Outputs (98)

Comparison of simple averaging and latent class modeling to estimate the area of land cover in the presence of reference data variability (2021)
Journal Article
Xing, D., Stehman, S. V., Foody, G. M., & Pengra, B. W. (2021). Comparison of simple averaging and latent class modeling to estimate the area of land cover in the presence of reference data variability. Land, 10(1), Article 35. https://doi.org/10.3390/land10010035

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. Estimates of the area or percent area of the land cover classes within a study region are often based on the reference land cover class labels assigned by analysts interpreting satellite image... Read More about Comparison of simple averaging and latent class modeling to estimate the area of land cover in the presence of reference data variability.

Object-Based Area-to-Point Regression Kriging for Pansharpening (2020)
Journal Article
Zhang, Y., Atkinson, P. M., Ling, F., Foody, G. M., Wang, Q., Ge, Y., Li, X., & Du, Y. (2021). Object-Based Area-to-Point Regression Kriging for Pansharpening. IEEE Transactions on Geoscience and Remote Sensing, 59(10), 8599-8614. https://doi.org/10.1109/TGRS.2020.3041724

IEEE Optical earth observation satellite sensors often provide a coarse spatial resolution (CR) multispectral (MS) image together with a fine spatial resolution (FR) panchromatic (PAN) image. Pansharpening is a technique applied to such satellite sen... Read More about Object-Based Area-to-Point Regression Kriging for Pansharpening.

Let your maps be fuzzy!—Class probabilities and floristic gradients as alternatives to crisp mapping for remote sensing of vegetation (2020)
Journal Article
Feilhauer, H., Zlinszky, A., Kania, A., Foody, G. M., Doktor, D., Lausch, A., & Schmidtlein, S. (2020). Let your maps be fuzzy!—Class probabilities and floristic gradients as alternatives to crisp mapping for remote sensing of vegetation. Remote Sensing in Ecology and Conservation, https://doi.org/10.1002/rse2.188

© 2020 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London. Mapping vegetation as hard classes based on remote sensing data is a frequently applied approach, even thou... Read More about Let your maps be fuzzy!—Class probabilities and floristic gradients as alternatives to crisp mapping for remote sensing of vegetation.

Cloud detection in Landsat-8 imagery in Google Earth Engine based on a deep convolutional neural network (2020)
Journal Article
Yin, Z., Ling, F., Foody, G. M., Li, X., & Du, Y. (2020). Cloud detection in Landsat-8 imagery in Google Earth Engine based on a deep convolutional neural network. Remote Sensing Letters, 11(12), 1181-1190. https://doi.org/10.1080/2150704X.2020.1833096

© 2020 Informa UK Limited, trading as Taylor & Francis Group. Google Earth Engine (GEE) provides a convenient platform for applications based on optical satellite imagery of large areas. With such data sets, the detection of cloud is often a necess... Read More about Cloud detection in Landsat-8 imagery in Google Earth Engine based on a deep convolutional neural network.

Remote sensing of fish-processing in the Sundarbans Reserve Forest, Bangladesh: an insight into the modern slavery-environment nexus in the coastal fringe (2020)
Journal Article
Jackson, B., Boyd, D. S., Ives, C. D., Decker Sparks, J. L., Foody, G. M., Marsh, S., & Bales, K. (2020). Remote sensing of fish-processing in the Sundarbans Reserve Forest, Bangladesh: an insight into the modern slavery-environment nexus in the coastal fringe. Maritime Studies, 19(4), 429–444. https://doi.org/10.1007/s40152-020-00199-7

© 2020, The Author(s). Land-based fish-processing activities in coastal fringe areas and their social-ecological impacts have often been overlooked by marine scientists and antislavery groups. Using remote sensing methods, the location and impacts of... Read More about Remote sensing of fish-processing in the Sundarbans Reserve Forest, Bangladesh: an insight into the modern slavery-environment nexus in the coastal fringe.

