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Brian Alan Johnson
Natural Resources and Ecosystem Services Area, Institute for Global Environmental Strategies, 2108-1 Kamiyamaguchi, Hayama, Kanagawa 240-0115, Japan

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Journal article
Published: 06 July 2021 in Remote Sensing
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Lichen is an important food source for caribou in Canada. Lichen mapping using remote sensing (RS) images could be a challenging task, however, as lichens generally appear in unevenly distributed, small patches, and could resemble surficial features. Moreover, collecting lichen labeled data (reference data) is expensive, which restricts the application of many robust supervised classification models that generally demand a large quantity of labeled data. The goal of this study was to investigate the potential of using a very-high-spatial resolution (1-cm) lichen map of a small sample site (e.g., generated based on a single UAV scene and using field data) to train a subsequent classifier to map caribou lichen over a much larger area (~0.04 km2 vs. ~195 km2) and a lower spatial resolution image (in this case, a 50-cm WorldView-2 image). The limited labeled data from the sample site were also partially noisy due to spatial and temporal mismatching issues. For this, we deployed a recently proposed Teacher-Student semi-supervised learning (SSL) approach (based on U-Net and U-Net++ networks) involving unlabeled data to assist with improving the model performance. Our experiments showed that it was possible to scale-up the UAV-derived lichen map to the WorldView-2 scale with reasonable accuracy (overall accuracy of 85.28% and F1-socre of 84.38%) without collecting any samples directly in the WorldView-2 scene. We also found that our noisy labels were partially beneficial to the SSL robustness because they improved the false positive rate compared to the use of a cleaner training set directly collected within the same area in the WorldView-2 image. As a result, this research opens new insights into how current very high-resolution, small-scale caribou lichen maps can be used for generating more accurate large-scale caribou lichen maps from high-resolution satellite imagery.

ACS Style

Shahab Jozdani; Dongmei Chen; Wenjun Chen; Sylvain Leblanc; Christian Prévost; Julie Lovitt; Liming He; Brian Johnson. Leveraging Deep Neural Networks to Map Caribou Lichen in High-Resolution Satellite Images Based on a Small-Scale, Noisy UAV-Derived Map. Remote Sensing 2021, 13, 2658 .

AMA Style

Shahab Jozdani, Dongmei Chen, Wenjun Chen, Sylvain Leblanc, Christian Prévost, Julie Lovitt, Liming He, Brian Johnson. Leveraging Deep Neural Networks to Map Caribou Lichen in High-Resolution Satellite Images Based on a Small-Scale, Noisy UAV-Derived Map. Remote Sensing. 2021; 13 (14):2658.

Chicago/Turabian Style

Shahab Jozdani; Dongmei Chen; Wenjun Chen; Sylvain Leblanc; Christian Prévost; Julie Lovitt; Liming He; Brian Johnson. 2021. "Leveraging Deep Neural Networks to Map Caribou Lichen in High-Resolution Satellite Images Based on a Small-Scale, Noisy UAV-Derived Map." Remote Sensing 13, no. 14: 2658.

Journal article
Published: 03 June 2021 in Sustainability
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Widespread urban expansion around the world, combined with rapid demographic and climatic changes, has resulted in serious pollution issues in many coastal water bodies. To help formulate coastal management strategies to mitigate the impacts of these extreme changes (e.g., local land-use or climate change adaptation policies), research methodologies that incorporate participatory approaches alongside with computer simulation modeling tools have potential to be particularly effective. One such research methodology, called the “Participatory Coastal Land-Use Management” (PCLM) approach, consists of three major steps: (a) participatory approach to find key drivers responsible for the water quality deterioration, (b) scenario analysis using different computer simulation modeling tools for impact assessment, and (c) using these scientific evidences for developing adaptation and mitigation measures. In this study, we have applied PCLM approach in the Kendrapara district of India (focusing on the Brahmani River basin), a rapidly urbanizing area on the country’s east coast to evaluate current status and predict its future conditions. The participatory approach involved key informant interviews to determine key drivers of water quality degradation, which served as an input for scenario analysis and hydrological simulation in the next step. Future river water quality (BOD and Total coliform (Tot. coli) as important parameters) was simulated using the Water Evaluation and Planning (WEAP) tool, considering a different plausible future scenario (to 2050) incorporating diverse drivers and pressures (i.e., population growth, land-use change, and climate change). Water samples (collected in 2018) indicated that the Brahmani River in this district was already moderately-to-extremely polluted in comparison to the desirable water quality (Class B), and modeling results indicated that the river water quality is likely to further deteriorate by 2050 under all of the considered scenarios. Demographic changes emerged as the major driver affecting the future water quality deterioration (68% and 69% for BOD and Tot. coli respectively), whereas climate change had the lowest impact on river water quality (12% and 13% for BOD and Tot. coli respectively), although the impact was not negligible. Scientific evidence to understand the impacts of future changes can help in developing diverse plausible coastal zone management approaches for ensuring sustainable management of water resources in the region. The PCLM approach, by having active stakeholder involvement, can help in co-generation of the coastal management options followed by open access free software, and models can play a relevant cost-effective approach to enhance science-policy interface for conservation of natural resources.

ACS Style

Pankaj Kumar; Rajarshi Dasgupta; Shalini Dhyani; Rakesh Kadaverugu; Brian Johnson; Shizuka Hashimoto; Netrananda Sahu; Ram Avtar; Osamu Saito; Shamik Chakraborty; Binaya Mishra. Scenario-Based Hydrological Modeling for Designing Climate-Resilient Coastal Water Resource Management Measures: Lessons from Brahmani River, Odisha, Eastern India. Sustainability 2021, 13, 6339 .

AMA Style

Pankaj Kumar, Rajarshi Dasgupta, Shalini Dhyani, Rakesh Kadaverugu, Brian Johnson, Shizuka Hashimoto, Netrananda Sahu, Ram Avtar, Osamu Saito, Shamik Chakraborty, Binaya Mishra. Scenario-Based Hydrological Modeling for Designing Climate-Resilient Coastal Water Resource Management Measures: Lessons from Brahmani River, Odisha, Eastern India. Sustainability. 2021; 13 (11):6339.

