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Lianas (woody vines) play a key role in tropical forest dynamics because of their strong influence on tree growth, mortality and regeneration. Assessing liana infestation over large areas is critical to understand the factors that drive their spatial distribution and to monitor change over time. However, it currently remains unclear whether satellite-based imagery can be used to detect liana infestation across closed-canopy forests and therefore if satellite-observed changes in liana infestation can be detected over time and in response to climatic conditions. Here, we aim to determine the efficacy of satellite-based remote sensing for the detection of spatial and temporal patterns of liana infestation across a primary and selectively logged aseasonal forest in Sabah, Borneo. We used predicted liana infestation derived from airborne hyperspectral data to train a neural network classification for prediction across four Sentinel-2 satellite-based images from 2016 to 2019. Our results showed that liana infestation was positively related to an increase in Greenness Index (GI), a simple metric relating to the amount of photosynthetically active green leaves. Furthermore, this relationship was observed in different forest types and during (2016), as well as after (2017–2019), an El Niño-induced drought. Using a neural network classification, we assessed liana infestation over time and showed an increase in the percentage of severely (>75%) liana infested pixels from 12.9% ± 0.63 (95% CI) in 2016 to 17.3% ± 2 in 2019. This implies that reports of increasing liana abundance may be more wide-spread than currently assumed. This is the first study to show that liana infestation can be accurately detected across closed-canopy tropical forests using satellite-based imagery. Furthermore, the detection of liana infestation during both dry and wet years and across forest types suggests this method should be broadly applicable across tropical forests. This work therefore advances our ability to explore the drivers responsible for patterns of liana infestation at multiple spatial and temporal scales and to quantify liana-induced impacts on carbon dynamics in tropical forests globally.
Chris Chandler; Geertje van der Heijden; Doreen Boyd; Giles Foody. Detection of Spatial and Temporal Patterns of Liana Infestation Using Satellite-Derived Imagery. Remote Sensing 2021, 13, 2774 .
AMA StyleChris Chandler, Geertje van der Heijden, Doreen Boyd, Giles Foody. Detection of Spatial and Temporal Patterns of Liana Infestation Using Satellite-Derived Imagery. Remote Sensing. 2021; 13 (14):2774.
Chicago/Turabian StyleChris Chandler; Geertje van der Heijden; Doreen Boyd; Giles Foody. 2021. "Detection of Spatial and Temporal Patterns of Liana Infestation Using Satellite-Derived Imagery." Remote Sensing 13, no. 14: 2774.
The surface urban heat island (SUHI) effect poses a significant threat to the urban environment and public health. This paper utilized the Local Climate Zone (LCZ) classification and land surface temperature (LST) data to analyze the seasonal dynamics of SUHI in Wuhan based on the Google Earth Engine platform. In addition, the SUHI intensity derived from the traditional urban–rural dichotomy was also calculated for comparison. Seasonal SUHI analysis showed that (1) both LCZ classification and the urban–rural dichotomy confirmed that Wuhan’s SHUI effect was the strongest in summer, followed by spring, autumn and winter; (2) the maximum SUHI intensity derived from LCZ classification reached 6.53 °C, which indicated that the SUHI effect was very significant in Wuhan; (3) LCZ 8 (i.e., large low-rise) had the maximum LST value and LCZ G (i.e., water) had the minimum LST value in all seasons; (4) the LST values of compact high-rise/midrise/low-rise (i.e., LCZ 1–3) were higher than those of open high-rise/midrise/low-rise (i.e., LCZ 4–6) in all seasons, which indicated that building density had a positive correlation with LST; (5) the LST values of dense trees (i.e., LCZ A) were less than those of scattered trees (i.e., LCZ B) in all seasons, which indicated that vegetation density had a negative correlation with LST. This paper provides some useful information for urban planning and contributes to the healthy and sustainable development of Wuhan.
Lingfei Shi; Feng Ling; Giles Foody; Zhen Yang; Xixi Liu; Yun Du. Seasonal SUHI Analysis Using Local Climate Zone Classification: A Case Study of Wuhan, China. International Journal of Environmental Research and Public Health 2021, 18, 7242 .
AMA StyleLingfei Shi, Feng Ling, Giles Foody, Zhen Yang, Xixi Liu, Yun Du. Seasonal SUHI Analysis Using Local Climate Zone Classification: A Case Study of Wuhan, China. International Journal of Environmental Research and Public Health. 2021; 18 (14):7242.
Chicago/Turabian StyleLingfei Shi; Feng Ling; Giles Foody; Zhen Yang; Xixi Liu; Yun Du. 2021. "Seasonal SUHI Analysis Using Local Climate Zone Classification: A Case Study of Wuhan, China." International Journal of Environmental Research and Public Health 18, no. 14: 7242.
Thematic maps are often derived from remotely sensed imagery via a supervised image classification analysis. The training and testing stages of a supervised image classification may proceed ignorant of the presence of some classes in the region to be mapped. This violates the assumption of an exhaustively defined set of classes that is often made in classification analyses. In such circumstances, the overall accuracy of a thematic map produced by the application of a trained classifier will be less than the accuracy of the classification of the test set by the same classifier. This situation arises because the cases of an untrained class can normally only be commissioned into the set of trained classes. Simple mathematical relationships between classification and map accuracy are shown for assessments of overall, user's and producer's accuracy. For example, it is shown that in a simple scenario the accuracy of a thematic map is less than that of a classification, scaling as a function of the abundance of the untrained class(es). Impacts on other estimates made from thematic maps, such as class areal extent, are also briefly discussed. When using a thematic map, care is needed in interpreting and using classification accuracy assessments as sometimes they may not reflect properties of the map well.
Giles M. Foody. Impacts of ignorance on the accuracy of image classification and thematic mapping. Remote Sensing of Environment 2021, 259, 112367 .