Superresolution Land Cover Mapping Using a Generative Adversarial Network (2020)
Journal Article
Shang, C., Li, X., Foody, G. M., Du, Y., & Ling, F. (2022). Superresolution Land Cover Mapping Using a Generative Adversarial Network. IEEE Geoscience and Remote Sensing Letters, 19, Article 6000105. https://doi.org/10.1109/LGRS.2020.3020395

Superresolution mapping (SRM) is a commonly used method to cope with the problem of mixed pixels when predicting the spatial distribution within low-resolution pixels. Central to the popular SRM method is the spatial pattern model, which is utilized... Read More about Superresolution Land Cover Mapping Using a Generative Adversarial Network.

Monitoring surface water area variations of reservoirs using daily MODIS images by exploring sub-pixel information (2020)
Journal Article
Ling, F., Li, X., Foody, G. M., Boyd, D., Ge, Y., Li, X., & Du, Y. (2020). Monitoring surface water area variations of reservoirs using daily MODIS images by exploring sub-pixel information. ISPRS Journal of Photogrammetry and Remote Sensing, 168, 141-152. https://doi.org/10.1016/j.isprsjprs.2020.08.008

© 2020 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Information on the temporal variation of surface water area of reservoirs is fundamental for water resource management and is often monitored by satellite remote sensing... Read More about Monitoring surface water area variations of reservoirs using daily MODIS images by exploring sub-pixel information.

Active restoration accelerates the carbon recovery of human-modified tropical forests (2020)
Journal Article
Philipson, C. D., Cutler, M. E. J., Brodrick, P. G., Asner, G. P., Boyd, D. S., Costa, P. M., Fiddes, J., Foody, G. M., Van Der Heijden, G. M., Ledo, A., Lincoln, P. R., Margrove, J. A., Martin, R. E., Milne, S., Pinard, M. A., Reynolds, G., Snoep, M., Tangki, H., Wai, Y. S., Wheeler, C. E., & Burslem, D. F. R. P. (2020). Active restoration accelerates the carbon recovery of human-modified tropical forests. Science, 369(6505), 838-841. https://doi.org/10.1126/science.aay4490

More than half of all tropical forests are degraded by human impacts, leaving them threatened with conversion to agricultural plantations and risking substantial biodiversity and carbon losses. Restoration could accelerate recovery of aboveground car... Read More about Active restoration accelerates the carbon recovery of human-modified tropical forests.

Spatiotemporal Fusion of Land Surface Temperature Based on a Convolutional Neural Network (2020)
Journal Article
Yin, Z., Wu, P., Foody, G. M., Wu, Y., Liu, Z., Du, Y., & Ling, F. (2021). Spatiotemporal Fusion of Land Surface Temperature Based on a Convolutional Neural Network. IEEE Transactions on Geoscience and Remote Sensing, 59(2), 1808-1822. https://doi.org/10.1109/TGRS.2020.2999943

© 1980-2012 IEEE. Due to the tradeoff between spatial and temporal resolutions commonly encountered in remote sensing, no single satellite sensor can provide fine spatial resolution land surface temperature (LST) products with frequent coverage. This... Read More about Spatiotemporal Fusion of Land Surface Temperature Based on a Convolutional Neural Network.

Iterative Training Sample Expansion to Increase and Balance the Accuracy of Land Classification from VHR Imagery (2020)
Journal Article
Lv, Z., Li, G., Jin, Z., Benediktsson, J. A., & Foody, G. M. (2021). Iterative Training Sample Expansion to Increase and Balance the Accuracy of Land Classification from VHR Imagery. IEEE Transactions on Geoscience and Remote Sensing, 59(1), 139-150. https://doi.org/10.1109/TGRS.2020.2996064

© 1980-2012 IEEE. Imbalanced training sets are known to produce suboptimal maps for supervised classification. Therefore, one challenge in mapping land cover is acquiring training data that will allow classification with high overall accuracy (OA) in... Read More about Iterative Training Sample Expansion to Increase and Balance the Accuracy of Land Classification from VHR Imagery.