Chicago/Turabian Style

Pankaj Kumar; Rajarshi Dasgupta; Shalini Dhyani; Rakesh Kadaverugu; Brian Johnson; Shizuka Hashimoto; Netrananda Sahu; Ram Avtar; Osamu Saito; Shamik Chakraborty; Binaya Mishra. 2021. "Scenario-Based Hydrological Modeling for Designing Climate-Resilient Coastal Water Resource Management Measures: Lessons from Brahmani River, Odisha, Eastern India." Sustainability 13, no. 11: 6339.

Review
Published: 14 May 2021 in Water
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Water scarcity, together with the projected impacts of water stress worldwide, has led to a rapid increase in research on measuring water security. However, water security has been conceptualized under different perspectives, including various aspects and dimensions. Since public health is also an integral part of water security, it is necessary to understand how health has been incorporated as a dimension in the existing water security frameworks. While supply–demand and governance narratives dominated several popular water security frameworks, studies that are specifically designed for public health purposes are generally lacking. This research aims to address this gap, firstly by assessing the multiple thematic dimensions of water security frameworks in scientific disclosure; and secondly by looking into the public health dimensions and evaluating their importance and integration in the existing water security frameworks. For this, a systematic review of the Scopus database was undertaken using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A detailed review analysis of 77 relevant papers was performed. The result shows that 11 distinct dimensions have been used to design the existing water security framework. Although public health aspects were mentioned in 51% of the papers, direct health impacts were considered only by 18%, and indirect health impacts or mediators were considered by 33% of the papers. Among direct health impacts, diarrhea is the most prevalent one considered for developing a water security framework. Among different indirect or mediating factors, poor accessibility and availability of water resources in terms of time and distance is a big determinant for causing mental illnesses, such as stress or anxiety, which are being considered when framing water security framework, particularly in developing nations. Water quantity is more of a common issue for both developed and developing countries, water quality and mismanagement of water supply-related infrastructure is the main concern for developing nations, which proved to be the biggest hurdle for achieving water security. It is also necessary to consider how people treat and consume the water available to them. The result of this study sheds light on existing gaps for different water security frameworks and provides policy-relevant guidelines for its betterment. Also, it stressed that a more wide and holistic approach must be considered when framing a water security framework to result in sustainable water management and human well-being.

ACS Style

Sushila Paudel; Pankaj Kumar; Rajarshi Dasgupta; Brian Johnson; Ram Avtar; Rajib Shaw; Binaya Mishra; Sakiko Kanbara. Nexus between Water Security Framework and Public Health: A Comprehensive Scientific Review. Water 2021, 13, 1365 .

AMA Style

Sushila Paudel, Pankaj Kumar, Rajarshi Dasgupta, Brian Johnson, Ram Avtar, Rajib Shaw, Binaya Mishra, Sakiko Kanbara. Nexus between Water Security Framework and Public Health: A Comprehensive Scientific Review. Water. 2021; 13 (10):1365.

Chicago/Turabian Style

Sushila Paudel; Pankaj Kumar; Rajarshi Dasgupta; Brian Johnson; Ram Avtar; Rajib Shaw; Binaya Mishra; Sakiko Kanbara. 2021. "Nexus between Water Security Framework and Public Health: A Comprehensive Scientific Review." Water 13, no. 10: 1365.

Journal article
Published: 30 April 2021 in Conservation
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Biodiversity knowledge is communicated by scientists to policymakers at the biodiversity “science-policy interface” (SPI). Although the biodiversity SPI is the subject of a growing body of literature, gaps in our understanding include the efficacy of mechanisms to bridge the interface, the quality of information exchanged between science and policy, and the inclusivity of stakeholders involved. To improve this understanding, we surveyed an important but under-studied group—biodiversity policymakers and scientific advisors representing their respective countries in negotiations of the Convention on Biological Diversity (CBD) and the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES). We found that a wide variety of SPI mechanisms were being used. Overall, they were considered to be sufficiently effective, improving over time, and supplied with information of adequate quality. Most respondents, however, agreed that key actors were still missing from the biodiversity SPI.

ACS Style

André Mader; Brian Johnson; Yuki Ohashi; Isabella Fenstermaker. Country Representatives’ Perceptions of the Biodiversity Science-Policy Interface. Conservation 2021, 1, 73 -80.

AMA Style

André Mader, Brian Johnson, Yuki Ohashi, Isabella Fenstermaker. Country Representatives’ Perceptions of the Biodiversity Science-Policy Interface. Conservation. 2021; 1 (2):73-80.

Chicago/Turabian Style

André Mader; Brian Johnson; Yuki Ohashi; Isabella Fenstermaker. 2021. "Country Representatives’ Perceptions of the Biodiversity Science-Policy Interface." Conservation 1, no. 2: 73-80.

Accepted manuscript
Published: 12 February 2021 in Environmental Research Letters
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With the intensifying challenges of global sustainability and biodiversity conservation, the monitoring of the world's remaining forests has become more important than ever. Today, Earth observation technologies, particularly remote sensing, are at the forefront of forest cover monitoring worldwide. Given the current conceptual understanding of what a forest is, canopy cover threshold values are used to map forest cover from remote sensing imagery and produce categorical data products such as forest/non-forest (F/NF) maps. However, multi-temporal categorical map products have important limitations because they inadequately represent the actual status of forest landscapes and the trajectories of forest cover changes as a result of the thresholding effect. Here, we examined the potential of using remotely sensed tree canopy cover (TCC) datasets, which are continuous data products, to complement F/NF maps for forest cover monitoring. We developed a conceptual analytical framework for forest cover monitoring using both types of data products and applied it to the forests of Southeast Asia. We conclude that TCC datasets and the statistics derived from them can be used to complement the information provided by categorical F/NF maps. TCC-based indicators (i.e., losses, gains, and net changes) can help in monitoring not only deforestation but also forest degradation and forest cover enhancement, all of which are highly relevant to the 2030 Agenda for Sustainable Development and other global forest cover monitoring–related initiatives. We recommend that future research should focus on the production, application, and evaluation of TCC datasets to advance the current understanding of how accurately these products can capture changes in forest landscapes across space and time.

ACS Style

Ronald C. Estoque; Brian Alan Johnson; Yan Gao; Rajarshi DasGupta; Makoto Ooba; Takuya Togawa; Yasuaki Hijioka; Yuji Murayama; Lilito D Gavina; Rodel D Lasco; Shogo Nakamura. Remotely sensed tree canopy cover–based indicators for monitoring global sustainability and environmental initiatives. Environmental Research Letters 2021, 1 .