AMA StyleGiles M. Foody. Impacts of ignorance on the accuracy of image classification and thematic mapping. Remote Sensing of Environment. 2021; 259 ():112367.
Chicago/Turabian StyleGiles M. Foody. 2021. "Impacts of ignorance on the accuracy of image classification and thematic mapping." Remote Sensing of Environment 259, no. : 112367.
Aim The majority of work done to gather information on the Earth's biodiversity has been carried out using in‐situ data, with known issues related to epistemology (e.g., species determination and taxonomy), spatial uncertainty, logistics (time and costs), among others. An alternative way to gather information about spatial ecosystem variability is the use of satellite remote sensing. It works as a powerful tool for attaining rapid and standardized information. Several metrics used to calculate remotely sensed diversity of ecosystems are based on Shannon’s information theory, namely on the differences in relative abundance of pixel reflectances in a certain area. Additional metrics like the Rao’s quadratic entropy allow the use of spectral distance beside abundance, but they are point descriptors of diversity, that is they can account only for a part of the whole diversity continuum. The aim of this paper is thus to generalize the Rao’s quadratic entropy by proposing its parameterization for the first time. Innovation The parametric Rao’s quadratic entropy, coded in R, (a) allows the representation of the whole continuum of potential diversity indices in one formula, and (b) starting from the Rao’s quadratic entropy, allows the explicit use of distances among pixel reflectance values, together with relative abundances. Main conclusions The proposed unifying measure is an integration between abundance‐ and distance‐based algorithms to map the continuum of diversity given a satellite image at any spatial scale. Being part of the rasterdiv R package, the proposed method is expected to ensure high robustness and reproducibility.
Duccio Rocchini; Matteo Marcantonio; Daniele Da Re; Giovanni Bacaro; Enrico Feoli; Giles M. Foody; Reinhard Furrer; Ryan J. Harrigan; David Kleijn; Martina Iannacito; Jonathan Lenoir; Meixi Lin; Marco Malavasi; Elisa Marchetto; Rachel S. Meyer; Vítězslav Moudry; Fabian D. Schneider; Petra Šímová; Andrew H. Thornhill; Elisa Thouverai; Saverio Vicario; Robert K. Wayne; Carlo Ricotta. From zero to infinity: Minimum to maximum diversity of the planet by spatio‐parametric Rao’s quadratic entropy. Global Ecology and Biogeography 2021, 30, 1153 -1162.
AMA StyleDuccio Rocchini, Matteo Marcantonio, Daniele Da Re, Giovanni Bacaro, Enrico Feoli, Giles M. Foody, Reinhard Furrer, Ryan J. Harrigan, David Kleijn, Martina Iannacito, Jonathan Lenoir, Meixi Lin, Marco Malavasi, Elisa Marchetto, Rachel S. Meyer, Vítězslav Moudry, Fabian D. Schneider, Petra Šímová, Andrew H. Thornhill, Elisa Thouverai, Saverio Vicario, Robert K. Wayne, Carlo Ricotta. From zero to infinity: Minimum to maximum diversity of the planet by spatio‐parametric Rao’s quadratic entropy. Global Ecology and Biogeography. 2021; 30 (5):1153-1162.
Chicago/Turabian StyleDuccio Rocchini; Matteo Marcantonio; Daniele Da Re; Giovanni Bacaro; Enrico Feoli; Giles M. Foody; Reinhard Furrer; Ryan J. Harrigan; David Kleijn; Martina Iannacito; Jonathan Lenoir; Meixi Lin; Marco Malavasi; Elisa Marchetto; Rachel S. Meyer; Vítězslav Moudry; Fabian D. Schneider; Petra Šímová; Andrew H. Thornhill; Elisa Thouverai; Saverio Vicario; Robert K. Wayne; Carlo Ricotta. 2021. "From zero to infinity: Minimum to maximum diversity of the planet by spatio‐parametric Rao’s quadratic entropy." Global Ecology and Biogeography 30, no. 5: 1153-1162.
Ecosystem heterogeneity has been widely recognized as a key ecological feature, influencing several ecological functions, since it is strictly related to several ecological functions like diversity patterns and change, metapopulation dynamics, population connectivity, or gene flow. In this paper, we present a new R package ‐ rasterdiv ‐ to calculate heterogeneity indices based on remotely sensed data. We also provide an ecological application at the landscape scale and demonstrate its power in revealing potentially hidden heterogeneity patterns. The rasterdiv package allows calculating multiple indices, robustly rooted in Information Theory, and based on reproducible open source algorithms.
Duccio Rocchini; Elisa Thouverai; Matteo Marcantonio; Martina Iannacito; Daniele Da Re; Michele Torresani; Giovanni Bacaro; Manuele Bazzichetto; Alessandra Bernardi; Giles M. Foody; Reinhard Furrer; David Kleijn; Stefano Larsen; Jonathan Lenoir; Marco Malavasi; Elisa Marchetto; Filippo Messori; Alessandro Montaghi; Vítězslav Moudrý; Babak Naimi; Carlo Ricotta; Micol Rossini; Francesco Santi; Maria J. Santos; Michael E. Schaepman; Fabian D. Schneider; Leila Schuh; Sonia Silvestri; Petra Ŝímová; Andrew K. Skidmore; Clara Tattoni; Enrico Tordoni; Saverio Vicario; Piero Zannini; Martin Wegmann. rasterdiv—An Information Theory tailored R package for measuring ecosystem heterogeneity from space: To the origin and back. Methods in Ecology and Evolution 2021, 12, 1093 -1102.
