Land cover fraction mapping across global biomes with Landsat data, spatially generalized regression models and spectral-temporal metrics

Mapping land cover in highly heterogeneous landscapes is challenging, and classifications have inherent limitations where the spatial resolution of remotely sensed data exceeds the size of small objects. For example, classifications based on 30-m Landsat data do not capture urban or other heterogene...

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Vydané v:	Remote sensing of environment Ročník 311; s. 114260
Hlavní autori:	Schug, Franz, Pfoch, Kira A., Pham, Vu-Dong, van der Linden, Sebastian, Okujeni, Akpona, Frantz, David, Radeloff, Volker C.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Inc 01.09.2024
Predmet:	ecosystems environment land cover Landsat Model transfer Neural network regression remote sensing Synthetic training data Weighted training data Synthetic training data Model transfer Neural network regression Weighted training data
ISSN:	0034-4257
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Abstract	Mapping land cover in highly heterogeneous landscapes is challenging, and classifications have inherent limitations where the spatial resolution of remotely sensed data exceeds the size of small objects. For example, classifications based on 30-m Landsat data do not capture urban or other heterogeneous environments well. This limitation may be overcome by quantifying the subpixel fractions of different land cover types. However, the selection process and transferability of models designed for subpixel land cover mapping across biomes is yet challenging. We asked to what extent (a) locally trained models can be used for sub-pixel land cover fraction estimates in other biomes, and (b) training data from different regions can be combined into spatially generalized models to quantify fractions across global biomes. We applied machine learning regression-based fraction mapping to quantify land cover fractions of 18 regions in five biomes using Landsat data from 2022. We used spectral-temporal metrics to incorporate intra-annual temporal information and compared the performance of local, spatially transferred, and spatially generalized models. Local models performed best when applied to their respective sites (average mean absolute error, MAE, 9–18%), and also well when transferred to other sites within the same biome, but not consistently so for out-of-biome sites. However, spatially generalized models that combined input data from many sites worked very well when analyzing sites in many different biomes, and their MAE values were only slightly higher than those of the respective local models. A weighted training data selection approach, preferring training data with a lower spectral distance to the image data to be predicted, further enhanced the performance of generalized models. Our results suggest that spatially generalized regression-based fraction models can support multi-class sub-pixel fraction estimates based on medium-resolution satellite images globally. Such products would have great value for environmental monitoring in heterogeneous environments and where land cover varies along spatial or temporal gradients. •Spectral-temporal metrics from multi-spectral data distinguish land cover fractions.•Local models accurately map land cover fractions in all biomes.•Spatially generalized models yielded accurate results across sites and biomes.•Weighted training data especially useful for global subpixel fraction maps.
AbstractList	Mapping land cover in highly heterogeneous landscapes is challenging, and classifications have inherent limitations where the spatial resolution of remotely sensed data exceeds the size of small objects. For example, classifications based on 30-m Landsat data do not capture urban or other heterogeneous environments well. This limitation may be overcome by quantifying the subpixel fractions of different land cover types. However, the selection process and transferability of models designed for subpixel land cover mapping across biomes is yet challenging. We asked to what extent (a) locally trained models can be used for sub-pixel land cover fraction estimates in other biomes, and (b) training data from different regions can be combined into spatially generalized models to quantify fractions across global biomes. We applied machine learning regression-based fraction mapping to quantify land cover fractions of 18 regions in five biomes using Landsat data from 2022. We used spectral-temporal metrics to incorporate intra-annual temporal information and compared the performance of local, spatially transferred, and spatially generalized models. Local models performed best when applied to their respective sites (average mean absolute error, MAE, 9–18%), and also well when transferred to other sites within the same biome, but not consistently so for out-of-biome sites. However, spatially generalized models that combined input data from many sites worked very well when analyzing sites in many different biomes, and their MAE values were only slightly higher than those of the respective local models. A weighted training data selection