AMA Style

Ronald C. Estoque, Brian Alan Johnson, Yan Gao, Rajarshi DasGupta, Makoto Ooba, Takuya Togawa, Yasuaki Hijioka, Yuji Murayama, Lilito D Gavina, Rodel D Lasco, Shogo Nakamura. Remotely sensed tree canopy cover–based indicators for monitoring global sustainability and environmental initiatives. Environmental Research Letters. 2021; ():1.

Chicago/Turabian Style

Ronald C. Estoque; Brian Alan Johnson; Yan Gao; Rajarshi DasGupta; Makoto Ooba; Takuya Togawa; Yasuaki Hijioka; Yuji Murayama; Lilito D Gavina; Rodel D Lasco; Shogo Nakamura. 2021. "Remotely sensed tree canopy cover–based indicators for monitoring global sustainability and environmental initiatives." Environmental Research Letters , no. : 1.

Review
Published: 27 January 2021 in Remote Sensing
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Remote sensing technology has seen a massive rise in popularity over the last two decades, becoming an integral part of our lives. Space-based satellite technologies facilitated access to the inaccessible terrains, helped humanitarian teams, support complex emergencies, and contributed to monitoring and verifying conflict zones. The scoping phase of this review investigated the utility of the role of remote sensing application to complement international peace and security activities owing to their ability to provide objective near real-time insights at the ground level. The first part of this review looks into the major research concepts and implementation of remote sensing-based techniques for international peace and security applications and presented a meta-analysis on how advanced sensor capabilities can support various aspects of peace and security. With key examples, we demonstrated how this technology assemblage enacts multiple versions of peace and security: for refugee relief operations, in armed conflicts monitoring, tracking acts of genocide, providing evidence in courts of law, and assessing contravention in human rights. The second part of this review anticipates future challenges that can hinder the applicative capabilities of remote sensing in peace and security. Varying types of sensors pose discrepancies in image classifications and issues like cost, resolution, and difficulty of ground-truth in conflict areas. With emerging technologies and sufficient secondary resources available, remote sensing plays a vital operational tool in conflict-affected areas by supporting an extensive diversity in public policy actions for peacekeeping processes.

ACS Style

Ram Avtar; Asma Kouser; Ashwani Kumar; Deepak Singh; Prakhar Misra; Ankita Gupta; Ali Yunus; Pankaj Kumar; Brian Johnson; Rajarshi Dasgupta; Netrananda Sahu; Andi Besse Rimba. Remote Sensing for International Peace and Security: Its Role and Implications. Remote Sensing 2021, 13, 439 .

AMA Style

Ram Avtar, Asma Kouser, Ashwani Kumar, Deepak Singh, Prakhar Misra, Ankita Gupta, Ali Yunus, Pankaj Kumar, Brian Johnson, Rajarshi Dasgupta, Netrananda Sahu, Andi Besse Rimba. Remote Sensing for International Peace and Security: Its Role and Implications. Remote Sensing. 2021; 13 (3):439.

Chicago/Turabian Style

Ram Avtar; Asma Kouser; Ashwani Kumar; Deepak Singh; Prakhar Misra; Ankita Gupta; Ali Yunus; Pankaj Kumar; Brian Johnson; Rajarshi Dasgupta; Netrananda Sahu; Andi Besse Rimba. 2021. "Remote Sensing for International Peace and Security: Its Role and Implications." Remote Sensing 13, no. 3: 439.

Journal article
Published: 18 January 2021 in Water
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Considering the well-documented impacts of land-use change on water resources and the rapid land-use conversions occurring throughout Africa, in this study, we conducted a spatiotemporal analysis of surface water quality and its relation with the land use and land cover (LULC) pattern in Mokopane, Limpopo province of South Africa. Various physico-chemical parameters were analyzed for surface water samples collected from five sampling locations from 2016 to 2020. Time-series analysis of key surface water quality parameters was performed to identify the essential hydrological processes governing water quality. The analyzed water quality data were also used to calculate the heavy metal pollution index (HPI), heavy metal evaluation index (HEI) and weighted water quality index (WQI). Also, the spatial trend of water quality is compared with LULC changes from 2015 to 2020. Results revealed that the concentration of most of the physico-chemical parameters in the water samples was beyond the World Health Organization (WHO) adopted permissible limit, except for a few parameters in some locations. Based on the calculated values of HPI and HEI, water quality samples were categorized as low to moderately polluted water bodies, whereas all water samples fell under the poor category (>100) and beyond based on the calculated WQI. Looking precisely at the water quality’s temporal trend, it is found that most of the sampling shows a deteriorating trend from 2016 to 2019. However, the year 2020 shows a slightly improving trend on water quality, which can be justified by lowering human activities during the lockdown period imposed by COVID-19. Land use has a significant relationship with surface water quality, and it was evident that built-up land had a more significant negative impact on water quality than the other land use classes. Both natural processes (rock weathering) and anthropogenic activities (wastewater discharge, industrial activities etc.) were found to be playing a vital role in water quality evolution. This study suggests that continuous assessment and monitoring of the spatial and temporal variability of water quality in Limpopo is important to control pollution and health safety in the future.

ACS Style

Mmasabata Molekoa; Ram Avtar; Pankaj Kumar; Huynh Thu Minh; Rajarshi Dasgupta; Brian Johnson; Netrananda Sahu; Ram Verma; Ali Yunus. Spatio-Temporal Analysis of Surface Water Quality in Mokopane Area, Limpopo, South Africa. Water 2021, 13, 220 .

AMA Style

Mmasabata Molekoa, Ram Avtar, Pankaj Kumar, Huynh Thu Minh, Rajarshi Dasgupta, Brian Johnson, Netrananda Sahu, Ram Verma, Ali Yunus. Spatio-Temporal Analysis of Surface Water Quality in Mokopane Area, Limpopo, South Africa. Water. 2021; 13 (2):220.

Chicago/Turabian Style

Mmasabata Molekoa; Ram Avtar; Pankaj Kumar; Huynh Thu Minh; Rajarshi Dasgupta; Brian Johnson; Netrananda Sahu; Ram Verma; Ali Yunus. 2021. "Spatio-Temporal Analysis of Surface Water Quality in Mokopane Area, Limpopo, South Africa." Water 13, no. 2: 220.