AMA StyleDuccio Rocchini, Elisa Thouverai, Matteo Marcantonio, Martina Iannacito, Daniele Da Re, Michele Torresani, Giovanni Bacaro, Manuele Bazzichetto, Alessandra Bernardi, Giles M. Foody, Reinhard Furrer, David Kleijn, Stefano Larsen, Jonathan Lenoir, Marco Malavasi, Elisa Marchetto, Filippo Messori, Alessandro Montaghi, Vítězslav Moudrý, Babak Naimi, Carlo Ricotta, Micol Rossini, Francesco Santi, Maria J. Santos, Michael E. Schaepman, Fabian D. Schneider, Leila Schuh, Sonia Silvestri, Petra Ŝímová, Andrew K. Skidmore, Clara Tattoni, Enrico Tordoni, Saverio Vicario, Piero Zannini, Martin Wegmann. rasterdiv—An Information Theory tailored R package for measuring ecosystem heterogeneity from space: To the origin and back. Methods in Ecology and Evolution. 2021; 12 (6):1093-1102.
Chicago/Turabian StyleDuccio Rocchini; Elisa Thouverai; Matteo Marcantonio; Martina Iannacito; Daniele Da Re; Michele Torresani; Giovanni Bacaro; Manuele Bazzichetto; Alessandra Bernardi; Giles M. Foody; Reinhard Furrer; David Kleijn; Stefano Larsen; Jonathan Lenoir; Marco Malavasi; Elisa Marchetto; Filippo Messori; Alessandro Montaghi; Vítězslav Moudrý; Babak Naimi; Carlo Ricotta; Micol Rossini; Francesco Santi; Maria J. Santos; Michael E. Schaepman; Fabian D. Schneider; Leila Schuh; Sonia Silvestri; Petra Ŝímová; Andrew K. Skidmore; Clara Tattoni; Enrico Tordoni; Saverio Vicario; Piero Zannini; Martin Wegmann. 2021. "rasterdiv—An Information Theory tailored R package for measuring ecosystem heterogeneity from space: To the origin and back." Methods in Ecology and Evolution 12, no. 6: 1093-1102.
The ability to accurately assess liana (woody vine) infestation at the landscape level is essential to quantify their impact on carbon dynamics and help inform targeted forest management and conservation action. Remote sensing techniques provide potential solutions for assessing liana infestation at broader spatial scales. However, their use so far has been limited to seasonal forests, where there is a high spectral contrast between lianas and trees. Additionally, the ability to align the spatial units of remotely sensed data with canopy observations of liana infestation requires further attention. We combined airborne hyperspectral and LiDAR data with a neural network machine learning classification to assess the distribution of liana infestation at the landscape‐level across an aseasonal primary forest in Sabah, Malaysia. We tested whether an object‐based classification was more effective at predicting liana infestation when compared to a pixel‐based classification. We found a stronger relationship between predicted and observed liana infestation when using a pixel‐based approach (RMSD = 27.0% ± 0.80) in comparison to an object‐based approach (RMSD = 32.6% ± 4.84). However, there was no significant difference in accuracy for object‐ versus pixel‐based classifications when liana infestation was grouped into three classes; Low [0–30%], Medium [31–69%] and High [70–100%] (McNemar’s χ2 = 0.211, P = 0.65). We demonstrate, for the first time, that remote sensing approaches are effective in accurately assessing liana infestation at a landscape scale in an aseasonal tropical forest. Our results indicate potential limitations in object‐based approaches which require refinement in order to accurately segment imagery across contiguous closed‐canopy forests. We conclude that the decision on whether to use a pixel‐ or object‐based approach may depend on the structure of the forest and the ultimate application of the resulting output. Both approaches will provide a valuable tool to inform effective conservation and forest management.
Chris J. Chandler; Geertje M. F. van der Heijden; Doreen S. Boyd; Mark E. J. Cutler; Hugo Costa; Reuben Nilus; Giles M. Foody. Remote sensing liana infestation in an aseasonal tropical forest: addressing mismatch in spatial units of analyses. Remote Sensing in Ecology and Conservation 2021, 1 .
AMA StyleChris J. Chandler, Geertje M. F. van der Heijden, Doreen S. Boyd, Mark E. J. Cutler, Hugo Costa, Reuben Nilus, Giles M. Foody. Remote sensing liana infestation in an aseasonal tropical forest: addressing mismatch in spatial units of analyses. Remote Sensing in Ecology and Conservation. 2021; ():1.
Chicago/Turabian StyleChris J. Chandler; Geertje M. F. van der Heijden; Doreen S. Boyd; Mark E. J. Cutler; Hugo Costa; Reuben Nilus; Giles M. Foody. 2021. "Remote sensing liana infestation in an aseasonal tropical forest: addressing mismatch in spatial units of analyses." Remote Sensing in Ecology and Conservation , no. : 1.
Ecosystem heterogeneity has been widely recognized as a key ecological feature, influencing several ecological functions, since it is strictly related to several ecological functions like diversity patterns and change, metapopulation dynamics, population connectivity, or gene flow. In this paper, we present a new R package - rasterdiv - to calculate heterogeneity indices based on remotely sensed data. We also provide an ecological application at the landscape scale and demonstrate its power in revealing potentially hidden heterogeneity patterns. The rasterdiv package allows calculating multiple indices, robustly rooted in Information Theory, and based on reproducible open source algorithms.
Duccio Rocchini; Elisa Thouverai; Matteo Marcantonio; Martina Iannacito; Daniele Da Re; Michele Torresani; Giovanni Bacaro; Manuele Bazzichetto; Alessandra Bernardi; Giles M. Foody; Reinhard Furrer; David Kleijn; Stefano Larsen; Jonathan Lenoir; Marco Malavasi; Elisa Marchetto; Filippo Messori; Alessandro Montaghi; Vítězslav Moudrý; Babak Naimi; Carlo Ricotta; Micol Rossini; Francesco Santi; Maria J. Santos; Michael Schaepman; Fabian Schneider; Leila Schuh; Sonia Silvestri; Petra Šímová; Andrew K. Skidmore; Clara Tattoni; Enrico Tordoni; Saverio Vicario; Piero Zannini; Martin Wegmann. rasterdiv - an Information Theory tailored R package for measuring ecosystem heterogeneity from space: to the origin and back. 2021, 1 .
