@article{sauraEffectsRemoteSensor2004,
  title = {Effects of Remote Sensor Spatial Resolution and Data Aggregation on Selected Fragmentation Indices},
  author = {Saura, Santiago},
  date = {2004},
  journaltitle = {Landscape Ecology},
  volume = {19},
  pages = {197--209},
  issn = {1572-9761},
  doi = {10.1023/B:LAND.0000021724.60785.65},
  url = {https://doi.org/10.1023/B:LAND.0000021724.60785.65},
  abstract = {Analyzing the effect of scale on landscape pattern indices has been a key research topic in landscape ecology. The lack of comparability of fragmentation indices across spatial resolutions seriously limits their usefulness while multi-scale remotely sensed data are becoming increasingly available. In this paper, we examine the effect of spatial resolution on six common fragmentation indices that are being used within the Third Spanish National Forest Inventory. We analyse categorical data derived from simultaneously gathered Landsat-TM and IRS-WiFS satellite images, as well as TM patterns aggregated to coarser resolutions through majority rules. In general, majority rules tend to produce more fragmented patterns than actual sensor ones. It is suggested that sensor point spread function should be specifically considered to improve comparability among satellite images of varying pixel sizes. Power scaling-laws were found between spatial resolution and several fragmentation indices, with mean prediction errors under 10\,\% for number of patches and mean patch size and under 5\,\% for edge length. All metrics but patch cohesion indicate lower fragmentation at coarser spatial resolutions. In fact, an arbitrarily large value of patch cohesion can be obtained by resampling the pattern to smaller pixel sizes. An explanation and simple solution for correcting this undesired behaviour is provided. Landscape division and largest patch index were found to be the least sensitive indices to spatial resolution effects.},
  keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-13462857,ecology,fragmentation,indices,landscape-modelling,multi-scale,power-law,remote-sensing,spatial-pattern},
  number = {2}
}

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In this paper, we examine the effect of spatial resolution on six common fragmentation indices that are being used within the Third Spanish National Forest Inventory. We analyse categorical data derived from simultaneously gathered Landsat-TM and IRS-WiFS satellite images, as well as TM patterns aggregated to coarser resolutions through majority rules. In general, majority rules tend to produce more fragmented patterns than actual sensor ones. It is suggested that sensor point spread function should be specifically considered to improve comparability among satellite images of varying pixel sizes. Power scaling-laws were found between spatial resolution and several fragmentation indices, with mean prediction errors under 10\\,% for number of patches and mean patch size and under 5\\,% for edge length. All metrics but patch cohesion indicate lower fragmentation at coarser spatial resolutions. In fact, an arbitrarily large value of patch cohesion can be obtained by resampling the pattern to smaller pixel sizes. An explanation and simple solution for correcting this undesired behaviour is provided. Landscape division and largest patch index were found to be the least sensitive indices to spatial resolution effects.","keywords":"*imported-from-citeulike-INRMM,~INRMM-MiD:c-13462857,ecology,fragmentation,indices,landscape-modelling,multi-scale,power-law,remote-sensing,spatial-pattern","number":"2","bibtex":"@article{sauraEffectsRemoteSensor2004,\n title = {Effects of Remote Sensor Spatial Resolution and Data Aggregation on Selected Fragmentation Indices},\n author = {Saura, Santiago},\n date = {2004},\n journaltitle = {Landscape Ecology},\n volume = {19},\n pages = {197--209},\n issn = {1572-9761},\n doi = {10.1023/B:LAND.0000021724.60785.65},\n url = {https://doi.org/10.1023/B:LAND.0000021724.60785.65},\n abstract = {Analyzing the effect of scale on landscape pattern indices has been a key research topic in landscape ecology. The lack of comparability of fragmentation indices across spatial resolutions seriously limits their usefulness while multi-scale remotely sensed data are becoming increasingly available. In this paper, we examine the effect of spatial resolution on six common fragmentation indices that are being used within the Third Spanish National Forest Inventory. We analyse categorical data derived from simultaneously gathered Landsat-TM and IRS-WiFS satellite images, as well as TM patterns aggregated to coarser resolutions through majority rules. In general, majority rules tend to produce more fragmented patterns than actual sensor ones. It is suggested that sensor point spread function should be specifically considered to improve comparability among satellite images of varying pixel sizes. Power scaling-laws were found between spatial resolution and several fragmentation indices, with mean prediction errors under 10\\,\\% for number of patches and mean patch size and under 5\\,\\% for edge length. All metrics but patch cohesion indicate lower fragmentation at coarser spatial resolutions. In fact, an arbitrarily large value of patch cohesion can be obtained by resampling the pattern to smaller pixel sizes. An explanation and simple solution for correcting this undesired behaviour is provided. 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