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Spatial–Temporal Analysis of Greenness and Its Relationship with Poverty in China (2024)
Journal Article
Xie, W., Ge, Y., Hamm, N. A. S., Foody, G. M., & Ren, Z. (2024). Spatial–Temporal Analysis of Greenness and Its Relationship with Poverty in China. Remote Sensing, 16(21), Article 3938. https://doi.org/10.3390/rs16213938

Ecological environmental protection and poverty alleviation are of great significance for the study of human–land relationship coordination and sustainable development, and they have also been a focus of attention in China in the past few decades. In... Read More about Spatial–Temporal Analysis of Greenness and Its Relationship with Poverty in China.

Addressing multiple facets of bias and uncertainty in continental scale biodiversity databases (2024)
Journal Article
Marchetto, E., Livornese, M., Sabatini, F. M., Tordoni, E., Da Re, D., Lenoir, J., Testolin, R., Bacaro, G., Cazzolla Gatti, R., Chiarucci, A., Foody, G. M., Gábor, L., Groom, Q., Iaria, J., Malavasi, M., Moudrý, V., Santovito, D., Šímová, P., Zannini, P., & Rocchini, D. (2024). Addressing multiple facets of bias and uncertainty in continental scale biodiversity databases. Biodiversity Informatics, 18, https://doi.org/10.17161/bi.v18i.21810

The availability of biodiversity databases is expanding at unprecedented rates. Nevertheless, species occurrence data can be intrinsically biased and contain uncertainties that impact the accuracy and reliability of biodiversity estimates. In this st... Read More about Addressing multiple facets of bias and uncertainty in continental scale biodiversity databases.

Under the mantra: 'Make use of colorblind friendly graphs' (2024)
Journal Article
Rocchini, D., Chieffallo, L., Thouverai, E., D'Introno, R., Dagostin, F., Donini, E., Foody, G., Garnier, S., Mazzochini, G. G., Moudry, V., Rudis, B., Simova, P., Torresani, M., & Nowosad, J. (2024). Under the mantra: 'Make use of colorblind friendly graphs'. Environmetrics, 35(6), Article e2877. https://doi.org/10.1002/env.2877

Colorblindness is a genetic condition that affects a person's ability to accurately perceive colors. Several papers still exist making use of rainbow colors palette to show output. In such cases, for colorblind people such graphs are meaningless. In... Read More about Under the mantra: 'Make use of colorblind friendly graphs'.

Resolving data gaps in global surface water monthly records through a self-supervised deep learning strategy (2024)
Journal Article
Hao, Z., Cai, X., Ge, Y., Foody, G., Li, X., Yin, Z., Du, Y., & Ling, F. (2024). Resolving data gaps in global surface water monthly records through a self-supervised deep learning strategy. Journal of Hydrology, 640, Article 131673. https://doi.org/10.1016/j.jhydrol.2024.131673

The distribution of land surface water bodies is constantly changing. Monitoring these changes is critical for both humanity and the ecological system. The Joint Research Centre Global Surface Water (GSW) dataset is crucial in monitoring global water... Read More about Resolving data gaps in global surface water monthly records through a self-supervised deep learning strategy.

Reviewing the Spectral Variation Hypothesis: Twenty years in the tumultuous sea of biodiversity estimation by remote sensing (2024)
Journal Article
Torresani, M., Rossi, C., Perrone, M., Hauser, L. T., Féret, J.-B., Moudrý, V., Simova, P., Ricotta, C., Foody, G. M., Kacic, P., Feilhauer, H., Malavasi, M., Tognetti, R., & Rocchini, D. (2024). Reviewing the Spectral Variation Hypothesis: Twenty years in the tumultuous sea of biodiversity estimation by remote sensing. Ecological Informatics, 82, Article 102702. https://doi.org/10.1016/j.ecoinf.2024.102702

Twenty years ago, the Spectral Variation Hypothesis (SVH) was formulated as a means to link between different aspects of biodiversity and spatial patterns of spectral data (e.g. reflectance) measured from optical remote sensing. This hypothesis initi... Read More about Reviewing the Spectral Variation Hypothesis: Twenty years in the tumultuous sea of biodiversity estimation by remote sensing.

DeepWaterFraction: A globally applicable, self-training deep learning approach for percent surface water area estimation from Landsat mission imagery (2024)
Journal Article
Hao, Z., Foody, G., Ge, Y., Cai, X., Du, Y., & Ling, F. (2024). DeepWaterFraction: A globally applicable, self-training deep learning approach for percent surface water area estimation from Landsat mission imagery. Journal of Hydrology, 638, Article 131512. https://doi.org/10.1016/j.jhydrol.2024.131512

Surface water area estimation is essential for understanding global environmental dynamics, yet it presents significant challenges, particularly when dealing with small water bodies like ponds and narrow width rivers. Surface water areas for these sm... Read More about DeepWaterFraction: A globally applicable, self-training deep learning approach for percent surface water area estimation from Landsat mission imagery.

