Canadian Journal of Remote Sensing最新文献_第9页

Quantifying Woody Plant Encroachment in Grasslands: A Review on Remote Sensing Approaches 草地木本植物入侵的遥感定量研究进展

IF 2.6 4区地球科学

Canadian Journal of Remote Sensing Pub Date : 2022-03-22 DOI: 10.1080/07038992.2022.2039060

I. Soubry, Xulin Guo

{"title":"Quantifying Woody Plant Encroachment in Grasslands: A Review on Remote Sensing Approaches","authors":"I. Soubry, Xulin Guo","doi":"10.1080/07038992.2022.2039060","DOIUrl":"https://doi.org/10.1080/07038992.2022.2039060","url":null,"abstract":"Abstract Grasslands are an important global ecosystem, providing essential ecological and economic ecosystem services. Over the last couple decades, as a result of climate change and human activities, nearly 50% of global grasslands have degraded. Woody plant encroachment (WPE), one of the outcomes of climate change and human-related activities, negatively affects grasslands’ ecology, as well as their ability to produce food for livestock, habitats for wildlife, and economic returns for rangeland managers. Long-term monitoring of grassland status can facilitate grassland restoration. Additionally, the study of factors that influence grassland dynamics (e.g., grazing, fire, land use, or climate) can help in the restoration of grasslands. Remote sensing (RS) has been used to map the spatiotemporal distribution of WPE by using a wide variety of sensors and methods, necessitating a review on the effectiveness of RS data for WPE monitoring. Based on the importance of RS data and the rate at which grassland ecosystems are changing, this paper provides a literature review on a theoretical basis for quantifying WPE using RS and on existing RS approaches for WPE monitoring. Lastly, it identifies the current challenges associated with quantifying spatio-temporal variability in WPE that future research will need to overcome.","PeriodicalId":48843,"journal":{"name":"Canadian Journal of Remote Sensing","volume":"48 1","pages":"337 - 378"},"PeriodicalIF":2.6,"publicationDate":"2022-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48214421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Retrieval of Lake Ice Characteristics from SAR Imagery 从SAR图像中反演湖泊冰特征

IF 2.6 4区地球科学

Canadian Journal of Remote Sensing Pub Date : 2022-03-17 DOI: 10.1080/07038992.2022.2042227

G. Siles, R. Leconte, D. Peters

{"title":"Retrieval of Lake Ice Characteristics from SAR Imagery","authors":"G. Siles, R. Leconte, D. Peters","doi":"10.1080/07038992.2022.2042227","DOIUrl":"https://doi.org/10.1080/07038992.2022.2042227","url":null,"abstract":"Abstract Boreal lakes ecosystems can remain partially or completely covered by ice and snow during an important portion of the year. Alterations of lake and river ice properties can deteriorate the conditions of local ice roads, negatively influencing Nordic communities and economical activities. Monitoring of lake ice characteristics and dynamics is therefore crucial. In this study, Sentinel-1 Synthetic Aperture Radar (SAR) satellite imagery is exploited to evaluate changes in the ice regime over shallow and deep high-latitude lakes during the winters of 2018 and 2019. The methodology proposed, based on the combined analysis of SAR intensity and interferometric coherence maps, enables the extraction of important characteristics of ice dynamics. Overall, the lake ice thickness change derived from Differential Interferometric SAR (D-InSAR) increases with the lake depth. The D-InSAR-derived mean rate of growth, in general, agrees with the one estimated from records of in-situ ice thickness measurements. The methodology presented herein could be temporally extended to support the understanding of historical and current climate conditions. This could be done by using archived and newly available imagery to improve lake ice models.","PeriodicalId":48843,"journal":{"name":"Canadian Journal of Remote Sensing","volume":"48 1","pages":"379 - 399"},"PeriodicalIF":2.6,"publicationDate":"2022-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41985396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

A New Endmember Extraction Method Based on Least Squares 一种新的基于最小二乘法的端元提取方法

IF 2.6 4区地球科学

Canadian Journal of Remote Sensing Pub Date : 2022-03-04 DOI: 10.1080/07038992.2021.1992594

Guangyi Chen, A. Krzyżak, S. Qian

引用次数: 1

Toward Targeted Change Detection with Heterogeneous Remote Sensing Images for Forest Mortality Mapping 面向森林死亡率制图的异质遥感图像目标变化检测

IF 2.6 4区地球科学

Canadian Journal of Remote Sensing Pub Date : 2022-02-28 DOI: 10.1080/07038992.2022.2135497

Jorgen A. Agersborg, L. T. Luppino, S. Anfinsen, J. U. Jepsen

引用次数: 2

Combined Spatial-Spectral Schroedinger Eigenmaps with Multiple Kernel Learning for Hyperspectral Image Classification Using a Low Number of Training Samples 结合空间-光谱薛定谔特征映射和多核学习的低样本高光谱图像分类

IF 2.6 4区地球科学

Canadian Journal of Remote Sensing Pub Date : 2022-01-31 DOI: 10.1080/07038992.2021.1978840

