Article
Environmental Sciences
Yanchao Zhang, Wen Yang, Ying Sun, Christine Chang, Jiya Yu, Wenbo Zhang
Summary: This study examined the fusion of spectral bands information and vegetation indices for almond plantation classification using different machine learning algorithms. It was found that spectral information can be used for ground classification, with SVM performing the best among the algorithms tested. The combination of multispectral bands and vegetation indices can improve classification accuracy, with specific vegetation indices like NDEGE, NDVIG, and NDVGE showing consistent performance in enhancing accuracy.
Article
Biodiversity Conservation
Franziska Wolff, Tiina H. M. Kolari, Miguel Villoslada, Teemu Tahvanainen, Pasi Korpelainen, Pedro A. P. Zamboni, Timo Kumpula
Summary: Plant communities of mires are important for ecological processes such as carbon storage and gas fluxes. Mapping mire vegetation using UAVs can provide valuable information for ecosystem assessment. However, accurate mapping of plant communities remains challenging due to overlapping spectral signatures of plant species.
ECOLOGICAL INDICATORS
(2023)
Article
Environmental Sciences
Francisco Arguello, Dora B. Heras, Alberto S. Garea, Pablo Quesada-Barriuso
Summary: This article presents a method for automatically monitoring river basins in Galicia, Spain, using multispectral images and image processing algorithms to determine the state of vegetation and detect man-made structures occupying the river basin. By selecting techniques and algorithms for fast execution and efficient use of computational resources, the proposed approach proves to achieve the monitoring goal with speed and precision.
Article
Environmental Sciences
Kai O. Bergmueller, Mark C. Vanderwel
Summary: This study used spectral information from UAV imagery to predict tree mortality in 38 forest stands in western Canada. The inclusion of multispectral indices improved the prediction accuracy, and different tree species had varying levels of prediction performance. However, all models tended to overpredict tree mortality.
Article
Environmental Sciences
Amarasingam Narmilan, Felipe Gonzalez, Arachchige Surantha Ashan Salgadoe, Unupen Widanelage Lahiru Madhushanka Kumarasiri, Hettiarachchige Asiri Sampageeth Weerasinghe, Buddhika Rasanjana Kulasekara
Summary: This research utilizes unmanned aerial vehicles and spectral vegetation indices to infer chlorophyll content in sugarcane crops and compares the performance of multiple machine learning algorithms in predicting chlorophyll content. The findings demonstrate the accuracy of estimating chlorophyll content using multispectral UAVs and emphasize the importance of this methodology in crop nutrition management in sugarcane plantations.
Article
Environmental Sciences
Quan Yin, Yuting Zhang, Weilong Li, Jianjun Wang, Weiling Wang, Irshad Ahmad, Guisheng Zhou, Zhongyang Huo
Summary: This study uses UAV multispectral imagery to monitor crop stress during the pre-heading stage in the mid-lower Yangtze River area of China, and proposes a fused model based on LSTM that achieves high accuracy and robust generalization, aiding in mitigating winter wheat frost risks and increasing yields.
Article
Biodiversity Conservation
Ning Wang, Yuchuan Guo, Xuan Wei, Mingtong Zhou, Huijing Wang, Yunbao Bai
Summary: This study used unmanned aerial vehicles and machine learning methods to map and predict the natural vegetation of an oasis in the Taklamakan Desert in China. The results showed that using a combination of visible and multispectral vegetation indices can effectively obtain the fractional vegetation cover of sparse vegetation.
ECOLOGICAL INDICATORS
(2022)
Article
Biochemical Research Methods
Wen Pan, Xiaoyu Wang, Yan Sun, Jia Wang, Yanjie Li, Sheng Li
Summary: In this study, UAV multispectral remote sensing data was used to detect vegetation in karst areas, and the performance of four machine learning models was compared. The results showed that the Gradient Boosting Machine model achieved the highest accuracy in detecting karst vegetation. This study provided a methodological reference for vegetation detection in karst areas in eastern China.