The effects of extreme heat on human health in tropical Africa (2024)
Journal Article
Kunda, J. J., Gosling, S. N., & Foody, G. M. (2024). The effects of extreme heat on human health in tropical Africa. International Journal of Biometeorology, 68, 1015-1033. https://doi.org/10.1007/s00484-024-02650-4

This review examines high-quality research evidence that synthesises the effects of extreme heat on human health in tropical Africa. Web of Science (WoS) was used to identify research articles on the effects extreme heat, humidity, Wet-bulb Globe Tem... Read More about The effects of extreme heat on human health in tropical Africa.

Ground Truth in Classification Accuracy Assessment: Myth and Reality (2024)
Journal Article
Foody, G. M. (2024). Ground Truth in Classification Accuracy Assessment: Myth and Reality. Geomatics, 4(1), 81-90. https://doi.org/10.3390/geomatics4010005

The ground reference dataset used in the assessment of classification accuracy is typically assumed implicitly to be perfect (i.e., 100% correct and representing ground truth). Rarely is this assumption valid, and errors in the ground dataset can cau... Read More about Ground Truth in Classification Accuracy Assessment: Myth and Reality.

Optical and radar Earth observation data for upscaling methane emissions linked to permafrost degradation in sub-Arctic peatlands in northern Sweden (2023)
Journal Article
Sjögersten, S., Ledger, M., Siewert, M., de la Barreda-Bautista, B., Sowter, A., Gee, D., Foody, G., & Boyd, D. S. (2023). Optical and radar Earth observation data for upscaling methane emissions linked to permafrost degradation in sub-Arctic peatlands in northern Sweden. Biogeosciences, 20(20), 4221-4239. https://doi.org/10.5194/bg-20-4221-2023

Permafrost thaw in Arctic regions is increasing methane (CH4) emissions into the atmosphere, but quantification of such emissions is difficult given the large and remote areas impacted. Hence, Earth observation (EO) data are critical for assessing pe... Read More about Optical and radar Earth observation data for upscaling methane emissions linked to permafrost degradation in sub-Arctic peatlands in northern Sweden.

Challenges in the real world use of classification accuracy metrics: From recall and precision to the Matthews correlation coefficient (2023)
Journal Article
Foody, G. M. (2023). Challenges in the real world use of classification accuracy metrics: From recall and precision to the Matthews correlation coefficient. PLoS ONE, 18(10), Article e0291908. https://doi.org/10.1371/journal.pone.0291908

The accuracy of a classification is fundamental to its interpretation, use and ultimately decision making. Unfortunately, the apparent accuracy assessed can differ greatly from the true accuracy. Mis-estimation of classification accuracy metrics and... Read More about Challenges in the real world use of classification accuracy metrics: From recall and precision to the Matthews correlation coefficient.

Unmixing-based Spatiotemporal Image Fusion Based on the Self-trained Random Forest Regression and Residual Compensation (2023)
Journal Article
Li, X., Wang, Y., Zhang, Y., Hou, S., Zhou, P., Wang, X., …Foody, G. (2023). Unmixing-based Spatiotemporal Image Fusion Based on the Self-trained Random Forest Regression and Residual Compensation. IEEE Transactions on Geoscience and Remote Sensing, 61, Article 5406319. https://doi.org/10.1109/tgrs.2023.3308902

Spatiotemporal satellite image fusion (STIF) has been widely applied in land surface monitoring to generate high spatial and high temporal reflectance images from satellite sensors. This paper proposed a new unmixing-based spatiotemporal fusion metho... Read More about Unmixing-based Spatiotemporal Image Fusion Based on the Self-trained Random Forest Regression and Residual Compensation.

Regression-based surface water fraction mapping using a synthetic spectral library for monitoring small water bodies (2023)
Journal Article
Wang, Y., Foody, G., Li, X., Zhang, Y., Zhou, P., & Du, Y. (2023). Regression-based surface water fraction mapping using a synthetic spectral library for monitoring small water bodies. GIScience and Remote Sensing, 60(1), Article 2217573. https://doi.org/10.1080/15481603.2023.2217573

Small water bodies (SWBs), such as ponds and on-farm reservoirs, are a key part of the hydrological system and play important roles in diverse domains from agriculture to conservation. The monitoring of SWBs has been greatly facilitated by medium-spa... Read More about Regression-based surface water fraction mapping using a synthetic spectral library for monitoring small water bodies.

