Pub Date : 2023-11-10DOI: 10.1080/15481603.2023.2271246
Ye Ma, Zhen Zhen, Fengri Li, Fujuan Feng, Yinghui Zhao
Large-scale forest composition mapping and change monitoring are essential for regional and national forest resource management, monitoring, and carbon stock assessment. However, the existing large...
{"title":"An innovative lightweight 1D-CNN model for efficient monitoring of large-scale forest composition: a case study of Heilongjiang Province, China","authors":"Ye Ma, Zhen Zhen, Fengri Li, Fujuan Feng, Yinghui Zhao","doi":"10.1080/15481603.2023.2271246","DOIUrl":"https://doi.org/10.1080/15481603.2023.2271246","url":null,"abstract":"Large-scale forest composition mapping and change monitoring are essential for regional and national forest resource management, monitoring, and carbon stock assessment. However, the existing large...","PeriodicalId":55091,"journal":{"name":"GIScience & Remote Sensing","volume":"42 9","pages":""},"PeriodicalIF":6.7,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72365799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-09DOI: 10.1080/15481603.2023.2275424
Xiaohan Zhang, Wei Han, Jun Li, Lizhe Wang
Accurate bathymetric information is an important foundation for marine resource development and nearshore ecological protection. Existing empirical algorithms can estimate water depth from high res...
准确的水深信息是海洋资源开发和近岸生态保护的重要基础。现有的经验算法可以从高分辨率估计水深。。。
{"title":"Nearshore bathymetry estimation through dual-time phase satellite imagery in the absence of in-situ data","authors":"Xiaohan Zhang, Wei Han, Jun Li, Lizhe Wang","doi":"10.1080/15481603.2023.2275424","DOIUrl":"https://doi.org/10.1080/15481603.2023.2275424","url":null,"abstract":"Accurate bathymetric information is an important foundation for marine resource development and nearshore ecological protection. Existing empirical algorithms can estimate water depth from high res...","PeriodicalId":55091,"journal":{"name":"GIScience & Remote Sensing","volume":"64 14","pages":""},"PeriodicalIF":6.7,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71524933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-30DOI: 10.1080/15481603.2023.2275427
Baode Jiang, Shaofen Xu, Zhiwei Li
Polyline simplification is crucial for cartography and spatial database management. In recent decades, various rule-based algorithms for vector polyline simplification have been proposed. However, ...
{"title":"Polyline simplification using a region proposal network integrating raster and vector features","authors":"Baode Jiang, Shaofen Xu, Zhiwei Li","doi":"10.1080/15481603.2023.2275427","DOIUrl":"https://doi.org/10.1080/15481603.2023.2275427","url":null,"abstract":"Polyline simplification is crucial for cartography and spatial database management. In recent decades, various rule-based algorithms for vector polyline simplification have been proposed. However, ...","PeriodicalId":55091,"journal":{"name":"GIScience & Remote Sensing","volume":"66 4","pages":""},"PeriodicalIF":6.7,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71507431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-26DOI: 10.1080/15481603.2023.2270814
Binbin Fan, Geping Luo, Olaf Hellwich, Xuguo Shi, Friday U. Ochege
The Mountain-Oasis-Desert System (MODS) is the fundamental landscape component within the vast arid region of Central Asia. Human activities and natural processes cause surface displacement in the ...
山地绿洲沙漠系统（MODS）是中亚广大干旱地区的基本景观组成部分。人类活动和自然过程导致了。。。
{"title":"Surface deformation detection and attribution in the Mountain-Oasis-Desert Landscape in north Tianshan Mountains","authors":"Binbin Fan, Geping Luo, Olaf Hellwich, Xuguo Shi, Friday U. Ochege","doi":"10.1080/15481603.2023.2270814","DOIUrl":"https://doi.org/10.1080/15481603.2023.2270814","url":null,"abstract":"The Mountain-Oasis-Desert System (MODS) is the fundamental landscape component within the vast arid region of Central Asia. Human activities and natural processes cause surface displacement in the ...","PeriodicalId":55091,"journal":{"name":"GIScience & Remote Sensing","volume":"51 15","pages":""},"PeriodicalIF":6.7,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71514439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-26DOI: 10.1080/15481603.2023.2270791
Marco Assiri, Anna Sartori, Antonio Persichetti, Cristiano Miele, Regine Anne Faelga, Tegan Blount, Sonia Silvestri
Aboveground biomass (AGB) can serve as an indicator when estimating various biogeochemical processes in peatlands, an ecosystem which provides countless ecosystem services and plays a key role in c...