approach, preferring training data with a lower spectral distance to the image data to be predicted, further enhanced the performance of generalized models. Our results suggest that spatially generalized regression-based fraction models can support multi-class sub-pixel fraction estimates based on medium-resolution satellite images globally. Such products would have great value for environmental monitoring in heterogeneous environments and where land cover varies along spatial or temporal gradients. Mapping land cover in highly heterogeneous landscapes is challenging, and classifications have inherent limitations where the spatial resolution of remotely sensed data exceeds the size of small objects. For example, classifications based on 30-m Landsat data do not capture urban or other heterogeneous environments well. This limitation may be overcome by quantifying the subpixel fractions of different land cover types. However, the selection process and transferability of models designed for subpixel land cover mapping across biomes is yet challenging. We asked to what extent (a) locally trained models can be used for sub-pixel land cover fraction estimates in other biomes, and (b) training data from different regions can be combined into spatially generalized models to quantify fractions across global biomes. We applied machine learning regression-based fraction mapping to quantify land cover fractions of 18 regions in five biomes using Landsat data from 2022. We used spectral-temporal metrics to incorporate intra-annual temporal information and compared the performance of local, spatially transferred, and spatially generalized models. Local models performed best when applied to their respective sites (average mean absolute error, MAE, 9–18%), and also well when transferred to other sites within the same biome, but not consistently so for out-of-biome sites. However, spatially generalized models that combined input data from many sites worked very well when analyzing sites in many different biomes, and their MAE values were only slightly higher than those of the respective local models. A weighted training data selection approach, preferring training data with a lower spectral distance to the image data to be predicted, further enhanced the performance of generalized models. Our results suggest that spatially generalized regression-based fraction models can support multi-class sub-pixel fraction estimates based on medium-resolution satellite images globally. Such products would have great value for environmental monitoring in heterogeneous environments and where land cover varies along spatial or temporal gradients. •Spectral-temporal metrics from multi-spectral data distinguish land cover fractions.•Local models accurately map land cover fractions in all biomes.•Spatially generalized models yielded accurate results across sites and biomes.•Weighted training data especially useful for global subpixel fraction maps.
ArticleNumber	114260
Author	Frantz, David Pfoch, Kira A. Pham, Vu-Dong van der Linden, Sebastian Okujeni, Akpona Schug, Franz Radeloff, Volker C.
Author_xml	– sequence: 1 givenname: Franz surname: Schug fullname: Schug, Franz email: fschug@wisc.edu organization: SILVIS Lab, Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Dr., Madison, WI 53706, USA – sequence: 2 givenname: Kira A. surname: Pfoch fullname: Pfoch, Kira A. email: pfoch@wisc.edu organization: SILVIS Lab, Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Dr., Madison, WI 53706, USA – sequence: 3 givenname: Vu-Dong surname: Pham fullname: Pham, Vu-Dong email: vudong.pham@uni-greifswald.de organization: Earth Observation and Geoinformation Science Lab, Institute of Geography and Geology, University of Greifswald, Friedrich-Ludwig-Jahn-Str. 16, 17489 Greifswald, Germany – sequence: 4 givenname: Sebastian surname: van der Linden fullname: van der Linden, Sebastian email: sebastian.linden@uni-greifswald.de organization: Earth Observation and Geoinformation Science Lab, Institute of Geography and Geology, University of Greifswald, Friedrich-Ludwig-Jahn-Str. 16, 17489 Greifswald, Germany – sequence: 5 givenname: Akpona surname: Okujeni fullname: Okujeni, Akpona email: akpona.okujeni@geo.hu-berlin.de organization: Integrative Research Institute on Transformations of Human-Environment Systems (IRI THESys), Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany – sequence: 6 givenname: David surname: Frantz fullname: Frantz, David email: david.frantz@uni-trier.de organization: Geoinformatics – Spatial Data Science, Trier University, 54286 Trier, Germany – sequence: 7 givenname: Volker C. surname: Radeloff fullname: Radeloff, Volker C. email: radeloff@wisc.edu organization: SILVIS Lab, Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Dr., Madison, WI 53706, USA
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SubjectTerms	ecosystems environment land cover Landsat Model transfer Neural network regression remote sensing Synthetic training data Weighted training data
Title	Land cover fraction mapping across global biomes with Landsat data, spatially generalized regression models and spectral-temporal metrics
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