AMA StyleDuccio Rocchini, Elisa Thouverai, Matteo Marcantonio, Martina Iannacito, Daniele Da Re, Michele Torresani, Giovanni Bacaro, Manuele Bazzichetto, Alessandra Bernardi, Giles M. Foody, Reinhard Furrer, David Kleijn, Stefano Larsen, Jonathan Lenoir, Marco Malavasi, Elisa Marchetto, Filippo Messori, Alessandro Montaghi, Vítězslav Moudrý, Babak Naimi, Carlo Ricotta, Micol Rossini, Francesco Santi, Maria J. Santos, Michael Schaepman, Fabian Schneider, Leila Schuh, Sonia Silvestri, Petra Šímová, Andrew K. Skidmore, Clara Tattoni, Enrico Tordoni, Saverio Vicario, Piero Zannini, Martin Wegmann. rasterdiv - an Information Theory tailored R package for measuring ecosystem heterogeneity from space: to the origin and back. . 2021; ():1.
Chicago/Turabian StyleDuccio Rocchini; Elisa Thouverai; Matteo Marcantonio; Martina Iannacito; Daniele Da Re; Michele Torresani; Giovanni Bacaro; Manuele Bazzichetto; Alessandra Bernardi; Giles M. Foody; Reinhard Furrer; David Kleijn; Stefano Larsen; Jonathan Lenoir; Marco Malavasi; Elisa Marchetto; Filippo Messori; Alessandro Montaghi; Vítězslav Moudrý; Babak Naimi; Carlo Ricotta; Micol Rossini; Francesco Santi; Maria J. Santos; Michael Schaepman; Fabian Schneider; Leila Schuh; Sonia Silvestri; Petra Šímová; Andrew K. Skidmore; Clara Tattoni; Enrico Tordoni; Saverio Vicario; Piero Zannini; Martin Wegmann. 2021. "rasterdiv - an Information Theory tailored R package for measuring ecosystem heterogeneity from space: to the origin and back." , no. : 1.
Nutrient input through submarine groundwater discharge (SGD) often plays a significant role in primary productivity and nutrient cycling in the coastal areas. Understanding relationships between SGD and topo-hydrological and geo-environmental characteristics of upstream zones is essential for sustainable development in these areas. However, these important relationships have not yet been completely explored using data-mining approaches, especially in arid and semi-arid coastal lands. Here, Landsat 8 thermal sensor data were used to identify potential sites of SGD at a regional scale. Relationships between the remotely-sensed sea surface temperature (SST) patterns and geo-environmental variables of upland watersheds were analyzed using logistic regression model for the first time. The accuracy of the predictions was evaluated using the area under the receiver operating characteristic curve (AUC-ROC) metric. A highly accurate model, with the AUC-ROC of 96.6%, was generated. Moreover, the results indicated that the percentage of karstic lithological formation and topographic wetness index were key variables influencing SGD phenomenon and spatial distribution in the northern coastal areas of the Persian Gulf. The adopted methodology and applied metrics can be transferred to other coastal regions as a rapid assessment procedure for SGD site detection. Moreover, the results can help planners and decision-makers to develop efficient environmental management strategies and the design of comprehensive sustainable development policies.
Aliakbar Samani; Mohsen Farzin; Omid Rahmati; Sadat Feiznia; Gholam Kazemi; Giles Foody; Assefa Melesse. Scrutinizing Relationships between Submarine Groundwater Discharge and Upstream Areas Using Thermal Remote Sensing: A Case Study in the Northern Persian Gulf. Remote Sensing 2021, 13, 358 .
AMA StyleAliakbar Samani, Mohsen Farzin, Omid Rahmati, Sadat Feiznia, Gholam Kazemi, Giles Foody, Assefa Melesse. Scrutinizing Relationships between Submarine Groundwater Discharge and Upstream Areas Using Thermal Remote Sensing: A Case Study in the Northern Persian Gulf. Remote Sensing. 2021; 13 (3):358.
Chicago/Turabian StyleAliakbar Samani; Mohsen Farzin; Omid Rahmati; Sadat Feiznia; Gholam Kazemi; Giles Foody; Assefa Melesse. 2021. "Scrutinizing Relationships between Submarine Groundwater Discharge and Upstream Areas Using Thermal Remote Sensing: A Case Study in the Northern Persian Gulf." Remote Sensing 13, no. 3: 358.
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 imagery and other ancillary spatial data. Different analysts interpreting the same spatial unit will not always agree on the land cover class label that should be assigned. Two approaches for accommodating interpreter variability when estimating the area are simple averaging (SA) and latent class modeling (LCM). This study compares agreement between area estimates obtained from SA and LCM using reference data obtained by seven trained, professional interpreters who independently interpreted an annual time series of land cover reference class labels for 300 sampled Landsat pixels. We also compare the variability of the LCM and SA area estimates over different numbers of interpreters and different subsets of interpreters within each interpreter group size, and examine area estimates of three land cover classes (forest, developed, and wetland) and three change types (forest gain, forest loss, and developed gain). Differences between the area estimates obtained from SA and LCM are most pronounced for the estimates of wetland and the three change types. The percent area estimates of these rare classes were usually greater for LCM compared to SA, with the differences between LCM and SA increasing as the number of interpreters providing the reference data increased. The LCM area estimates generally had larger standard deviations and greater ranges over different subsets of interpreters, indicating greater sensitivity to the selection of the individual interpreters who carried out the reference class labeling.
Dingfan Xing; Stephen V. Stehman; Giles M. Foody; Bruce W. Pengra. Comparison of Simple Averaging and Latent Class Modeling to Estimate the Area of Land Cover in the Presence of Reference Data Variability. Land 2021, 10, 35 .