Shirin Hassanzadeh, H. Danyali, M. Helfroush

{"title":"Combined Spatial-Spectral Schroedinger Eigenmaps with Multiple Kernel Learning for Hyperspectral Image Classification Using a Low Number of Training Samples","authors":"Shirin Hassanzadeh, H. Danyali, M. Helfroush","doi":"10.1080/07038992.2021.1978840","DOIUrl":"https://doi.org/10.1080/07038992.2021.1978840","url":null,"abstract":"Abstract The classification of hyperspectral images is one of the most popular fields in remote sensing applications. It should be noted that spectral and spatial features have critical roles in this research area. This paper proposes a method based on spatial-spectral Schroedinger eigenmaps (SSSE) and multiple kernel learning (MKL) to classify hyperspectral images more efficiently while using a low number of training samples. In the proposed method, first SSSE is applied to spectral domain in order to extract significant features and reduce dimension of the original image. Then MKL is utilized to enhance the feature learning process and obtain an optimum combination of some specified kernels. Finally, the classification is carried out by substituting the optimal kernel in support vector machine (SVM) algorithm. Experimental results show that the proposed method improves classification accuracy significantly and provides highly efficient results in the case of a small number of training samples. Furthermore, the computation time of the proposed method is much lower than the state-of-the-art MKL methods.","PeriodicalId":48843,"journal":{"name":"Canadian Journal of Remote Sensing","volume":"48 1","pages":"579 - 591"},"PeriodicalIF":2.6,"publicationDate":"2022-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47194289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Multi-Resolution-Based Deep Learning Approach for Rice Field Monitoring 基于多分辨率的深度学习稻田监测方法

IF 2.6 4区地球科学

Canadian Journal of Remote Sensing Pub Date : 2022-01-27 DOI: 10.1080/07038992.2021.2010036

Yasir Afaq, Ankush Manocha

{"title":"Multi-Resolution-Based Deep Learning Approach for Rice Field Monitoring","authors":"Yasir Afaq, Ankush Manocha","doi":"10.1080/07038992.2021.2010036","DOIUrl":"https://doi.org/10.1080/07038992.2021.2010036","url":null,"abstract":"Abstract In India, agribusiness is directly dependent on the precise monitoring of paddy areas to take considerable supportive actions toward food security. For this, satellite-based data is considered one of the effective solutions. The goal of this study is to design an intelligent framework to determine the crop area by using satellite data that is easily available. In this article, a Multi-resolution Deep Neural Network (MR-DNN) is proposed to determine rice fields by performing multi-streaming classification. The task of prediction is performed on Landsat 8 satellite images with high spatial resolution. The prediction performance of the proposed model is justified by comparing the calculated outcomes from a few selected methods. The proposed model has achieved the highest prediction performance in terms of the F1 score with the accuracy of 95.40% and 95.12% for Punjab and West-Bengal dataset as compared to the selected models, such as DeepLabV3+, Convolutional Neural Network (CNN), Support Vector Machine (SVM), Random Forest (RF), Light-Gradient Boosting Method (LGBM), eXtreme Gradient Boosting (XGBoost), Spectral, and Threshold. In this manner, the empirical evaluation defines the prediction performance of the proposed model over the visual interpretation of the maps as well as seasonal impacts.","PeriodicalId":48843,"journal":{"name":"Canadian Journal of Remote Sensing","volume":"48 1","pages":"278 - 298"},"PeriodicalIF":2.6,"publicationDate":"2022-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48075865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Pix2Pix Network to Estimate Agricultural Near Infrared Images from RGB Data 基于RGB数据估计农业近红外图像的Pix2Pix网络

IF 2.6 4区地球科学

Canadian Journal of Remote Sensing Pub Date : 2022-01-27 DOI: 10.1080/07038992.2021.2016056

Daniel Caio de Lima, D. Saqui, S. A. T. Mpinda, J. H. Saito

{"title":"Pix2Pix Network to Estimate Agricultural Near Infrared Images from RGB Data","authors":"Daniel Caio de Lima, D. Saqui, S. A. T. Mpinda, J. H. Saito","doi":"10.1080/07038992.2021.2016056","DOIUrl":"https://doi.org/10.1080/07038992.2021.2016056","url":null,"abstract":"Abstract Remote sensing has been applied to agriculture, making it possible to acquire a large amount of data far away from crops, providing information for decision making by producers that can impact production costs and crops quality. One way of getting the production information is through vegetation indices, arithmetic operations that use spectral bands, especially the Near Infrared (NIR). However, sensors that capture this spectral information are very expensive for small producers to afford it. In a previous article, a pixel-to-pixel image synthesis model to estimate NIR images from RGB data using hyperspectral endmembers (pure hyperspectral signatures) was described. In this work, an image-to-image synthesis model, known as Pix2Pix, is used for estimating NIR images from low-cost RGB camera images. Pix2Pix is a kind of Generative Adversarial Networks (GANs), composed by two neural networks, a generator (G) and a discriminator (D), that compete. G learns to create images from a random noise inputs and D learns to verify if these images are real or fake. The results showed that the presented method generated NIR images quite similar to real ones, reaching a value of 0.912 on M3SIM similarity metric, outperforming results obtained with the previous endmembers method (0.775 on M3SIM).","PeriodicalId":48843,"journal":{"name":"Canadian Journal of Remote Sensing","volume":"48 1","pages":"299 - 315"},"PeriodicalIF":2.6,"publicationDate":"2022-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42034202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Regional Crop Characterization Using Multi-Temporal Optical and Synthetic Aperture Radar Earth Observations Data 利用多时相光学和合成孔径雷达对地观测数据进行区域作物特征描述

