Article
Environmental Sciences
Yubo Zhang, Jiuchun Yang, Dongyan Wang, Jing Wang, Lingxue Yu, Fengqin Yan, Liping Chang, Shuwen Zhang
Summary: This study introduces an integrated convolutional neural network (CNN) LUCC reconstruction and prediction model to meet the demand for fine-scale land use change. Through validation in a specific case study, the model achieved high-precision reconstruction and prediction, with an overall accuracy rate 9.38% higher than existing models.
Article
Ecology
Yisa Ginath Yuh, Wiktor Tracz, Damon Matthews, Sarah E. Turner
Summary: Machine learning (ML) models, including k-nearest neighbour (kNN), support vector machines (SVM), artificial neural networks (ANN), and random forests (RF), have been effectively applied to classify land use and land cover (LULC) types at various scales. However, their application in African tropical regions has been limited due to methodological challenges arising from the use of coarse-resolution satellite images. In this study, four ML algorithms were compared for LULC monitoring in northern Cameroon, and the random forests model showed the highest classification accuracy. Forest loss was observed in approximately 7% of the study area, with an expansion of croplands and built-up areas being the main factors. This research represents a novel application of ML approaches using coarse-resolution satellite images in an African tropical forest and savanna setting, providing important baseline data for policy development, conservation planning, and monitoring.
ECOLOGICAL INFORMATICS
(2023)
Article
Environmental Sciences
Masoud Fallah-Shorshani, Xiaozhe Yin, Rob McConnell, Scott Fruin, Meredith Franklin
Summary: Traffic-related noise, despite its detrimental health impacts, has not been adequately regulated or studied. This study aims to compare the performance of various models in estimating traffic noise and validate their accuracy.
ENVIRONMENT INTERNATIONAL
(2022)
Article
Environmental Sciences
Txomin Hermosilla, Michael A. Wulder, Joanne C. White, Nicholas C. Coops
Summary: Deriving land cover from remotely sensed data is essential for operational mapping and reporting programs, benefiting from free imagery access and improved technological capabilities. The accuracy of land cover maps depends on calibration data, classification models, and implementation methods.
REMOTE SENSING OF ENVIRONMENT
(2022)
Article
Environmental Sciences
Gorica Bratic, Daniele Oxoli, Maria Antonia Brovelli
Summary: Land cover information is crucial for sustainable development and decision-making. This study created a new benchmark dataset called MOLCA by integrating multiple existing high-resolution land cover datasets, covering Sub-Saharan Africa, the Amazon, and Siberia. MOLCA has a higher number of pixels and coverage for regions with limited training data availability, making it a valuable resource for future high-resolution land cover mapping.
Review
Environmental Sciences
Frane Gilic, Mateo Gasparovic, Martina Baucic
Summary: Data on land cover are crucial in assessing human impact on nature and the environment and vice versa. Earth observation (EO) satellite images have long been utilized to produce global and continental land cover maps, but challenges persist in the production process. This research analyzes recent land cover products, identifies the main steps in map production, highlights existing challenges, and provides directions for future research. Additionally, it presents an overview of EO satellite missions and classification algorithms commonly used for moderate resolution land cover mapping (10-30 m).
GEOCARTO INTERNATIONAL
(2023)
Article
Construction & Building Technology
Pei-Yi Wong, Hsiao-Yun Lee, Ling-Jyh Chen, Yu-Cheng Chen, Nai-Tzu Chen, Shih-Chun Candice Lung, Huey-Jen Su, Chih-Da Wu, Jose Guillermo Cedeno Laurent, Gary Adamkiewicz, John D. Spengler
Summary: This study introduced an alternative approach to estimating daily PM2.5 concentration in indoor environments of schools in a large area by integrating low-cost sensors, land-use predictors, and machine learning models. Results indicated that outdoor PM2.5 and distance to the nearest thermal power plant were the main factors influencing indoor PM2.5 estimation variations. Incorporating machine learning techniques significantly improved model performance compared to traditional land-use regression methods.