AMA StyleDingfan Xing, Stephen V. Stehman, Giles M. Foody, Bruce W. Pengra. Comparison of Simple Averaging and Latent Class Modeling to Estimate the Area of Land Cover in the Presence of Reference Data Variability. Land. 2021; 10 (1):35.
Chicago/Turabian StyleDingfan Xing; Stephen V. Stehman; Giles M. Foody; Bruce W. Pengra. 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, no. 1: 35.
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 sensor images to generate an FR MS image by injecting spatial detail taken from the FR PAN image while simultaneously preserving the spectral information of MS image. Pansharpening methods are mostly applied on a per-pixel basis and use the PAN image to extract spatial detail. However, many land cover objects in FR satellite sensor images are not illustrated as independent pixels, but as many spatially aggregated pixels that contain important semantic information. In this article, an object-based pansharpening approach, termed object-based area-to-point regression kriging (OATPRK), is proposed. OATPRK aims to fuse the MS and PAN images at the object-based scale and, thus, takes advantage of both the unified spectral information within the CR MS images and the spatial detail of the FR PAN image. OATPRK is composed of three stages: image segmentation, object-based regression, and residual downscaling. Three data sets acquired from IKONOS and Worldview-2 and 11 benchmark pansharpening algorithms were used to provide a comprehensive assessment of the proposed OATPRK approach. In both the synthetic and real experiments, OATPRK produced the most superior pan-sharpened results in terms of visual and quantitative assessment. OATPRK is a new conceptual method that advances the pixel-level geostatistical pansharpening approach to the object level and provides more accurate pan-sharpened MS images.
Yihang Zhang; Peter M. Atkinson; Feng Ling; Giles M. Foody; Qunming Wang; Yong Ge; Xiaodong Li; Yun Du. Object-Based Area-to-Point Regression Kriging for Pansharpening. IEEE Transactions on Geoscience and Remote Sensing 2020, PP, 1 -16.
AMA StyleYihang Zhang, Peter M. Atkinson, Feng Ling, Giles M. Foody, Qunming Wang, Yong Ge, Xiaodong Li, Yun Du. Object-Based Area-to-Point Regression Kriging for Pansharpening. IEEE Transactions on Geoscience and Remote Sensing. 2020; PP (99):1-16.
Chicago/Turabian StyleYihang Zhang; Peter M. Atkinson; Feng Ling; Giles M. Foody; Qunming Wang; Yong Ge; Xiaodong Li; Yun Du. 2020. "Object-Based Area-to-Point Regression Kriging for Pansharpening." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-16.
Mapping vegetation as hard classes based on remote sensing data is a frequently applied approach, even though this crisp, categorical representation is not in line with nature's fuzziness. Gradual transitions in plant species composition in ecotones and faint compositional differences across different patches are thus poorly described in the resulting maps. Several concepts promise to provide better vegetation maps. These include (1) fuzzy classification (a.k.a. soft classification) that takes the probability of an image pixel's class membership into account and (2) gradient mapping based on ordination, which describes plant species composition as a floristic continuum and avoids a categorical description of vegetation patterns. A systematic and comprehensive comparison of these approaches is missing to date. This paper hence gives an overview of the state of the art in fuzzy classification and gradient mapping and compares the approaches in a case study. The advantages and disadvantages of the approaches are discussed and their performance is compared to hard classification (a.k.a. crisp or boolean classification). Gradient mapping best conserves the information in the original data and does not require an a priori categorization. Fuzzy classification comes close in terms of information loss and likewise preserves the continuous nature of vegetation, however, still relying on a priori classification. The need for a priori classification may be a disadvantage or, in other cases, an advantage because it allows using categorical input data instead of the detailed vegetation records required for ordination. Both approaches support spatially explicit accuracy analyses, which further improves the usefulness of the output maps. Gradient mapping and fuzzy classification offer various advantages over hard classification, can always be transformed into a crisp map and are generally applicable to various data structures. We thus recommend the use of these approaches over hard classification for applications in ecological research.
Hannes Feilhauer; András Zlinszky; Adam Kania; Giles M. Foody; Daniel Doktor; Angela Lausch; Sebastian Schmidtlein. 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 2020, 7, 292 -305.
AMA StyleHannes Feilhauer, András Zlinszky, Adam Kania, Giles M. Foody, Daniel Doktor, Angela Lausch, Sebastian Schmidtlein. 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. 2020; 7 (2):292-305.
Chicago/Turabian StyleHannes Feilhauer; András Zlinszky; Adam Kania; Giles M. Foody; Daniel Doktor; Angela Lausch; Sebastian Schmidtlein. 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 7, no. 2: 292-305.
Greater awareness of the serious human rights abuses associated with the extraction and trade of cobalt in the Democratic Republic of the Congo (DRC) has applied increasing pressure for businesses to move towards more responsible and sustainable mineral sourcing. Artisanal and small-scale mining (ASM) activities in rural and remote locations may provide heightened opportunities to conceal the alleged human rights violations associated with mining, such as: hazardous working conditions, health impacts, child labour, child trafficking, and debt bondage. In this study, we investigate the feasibility of the Intermittent Small Baseline Subset (ISBAS) interferometric synthetic aperture radar (InSAR) method, teamed with high temporal frequency Sentinel-1 imagery, for monitoring ASM activity in rural locations of the “Copperbelt”, the DRC. The results show that the ISBAS descriptive variables (mean, standard deviation, minimum, and maximum) were significantly different (p-value = ≤ 0.05) between mining and non-mining areas. Additionally, a significant difference was found for the ISBAS descriptive variables mean, standard deviation, and minimum between the different mine types (industrial, surface, and tunnels). As expected, a high level of subsidence (i.e., negative ISBAS pixel value) was a clear indicator of mine activity. Trial activity thresholds were set for the descriptive variables mean (-2.43 mm/yr) and minimum (-5.36 mm/yr) to explore an ISBAS approach to active mine identification. The study concluded that the ISBAS method has great potential as a monitoring tool for ASM, with the ability to separate mining and non-mining areas based on surface motion values, and further distinguish the different mine types (industrial, surface, and tunnel). Ground data collection and further development of ISBAS analysis needs to be made to fully understand the value of an ISBAS-based ASM monitoring system. In particular, surrounding the impact of seasonality relative to longer-term trends in ASM activity.
