidence: 99%

Spectral Unmixing of Forest Crown Components at Close Range, Airborne and Simulated Sentinel-2 and EnMAP Spectral Imaging Scale

et al. 2015

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Forest biochemical and biophysical variables and their spatial and temporal distribution are essential inputs to process-orientated ecosystem models. To provide this information, imaging spectroscopy appears to be a promising tool. In this context, the present study investigates the potential of spectral unmixing to derive sub-pixel crown component fractions in a temperate deciduous forest ecosystem. However, the high proportion of foliage in this complex vegetation structure leads to the problem of saturation effects, when applying broadband vegetation indices. This study illustrates that multiple endmember spectral mixture analysis (MESMA) can contribute to overcoming this challenge. Reference OPEN ACCESSRemote Sens. 2015, 7 15362 fractional abundances, as well as spectral measurements of the canopy components, could be precisely determined from a crane measurement platform situated in a deciduous forest in North-East Germany. In contrast to most other studies, which only use leaf and soil endmembers, this experimental setup allowed for the inclusion of a bark endmember for the unmixing of components within the canopy. This study demonstrates that the inclusion of additional endmembers markedly improves the accuracy. A mean absolute error of 7.9% could be achieved for the fractional occurrence of the leaf endmember and 5.9% for the bark endmember. In order to evaluate the results of this field-based study for airborne and satellite-based remote sensing applications, a transfer to Airborne Imaging Spectrometer for Applications (AISA) and simulated Environmental Mapping and Analysis Program (EnMAP) and Sentinel-2 imagery was carried out. All sensors were capable of unmixing crown components with a mean absolute error ranging between 3% and 21%.

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“…In order to capture the change in image characteristics due to changes in resolution, and find relationships between accuracy and resolutions, it is necessary to build a framework to represent, analyze and classify images from multiple resolutions [32]. This framework could be a useful tool for selecting the appropriate spatial resolutions and analysis routines in further image classification [31,[33][34][35].…”

Section: Spatial Resolution Effectsmentioning

confidence: 99%

“…Many studies reported in the literature have used multiple resolution remote sensing data to study changes in vegetation indices [30,36], surface complexity and variation [1,[7][8][9]37], classification accuracy and errors [34,35,38], image representation and storage [31], and ecosystem and landscape analysis in general [39,40]. However, much of the previous research has been devoted to exploring the magnitude and impact of scale or resolution effects by aggregation of a single data set (i.e., re-sampling the data) [32].…”

Section: Spatial Resolution Effectsmentioning

confidence: 99%

Effects of Spatial and Spectral Resolutions on Fractal Dimensions in Forested Landscapes

et al. 2010

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Abstract:Recent work has shown that more research is needed in applying fractal analysis to multi-resolution remote sensing data for landscape characterization. The purpose of this study was to closely examine the impacts that spatial and spectral resolutions have on fractal dimensions using real-world multi-resolution remotely sensed data as opposed to the more conventional single resolution and aggregation approach. The study focused on fractal analysis of forested landscapes in the southeastern United States and Central America. Initially, the effects of spatial resolution on the computed fractal dimensions were examined using data from three instruments with different spatial resolutions. Based on the criteria of mean value and variation within the accepted ranges of fractal dimensions, it was determined that 30-m Landsat TM data were best able to capture the complexity of a forested landscape in Central America compared to 4-m IKONOS data and 250-m MODIS data. Also, among the spectral bands of Landsat TM images of four national forests in the southeastern United States, tests showed that the spatial indices of fractal dimensions are much more distinguishable in the visible bands than they are in the near-mid infrared bands. Thus, based solely on the fractal analysis, the fractal dimensions could have relatively higher chances to distinguish forest characteristics (e.g., stand sizes and species) in the 612Landsat TM visible wavelength bands than in the near-mid infrared bands. This study has focused on a relative comparison between visible and near-mid infrared wavelength bands; however it will be important to study in the future the effect of a combination of those bands such as the Normalized Difference Vegetation Index (NDVI) on fractal dimensions of forested landscapes.

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Remote sensing and the measurement of geographical entities in a forested environment. 1. The scale and spatial aggregation problem

Cited by 135 publications

References 15 publications

Selecting Appropriate Spatial Scale for Mapping Plastic-Mulched Farmland with Satellite Remote Sensing Imagery

Selecting Appropriate Spatial Scale for Mapping Plastic-Mulched Farmland with Satellite Remote Sensing Imagery

Spectral Unmixing of Forest Crown Components at Close Range, Airborne and Simulated Sentinel-2 and EnMAP Spectral Imaging Scale

Effects of Spatial and Spectral Resolutions on Fractal Dimensions in Forested Landscapes

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