Journal article
Published: 08 January 2021 in Remote Sensing
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Research on the service values of urban ecosystems is a hot topic of ecological studies in the current era of rapid urbanization. To quantitatively estimate the ecosystem service value in Chengdu, China from the perspectives of natural ecology and social ecology, the technologies of remote sensing (RS) and geographic information system (GIS) are utilized in this study to extract the land use type information from RS images of Chengdu in 2003, 2007, 2013 and 2018. Subsequently, a driver analysis of the ecosystem services of Chengdu was performed based on socioeconomic data from the last 16 years. The results indicated that: (1) from 2003 to 2018, the land utilization in Chengdu changed significantly, with the area of cultivated lands, forest lands and water decreasing remarkably, while the area of construction lands dramatically increased. (2) The ecosystem services value (ESV) of Chengdu decreased by 30.92% in the last 16 years, from CNY 2.4078 × 1010 in 2003 to CNY 1.6632 × 1010 in 2018. Based on a future simulation, the ESV is further predicted to be reduced to CNY 1.4261 × 1010 by 2033. (3) The ESV of Chengdu showed a negative correlation with the total population, the urbanization rate and the per capita GDP of the region, indicating that the ESV of the studied region was inter-coupled with the socioeconomic development and can be maintained at a high level through rationally regulating the socioeconomic structure.

ACS Style

Xiaoai Dai; Brian Alan Johnson; Penglan Luo; Kai Yang; Linxin Dong; Qiang Wang; Chao Liu; Naiwen Li; Heng Lu; Lei Ma; Zhengli Yang; Yuanzhi Yao. Estimation of Urban Ecosystem Services Value: A Case Study of Chengdu, Southwestern China. Remote Sensing 2021, 13, 207 .

AMA Style

Xiaoai Dai, Brian Alan Johnson, Penglan Luo, Kai Yang, Linxin Dong, Qiang Wang, Chao Liu, Naiwen Li, Heng Lu, Lei Ma, Zhengli Yang, Yuanzhi Yao. Estimation of Urban Ecosystem Services Value: A Case Study of Chengdu, Southwestern China. Remote Sensing. 2021; 13 (2):207.

Chicago/Turabian Style

Xiaoai Dai; Brian Alan Johnson; Penglan Luo; Kai Yang; Linxin Dong; Qiang Wang; Chao Liu; Naiwen Li; Heng Lu; Lei Ma; Zhengli Yang; Yuanzhi Yao. 2021. "Estimation of Urban Ecosystem Services Value: A Case Study of Chengdu, Southwestern China." Remote Sensing 13, no. 2: 207.

Journal article
Published: 02 August 2020 in Geomorphology
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Debris cover on glacier surfaces hampers the accurate detection of debris-covered ice using traditional techniques based on image band ratios. Therefore, this study tests a new automatic classification scheme for hierarchical mapping of glacier surfaces based on machine learning classifiers including k-nearest neighbors (KNN), support vector machine (SVM), gradient boosting (GB), decision tree (DT), random forest (RF) and multi-layer perceptron (MLP). Several raster layer combinations (synthetic aperture radar (SAR) coherence image derived from Sentinel-1 data, visible near-infrared to short wave infrared bands from Sentinel-2, thermal information from Landsat 8 and geomorphometric parameters from the Advanced Land Observing Satellite (ALOS) World 3D 30 m mesh (AW3D30) digital elevation model) were tested to delineate the debris-covered glaciers in the Gilgit-Baltistan, Pakistan and Shaksgam valley, China. The highest over classification accuracy (97%) was obtained using the RF classifier (followed by the GB and SVM with radial basis function kernel) and utilizing all of the multisensor Sentinel/Landsat/ALOS data. Notably, the RF classifier showed to be robust to parameter settings, fast and accurate for mapping debris-covered ice. GB classifier showed similar performance as RF despite it has a moderately lower accuracy compared to RF. Although SVM classifier has a slower in the speed of tuning hyper-parameter, it still performs the third-best classification accuracy. As the multisensory data we used is freely and (near-)globally available, our methodology potentially could be applied for precise delineation of debris-covered glaciers in other areas.

ACS Style

Haireti Alifu; Jean-Francois Vuillaume; Brian Alan Johnson; Yukiko Hirabayashi. Machine-learning classification of debris-covered glaciers using a combination of Sentinel-1/-2 (SAR/optical), Landsat 8 (thermal) and digital elevation data. Geomorphology 2020, 369, 107365 .

AMA Style

Haireti Alifu, Jean-Francois Vuillaume, Brian Alan Johnson, Yukiko Hirabayashi. Machine-learning classification of debris-covered glaciers using a combination of Sentinel-1/-2 (SAR/optical), Landsat 8 (thermal) and digital elevation data. Geomorphology. 2020; 369 ():107365.

Chicago/Turabian Style

Haireti Alifu; Jean-Francois Vuillaume; Brian Alan Johnson; Yukiko Hirabayashi. 2020. "Machine-learning classification of debris-covered glaciers using a combination of Sentinel-1/-2 (SAR/optical), Landsat 8 (thermal) and digital elevation data." Geomorphology 369, no. : 107365.

Editorial
Published: 01 June 2020 in Remote Sensing
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Image segmentation and geographic object-based image analysis (GEOBIA) were proposed around the turn of the century as a means to analyze high-spatial-resolution remote sensing images. Since then, object-based approaches have been used to analyze a wide range of images for numerous applications. In this Editorial, we present some highlights of image segmentation and GEOBIA research from the last two years (2018–2019), including a Special Issue published in the journal Remote Sensing. As a final contribution of this special issue, we have shared the views of 45 other researchers (corresponding authors of published papers on GEOBIA in 2018–2019) on the current state and future priorities of this field, gathered through an online survey. Most researchers surveyed acknowledged that image segmentation/GEOBIA approaches have achieved a high level of maturity, although the need for more free user-friendly software and tools, further automation, better integration with new machine-learning approaches (including deep learning), and more suitable accuracy assessment methods was frequently pointed out.

ACS Style

Brian Alan Johnson; Lei Ma. Image Segmentation and Object-Based Image Analysis for Environmental Monitoring: Recent Areas of Interest, Researchers’ Views on the Future Priorities. Remote Sensing 2020, 12, 1772 .

AMA Style

Brian Alan Johnson, Lei Ma. Image Segmentation and Object-Based Image Analysis for Environmental Monitoring: Recent Areas of Interest, Researchers’ Views on the Future Priorities. Remote Sensing. 2020; 12 (11):1772.

Chicago/Turabian Style

Brian Alan Johnson; Lei Ma. 2020. "Image Segmentation and Object-Based Image Analysis for Environmental Monitoring: Recent Areas of Interest, Researchers’ Views on the Future Priorities." Remote Sensing 12, no. 11: 1772.

