Remote Sensing

Article Geosciences, Multidisciplinary

Data-driven modeling of wildfire spread with stochastic cellular automata and latent spatio-temporal dynamics

Nicholas Grieshop, Christopher K. Wikle

Summary: We propose a Bayesian stochastic cellular automata modeling approach to model the spread of wildfires with uncertainty quantification. The model considers a dynamic neighborhood structure and captures additional spatial information, allowing for accurate prediction of fire states.

SPATIAL STATISTICS (2024)

Article Environmental Sciences

Use of a new Tibetan Plateau network for permafrost to characterize satellite-based products errors: An application to soil moisture and freeze/ thaw

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)

Article Environmental Sciences

Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on aerial lidar

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

Spatially constrained atmosphere and surface retrieval for imaging spectroscopy

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

Low-amplitude brittle deformations revealed by UAV surveys in alluvial fans along the northwest coast of Lake Baikal: Neotectonic significance and geological hazards

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

Evaluating the spatial patterns of US urban NOx emissions using TROPOMI NO2

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

Choosing a sample size allocation to strata based on trade-offs in precision when estimating accuracy and area of a rare class from a stratified sample

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

Wide-swath and high-resolution whisk-broom imaging and on-orbit performance of SDGSAT-1 thermal infrared spectrometer

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

Simulation of urban thermal anisotropy at remote sensing pixel scales: Evaluating three schemes using GUTA-T over Toulouse city

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

Global retrieval of the spectrum of terrestrial chlorophyll fluorescence: First results with TROPOMI

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

A vehicle imaging approach to acquire ground truth data for upscaling to satellite data: A case study for estimating harvesting dates

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

Improving estimates of sub-daily gross primary production from solar-induced chlorophyll fluorescence by accounting for light distribution within canopy

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

Comparison of different spectral indices to differentiate the impact of insect attack on planted forest stands

Sifiso Xulu, Nkanyiso Mbatha, Kabir Peerbhay, Michael Gebreslasie, Naeem Agjee

Summary: This study examines the impact of insect infestation on trees in South African eucalyptus plantations using remote sensing techniques and field surveys. The results demonstrate that the SWIR band and spectral indices are effective in detecting the effects of insect damage. Additionally, anomaly detection can differentiate insect damage from other disturbances, which is significant for data-poor regions.

REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT (2024)

Article Environmental Sciences

Correlating chlorophyll movement with wind speed and direction using satellite imagery: A case study of Devils Lake, North Dakota

Meera Gopinath Sujatha, Devarshi Patel, Ronald Marsh, Prakash Ranganathan

Summary: This paper investigates the locations of high concentrations of Chlorophyll-a (Chl-a) in Devils Lake, North Dakota using satellite images and ground-sampled data. The study reveals a positive correlation between Chl-a and wind direction, as well as a decrease in Chl-a concentration area on days with wind velocity greater than 3 m/s. Random Forest method shows higher accuracy in image segmentation compared to other methods.

REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT (2024)

Article Environmental Sciences

Analysis of the future potential impact of environmental and climate changes on wildfire spread in Ghana's ecological zones using a Random Forest (RF) machine learning approach

Kueshi Semanou Dahan, Raymond Abudu Kasei, Rikiatu Husseini, Mamadou Sarr, Mohammed Y. Said

Summary: Climate change is causing increased hazards and vulnerabilities in many parts of the world, including natural zones. This study used a random forest regression model to predict the impact of climate change on wildfire spread in the Guinean savannah and the forest-savannah mosaic in Ghana. The findings indicate that decreased rainfall and increased temperature will lead to more fire activity and spread in these ecological zones.

REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT (2024)

Article Environmental Sciences

High-resolution forest canopy cover estimation in ecodiverse landscape using machine learning and Google Earth Engine: Validity and reliability assessment

Hamdi A. Zurqani

Summary: Forest Canopy Cover (FCC) is an important factor in forest health and functioning, and this study successfully developed a large-scale FCC dataset with a 1-meter spatial resolution. The dataset accurately identified FCC in Arkansas and showed strong positive correlations with other datasets, providing valuable information for monitoring, forecasting, and managing forest resources.

REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT (2024)

Review Environmental Sciences

Crop monitoring by multimodal remote sensing: A review

Priyabrata Karmakar, Shyh Wei Teng, Manzur Murshed, Shaoning Pang, Yanyu Li, Hao Lin

Summary: Effective approaches to achieve food safety and security can prevent catastrophic situations, therefore, regular monitoring of agricultural crops is required. Multimodal remote sensing methods provide a comprehensive understanding of plant growth and development by integrating data from multiple sources, leading to more accurate and detailed crop analysis.

REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT (2024)

Article Environmental Sciences

Analysis of the causes of extreme precipitation in major cities of Peninsular India using remotely sensed data

Tharani Kotrike, Venkata Reddy Keesara, Venkataramana Sridhar

Summary: The frequent occurrence of Extreme Precipitation Events (EPEs) in Indian cities over the last decade has caused significant damage. Anthropogenic emissions, including aerosols, have been identified as contributing factors to the occurrence of EPEs. This study aims to investigate the influence of aerosols on EPEs by analyzing remotely sensed data. The results show that middle-level clouds, low AOD, thunderstorm states, and cloud top temperature are the main factors causing intensified precipitation during EPEs.

REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT (2024)

Article Environmental Sciences

Satellite-derived bathymetry from WorldView-2 based on linear and machine learning regression in the optically complex shallow water of the coral reef ecosystem of Kemujan island

Pramaditya Wicaksono, Setiawan Djody Harahap, Rani Hendriana

Summary: This study assesses the performance of different machine learning regression models in mapping the bathymetry of shallow water in coral reef ecosystems. The results show that random forest regression (RFR) outperforms support vector machine regression (SVR) and linear regression (LR) in terms of accuracy and precision.

REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT (2024)

Article Environmental Sciences

Deriving community vulnerability indices by analyzing multi-resolution space-borne data and demographic data for extreme weather events in global cities

Arvindh R. Sharma, Sunil Bhaskaran

Summary: This paper presents an approach to derive Community Vulnerability Index (CVI) to flooding by using an integrated geospatial computational model and illustrates the methodology for six global cities. The model provides an effective, adaptable, and cost-effective approach for vulnerability assessment and may provide information for policy makers to design appropriate resilience and adaptation efforts.

REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT (2024)