Chloe Brown; Anna Daniels; Doreen Boyd; Andrew Sowter; Giles Foody; Siddharth Kara. Investigating the Potential of Radar Interferometry for Monitoring Rural Artisanal Cobalt Mines in the Democratic Republic of the Congo. Sustainability 2020, 12, 9834 .
AMA StyleChloe Brown, Anna Daniels, Doreen Boyd, Andrew Sowter, Giles Foody, Siddharth Kara. Investigating the Potential of Radar Interferometry for Monitoring Rural Artisanal Cobalt Mines in the Democratic Republic of the Congo. Sustainability. 2020; 12 (23):9834.
Chicago/Turabian StyleChloe Brown; Anna Daniels; Doreen Boyd; Andrew Sowter; Giles Foody; Siddharth Kara. 2020. "Investigating the Potential of Radar Interferometry for Monitoring Rural Artisanal Cobalt Mines in the Democratic Republic of the Congo." Sustainability 12, no. 23: 9834.
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 fish-processing activities were assessed within a case study of Bangladesh’s Sundarbans mangrove forests. Ten fish-processing camps were identified, with some occurring in locations where human activity is banned. Environmental degradation included the removal of mangroves, erosion, and the destruction of protected areas. Previous studies have identified cases of labour exploitation and modern slavery occurring within the Sundarbans, and remote sensing was used to triangulate these claims by providing spatial and temporal analysis to increase the understanding of the operational trends at these locations. These findings were linked to the cyclical relationship between modern slavery and environmental degradation, whereby environmental damage is both a driver and result of workers subjected to modern slavery. Remote sensing can be used as an additional methodological tool to support the achievement of the Sustainable Development Goals (SDGs) and provide evidence to support the promotion of the “freedom dividend” which would have far-reaching economic, social, cultural, and environmental benefits. Satellite remote sensing is likely to play an important role going forward for understanding these issues but should be augmented with ground-based data collection methods.
Bethany Jackson; Doreen S. Boyd; Christopher D. Ives; Jessica L. Decker Sparks; Giles M. Foody; Stuart Marsh; Kevin Bales. 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 2020, 19, 429 -444.
AMA StyleBethany Jackson, Doreen S. Boyd, Christopher D. Ives, Jessica L. Decker Sparks, Giles M. Foody, Stuart Marsh, Kevin Bales. 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. 2020; 19 (4):429-444.
Chicago/Turabian StyleBethany Jackson; Doreen S. Boyd; Christopher D. Ives; Jessica L. Decker Sparks; Giles M. Foody; Stuart Marsh; Kevin Bales. 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, no. 4: 429-444.
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 to represent the land cover spatial distribution within mixed pixels. The use of an inappropriate spatial pattern model limits such SRM analyses. Alternative approaches, such as deep-learning-based algorithms, which learn the spatial pattern from training data through a convolutional neural network, have been shown to have considerable potential. Deep learning methods, however, are limited by issues such as the way the fraction images are utilized. Here, a novel SRM model based on a generative adversarial network (GAN), GAN-SRM, is proposed that uses an end-to-end network to address the main limitations of existing SRM methods. The potential of the proposed GAN-SRM model was assessed using four land cover subsets and compared to hard classification and several popular SRM methods. The experimental results show that of the set of methods explored, the GAN-SRM model was able to generate the most accurate high-resolution land cover maps.
Cheng Shang; Xiaodong Li; Giles M. Foody; Yun Du; Feng Ling. Superresolution Land Cover Mapping Using a Generative Adversarial Network. IEEE Geoscience and Remote Sensing Letters 2020, PP, 1 -5.
AMA StyleCheng Shang, Xiaodong Li, Giles M. Foody, Yun Du, Feng Ling. Superresolution Land Cover Mapping Using a Generative Adversarial Network. IEEE Geoscience and Remote Sensing Letters. 2020; PP (99):1-5.
Chicago/Turabian StyleCheng Shang; Xiaodong Li; Giles M. Foody; Yun Du; Feng Ling. 2020. "Superresolution Land Cover Mapping Using a Generative Adversarial Network." IEEE Geoscience and Remote Sensing Letters PP, no. 99: 1-5.
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 carbon density (ACD), but adoption of restoration is constrained by cost and uncertainties over effectiveness. We report a long-term comparison of ACD recovery rates between naturally regenerating and actively restored logged tropical forests. Restoration enhanced decadal ACD recovery by more than 50%, from 2.9 to 4.4 megagrams per hectare per year. This magnitude of response, coupled with modal values of restoration costs globally, would require higher carbon prices to justify investment in restoration. However, carbon prices required to fulfill the 2016 Paris climate agreement [$40 to $80 (USD) per tonne carbon dioxide equivalent] would provide an economic justification for tropical forest restoration.
Christopher D. Philipson; Mark E. J. Cutler; Philip G. Brodrick; Gregory P. Asner; Doreen S. Boyd; Pedro Moura Costa; Joel Fiddes; Giles M. Foody; Geertje M. F. Van Der Heijden; Alicia Ledo; Philippa R. Lincoln; James A. Margrove; Roberta E. Martin; Sol Milne; Michelle A. Pinard; Glen Reynolds; Martijn Snoep; Hamzah Tangki; Yap Sau Wai; Charlotte E. Wheeler; David F. R. P. Burslem. Active restoration accelerates the carbon recovery of human-modified tropical forests. Science 2020, 369, 838 -841.