Monitoring holopelagic Sargassum spp. along the Mexican Caribbean coast: understanding and addressing user requirements for satellite remote sensing (2023)
Journal Article
de la Barreda-Bautista, B., Metcalfe, S. E., Smith, G., Sjögersten, S., Boyd, D. S., Cerdeira-Estrada, S., …Foody, G. (2023). Monitoring holopelagic Sargassum spp. along the Mexican Caribbean coast: understanding and addressing user requirements for satellite remote sensing. Frontiers in Marine Science, 10, Article 1166000. https://doi.org/10.3389/fmars.2023.1166000

Massive influxes of holopelagic Sargassum spp. (Sargassum natans and S. fluitans) have been causing major economic, environmental and ecological problems along the Caribbean coast of Mexico. Predicting the arrival of the sargassum as an aid to addres... Read More about Monitoring holopelagic Sargassum spp. along the Mexican Caribbean coast: understanding and addressing user requirements for satellite remote sensing.

A quixotic view of spatial bias in modelling the distribution of species and their diversity (2023)
Journal Article
Rocchini, D., Tordoni, E., Marchetto, E., Marcantonio, M., Barbosa, A. M., Bazzichetto, M., …Malavasi, M. (2023). A quixotic view of spatial bias in modelling the distribution of species and their diversity. npj Biodiversity, 2(1), Article 10. https://doi.org/10.1038/s44185-023-00014-6

Ecological processes are often spatially and temporally structured, potentially leading to autocorrelation either in environmental variables or species distribution data. Because of that, spatially-biased in-situ samples or predictors might affect th... Read More about A quixotic view of spatial bias in modelling the distribution of species and their diversity.

Scientific maps should reach everyone: The cblindplot R package to let colour blind people visualise spatial patterns (2023)
Journal Article
Rocchini, D., Nowosad, J., D'Introno, R., Chieffallo, L., Bacaro, G., Gatti, R. C., …Thouverai, E. (2023). Scientific maps should reach everyone: The cblindplot R package to let colour blind people visualise spatial patterns. Ecological Informatics, 76, Article 102045. https://doi.org/10.1016/j.ecoinf.2023.102045

Maps represent powerful tools to show the spatial variation of a variable in a straightforward manner. A crucial aspect in map rendering for its interpretation by users is the gamut of colours used for displaying data. One part of this problem is lin... Read More about Scientific maps should reach everyone: The cblindplot R package to let colour blind people visualise spatial patterns.

Deep Feature and Domain Knowledge Fusion Network for Mapping Surface Water Bodies by Fusing Google Earth RGB and Sentinel-2 images (2023)
Journal Article
Zhou, P., Li, X., Foody, G. M., Boyd, D. S., Wang, X., Ling, F., …Du, Y. (2023). Deep Feature and Domain Knowledge Fusion Network for Mapping Surface Water Bodies by Fusing Google Earth RGB and Sentinel-2 images. IEEE Geoscience and Remote Sensing Letters, 1-1. https://doi.org/10.1109/LGRS.2023.3234306

Mapping surface water bodies from fine spatial resolution optical remote sensing imagery is essential for the understanding of the global hydrologic cycle. Although satellite data are useful for mapping, the limited spectral information captured by s... Read More about Deep Feature and Domain Knowledge Fusion Network for Mapping Surface Water Bodies by Fusing Google Earth RGB and Sentinel-2 images.

Global and Local Assessment of Image Classification Quality on an Overall and Per-Class Basis without Ground Reference Data (2022)
Journal Article
Foody, G. M. (2022). Global and Local Assessment of Image Classification Quality on an Overall and Per-Class Basis without Ground Reference Data. Remote Sensing, 14(21), Article 5380. https://doi.org/10.3390/rs14215380

Ground reference data are typically required to evaluate the quality of a supervised image classification analysis used to produce a thematic map from remotely sensed data. Acquiring a suitable ground data set for a rigorous assessment of classificat... Read More about Global and Local Assessment of Image Classification Quality on an Overall and Per-Class Basis without Ground Reference Data.

Double down on remote sensing for biodiversity estimation: a biological mindset (2022)
Journal Article
Rocchini, D., Torresani, M., Beierkuhnlein, C., Feoli, E., Foody, G. M., Lenoir, J., …Ricotta, C. (2022). Double down on remote sensing for biodiversity estimation: a biological mindset. Community Ecology, https://doi.org/10.1007/s42974-022-00113-7

In the light of unprecedented planetary changes in biodiversity, real-time and accurate ecosystem and biodiversity assessments are becoming increasingly essential for informing policy and sustainable development. Biodiversity monitoring is a challeng... Read More about Double down on remote sensing for biodiversity estimation: a biological mindset.

The spectral species concept in living color (2022)
Journal Article
Rocchini, D., Santos, M. J., Ustin, S. L., Féret, J. B., Asner, G. P., Beierkuhnlein, C., …Lenoir, J. (2022). The spectral species concept in living color. Journal of Geophysical Research: Biogeosciences, 127(9), Article e2022JG007026. https://doi.org/10.1029/2022JG007026

Biodiversity monitoring is an almost inconceivable challenge at the scale of the entire Earth. The current (and soon to be flown) generation of spaceborne and airborne optical sensors (i.e., imaging spectrometers) can collect detailed information at... Read More about The spectral species concept in living color.