Reply
Published: 31 May 2020 in Remote Sensing
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Land use/land cover (LULC) maps are now being used across disciplines for many different types of applications, e.g., to analyze urban heat islands or rainfall-runoff dynamics. Traditional map accuracy metrics are limited in this regard, as they only assess LULC map thematic accuracy. In reality, some types of misclassification lead to larger estimation errors for these specific applications. In a previous study, we developed a new map accuracy metric (referred to here as “JJ19”) to assess the accuracy of local climate zone maps for urban microclimate analysis. In the previous work, we also attempted to reproduce another metric (weighted accuracy (WA)) proposed for this purpose, but misinterpreted it due to a lack of methodological information available (principally, the lack of a confusion matrix to demonstrate how WA was derived). We sincerely thank the authors of Bechtel et al. 2019 for providing more information on WA in response to our previous study and are happy to report that we found that the metric is now both reproducible and valid. On the other hand, we found some other aspects of Bechtel et al. 2019’s study to be inaccurate, particularly their claims regarding the suitability of the JJ19 metric. Finally, we made a minor improvement to the JJ19 metric based on Bechtel et al.’s comments.

ACS Style

Brian Alan Johnson; Shahab Eddin Jozdani. Confusion Matrices Help Prevent Reader Confusion: Reply to Bechtel, B., et al. A Weighted Accuracy Measure for Land Cover Mapping: Comment on Johnson et al. Local Climate Zone (LCZ) Map Accuracy Assessments Should Account for Land Cover Physical Characteristics that Affect the Local Thermal Environment, Remote Sens. 2019, 11, 2420. Remote Sensing 2020, 12, 1771 .

AMA Style

Brian Alan Johnson, Shahab Eddin Jozdani. Confusion Matrices Help Prevent Reader Confusion: Reply to Bechtel, B., et al. A Weighted Accuracy Measure for Land Cover Mapping: Comment on Johnson et al. Local Climate Zone (LCZ) Map Accuracy Assessments Should Account for Land Cover Physical Characteristics that Affect the Local Thermal Environment, Remote Sens. 2019, 11, 2420. Remote Sensing. 2020; 12 (11):1771.

Chicago/Turabian Style

Brian Alan Johnson; Shahab Eddin Jozdani. 2020. "Confusion Matrices Help Prevent Reader Confusion: Reply to Bechtel, B., et al. A Weighted Accuracy Measure for Land Cover Mapping: Comment on Johnson et al. Local Climate Zone (LCZ) Map Accuracy Assessments Should Account for Land Cover Physical Characteristics that Affect the Local Thermal Environment, Remote Sens. 2019, 11, 2420." Remote Sensing 12, no. 11: 1771.

Journal article
Published: 19 April 2020 in Water
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Due to the cumulative effects of rapid urbanization, population growth and climate change, many inland and coastal water bodies around the world are experiencing severe water pollution. To help make land-use and climate change adaptation policies more effective at a local scale, this study used a combination of participatory approaches and computer simulation modeling. This methodology (called the “Participatory Watershed Land-use Management” (PWLM) approach) consist of four major steps: (a) Scenario analysis, (b) impact assessment, (c) developing adaptation and mitigation measures and its integration in local government policies, and (d) improvement of land use plan. As a test case, we conducted PWLM in the Santa Rosa Sub-watershed of the Philippines, a rapidly urbanizing area outside Metro Manila. The scenario analysis step involved a participatory land-use mapping activity (to understand future likely land-use changes), as well as GCM precipitation and temperature data downscaling (to understand the local climate scenarios). For impact assessment, the Water Evaluation and Planning (WEAP) tool was used to simulate future river water quality (BOD and E. coli) under a Business as Usual (BAU) scenario and several alternative future scenarios considering different drivers and pressures (to 2030). Water samples from the Santa Rosa River in 2015 showed that BOD values ranged from 13 to 52 mg/L; indicating that the river is already moderately to extremely polluted compared to desirable water quality (class B). In the future scenarios, we found that water quality will deteriorate further by 2030 under all scenarios. Population growth was found to have the highest impact on future water quality deterioration, while climate change had the lowest (although not negligible). After the impact assessment, different mitigation measures were suggested in a stakeholder consultation workshop, and of them (enhanced capacity of wastewater treatment plants (WWTPs), and increased sewerage connection rate) were adopted to generate a final scenario including countermeasures. The main benefit of the PWLM approach are its high level of stakeholder involvement (through co-generation of the research) and use of free (for developing countries) software and models, both of which contribute to an enhanced science-policy interface.

ACS Style

Pankaj Kumar; Brian Alan Johnson; Rajarshi Dasgupta; Ram Avtar; Shamik Chakraborty; Masayuki Kawai; Damasa B. Magcale-Macandog. Participatory Approach for More Robust Water Resource Management: Case Study of the Santa Rosa Sub-Watershed of the Philippines. Water 2020, 12, 1172 .

AMA Style

Pankaj Kumar, Brian Alan Johnson, Rajarshi Dasgupta, Ram Avtar, Shamik Chakraborty, Masayuki Kawai, Damasa B. Magcale-Macandog. Participatory Approach for More Robust Water Resource Management: Case Study of the Santa Rosa Sub-Watershed of the Philippines. Water. 2020; 12 (4):1172.

Chicago/Turabian Style

Pankaj Kumar; Brian Alan Johnson; Rajarshi Dasgupta; Ram Avtar; Shamik Chakraborty; Masayuki Kawai; Damasa B. Magcale-Macandog. 2020. "Participatory Approach for More Robust Water Resource Management: Case Study of the Santa Rosa Sub-Watershed of the Philippines." Water 12, no. 4: 1172.