Article
Agricultural Engineering
Juan Villacres, Fernando A. Auat Cheein
Summary: This paper presents the construction of fuel moisture content (FMC) maps expressed as vegetation indices (VIs) in a point cloud for the development of fire susceptibility models in forested areas. Multispectral images captured by a camera mounted on an unmanned aerial vehicle were used to create the point cloud, and VIs were estimated in the forest canopy points. The results showed that the combination of ground filtering and VIs thresholding achieved high accuracy and recall rates for canopy points segmentation. Gaussian process retrieval (GPR) showed good performance in recovering the VIs.
BIOSYSTEMS ENGINEERING
(2022)
Article
Agriculture, Multidisciplinary
Mingchao Shao, Chenwei Nie, Aijun Zhang, Liangsheng Shi, Yuanyuan Zha, Honggen Xu, Hongye Yang, Xun Yu, Yi Bai, Shuaibing Liu, Minghan Cheng, Tao Lin, Ningbo Cui, Wenbin Wu, Xiuliang Jin
Summary: This study used deep learning segmentation methods to quantify the impact of maize tassels on LAI estimates and evaluated the influence of variable quantity on LAI estimates. The results show that the VGG-encoded U-Net model achieved the highest accuracy in segmenting the multispectral dataset. The growth of tassels affects the segmentation accuracy, and tassels have the greatest impact on the modified nonlinear vegetation index. Removing tassels from images significantly improves the accuracy of LAI estimation using the gradient-boosting decision tree method. The estimation method using nine vegetation indices achieved the highest accuracy.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2023)
Article
Forestry
Bruno Rodrigues de Oliveira, Arlindo Ananias Pereira da Silva, Larissa Pereira Ribeiro Teodoro, Gileno Brito de Azevedo, Glauce Tais de Oliveira Sousa Azevedo, Fabio Henrique Rojo Baio, Renato Lustosa Sobrinho, Carlos Antonio da Silva Junior, Paulo Eduardo Teodoro
Summary: The study evaluated the use of ML techniques to classify the growth of five species of eucalyptus and Corymbria citriodora, recognizing the species based on their growth using vegetation indices and spectral bands.
FOREST ECOLOGY AND MANAGEMENT
(2021)
Article
Agriculture, Multidisciplinary
Sevda Tasan, Bilal Cemek, Mehmet Tasan, Aslihan Canturk
Summary: Estimating crop yields is crucial for agricultural planning, and remote sensing products like VI are commonly used for this purpose. This study aimed to predict eggplant yield using VIs and machine learning methods, with the best results achieved by the ANN model based on PCA inputs.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2022)
Article
Agronomy
Hanchao Liu, Yuan Qi, Wenwei Xiao, Haoxin Tian, Dehua Zhao, Ke Zhang, Junqi Xiao, Xiaoyang Lu, Yubin Lan, Yali Zhang
Summary: This study used remote sensing images obtained with a UAV and vegetation indices to accurately identify the male and female parents of hybrid rice using pixel-based supervised classification and sample-based object-oriented classification methods. The ExGR index showed the best performance in classification. This method can provide a reference for determining optimal pollination timing for hybrid rice in large-scale seed production farms.
Article
Engineering, Electrical & Electronic
Ziyu Liu, Han Zhu, Zhenzhong Chen
Summary: In this article, a spectral resolution enhancement method based on the generative adversarial network framework is proposed, without introducing additional spectral responses prior. A spatial spectral feature attention module is introduced to adaptively rescale informative features for capturing interdependencies in the spectral and spatial dimensions. The experiments show the superiority of the proposed method compared to other state-of-the-art methods on both synthetic Landsat 8 and Sentinel-2 radiance data and real coregistered advanced land image and Hyperion (MS and HS) images.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2023)
Article
Geochemistry & Geophysics
Jie Fang, Guanghua He, Zhijie Zhu, Bahari Issa M. Attaher, Jian Xue
Summary: This article presents a spatial-spectral decoupling interaction network for multispectral imagery change detection. The network can exploit the underlying information of the multispectral imagery by simultaneously considering the discriminative attribute of each pixel and the robust spatial structure of the corresponding patch.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Environmental Sciences
Meshal M. Abdullah, Amjad T. Assi, Mansour T. Abdullah, Rusty A. Feagin
LAND DEGRADATION & DEVELOPMENT
(2020)
Article
Ecology
Rusty A. Feagin, Norman Johns, Thomas P. Huff, Meshal M. Abdullah, Kristin Fritz-Grammond
Article
Plant Sciences
Meshal M. Abdullah, Zahraa M. Al-Ali, Mansour T. Abdullah, Bader Al-Anzi
Summary: This study investigates advanced vegetation monitoring methods using UAVs and remote sensing techniques in Kuwait's Al Abdali protected site. The results show a significant increase in vegetation coverage in annual plants after extreme rainfall events, with a correlation found between plant coverage density and shrub height. The use of UAVs is recommended for future ecological studies and restoration programs in desert ecosystems.