{"title":"Leaf area index and aboveground biomass estimation of an alpine peatland with a UAV multi-sensor approach","authors":"Marco Assiri, Anna Sartori, Antonio Persichetti, Cristiano Miele, Regine Anne Faelga, Tegan Blount, Sonia Silvestri","doi":"10.1080/15481603.2023.2270791","DOIUrl":"https://doi.org/10.1080/15481603.2023.2270791","url":null,"abstract":"Aboveground biomass (AGB) can serve as an indicator when estimating various biogeochemical processes in peatlands, an ecosystem which provides countless ecosystem services and plays a key role in c...","PeriodicalId":55091,"journal":{"name":"GIScience & Remote Sensing","volume":"67 4","pages":""},"PeriodicalIF":6.7,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71507425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Deep learning techniques have been applied to extract road areas from remote sensing images, leveraging their efficient and intelligent advantages. However, the contradiction between the effective ...
深度学习技术已被应用于从遥感图像中提取道路区域，利用其高效和智能的优势。然而，有效。。。
{"title":"D-FusionNet: road extraction from remote sensing images using dilated convolutional block","authors":"Ruixuan Zhang, Wu Zhu, Yankui Li, Tiansheng Song, Zhenhong Li, Wenjing Yang, Luyao Yang, Tian Zhou, Xuanyu Xu","doi":"10.1080/15481603.2023.2270806","DOIUrl":"https://doi.org/10.1080/15481603.2023.2270806","url":null,"abstract":"Deep learning techniques have been applied to extract road areas from remote sensing images, leveraging their efficient and intelligent advantages. However, the contradiction between the effective ...","PeriodicalId":55091,"journal":{"name":"GIScience & Remote Sensing","volume":"68 4","pages":""},"PeriodicalIF":6.7,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71507419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-23DOI: 10.1080/15481603.2023.2267851
Jasper Steenvoorden, Juul Limpens
Northern peatland functions are strongly associated with vegetation structure and composition. While large-scale monitoring of functions through remotely sensed mapping of vegetation patterns is th...
{"title":"Upscaling peatland mapping with drone-derived imagery: impact of spatial resolution and vegetation characteristics","authors":"Jasper Steenvoorden, Juul Limpens","doi":"10.1080/15481603.2023.2267851","DOIUrl":"https://doi.org/10.1080/15481603.2023.2267851","url":null,"abstract":"Northern peatland functions are strongly associated with vegetation structure and composition. While large-scale monitoring of functions through remotely sensed mapping of vegetation patterns is th...","PeriodicalId":55091,"journal":{"name":"GIScience & Remote Sensing","volume":"67 3","pages":""},"PeriodicalIF":6.7,"publicationDate":"2023-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71507426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-31DOI: 10.1080/15481603.2022.2112391
Yichen Lu, T. James, C. Schillaci, Aldo Lipani
ABSTRACT Accurately monitoring the variation of snow cover from remote sensing is vital since it assists in various fields including prediction of floods, control of runoff values, and the ice regime of rivers. Spectral indices methods are traditional ways to realize snow segmentation, including the most common one – the Normalized Difference Snow Index (NDSI), which utilizes the combination of green and short-wave infrared (SWIR) bands. In addition, spectral indices methods heavily depend on the optimal threshold to determine the accuracy, making it time-consuming to find optimal values for different places. Convolutional neural networks ensemble model with DeepLabV3+ was employed as sub-models for snow segmentation using (Sentinel-2), which aims to distinguish clouds and water body from snow. The imagery dataset generated in this article contains sites in global alpine regions such as Tibetan Plateau in China, the Alps in Switzerland, Alaska in the United States, Southern Patagonian Icefield in Chile, Tsylos Provincial Park, Tatsamenie Peak, and Dalton Peak in Canada. To overcome the limitation of DeepLabV3+, which only accepts three channels as input features, and the need to use six features: green, red, blue, near-infraRed, SWIR, and NDSI, 20 three-channel DeepLabV3+ sub-models, were constructed with different combinations of three features and then ensembled together. The proposed ensemble model showed superior performance than benchmark spectral indices method, with mIoU values ranging from 0.8075 to 0.9538 in different test sites. The results of this project contribute to the development of automated snow segmentation tools to assist earth observation applications.
