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2. The Spectral Species Concept in Living Color

Author

Rocchini, Duccio, Santos, Maria J, Ustin, Susan L, Féret, Jean‐Baptiste, Asner, Gregory P, Beierkuhnlein, Carl, Dalponte, Michele, Feilhauer, Hannes, Foody, Giles M, Geller, Gary N, Gillespie, Thomas W, He, Kate S, Kleijn, David, Leitão, Pedro J, Malavasi, Marco, Moudrý, Vítězslav, Müllerová, Jana, Nagendra, Harini, Normand, Signe, Ricotta, Carlo, Schaepman, Michael E, Schmidtlein, Sebastian, Skidmore, Andrew K, Šímová, Petra, Torresani, Michele, Townsend, Philip A, Turner, Woody, Vihervaara, Petteri, Wegmann, Martin, and Lenoir, Jonathan

Subjects
Life Below Water, airborne sensors, biodiversity, ecoinformatics, hyperspectral images, plant optical types, remote sensing, satellite imagery, vegetation communities, Geophysics
Abstract

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 unprecedented spatial, temporal, and spectral resolutions. These new data streams are preceded by a revolution in modeling and analytics that can utilize the richness of these datasets to measure a wide range of plant traits, community composition, and ecosystem functions. At the heart of this framework for monitoring plant biodiversity is the idea of remotely identifying species by making use of the 'spectral species' concept. In theory, the spectral species concept can be defined as a species characterized by a unique spectral signature and thus remotely detectable within pixel units of a spectral image. In reality, depending on spatial resolution, pixels may contain several species which renders species-specific assignment of spectral information more challenging. The aim of this paper is to review the spectral species concept and relate it to underlying ecological principles, while also discussing the complexities, challenges and opportunities to apply this concept given current and future scientific advances in remote sensing.

Published
2022

5. A quixotic view of spatial bias in modelling the distribution of species and their diversity

Author

Rocchini, Duccio, Tordoni, Enrico, Marchetto, Elisa, Marcantonio, Matteo, Barbosa, A. Márcia, Bazzichetto, Manuele, Beierkuhnlein, Carl, Castelnuovo, Elisa, Gatti, Roberto Cazzolla, Chiarucci, Alessandro, Chieffallo, Ludovico, Da Re, Daniele, Di Musciano, Michele, Foody, Giles M., Gabor, Lukas, Garzon-Lopez, Carol X., Guisan, Antoine, Hattab, Tarek, Hortal, Joaquin, Kunin, William E., Jordán, Ferenc, Lenoir, Jonathan, Mirri, Silvia, Moudrý, Vítězslav, Naimi, Babak, Nowosad, Jakub, Sabatini, Francesco Maria, Schweiger, Andreas H., Šímová, Petra, Tessarolo, Geiziane, Zannini, Piero, and Malavasi, Marco

Published
2023
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6. Cloud detection in Landsat-8 imagery in Google Earth Engine based on a deep neural network

Author

Yin, Zhixiang, Ling, Feng, Foody, Giles M., Li, Xinyan, and Du, Yun

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Machine Learning
Abstract

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 necessary prerequisite step. Recently, deep learning-based cloud detection methods have shown their potential for cloud detection but they can only be applied locally, leading to inefficient data downloading time and storage problems. This letter proposes a method to directly perform cloud detection in Landsat-8 imagery in GEE based on deep learning (DeepGEE-CD). A deep neural network (DNN) was first trained locally, and then the trained DNN was deployed in the JavaScript client of GEE. An experiment was undertaken to validate the proposed method with a set of Landsat-8 images and the results show that DeepGEE-CD outperformed the widely used function of mask (Fmask) algorithm. The proposed DeepGEE-CD approach can accurately detect cloud in Landsat-8 imagery without downloading it, making it a promising method for routine cloud detection of Landsat-8 imagery in GEE.

Published
2020

21. Spectral Characteristics of Beached Sargassum in Response to Drying and Decay over Time

Author

Chandler, Chris J., primary, Ávila-Mosqueda, Silvia Valery, additional, Salas-Acosta, Evelyn Raquel, additional, Magaña-Gallegos, Eden, additional, Mancera, Edgar Escalante, additional, Reali, Miguel Angel Gómez, additional, de la Barreda-Bautista, Betsabé, additional, Boyd, Doreen S., additional, Metcalfe, Sarah E., additional, Sjogersten, Sofie, additional, van Tussenbroek, Brigitta, additional, Silva, Rodolfo, additional, and Foody, Giles M., additional

Published
2023
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30. Measuring and Modelling Biodiversity from Space

Author

Gillespie, Thomas, Foody, Giles M., Rocchini, Duccio, Giorgi, Ana Paula, and Saatchi, Sassan

Subjects
biogeography, conservation planning, diversity modelling, remote sensing, species distribution modelling
Abstract

The Earth is undergoing an accelerated rate of native ecosystem conversion and degradation and there is increased interest in measuring and modelling biodiversity from space. Biogeographers have a long-standing interest in measuring patterns of species occurrence and distributional movements and an interest in modelling species distributions and patterns of diversity. Much progress has been made in identifying plant species from space using high-resolution satellites (QuickBird, IKONOS), while the measurement of species movements has become commonplace with the ARGOS satellite tracking system which has been used to track the movements of thousands of individual animals. There have been signifi cant advances in land-cover classifi cations by combining data from multi-passive and active sensors, and new classifi cation techniques. Species distribution modelling has been growing at a striking rate and the incorporation of spaceborne data on climate, topography, land cover, and vegetation structure has great potential to improve models. There have been signifi cant advances in modelling species richness, alpha diversity, and beta diversity using multisensors to quantify land-cover classifi cations and landscape metrics, measures of productivity, and measures of heterogeneity. Remote sensing of nature reserves can provide natural resources managers with near real-time data within and around reserves that can be used to support conservation efforts anywhere in the world. Future research should focus on incorporating recent spaceborne sensors, more extensive integration of available spaceborne imagery, and the collection and dissemination of high-quality fi eld data. This will improve our understanding of the distribution of life on earth.