Article
Environmental Sciences
Meshal M. Abdullah, Zahraa M. Al-Ali, Mansour T. Abdullah, Shruthi Srinivasan, Amjad T. Assi, Sara Al Atiqi
Summary: This study focused on evaluating factors influencing the growth of perennial shrubs by integrating field-based experiments and spatial analysis. The research found that soil properties significantly impact vegetation biomass and growth, while annual plants contribute to the growth of perennial shrubs.
JOURNAL OF ENVIRONMENTAL MANAGEMENT
(2021)
Article
Ecology
Naseraldeen Asadalla, Meshal M. Abdullah, Zahraa M. Al-Ali, Mansour T. Abdullah
Summary: By integrating remote sensing techniques and MaxEnt modeling, this study developed a restoration approach to determine and rank hotspots for revegetation and restoration planning of native desert plant communities. The results showed that different native desert plant communities have varied suitable habitats based on environmental factors, with a vegetation restoration approach designed to classify locations for future restoration and revegetation planning.
RESTORATION ECOLOGY
(2021)
Article
Environmental Sciences
Meshal Abdullah, Zahraa Al-Ali, Ammar Abulibdeh, Midhun Mohan, Shruthi Srinivasan, Talal Al-Awadhi
Summary: Hydrocarbon-contaminated soils in arid regions pose a significant environmental challenge. This study utilizes remote sensing techniques to investigate the behavior of annual and perennial desert plants in different types of oil-contaminated soils in the Kuwait Desert. The results show that vegetation growth is influenced by soil type, with higher distribution rates observed for annual plants. The study also highlights the relationship between vegetation growth on clean sediment layers and vegetation growth on hydrocarbon-contaminated soils, as well as the accumulation of nutrient-rich soil due to aeolian sediment remobilization.
ENVIRONMENTAL RESEARCH
(2023)
Review
Environmental Sciences
Nisha Singh, Meshal M. Abdullah, Xingmao Ma, Virender K. Sharma
Summary: The increasing use of plastic products in agriculture has led to the accumulation of microplastics and nanoplastics in agricultural soils, which raises concerns about their impact on soil health, crop productivity, and food safety. The interaction between plants, soil, and microplastics/nanoplastics can negatively affect plant growth and induce oxidative stress. However, the current knowledge about the fate and impact of microplastics/nanoplastics in the plant-soil nexus is limited, hindering further progress in this field. This review aims to address this gap by summarizing the sources, investigation techniques, impact on soil properties, accumulation in plants, and possible phytotoxicity mechanisms of microplastics/nanoplastics in agriculture.
CRITICAL REVIEWS IN ENVIRONMENTAL SCIENCE AND TECHNOLOGY
(2023)
Article
Multidisciplinary Sciences
Meshal M. Abdullah, Zahraa M. Al-Ali, Shruthi Srinivasan
Summary: This study utilizes UAV technology to develop a method for estimating and monitoring the biomass and carbon stock of desert shrubs, providing ecosystem managers with an effective tool for assessment and monitoring.
Article
Environmental Sciences
Zahraa M. Al-Ali, Meshal M. Abdullah, Amjad A. Assi, Mansour S. Alhumimidi, Al-Qurnawi S. Wasan, Thamer S. Ali
Summary: The key to successful implementation of desert native vegetation recovery plans lies in law enforcement and massive media awareness, with studies during the COVID-19 pandemic showing the resilience of desert ecosystems. Human activities, such as quarrying and overgrazing, are identified as the main drivers of desert vegetation deterioration, while precipitation plays a significant role in vegetation recovery.
REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT
(2021)
Article
Green & Sustainable Science & Technology
Ahmed Alqallaf, Bader Al-Anzi, Meshal Alabdullah