{"title":"Snow detection in alpine regions with Convolutional Neural Networks: discriminating snow from cold clouds and water body","authors":"Yichen Lu, T. James, C. Schillaci, Aldo Lipani","doi":"10.1080/15481603.2022.2112391","DOIUrl":"https://doi.org/10.1080/15481603.2022.2112391","url":null,"abstract":"ABSTRACT Accurately monitoring the variation of snow cover from remote sensing is vital since it assists in various fields including prediction of floods, control of runoff values, and the ice regime of rivers. Spectral indices methods are traditional ways to realize snow segmentation, including the most common one – the Normalized Difference Snow Index (NDSI), which utilizes the combination of green and short-wave infrared (SWIR) bands. In addition, spectral indices methods heavily depend on the optimal threshold to determine the accuracy, making it time-consuming to find optimal values for different places. Convolutional neural networks ensemble model with DeepLabV3+ was employed as sub-models for snow segmentation using (Sentinel-2), which aims to distinguish clouds and water body from snow. The imagery dataset generated in this article contains sites in global alpine regions such as Tibetan Plateau in China, the Alps in Switzerland, Alaska in the United States, Southern Patagonian Icefield in Chile, Tsylos Provincial Park, Tatsamenie Peak, and Dalton Peak in Canada. To overcome the limitation of DeepLabV3+, which only accepts three channels as input features, and the need to use six features: green, red, blue, near-infraRed, SWIR, and NDSI, 20 three-channel DeepLabV3+ sub-models, were constructed with different combinations of three features and then ensembled together. The proposed ensemble model showed superior performance than benchmark spectral indices method, with mIoU values ranging from 0.8075 to 0.9538 in different test sites. The results of this project contribute to the development of automated snow segmentation tools to assist earth observation applications.","PeriodicalId":55091,"journal":{"name":"GIScience & Remote Sensing","volume":"59 1","pages":"1321 - 1343"},"PeriodicalIF":6.7,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44868738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-13DOI: 10.1080/15481603.2022.2158521
Wenting Wu, Chao Zhi, C. Chen, B. Tian, Zuoqi Chen, Hua Su
ABSTRACT Intertidal vegetation plays an essential role in habitat provision for waterbirds but suffers great losses due to human activities. However, it is challenging in tracking the human-driven loss and degradation of intertidal vegetation due to rapid urbanization in a high temporal resolution. In this study, a methodological framework based on full Landsat time-series (FLTS) is proposed to detect the year of change (YOC) of intertidal vegetation converted to impervious surfaces (ISs) and artificial ponds (APs), and the condition of the remaining intertidal vegetation was also assessed by FLTS, in the Fujian province, a subtropical coastal area lying in southeast China. The accuracies of the YOC detection of intertidal vegetation converted to IS and AP were 91.84% and 72.73%, with mean absolute errors of 0.26 and 1.06, respectively. The total areas of intertidal vegetation encroached by IS and AP were 31.68 km2 and 23.85 km2, respectively. Most ISs were developed later than 2010, and most APs were developed earlier than 2005, which are highly related to the implementation of local policies for economic development. The remaining intertidal vegetation in growing, stable, and degraded conditions were 43.05%, 56.38%, and 0.57%, respectively. The results indicated that areas of intertidal vegetation were reclaimed for anthropogenic uses at a considerable rate, although the intertidal vegetation still increased owing to natural development after the establishment of natural reserves. The study demonstrates that the FLTS has capacities in monitoring the dynamics in coastal zones solely for its dense earth observations.