BUILDING AND ENVIRONMENT
(2022)
Article
Environmental Sciences
Hemani Parikh, Samir Patel, Vibha Patel
Summary: This paper compares the application of deep learning and transform domain feature extraction techniques in land cover classification and evaluates the performance of different features. Classification accuracy can be improved through feature fusion and the use of synthetic minority oversampling technique.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Article
Environmental Sciences
Jiarui Zhang, Zhiyi Fu, Yilin Zhu, Bin Wang, Keran Sun, Feng Zhang
Summary: Land cover mapping is crucial for global resource monitoring, sustainable development research, and effective management. However, the complexity and computational requirements often cause delays in data processing and product publication, posing challenges for creating large-area products for monitoring dynamic land cover. This study proposes the HALF framework to automate and improve the efficiency of land cover mapping processes.
Article
Environmental Sciences
Auwal Aliyu, Muhammad Isma'il, Sule Muhammad Zubairu, Ibrahim Yahaya Gwio-kura, Abubakar Abdullahi, Babakaka Abdulsalam Abubakar, Muntaka Mansur
Summary: The land use and land cover in Yola North Local Government Area of Nigeria has experienced significant changes due to urbanization, leading to potential agricultural land loss and reduced food supply. This research develops an efficient framework for continuous monitoring of land use changes using machine learning and geospatial data, which is important for urban planning, natural resource management, and environmental conservation.
ENVIRONMENTAL MONITORING AND ASSESSMENT
(2023)
Article
Environmental Sciences
Guanyao Xie, Simona Niculescu
Summary: LCLU is a crucial topic for coastal areas, and accurate multiannual change detection is essential. In this study, CNN outperformed SVM and RF, significantly improving classification performance with a higher accuracy of up to 90%.
Article
Geography, Physical
Zuzanna M. Swirad, Adam P. Young
Summary: This study quantified coastal cliff erosion along 866 km of the California coastline using airborne LiDAR data, revealing a net volume loss of 1.24 x 10(7) m(3) and an average erosion rate of 2.47 m(3) yr(-1) per meter of coastline, with a mean cliff retreat rate of 0.06 m yr(-1). Spatial variations in cliff retreat rates were observed, with the highest rates in Humboldt County (0.18 m yr(-1)) and the lowest in Orange County (0.003 m yr(-1)).
Article
Construction & Building Technology
Abdulla-Al Kafy, Milan Saha, Abdullah-Al-Faisal, Zullyadini A. Rahaman, Muhammad Tauhidur Rahman, Desheng Liu, Md Abdul Fattah, Abdullah Al Rakib, Ahmad E. AlDousari, Sk Nafiz Rahaman, Md Zakaria Hasan, Md Ahasanul Karim Ahasan
Summary: Changes in land use/land cover and land surface temperatures significantly affect urban heat island effects. This study assesses and predicts the thermal characteristics of Sylhet city in Bangladesh. The results show continued expansion of urban built-up areas and reductions in green cover and water bodies, which will have an impact on the heat island effect. Effective strategies are provided to mitigate the heat island effects and ensure sustainable and eco-friendly urban development.
BUILDING AND ENVIRONMENT
(2022)
Article
Geochemistry & Geophysics
Jining Yan, Lizhe Wang, Haixu He, Dong Liang, Weijing Song, Wei Han
Summary: This paper proposes a deep transfer learning model based on similarity measurement for adaptive change detection in time series. By clustering with standard dynamic time warping distance and nonlinear fitting with time convolutional network, the model achieves high accuracy and efficiency in change detection.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Environmental Studies
Ashique Vadakkuveettil, Aakriti Grover
Summary: The unbalanced shift in land use and cover patterns due to urban growth increases land surface temperature and leads to the emergence of surface urban heat islands. This study examines the impact of evolving land use and cover patterns on land surface temperature and their correlation with vegetation index and building index. The results show a significant decrease in vegetated areas and an increase in built-up areas, with a corresponding rise in land surface temperature. It is crucial for urban planners and policymakers to address these imbalances and implement sustainable development measures, such as urban greening, to control the rising land surface temperature.
