Results 71 to 80 of about 643,359 (305)

Land use/land cover classification using machine learning models

International Journal of Electrical and Computer Engineering (IJECE), 2022
<p>An ensemble model has been proposed in this work by combining the extreme gradient boosting classification (XGBoost) model with support vector machine (SVM) for land use and land cover classification (LULCC). We have used the multispectral Landsat-8 operational land imager sensor (OLI) data with six spectral bands in the electromagnetic ...
Subhra Swetanisha, Amiya Ranjan Panda, Dayal Kumar Behera   +2 more
openaire   +2 more sources

A Framework for Evaluating Land Use and Land Cover Classification Using Convolutional Neural Networks [PDF]

, 2019
Analyzing land use and land cover (LULC) using remote sensing (RS) imagery is essential for many environmental and social applications. The increase in availability of RS data has led to the development of new techniques for digital pattern ...
Carranza García, Manuel, García Gutiérrez, Jorge, Riquelme Santos, José Cristóbal   +2 more
core   +1 more source

Bioenergy Cropping Reduces the Spatiotemporal Scaling of Soil Bacterial Biodiversity

Advanced Science, EarlyView.
Consistent with patterns observed in plant and animal communities, soil bacterial communities exhibit significant species–time–area and phylogenetic–time–area relationships independent of nested structure. Bioenergy cropping significantly reduces the spatiotemporal scaling rates, particularly in sandy loam soils.
Zhencheng Ye, Jialiang Kuang, Colin T. Bates, Arthur Escalas, Daliang Ning, Liyou Wu, Suo Liu, Sihang Deng, Jiesi Lei, Xiangwen Chen, Jennifer Pett‐Ridge, Malay Saha, Lauren Hale, Gangsheng Wang, Renmao Tian, Ying Fu, Yu Tang, Mary Firestone, Jizhong Zhou, Yunfeng Yang   +19 more
wiley   +1 more source

Land Cover Classification from Multi-temporal, Multi-spectral Remotely Sensed Imagery using Patch-Based Recurrent Neural Networks

, 2017
Sustainability of the global environment is dependent on the accurate land cover information over large areas. Even with the increased number of satellite systems and sensors acquiring data with improved spectral, spatial, radiometric and temporal ...
Liu, Xiuwen, Sharma, Atharva, Yang, Xiaojun   +2 more
core   +1 more source

Global Nitrogen Deposition Promotes Carbon Sink Formation in Terrestrial Ecosystems

Advanced Science, EarlyView.
Nitrogen deposition alleviates ecosystem N limitation and enhances carbon sinks. Using 829 observations, we show 36% of deposited N is retained globally (39.15 Tg N yr−1), with distinct NHx and NOy contributions. This retention drives a terrestrial C sink of 0.88 Pg C yr−1 (25.48%), highlighting the importance of pool‐specific C:N stoichiometry ...
Lei Li, Mingyu Xie, Nan Jia, Li‐Dong Mo, Qiang Yu, Fanjiang Zeng, Xiangyi Li   +6 more
wiley   +1 more source

Biometry Protocol [PDF]

, 2003
The purpose of this resource is to measure and classify the plant life at a Land Cover Site to help determine the MUC classification.
The GLOBE Program, University Corporation for Atmospheric Research (UCAR)
core  

Uncertainties in classification system conversion and an analysis of inconsistencies in global land cover products [PDF]

, 2017
In this study, using the common classification systems of IGBP-17, IGBP-9, IPCC-5 and TC (vegetation, wetlands and others only), we studied spatial and areal inconsistencies in the three most recent multi-resource land cover products in a complex ...
De Maeyer, Philippe, Kurban, Alishir, Ma, Mingguo, Zhang, Miao   +3 more
core   +2 more sources

Integration of Spatiotemporal Multi‐Omics in Peach Fruit Unravels a Metabolic Niche and the Genetic Basis of Trichome‐Mediated Stress Adaptation

Advanced Science, EarlyView.
This study constructed the first spatiotemporal multi‐omics map of peach fruit and discovered a key candidate gene that synergistically regulates trichome development and drought tolerance through the jasmonic acid signaling pathway, providing insights into the coupling mechanism between development and stress resistance.
Zhixin Liu, Ke Cao, Liping Guan, Aizhi Qin, Yong Li, Zihao Zhao, Yinpeng Zhang, Yaping Zhou, Lirong Wang, Xuwu Sun   +9 more
wiley   +1 more source

Plant Genetic Engineering: Technological Pathways, Application Scenarios, and Future Directions

Advanced Science, EarlyView.
This review maps the fast‐evolving landscape of plant genetic engineering, linking enabling platforms with trait‐focused applications in architecture optimization, stress resilience, yield improvement, and quality enhancement. It highlights how genome editing, transgenic strategies, and emerging multi‐gene approaches reshape breeding pipelines, while ...
Peilin Wang, Wenzhi Wang, Dongjiao Wang, Qibin Wu, Youxiong Que   +4 more
wiley   +1 more source

Applying a deep learning pipeline to classify land cover from low-quality historical RGB imagery [PDF]

PeerJ Computer Science
Land use and land cover (LULC) classification is becoming faster and more accurate thanks to new deep learning algorithms. Moreover, new high spectral- and spatial-resolution datasets offer opportunities to classify land cover with greater accuracy and ...
Harold N. Eyster, Brian Beckage
doaj   +2 more sources