{"title":"Detecting annual anthropogenic encroachment on intertidal vegetation using full Landsat time-series in Fujian, China","authors":"Wenting Wu, Chao Zhi, C. Chen, B. Tian, Zuoqi Chen, Hua Su","doi":"10.1080/15481603.2022.2158521","DOIUrl":"https://doi.org/10.1080/15481603.2022.2158521","url":null,"abstract":"ABSTRACT Intertidal vegetation plays an essential role in habitat provision for waterbirds but suffers great losses due to human activities. However, it is challenging in tracking the human-driven loss and degradation of intertidal vegetation due to rapid urbanization in a high temporal resolution. In this study, a methodological framework based on full Landsat time-series (FLTS) is proposed to detect the year of change (YOC) of intertidal vegetation converted to impervious surfaces (ISs) and artificial ponds (APs), and the condition of the remaining intertidal vegetation was also assessed by FLTS, in the Fujian province, a subtropical coastal area lying in southeast China. The accuracies of the YOC detection of intertidal vegetation converted to IS and AP were 91.84% and 72.73%, with mean absolute errors of 0.26 and 1.06, respectively. The total areas of intertidal vegetation encroached by IS and AP were 31.68 km2 and 23.85 km2, respectively. Most ISs were developed later than 2010, and most APs were developed earlier than 2005, which are highly related to the implementation of local policies for economic development. The remaining intertidal vegetation in growing, stable, and degraded conditions were 43.05%, 56.38%, and 0.57%, respectively. The results indicated that areas of intertidal vegetation were reclaimed for anthropogenic uses at a considerable rate, although the intertidal vegetation still increased owing to natural development after the establishment of natural reserves. The study demonstrates that the FLTS has capacities in monitoring the dynamics in coastal zones solely for its dense earth observations.","PeriodicalId":55091,"journal":{"name":"GIScience & Remote Sensing","volume":"59 1","pages":"2266 - 2282"},"PeriodicalIF":6.7,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46916293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
ABSTRACT Accurate crop mapping is critical for agricultural applications. Although studies have combined deep learning methods and time-series satellite images to crop classification with satisfactory results, most of them focused on supervised methods, which are usually applicable to a specific domain and lose their validity in new domains. Unsupervised domain adaptation (UDA) was proposed to solve this limitation by transferring knowledge from source domains with labeled samples to target domains with unlabeled samples. Particularly, multi-source UDA (MUDA) is a powerful extension that leverages knowledge from multiple source domains and can achieve better results in the target domain than single-source UDA (SUDA). However, few studies have explored the potential of MUDA for crop mapping. This study proposed a MUDA crop classification model (MUCCM) for unsupervised crop mapping. Specifically, 11 states in the U.S. were selected as the multi-source domains, and three provinces in Northeast China were selected as individual target domains. Ten spectral bands and five vegetation indexes were collected at a 10-day interval from time-series Sentinel-2 images to build the MUCCM. Subsequently, a SUDA model Domain Adversarial Neural Network (DANN) and two direct transfer methods, namely, the deep neural network and random forest, were constructed and compared with the MUCCM. The results indicated that the UDA models outperformed the direct transfer models significantly, and the MUCCM was superior to the DANN, achieving the highest classification accuracy (OA>85%) in each target domain. In addition, the MUCCM also performed best in in-season forecasting and crop mapping. This study is the first to apply a MUDA to crop classification and demonstrate a novel, effective solution for high-performance crop mapping in regions without labeled samples.
{"title":"Exploring the potential of multi-source unsupervised domain adaptation in crop mapping using Sentinel-2 images","authors":"Yumiao Wang, Luwei Feng, Weiwei Sun, Zuxun Zhang, Hanyu Zhang, Gang Yang, Xiangchao Meng","doi":"10.1080/15481603.2022.2156123","DOIUrl":"https://doi.org/10.1080/15481603.2022.2156123","url":null,"abstract":"ABSTRACT Accurate crop mapping is critical for agricultural applications. Although studies have combined deep learning methods and time-series satellite images to crop classification with satisfactory results, most of them focused on supervised methods, which are usually applicable to a specific domain and lose their validity in new domains. Unsupervised domain adaptation (UDA) was proposed to solve this limitation by transferring knowledge from source domains with labeled samples to target domains with unlabeled samples. Particularly, multi-source UDA (MUDA) is a powerful extension that leverages knowledge from multiple source domains and can achieve better results in the target domain than single-source UDA (SUDA). However, few studies have explored the potential of MUDA for crop mapping. This study proposed a MUDA crop classification model (MUCCM) for unsupervised crop mapping. Specifically, 11 states in the U.S. were selected as the multi-source domains, and three provinces in Northeast China were selected as individual target domains. Ten spectral bands and five vegetation indexes were collected at a 10-day interval from time-series Sentinel-2 images to build the MUCCM. Subsequently, a SUDA model Domain Adversarial Neural Network (DANN) and two direct transfer methods, namely, the deep neural network and random forest, were constructed and compared with the MUCCM. The results indicated that the UDA models outperformed the direct transfer models significantly, and the MUCCM was superior to the DANN, achieving the highest classification accuracy (OA>85%) in each target domain. In addition, the MUCCM also performed best in in-season forecasting and crop mapping. This study is the first to apply a MUDA to crop classification and demonstrate a novel, effective solution for high-performance crop mapping in regions without labeled samples.","PeriodicalId":55091,"journal":{"name":"GIScience & Remote Sensing","volume":"59 1","pages":"2247 - 2265"},"PeriodicalIF":6.7,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48355353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：