Journal article
Published: 08 April 2020 in Remote Sensing
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Spatial urban growth and its impact on land surface temperature (LST) is a high priority environmental issue for urban policy. Although the impact of horizontal spatial growth of cities on LST is well studied, the impact of the vertical spatial distribution of buildings on LST is under-investigated. This is particularly true for cities in sub-tropical developing countries. In this study, TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-XDEM), Advanced Spaceborne Thermal Emission and Reflection (ASTER)-Global Digital Elevation Model (GDEM), and ALOS World 3D-30m (AW3D30) based Digital Surface Model (DSM) data were used to investigate the vertical growth of the Dhaka Metropolitan Area (DMA) in Bangladesh. Thermal Infrared (TIR) data (10.6-11.2µm) of Landsat-8 were used to investigate the seasonal variations in LST. Thereafter, the impact of horizontal and vertical spatial growth on LST was studied. The result showed that: (a) TanDEM-X DSM derived building height had a higher accuracy as compared to other existing DSM that reveals mean building height of the Dhaka city is approximately 10 m, (b) built-up areas were estimated to cover approximately 94%, 88%, and 44% in Dhaka South City Corporation (DSCC), Dhaka North City Corporation (DNCC), and Fringe areas, respectively, of DMA using a Support Vector Machine (SVM) classification method, (c) the built-up showed a strong relationship with LST (Kendall tau coefficient of 0.625 in summer and 0.483 in winter) in comparison to vertical growth (Kendall tau coefficient of 0.156 in the summer and 0.059 in the winter), and (d) the ‘low height-high density’ areas showed high LST in both seasons. This study suggests that vertical development is better than horizontal development for providing enough open spaces, green spaces, and preserving natural features. This study provides city planners with a better understating of sustainable urban planning and can promote the formulation of action plans for appropriate urban development policies.

ACS Style

Mustafizur Rahman; Ram Avtar; Ali P. Yunus; Jie Dou; Prakhar Misra; Wataru Takeuchi; Netrananda Sahu; Pankaj Kumar; Brian Alan Johnson; Rajarshi Dasgupta; Ali Kharrazi; Shamik Chakraborty; Tonni Agustiono Kurniawan. Monitoring Effect of Spatial Growth on Land Surface Temperature in Dhaka. Remote Sensing 2020, 12, 1191 .

AMA Style

Mustafizur Rahman, Ram Avtar, Ali P. Yunus, Jie Dou, Prakhar Misra, Wataru Takeuchi, Netrananda Sahu, Pankaj Kumar, Brian Alan Johnson, Rajarshi Dasgupta, Ali Kharrazi, Shamik Chakraborty, Tonni Agustiono Kurniawan. Monitoring Effect of Spatial Growth on Land Surface Temperature in Dhaka. Remote Sensing. 2020; 12 (7):1191.

Chicago/Turabian Style

Mustafizur Rahman; Ram Avtar; Ali P. Yunus; Jie Dou; Prakhar Misra; Wataru Takeuchi; Netrananda Sahu; Pankaj Kumar; Brian Alan Johnson; Rajarshi Dasgupta; Ali Kharrazi; Shamik Chakraborty; Tonni Agustiono Kurniawan. 2020. "Monitoring Effect of Spatial Growth on Land Surface Temperature in Dhaka." Remote Sensing 12, no. 7: 1191.

Journal article
Published: 01 February 2020 in Land
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In this study, we measured and characterized the relative dielectric constant of mineral soils over the 0.3–3.0 frequency range, and compared our measurements with values of three dielectric constant simulation models (the Wang, Dobson, and Mironov models). The interrelationship between land cover and soil texture with respect to the dielectric constant was also investigated. Topsoil samples (0–10 cm) were collected from homogenous areas based on a land unit map of the study site, located in the Gamsar Plain in northern Iran. The field soil samples were then analyzed in the laboratory using a dielectric probe toolkit to measure the soil dielectric constant. In addition, we analyzed the behaviors of the dielectric constant of the soil samples under a variety of moisture content and soil fraction conditions (after oven-drying the field samples), with the goal of better understanding how these factors affect microwave remote sensing backscattering characteristics. Our laboratory dielectric constant measurements of the real part (ε′) of the frequency dependence between the factors showed the best agreement with the results obtained by the Mironov, Dobson, and Wang models, respectively, but our laboratory measurements of the imaginary part (ε″) did not respond well and showed a higher value in low frequency because of salinity impacts. All data were analyzed by integrating them with other geophysical data in GIS, such as land cover and soil textures. The result of the dielectric constant properties analysis showed that land cover influences the moisture condition, even within the same soil texture type.

ACS Style

Saeid Gharechelou; Ryutaro Tateishi; Brian A. Johnson. Mineral Soil Texture–Land Cover Dependency on Microwave Dielectric Models in an Arid Environment. Land 2020, 9, 39 .

AMA Style

Saeid Gharechelou, Ryutaro Tateishi, Brian A. Johnson. Mineral Soil Texture–Land Cover Dependency on Microwave Dielectric Models in an Arid Environment. Land. 2020; 9 (2):39.

Chicago/Turabian Style

Saeid Gharechelou; Ryutaro Tateishi; Brian A. Johnson. 2020. "Mineral Soil Texture–Land Cover Dependency on Microwave Dielectric Models in an Arid Environment." Land 9, no. 2: 39.

Review
Published: 10 December 2019 in ISPRS Journal of Photogrammetry and Remote Sensing
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Green fractional vegetation cover (fc) is an important phenotypic factor in the fields of agriculture, forestry, and ecology. Spatially explicit monitoring of fc via relative vegetation abundance (RA) algorithms, especially those based on scaled maximum/minimum vegetation index (VI) values, has been widely investigated in remote sensing research. Although many studies have explored the effectiveness of RA algorithms over the past 30 years, a literature review summarizing the corresponding theoretical background, issues, current state-of-the-art techniques, challenges, and prospects has not yet been published. The overall objective of the present study was to accomplish a comprehensive and systematic review of RA algorithms considering these factors based on the scientific papers published from January 1990 to November 2019. This review revealed that the key issues related to RA algorithms is the determination of the appropriate normalized difference vegetation index (NDVI) values of the full vegetation cover and bare soil (denoted hereafter by NDVI∞ and NDVIs, respectively). The existing methods used to correct for these issues were investigated, and their advantages and disadvantages are discussed in depth. In literature trends, we found that the number of reported studies in which RA algorithms were used has increased consistently over time, and that most authors tend to utilize the linear NDVI model, rather than other models in the RA algorithm family. We also found that RA algorithms have been utilized to analyze the images with spatial resolutions ranging from the sub-meter to kilometer, most commonly, using images of 30-m spatial resolution. Finally, current challenges and forward-looking insights in remote estimation of fc using RA algorithms are discussed to guide future research and directions.

ACS Style

Lin Gao; Xiaofei Wang; Brian Alan Johnson; Qingjiu Tian; Yu Wang; Jochem Verrelst; Xihan Mu; Xingfa Gu. Remote sensing algorithms for estimation of fractional vegetation cover using pure vegetation index values: A review. ISPRS Journal of Photogrammetry and Remote Sensing 2019, 159, 364 -377.