AMA StyleChristopher D. Philipson, Mark E. J. Cutler, Philip G. Brodrick, Gregory P. Asner, Doreen S. Boyd, Pedro Moura Costa, Joel Fiddes, Giles M. Foody, Geertje M. F. Van Der Heijden, Alicia Ledo, Philippa R. Lincoln, James A. Margrove, Roberta E. Martin, Sol Milne, Michelle A. Pinard, Glen Reynolds, Martijn Snoep, Hamzah Tangki, Yap Sau Wai, Charlotte E. Wheeler, David F. R. P. Burslem. Active restoration accelerates the carbon recovery of human-modified tropical forests. Science. 2020; 369 (6505):838-841.
Chicago/Turabian StyleChristopher D. Philipson; Mark E. J. Cutler; Philip G. Brodrick; Gregory P. Asner; Doreen S. Boyd; Pedro Moura Costa; Joel Fiddes; Giles M. Foody; Geertje M. F. Van Der Heijden; Alicia Ledo; Philippa R. Lincoln; James A. Margrove; Roberta E. Martin; Sol Milne; Michelle A. Pinard; Glen Reynolds; Martijn Snoep; Hamzah Tangki; Yap Sau Wai; Charlotte E. Wheeler; David F. R. P. Burslem. 2020. "Active restoration accelerates the carbon recovery of human-modified tropical forests." Science 369, no. 6505: 838-841.
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 situation greatly limits applications that require LST data with fine spatiotemporal resolution. Here, a deep learning-based spatiotemporal temperature fusion network (STTFN) method for the generation of fine spatiotemporal resolution LST products is proposed. In STTFN, a multiscale fusion convolutional neural network is employed to build the complex nonlinear relationship between input and output LSTs. Thus, unlike other LST spatiotemporal fusion approaches, STTFN is able to form the potentially complicated relationships through the use of training data without manually designed mathematical rules making it is more flexible and intelligent than other methods. In addition, two target fine spatial resolution LST images are predicted and then integrated by a spatiotemporal-consistency (STC)-weighting function to take advantage of STC of LST data. A set of analyses using two real LST data sets obtained from Landsat and moderate resolution imaging spectroradiometer (MODIS) were undertaken to evaluate the ability of STTFN to generate fine spatiotemporal resolution LST products. The results show that, compared with three classic fusion methods [the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), the spatiotemporal integrated temperature fusion model (STITFM), and the two-stream convolutional neural network for spatiotemporal image fusion (StfNet)], the proposed network produced the most accurate outputs [average root mean square error (RMSE) < 1.40 °C and average structural similarity (SSIM) > 0.971].
Zhixiang Yin; Penghai Wu; Giles M. Foody; Yanlan Wu; Zihan Liu; Yun Du; Feng Ling. Spatiotemporal Fusion of Land Surface Temperature Based on a Convolutional Neural Network. IEEE Transactions on Geoscience and Remote Sensing 2020, 59, 1808 -1822.
AMA StyleZhixiang Yin, Penghai Wu, Giles M. Foody, Yanlan Wu, Zihan Liu, Yun Du, Feng Ling. Spatiotemporal Fusion of Land Surface Temperature Based on a Convolutional Neural Network. IEEE Transactions on Geoscience and Remote Sensing. 2020; 59 (2):1808-1822.
Chicago/Turabian StyleZhixiang Yin; Penghai Wu; Giles M. Foody; Yanlan Wu; Zihan Liu; Yun Du; Feng Ling. 2020. "Spatiotemporal Fusion of Land Surface Temperature Based on a Convolutional Neural Network." IEEE Transactions on Geoscience and Remote Sensing 59, no. 2: 1808-1822.
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 which each class is also mapped onto similar user's accuracy. To solve this problem, we integrated local adaptive region and box-and-whisker plot (BP) techniques into an iterative algorithm to expand the size of the training sample for selected classes in this article. The major steps of the proposed algorithm are as follows. First, a very small initial training sample (ITS) for each class set is labeled manually. Second, potential new training samples are found within an adaptive region by conducting local spectral variation analysis. Lastly, three new training samples are acquired to capture information regarding intraclass variation; these samples lie in the lower, median, and upper quartiles of BP. After adding these new training samples to the ITS, classification is retrained and the process is continued iteratively until termination. The proposed approach was applied to three very high-resolution (VHR) remote-sensing images and compared with a set of cognate methods. The comparison demonstrated that the proposed approach produced the best result in terms of OA and exhibited superiority in balancing user's accuracy. For example, the proposed approach was typically 2%-10% more accurate than the compared methods in terms of OA and it generally yielded the most balanced classification.
Zhiyong Lv; Guangfei Li; Zhenong Jin; Jon Atli Benediktsson; Giles M. Foody. Iterative Training Sample Expansion to Increase and Balance the Accuracy of Land Classification From VHR Imagery. IEEE Transactions on Geoscience and Remote Sensing 2020, 59, 139 -150.
AMA StyleZhiyong Lv, Guangfei Li, Zhenong Jin, Jon Atli Benediktsson, Giles M. Foody. Iterative Training Sample Expansion to Increase and Balance the Accuracy of Land Classification From VHR Imagery. IEEE Transactions on Geoscience and Remote Sensing. 2020; 59 (1):139-150.
Chicago/Turabian StyleZhiyong Lv; Guangfei Li; Zhenong Jin; Jon Atli Benediktsson; Giles M. Foody. 2020. "Iterative Training Sample Expansion to Increase and Balance the Accuracy of Land Classification From VHR Imagery." IEEE Transactions on Geoscience and Remote Sensing 59, no. 1: 139-150.