Published
2008

35. Scientific maps should reach everyone: The cblindplot R package to let colour blind people visualise spatial patterns

Author

Rocchini, Duccio; https://orcid.org/0000-0003-0087-0594, Nowosad, Jakub; https://orcid.org/0000-0002-1057-3721, D’Introno, Rossella, Chieffallo, Ludovico, Bacaro, Giovanni, Gatti, Roberto Cazzolla, Foody, Giles M, Furrer, Reinhard; https://orcid.org/0000-0002-6319-2332, Gábor, Lukáš, Malavasi, Marco; https://orcid.org/0000-0002-9639-1784, Marcantonio, Matteo, Marchetto, Elisa, Moudrý, Vítězslav, Ricotta, Carlo, Šímová, Petra, Torresani, Michele, Thouverai, Elisa, Rocchini, Duccio; https://orcid.org/0000-0003-0087-0594, Nowosad, Jakub; https://orcid.org/0000-0002-1057-3721, D’Introno, Rossella, Chieffallo, Ludovico, Bacaro, Giovanni, Gatti, Roberto Cazzolla, Foody, Giles M, Furrer, Reinhard; https://orcid.org/0000-0002-6319-2332, Gábor, Lukáš, Malavasi, Marco; https://orcid.org/0000-0002-9639-1784, Marcantonio, Matteo, Marchetto, Elisa, Moudrý, Vítězslav, Ricotta, Carlo, Šímová, Petra, Torresani, Michele, and Thouverai, Elisa

Abstract

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 linked to the proportion of the human population that is colour blind and, therefore, highly sensitive to colour palette selection. The aim of this paper is to present the cblindplot R package and its founding function - cblind.plot() - which enables colour blind people to just enter an image in a coding workflow, simply set their colour blind deficiency type, and immediately get as output a colour blind friendly plot. We will first describe in detail colour blind problems, and then show a step by step example of the function being proposed. While examples exist to provide colour blind people with proper colour palettes, in such cases (i) the workflow include a separate import of the image and the application of a set of colour ramp palettes and (ii) albeit being well documented, there are many steps to be done before plotting an image with a colour blind friendly ramp palette. The function described in this paper, on the contrary, allows to (i) automatically call the image inside the function without any initial import step and (ii) explicitly refer to the colour blind deficiency type being experienced, to further automatically apply the proper colour ramp palette.

Published
2023

42. Citizen science for Earth Observation (Citzens4EO): understanding current use in the UK

Author

Boyd, Doreen S., Foody, Giles M., Brown, Chloe, Mazumdar, Suvodeep, Marshall, Harry, and Wardlaw, Jessica

Subjects
Geography: Geosciences, General Earth and Planetary Sciences
Abstract

The role of Earth observation (EO) data in addressing societal problems from environmental through to humanitarian should not be understated. Recent innovation in EO means provision of analysis ready data and data cubes, which allows for rapid use of EO data. This in combination with processing technologies, such as Google Earth Engine and open source algorithms/software for EO data integration and analyses, has afforded an explosion of information to answer research questions and/or inform policy making. However, there is still a need for both training and validation data within EO projects–often this can be challenging to obtain. It has been suggested that citizen science can help here to provide these data, yet there is some perceived hesitancy in using citizen science within EO projects. This paper reports on the Citizen Science 4 EO (Citizens4EO) project that aimed to obtain an in-depth understanding of researchers’ and practitioners’ experiences with citizen science data in EO within the UK. Through a mixed methods approach (online and in-depth surveys and a spotlight case study) it was found that although the benefits of using citizen science data in EO projects were many (and highlighted in the spotlighted “Slavery from Space” case study), there were a number of common concerns around using citizen science. These were around the mechanics of deploying citizen science and the unreliability of a potentially misinformed or undertrained citizen base. As such, comparing the results of this study with those of a similar survey undertaken in 2016, it is apparent that progress towards optimizing citizen science use in EO has been incremental but positive with evidence of the realization of the benefits of citizen science for EO (Citizens4EO). As such, we conclude by offering priority action areas to support further use of citizen science by the EO community within the UK, which ultimately should be adopted further afield.

Published
2022

43. Sub-Pixel Methods in Remote Sensing

Author

Foody, Giles M., Meer, Freek D. Van der, editor, Abrams, Michael, editor, Curran, Paul, editor, Dekker, Arnold, editor, Jong, Steven M. De, editor, and Schaepman, Michael, editor

Published
2004
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45. Uncertainty in ecosystem mapping by remote sensing

Author

Rocchini, Duccio, Foody, Giles M., Nagendra, Harini, Ricotta, Carlo, Anand, Madhur, He, Kate S., Amici, Valerio, Kleinschmit, Birgit, Förster, Michael, Schmidtlein, Sebastian, Feilhauer, Hannes, Ghisla, Anne, Metz, Markus, and Neteler, Markus

Published
2013
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48. Making (remote) sense of lianas

Author

Heijden, Geertje M. F., primary, Proctor, Ashley D. C., additional, Calders, Kim, additional, Chandler, Chris J., additional, Field, Richard, additional, Foody, Giles M., additional, Krishna Moorthy, Sruthi M., additional, Schnitzer, Stefan A., additional, Waite, Catherine E., additional, and Boyd, Doreen S., additional

Published
2022
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