AMA Style

Lin Gao, Xiaofei Wang, Brian Alan Johnson, Qingjiu Tian, Yu Wang, Jochem Verrelst, Xihan Mu, Xingfa Gu. Remote sensing algorithms for estimation of fractional vegetation cover using pure vegetation index values: A review. ISPRS Journal of Photogrammetry and Remote Sensing. 2019; 159 ():364-377.

Chicago/Turabian Style

Lin Gao; Xiaofei Wang; Brian Alan Johnson; Qingjiu Tian; Yu Wang; Jochem Verrelst; Xihan Mu; Xingfa Gu. 2019. "Remote sensing algorithms for estimation of fractional vegetation cover using pure vegetation index values: A review." ISPRS Journal of Photogrammetry and Remote Sensing 159, no. : 364-377.

Journal article
Published: 20 November 2019 in International Journal of Environmental Research and Public Health
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Just a few decades ago, Adyar River in India’s city of Chennai was an important source of water for various uses. Due to local and global changes (e.g., population growth and climate change), its ecosystem and overall water quality, including its aesthetic value, has deteriorated, and the water has become unsuitable for commercial uses. Adverse impacts of excessive population and changing climate are expected to continue in the future. Thus, this study focused on predicting the future water quality of the Adyar river under “business as usual” (BAU) and “suitable with measures” scenarios. The water evaluation and planning (WEAP) simulation tool was used for this study. Water quality simulation along a 19 km stretch of the Adyar River, from downstream of the Chembarambakkam to Adyar (Bay of Bengal) was carried out. In this analysis, clear indication of further deterioration of Adyar water quality by 2030 under the BAU scenario was evidenced. This would be rendering the river unsuitable for many aquatic species. Due to both climate change (i.e., increased temperature and precipitation) and population growth, the WEAP model results indicated that by 2030, biochemical oxygen demand (BOD) and Escherichia coli concentrations will increase by 26.7% and 8.3%, respectively. On the other hand, under the scenario with measures being taken, which assumes that “all wastewater generated locally will be collected and treated in WWTP with a capacity of 886 million liter per day (MLD),” the river water quality is expected to significantly improve by 2030. Specifically, the model results showed largely reduced concentrations of BOD and E. coli, respectively, to the tune of 74.2% and 98.4% compared to the BAU scenario. However, even under the scenario with measures being taken, water quality remains a concern, especially in the downstream area, when compared with class B (fishable surface water quality desirable by the national government). These results indicate that the current management policies and near future water resources management plan (i.e., the scenario including mitigating measures) are not adequate to check pollution levels to within the desirable limits. Thus, there is a need for transdisciplinary research into how the water quality can be further improved (e.g., through ecosystem restoration or river rehabilitation).

ACS Style

Pankaj Kumar; Rajarshi Dasgupta; Manish Ramaiah; Ram Avtar; Brian Alan Johnson; Binaya Kumar Mishra. Hydrological Simulation for Predicting the Future Water Quality of Adyar River, Chennai, India. International Journal of Environmental Research and Public Health 2019, 16, 4597 .

AMA Style

Pankaj Kumar, Rajarshi Dasgupta, Manish Ramaiah, Ram Avtar, Brian Alan Johnson, Binaya Kumar Mishra. Hydrological Simulation for Predicting the Future Water Quality of Adyar River, Chennai, India. International Journal of Environmental Research and Public Health. 2019; 16 (23):4597.

Chicago/Turabian Style

Pankaj Kumar; Rajarshi Dasgupta; Manish Ramaiah; Ram Avtar; Brian Alan Johnson; Binaya Kumar Mishra. 2019. "Hydrological Simulation for Predicting the Future Water Quality of Adyar River, Chennai, India." International Journal of Environmental Research and Public Health 16, no. 23: 4597.

Technical note
Published: 18 October 2019 in Remote Sensing
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Local climate zone (LCZ) maps are increasingly being used to help understand and model the urban microclimate, but traditional land use/land cover map (LULC) accuracy assessment approaches do not convey the accuracy at which LCZ maps depict the local thermal environment. 17 types of LCZs exist, each having unique physical characteristics that affect the local microclimate. Many studies have focused on generating LCZ maps using remote sensing data, but nearly all have used traditional LULC map accuracy metrics, which penalize all map classification errors equally, to evaluate the accuracy of these maps. Here, we proposed a new accuracy assessment approach that better explains the accuracy of the physical properties (i.e., surface structure, land cover, and anthropogenic heat emissions) depicted in an LCZ map, which allows for a better understanding of the accuracy at which the map portrays the local thermal environment.

ACS Style

Brian Alan Johnson; Shahab Eddin Jozdani. Local Climate Zone (LCZ) Map Accuracy Assessments Should Account for Land Cover Physical Characteristics that Affect the Local Thermal Environment. Remote Sensing 2019, 11, 2420 .

AMA Style

Brian Alan Johnson, Shahab Eddin Jozdani. Local Climate Zone (LCZ) Map Accuracy Assessments Should Account for Land Cover Physical Characteristics that Affect the Local Thermal Environment. Remote Sensing. 2019; 11 (20):2420.

Chicago/Turabian Style

Brian Alan Johnson; Shahab Eddin Jozdani. 2019. "Local Climate Zone (LCZ) Map Accuracy Assessments Should Account for Land Cover Physical Characteristics that Affect the Local Thermal Environment." Remote Sensing 11, no. 20: 2420.

Journal article
Published: 16 October 2019 in Global Ecology and Conservation
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Owing to the huge number of species observations that can be collected by non-professional scientists, “citizen science” has great potential to contribute to scientific knowledge on invasive alien species (IAS). Citizen science has existed for centuries, but the recent adoption of information and communications technology (ICT) in this field (e.g. web- or mobile application-based interfaces for citizen training and data generation) has led to a massive surge in popularity, mainly due to reduced geographic barriers to citizen participation. Several challenges exist, however, to effectively utilize citizen-generated data for monitoring IAS (or other species of interest) at the global scale. Here, we conducted a systematic analysis of citizen science initiatives collecting IAS data using ICT, hoping to better understand their scientific contributions and challenges, their similarities/differences, and their interconnections. Through a search of the Scopus database, we identified 26 initiatives whose data had been used in scientific publications related to IAS, and based our analyses on these initiatives. The most common scientific uses of these citizen science data were to visualize the spatial distribution of IAS, better understand their behaviour/phenology, and elucidate citizen science data quality issues. To alleviate data quality concerns, most initiatives (19/26) had mechanisms for verifying citizen observations, such as user-submitted photographs. While many initiatives collected similar data parameters for each species observation, only 54% of the initiatives had a practice of data sharing. This lack of data sharing causes fragmentation of the citizen-generated IAS data, and is likely inhibiting the wider usage of the data for scientific studies on IAS involving large geographic scales (e.g. regional or global) and/or broad taxonomic scopes. To reduce this fragmentation and better consolidate the collected citizen science data, finally we provide some general data sharing guidelines for citizen science initiatives as well as individual volunteers.