Land use and land cover (LULC) mapping is often undertaken by national mapping agencies, where these LULC products are used for different types of monitoring and reporting applications. Updating of LULC databases is often done on a multi-year cycle due to the high costs involved, so changes are only detected when mapping exercises are repeated. Consequently, the information on LULC can quickly become outdated and hence may be incorrect in some areas. In the current era of big data and Earth observation, change detection algorithms can be used to identify changes in urban areas, which can then be used to automatically update LULC databases on a more continuous basis. However, the change detection algorithm must be validated before the changes can be committed to authoritative databases such as those produced by national mapping agencies. This paper outlines a change detection algorithm for identifying construction sites, which represent ongoing changes in LU, developed in the framework of the LandSense project. We then use volunteered geographic information (VGI) captured through the use of mapathons from a range of different groups of contributors to validate these changes. In total, 105 contributors were involved in the mapathons, producing a total of 2778 observations. The 105 contributors were grouped according to six different user-profiles and were analyzed to understand the impact of the experience of the users on the accuracy assessment. Overall, the results show that the change detection algorithm is able to identify changes in residential land use to an adequate level of accuracy (85%) but changes in infrastructure and industrial sites had lower accuracies (57% and 75 %, respectively), requiring further improvements. In terms of user profiles, the experts in LULC from local authorities, researchers in LULC at the French national mapping agency (IGN), and first-year students with a basic knowledge of geographic information systems had the highest overall accuracies (86.2%, 93.2%, and 85.2%, respectively). Differences in how the users approach the task also emerged, e.g., local authorities used knowledge and context to try to identify types of change while those with no knowledge of LULC (i.e., normal citizens) were quicker to choose ‘Unknown’ when the visual interpretation of a class was more difficult.
A.-M. Olteanu-Raimond; L. See; M. Schultz; G. Foody; M. Riffler; T. Gasber; L. Jolivet; A. Le Bris; Y. Meneroux; L. Liu; M. Poupée; M. Gombert. Use of Automated Change Detection and VGI Sources for Identifying and Validating Urban Land Use Change. Remote Sensing 2020, 12, 1186 .
AMA StyleA.-M. Olteanu-Raimond, L. See, M. Schultz, G. Foody, M. Riffler, T. Gasber, L. Jolivet, A. Le Bris, Y. Meneroux, L. Liu, M. Poupée, M. Gombert. Use of Automated Change Detection and VGI Sources for Identifying and Validating Urban Land Use Change. Remote Sensing. 2020; 12 (7):1186.
Chicago/Turabian StyleA.-M. Olteanu-Raimond; L. See; M. Schultz; G. Foody; M. Riffler; T. Gasber; L. Jolivet; A. Le Bris; Y. Meneroux; L. Liu; M. Poupée; M. Gombert. 2020. "Use of Automated Change Detection and VGI Sources for Identifying and Validating Urban Land Use Change." Remote Sensing 12, no. 7: 1186.
The generation of land cover maps with both fine spatial and temporal resolution would aid the monitoring of change on the Earth’s surface. Spatio-temporal sub-pixel land cover mapping (STSPM) uses a few fine spatial resolution (FR) maps and a time series of coarse spatial resolution (CR) remote sensing images as input to generate FR land cover maps with a temporal frequency of the CR data set. Traditional STSPM selects spatially adjacent FR pixels within a local window as neighborhoods to model the land cover spatial dependence, which can be a source of error and uncertainty in the maps generated by the analysis. This paper proposes a new STSPM using FR remote sensing images that pre- and/or post-date the CR image as ancillary data to enhance the quality of the FR map outputs. Spectrally similar pixels within the locality of a target FR pixel in the ancillary data are likely to represent the same land cover class and hence such same-class pixels can provide spatial information to aid the analysis. Experimental results showed that the proposed STSPM predicted land cover maps more accurately than two comparative state-of-the-art STSPM algorithms.
Xiaodong Li; Rui Chen; Giles M. Foody; Lihui Wang; Xiaohong Yang; Yun Du; Feng Ling. Spatio-Temporal Sub-Pixel Land Cover Mapping of Remote Sensing Imagery Using Spatial Distribution Information From Same-Class Pixels. Remote Sensing 2020, 12, 503 .
AMA StyleXiaodong Li, Rui Chen, Giles M. Foody, Lihui Wang, Xiaohong Yang, Yun Du, Feng Ling. Spatio-Temporal Sub-Pixel Land Cover Mapping of Remote Sensing Imagery Using Spatial Distribution Information From Same-Class Pixels. Remote Sensing. 2020; 12 (3):503.
Chicago/Turabian StyleXiaodong Li; Rui Chen; Giles M. Foody; Lihui Wang; Xiaohong Yang; Yun Du; Feng Ling. 2020. "Spatio-Temporal Sub-Pixel Land Cover Mapping of Remote Sensing Imagery Using Spatial Distribution Information From Same-Class Pixels." Remote Sensing 12, no. 3: 503.
Feng Ling; Doreen Boyd; Yong Ge; Giles M. Foody; Xiaodong Li; Lihui Wang; Yihang Zhang; Lingfei Shi; Cheng Shang; Xinyan Li; Yun Du. Measuring River Wetted Width From Remotely Sensed Imagery at the Subpixel Scale With a Deep Convolutional Neural Network. Water Resources Research 2019, 55, 5631 -5649.
AMA StyleFeng Ling, Doreen Boyd, Yong Ge, Giles M. Foody, Xiaodong Li, Lihui Wang, Yihang Zhang, Lingfei Shi, Cheng Shang, Xinyan Li, Yun Du. Measuring River Wetted Width From Remotely Sensed Imagery at the Subpixel Scale With a Deep Convolutional Neural Network. Water Resources Research. 2019; 55 (7):5631-5649.
Chicago/Turabian StyleFeng Ling; Doreen Boyd; Yong Ge; Giles M. Foody; Xiaodong Li; Lihui Wang; Yihang Zhang; Lingfei Shi; Cheng Shang; Xinyan Li; Yun Du. 2019. "Measuring River Wetted Width From Remotely Sensed Imagery at the Subpixel Scale With a Deep Convolutional Neural Network." Water Resources Research 55, no. 7: 5631-5649.