IF 2.6 4区地球科学

Canadian Journal of Remote Sensing Pub Date : 2022-01-13 DOI: 10.1080/07038992.2021.2011180

Hazhir Bahrami, Saeid Homayouni, H. Mcnairn, M. Hosseini, M. Mahdianpari

{"title":"Regional Crop Characterization Using Multi-Temporal Optical and Synthetic Aperture Radar Earth Observations Data","authors":"Hazhir Bahrami, Saeid Homayouni, H. Mcnairn, M. Hosseini, M. Mahdianpari","doi":"10.1080/07038992.2021.2011180","DOIUrl":"https://doi.org/10.1080/07038992.2021.2011180","url":null,"abstract":"Abstract Crop biophysical parameters, such as Leaf Area Index (LAI) and biomass, are essential for estimating crop productivity, yield modeling, and agronomic management. This study used several features extracted from multi-temporal Sentinel-1 Synthetic Aperture Radar (SAR) and spectral vegetation indices extracted from Sentinel-2 optical data to estimate crop LAI and wet and dry biomass. Various machine learning algorithms, including Random Forest Regression (RFR), Support Vector Regression (SVR), and Artificial Neural Network (ANN), were trained and assessed for three major crops (wheat, soybeans and canola). ANN provided the best accuracy for all wheat parameters and soybean LAI and canola wet biomass and LAI. RFR led to higher accuracy for soybean dry and wet biomass. However, SVR could accurately estimate only canola dry biomass. All data were then pooled to investigate if a single algorithm could estimate biophysical parameters for all crops. The RFR model accurately estimated wet and dry biomass and LAI across all crop types in this scenario. This generic model is fast and accurate and can be easily applied for crop mapping and monitoring over large geographies using cloud computing platforms, such as Google Earth Engine.","PeriodicalId":48843,"journal":{"name":"Canadian Journal of Remote Sensing","volume":"48 1","pages":"258 - 277"},"PeriodicalIF":2.6,"publicationDate":"2022-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43144627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Application of Sentinel-1 SAR Data for Detecting a Nuclear Test Location in North Korea Sentinel-1 SAR数据在朝鲜核试验地点探测中的应用

IF 2.6 4区地球科学

Canadian Journal of Remote Sensing Pub Date : 2022-01-13 DOI: 10.1080/07038992.2021.2025348

Asset Akhmadiya, K. Moldamurat, M. Jamshidi, S. Brimzhanova, N. Nabiyev, Aigerim Kismanova

引用次数: 2

Detecting White Pine Needle Damage through Satellite Remote Sensing 利用卫星遥感探测白松针损害

IF 2.6 4区地球科学

Canadian Journal of Remote Sensing Pub Date : 2022-01-10 DOI: 10.1080/07038992.2021.2023317

Aaron Meneghini, Parinaz Rahimzadeh-Bajgiran, W. Livingston, A. Weiskittel

{"title":"Detecting White Pine Needle Damage through Satellite Remote Sensing","authors":"Aaron Meneghini, Parinaz Rahimzadeh-Bajgiran, W. Livingston, A. Weiskittel","doi":"10.1080/07038992.2021.2023317","DOIUrl":"https://doi.org/10.1080/07038992.2021.2023317","url":null,"abstract":"Abstract Eastern white pines (Pinus strobus L.) of New England forests have been recently impacted by a fungal disease known as White Pine Needle Damage (WPND), causing widespread needle damage. To complement current WPND monitoring methods based on field and aerial detection surveys, we evaluated the potential of satellite remote sensing technology to detect WPND outbreaks. Using Sentinel-2 spectral vegetation indices (SVIs), we directly visualized change overlapping WPND outbreaks and ran Random Forest machine learning classifiers for feature selection and WPND detection and severity classification. Direct visualization of WPND associated change was most effective through the Normalized Difference Infrared Index (NDII), which captured decreases in vegetation health conditions coinciding with peak WPND symptoms. We obtained good accuracies in binary (WPND vs. Non-WPND) detection (70%) and two-class severity modeling of WPND (75%). The highest accuracies were achieved using imagery from early to late summer. The most selected SVIs for modeling were the Carotenoid Reflectance Index1 (CRI1), the Sentinel-2 Red-Edge Position (S2REP), and the Normalized Difference Vegetation Index (NDVI). Our results suggest detecting severe WPND through fine resolution remote sensing is feasible. However, more work is needed to determine the effects of spatial, spectral, and temporal resolution of remote sensing data for detecting WPND severity levels.","PeriodicalId":48843,"journal":{"name":"Canadian Journal of Remote Sensing","volume":"48 1","pages":"239 - 257"},"PeriodicalIF":2.6,"publicationDate":"2022-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43666820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3