ACS Style

Brian Alan Johnson; Andre Mader; Rajarshi Dasgupta; Pankaj Kumar. Citizen science and invasive alien species: An analysis of citizen science initiatives using information and communications technology (ICT) to collect invasive alien species observations. Global Ecology and Conservation 2019, 21, e00812 .

AMA Style

Brian Alan Johnson, Andre Mader, Rajarshi Dasgupta, Pankaj Kumar. Citizen science and invasive alien species: An analysis of citizen science initiatives using information and communications technology (ICT) to collect invasive alien species observations. Global Ecology and Conservation. 2019; 21 ():e00812.

Chicago/Turabian Style

Brian Alan Johnson; Andre Mader; Rajarshi Dasgupta; Pankaj Kumar. 2019. "Citizen science and invasive alien species: An analysis of citizen science initiatives using information and communications technology (ICT) to collect invasive alien species observations." Global Ecology and Conservation 21, no. : e00812.

Journal article
Published: 19 July 2019 in Remote Sensing
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With the advent of high-spatial resolution (HSR) satellite imagery, urban land use/land cover (LULC) mapping has become one of the most popular applications in remote sensing. Due to the importance of context information (e.g., size/shape/texture) for classifying urban LULC features, Geographic Object-Based Image Analysis (GEOBIA) techniques are commonly employed for mapping urban areas. Regardless of adopting a pixel- or object-based framework, the selection of a suitable classifier is of critical importance for urban mapping. The popularity of deep learning (DL) (or deep neural networks (DNNs)) for image classification has recently skyrocketed, but it is still arguable if, or to what extent, DL methods can outperform other state-of-the art ensemble and/or Support Vector Machines (SVM) algorithms in the context of urban LULC classification using GEOBIA. In this study, we carried out an experimental comparison among different architectures of DNNs (i.e., regular deep multilayer perceptron (MLP), regular autoencoder (RAE), sparse, autoencoder (SAE), variational autoencoder (AE), convolutional neural networks (CNN)), common ensemble algorithms (Random Forests (RF), Bagging Trees (BT), Gradient Boosting Trees (GB), and Extreme Gradient Boosting (XGB)), and SVM to investigate their potential for urban mapping using a GEOBIA approach. We tested the classifiers on two RS images (with spatial resolutions of 30 cm and 50 cm). Based on our experiments, we drew three main conclusions: First, we found that the MLP model was the most accurate classifier. Second, unsupervised pretraining with the use of autoencoders led to no improvement in the classification result. In addition, the small difference in the classification accuracies of MLP from those of other models like SVM, GB, and XGB classifiers demonstrated that other state-of-the-art machine learning classifiers are still versatile enough to handle mapping of complex landscapes. Finally, the experiments showed that the integration of CNN and GEOBIA could not lead to more accurate results than the other classifiers applied.

ACS Style

Shahab Eddin Jozdani; Brian Alan Johnson; Dongmei Chen. Comparing Deep Neural Networks, Ensemble Classifiers, and Support Vector Machine Algorithms for Object-Based Urban Land Use/Land Cover Classification. Remote Sensing 2019, 11, 1713 .

AMA Style

Shahab Eddin Jozdani, Brian Alan Johnson, Dongmei Chen. Comparing Deep Neural Networks, Ensemble Classifiers, and Support Vector Machine Algorithms for Object-Based Urban Land Use/Land Cover Classification. Remote Sensing. 2019; 11 (14):1713.

Chicago/Turabian Style

Shahab Eddin Jozdani; Brian Alan Johnson; Dongmei Chen. 2019. "Comparing Deep Neural Networks, Ensemble Classifiers, and Support Vector Machine Algorithms for Object-Based Urban Land Use/Land Cover Classification." Remote Sensing 11, no. 14: 1713.

Review
Published: 28 April 2019 in ISPRS Journal of Photogrammetry and Remote Sensing
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Deep learning (DL) algorithms have seen a massive rise in popularity for remote-sensing image analysis over the past few years. In this study, the major DL concepts pertinent to remote-sensing are introduced, and more than 200 publications in this field, most of which were published during the last two years, are reviewed and analyzed. Initially, a meta-analysis was conducted to analyze the status of remote sensing DL studies in terms of the study targets, DL model(s) used, image spatial resolution(s), type of study area, and level of classification accuracy achieved. Subsequently, a detailed review is conducted to describe/discuss how DL has been applied for remote sensing image analysis tasks including image fusion, image registration, scene classification, object detection, land use and land cover (LULC) classification, segmentation, and object-based image analysis (OBIA). This review covers nearly every application and technology in the field of remote sensing, ranging from preprocessing to mapping. Finally, a conclusion regarding the current state-of-the art methods, a critical conclusion on open challenges, and directions for future research are presented.

ACS Style

Lei Ma; Yu Liu; Xueliang Zhang; Yuanxin Ye; Gaofei Yin; Brian Alan Johnson. Deep learning in remote sensing applications: A meta-analysis and review. ISPRS Journal of Photogrammetry and Remote Sensing 2019, 152, 166 -177.

AMA Style

Lei Ma, Yu Liu, Xueliang Zhang, Yuanxin Ye, Gaofei Yin, Brian Alan Johnson. Deep learning in remote sensing applications: A meta-analysis and review. ISPRS Journal of Photogrammetry and Remote Sensing. 2019; 152 ():166-177.

Chicago/Turabian Style

Lei Ma; Yu Liu; Xueliang Zhang; Yuanxin Ye; Gaofei Yin; Brian Alan Johnson. 2019. "Deep learning in remote sensing applications: A meta-analysis and review." ISPRS Journal of Photogrammetry and Remote Sensing 152, no. : 166-177.