Article
Biodiversity Conservation
Eric L. Bullock, Curtis E. Woodcock, Carlos Souza Jr, Pontus Olofsson
GLOBAL CHANGE BIOLOGY
(2020)
Article
Environmental Sciences
Curtis E. Woodcock, Thomas R. Loveland, Martin Herold, Marvin E. Bauer
REMOTE SENSING OF ENVIRONMENT
(2020)
Article
Environmental Sciences
Xiaojing Tang, Lucy R. Hutyra, Paulo Arevalo, Alessandro Baccini, Curtis E. Woodcock, Pontus Olofsson
SCIENCE OF THE TOTAL ENVIRONMENT
(2020)
Article
Environmental Sciences
Shi Qiu, Zhe Zhu, Curtis E. Woodcock
REMOTE SENSING OF ENVIRONMENT
(2020)
Article
Environmental Sciences
Eric L. Bullock, Curtis E. Woodcock
Summary: Forest carbon flux is the difference between carbon loss and CO2 removal due to photosynthesis. The Amazon rainforest contributes a quarter of global emissions from land use change, largely due to its size and carbon storage. Despite deforestation being the main contributor to carbon loss, degradation and natural disturbance also play significant roles.
SCIENCE OF THE TOTAL ENVIRONMENT
(2021)
Article
Environmental Sciences
Xiaojing Tang, Curtis E. Woodcock, Pontus Olofsson, Lucy R. Hutyra
Summary: This study analyzed the land use and land cover change in the Mekong River Basin from 2001 to 2019, showing that the establishment of plantations and agricultural expansion are the major components of land use change in the region. The study found that most deforested areas were converted into tree plantations, with carbon uptake from new plantations offsetting almost half of the emissions from deforestation in the area. Assessing post-deforestation land use is crucial for understanding the carbon consequences of land use change.
REMOTE SENSING OF ENVIRONMENT
(2021)
Article
Agronomy
Yulong Zhang, Conghe Song, Taehee Hwang, Kimberly Novick, John W. Coulston, James Vose, Matthew P. Dannenberg, Christopher R. Hakkenberg, Jiafu Mao, Curtis E. Woodcock
Summary: Land cover change (LCC) is a dynamic aspect of global environmental change with uncertain impacts on carbon budgets, and a study focusing on the conterminous United States found that LCC had a significant negative effect on Gross Primary Production (GPP) due to forest loss and urban expansion, partially offset by increases in crop area.
AGRICULTURAL AND FOREST METEOROLOGY
(2021)
Article
Environmental Sciences
Shijuan Chen, Curtis E. Woodcock, Eric L. Bullock, Paulo Arevalo, Paata Torchinava, Siqi Peng, Pontus Olofsson
Summary: Recent advancements in remote sensing data and cloud computing platforms have provided new opportunities for monitoring forest degradation in temperate regions. A new approach combining time series analysis and spectral mixture analysis successfully mapped and estimated the area of forest degradation in Georgia.
REMOTE SENSING OF ENVIRONMENT
(2021)
Article
Environmental Sciences
Yingtong Zhang, Curtis E. Woodcock, Shijuan Chen, Jonathan A. Wang, Damien Sulla-Menashe, Zhenpeng Zuo, Pontus Olofsson, Yetianjian Wang, Mark A. Friedl
Summary: The arctic and boreal biomes are undergoing changes in disturbance events due to increasing temperatures. The study used the CCDC algorithm to analyze Landsat observations and map causes of disturbance such as fire, logging, and pest damage. Disturbance rates due to logging remained constant while fires were more episodic, and insect damage was highest between 2005 and 2010.
REMOTE SENSING OF ENVIRONMENT
(2022)
Article
Environmental Sciences
Dekker Ehlers, Chao Wang, John Coulston, Yulong Zhang, Tamlin Pavelsky, Elizabeth Frankenberg, Curtis Woodcock, Conghe Song
Summary: The majority of the aboveground biomass on the Earth's land surface is stored in forests. However, accurate estimation of forest aboveground biomass (FAGB) remains challenging. This study proposed a new conceptual model using remotely sensed data to map FAGB. The model includes height metrics as the most important variables for estimating FAGB.
Article
Geography, Physical
Thomas R. Loveland, Martha C. Anderson, Justin L. Huntington, James R. Irons, David M. Johnson, Laura E. P. Rocchio, Curtis E. Woodcock, Michael A. Wulder
PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING
(2022)
Article
Environmental Sciences
Michael A. Wulder, David P. Roy, Volker C. Radeloff, Thomas R. Loveland, Martha C. Anderson, David M. Johnson, Sean Healey, Zhe Zhu, Theodore A. Scambos, Nima Pahlevan, Matthew Hansen, Noel Gorelick, Christopher J. Crawford, Jeffrey G. Masek, Txomin Hermosilla, Joanne C. White, Alan S. Belward, Crystal Schaaf, Curtis E. Woodcock, Justin L. Huntington, Leo Lymburner, Patrick Hostert, Feng Gao, Alexei Lyapustin, Jean-Francois Pekel, Peter Strobl, Bruce D. Cook
Summary: Since 1972, the Landsat program has provided 50 years of digital, multispectral, medium spatial resolution observations, playing a crucial role in scientific and technical advancements. The program's early years brought technological breakthroughs and established a template for global Earth observation missions. The knowledge gained from Landsat has been recognized for its economic and scientific value, leading to continuous improvement and increased usage through the introduction of free and open access to data.
REMOTE SENSING OF ENVIRONMENT
(2022)
Article
Environmental Sciences
Shi Qiu, Zhe Zhu, Pontus Olofsson, Curtis E. Woodcock, Suming Jin
Summary: We proposed a new image compositing algorithm (MAX-RNB) and evaluated it together with nine other algorithms. The results demonstrated that the performance of the algorithms varied depending on the compositing intervals and cloud cover. This study provides comprehensive guidance for selecting the most appropriate image compositing algorithm for different applications.
REMOTE SENSING OF ENVIRONMENT
(2023)
Article
Environmental Sciences
Xiaojing Tang, Eric L. Bullock, Pontus Olofsson, Curtis E. Woodcock
REMOTE SENSING OF ENVIRONMENT
(2020)
Article
Ecology
Eric L. Bullock, Christoph Nolte, Ana R. Segovia, Curtis E. Woodcock
REMOTE SENSING IN ECOLOGY AND CONSERVATION
(2020)
Article
Environmental Sciences
Ruonan Chen, Liangyun Liu, Xinjie Liu, Zhunqiao Liu, Lianhong Gu, Uwe Rascher
Summary: This study presents methods to accurately estimate sub-daily GPP from SIF in evergreen needleleaf forests and demonstrates that the interactions among light, canopy structure, and leaf physiology regulate the SIF-GPP relationship at the canopy scale.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Daniel L. Goldberg, Madankui Tao, Gaige Hunter Kerr, Siqi Ma, Daniel Q. Tong, Arlene M. Fiore, Angela F. Dickens, Zachariah E. Adelman, Susan C. Anenberg
Summary: A novel method is applied in this study to directly use satellite data to evaluate the spatial patterns of urban NOx emissions inventories. The results show that the 108 spatial surrogates used by NEMO are generally appropriate, but there may be underestimation in areas with dense intermodal facilities and overestimation in wealthy communities.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Zhuoyue Hu, Xiaoyan Li, Liyuan Li, Xiaofeng Su, Lin Yang, Yong Zhang, Xingjian Hu, Chun Lin, Yujun Tang, Jian Hao, Xiaojin Sun, Fansheng Chen
Summary: This paper proposes a whisk-broom imaging method using a long-linear-array detector and high-precision scanning mirror to achieve high-resolution and wide-swath thermal infrared data. The method has been implemented in the SDGs satellite and has shown promising test results.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Dandan Wang, Leiqiu Hu, James A. Voogt, Yunhao Chen, Ji Zhou, Gaijing Chang, Jinling Quan, Wenfeng Zhan, Zhizhong Kang
Summary: This study evaluates different schemes for determining model coefficients to quantify and correct the anisotropic impact from remote sensing LST for urban applications. The schemes have consistent results and accurately estimate parameter values, facilitating the broadening of parametric models.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Jamie Tolan, Hung - Yang, Benjamin Nosarzewski, Guillaume Couairon, Huy V. Vo, John Brandt, Justine Spore, Sayantan Majumdar, Daniel Haziza, Janaki Vamaraju, Theo Moutakanni, Piotr Bojanowski, Tracy Johns, Brian White, Tobias Tiecke, Camille Couprie
Summary: Vegetation structure mapping is crucial for understanding the global carbon cycle and monitoring nature-based approaches to climate adaptation and mitigation. This study presents the first high-resolution canopy height maps for California and Sao Paulo, achieved through the use of very high resolution satellite imagery and aerial lidar data. The maps provide valuable tools for forest structure assessment and land use monitoring.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Regina Eckert, Steffen Mauceri, David R. Thompson, Jay E. Fahlen, Philip G. Brodrick
Summary: In this paper, a mathematical framework is proposed to improve the retrieval of surface reflectance and atmospheric parameters by leveraging the expected spatial smoothness of the atmosphere. Experimental results show that this framework can reduce the surface reflectance retrieval error and surface-related biases.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Chongya Jiang, Kaiyu Guan, Yizhi Huang, Maxwell Jong
Summary: This study presents the Field Rover method, which uses vehicle-mounted cameras to collect ground truth data on crop harvesting status. The machine learning approach and remote sensing technology are employed to upscale the results to a regional scale. The accuracy of the remote sensing method in predicting crop harvesting dates is validated through comparison with satellite data.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Oksana V. Lunina, Anton A. Gladkov, Alexey V. Bochalgin
Summary: In this study, an unmanned aerial vehicle (UAV) was used to detect and map surface discontinuities with displacements of a few centimeters, indicating the presence of initial geological deformations. The study found that sediments of alluvial fans are susceptible to various tectonic and exogenous deformational processes, and the interpretation of ultra-high resolution UAV images can help recognize low-amplitude brittle deformations at an early stage. UAV surveys are critical for discerning neotectonic activity and its related hazards over short observation periods.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Feng Zhao, Weiwei Ma, Jun Zhao, Yiqing Guo, Mateen Tariq, Juan Li
Summary: This study presents a data-driven approach to reconstruct the terrestrial SIF spectrum using measurements from the TROPOMI instrument on Sentinel-5 precursor mission. The reconstructed SIF spectrum shows improved spatiotemporal distributions and demonstrates consistency with other datasets, indicating its potential for better understanding of the ecosystem function.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Stephen Stehman, John E. Wagner
Summary: This article investigates optimal sample allocation in stratified random sampling for estimation of accuracy and proportion of area in applications where the target class is rare. The study finds that precision of estimated accuracy has a stronger impact on sample allocation than estimation of proportion of area, and the trade-offs among these estimates become more pronounced as the target class becomes rarer. The results provide quantitative evidence to guide sample allocation decisions in specific applications.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Jingyao Zheng, Tianjie Zhao, Haishen Lu, Defu Zou, Nemesio Rodriguez-Fernandez, Arnaud Mialon, Philippe Richaume, Jianshe Xiao, Jun Ma, Lei Fan, Peilin Song, Yonghua Zhu, Rui Li, Panpan Yao, Qingqing Yang, Shaojie Du, Zhen Wang, Zhiqing Peng, Yuyang Xiong, Zanpin Xing, Lin Zhao, Yann Kerr, Jiancheng Shi
Summary: Soil moisture and freeze/thaw (F/T) play a crucial role in water and heat exchanges at the land-atmosphere interface. This study reports the establishment of a wireless sensor network for soil moisture and temperature over the permafrost region of Tibetan Plateau. Satellite-based surface soil moisture (SSM) and F/T products were evaluated using ground-based measurements. The results show the reliability of L-band passive microwave SSM and F/T products, while existing F/T products display earlier freezing and later thawing, leading to unsatisfactory accuracy.
REMOTE SENSING OF ENVIRONMENT
(2024)