Pub Date : 2024-06-01DOI: 10.14358/pers.24-00001r2
Stephanie A. Insalaco, Hannah V. Herrero, Russ Limber, Clancy Oliver, William B. Wolfson
The ecosystem of Mosquito Lagoon, Florida, has been rapidly deteriorating since the 2010s, with a notable decline in keystone seagrass species. Seagrass is vital for many species in the lagoon, but nutrient overloading, algal blooms, boating, manatee grazing, and other factors have led to its loss. To understand this decline, a deep neural network analyzed Landsat imagery from 2000 to 2020. Results showed significant seagrass loss post-2013, coinciding with the 2011–2013 super algal bloom. Seagrass abundance varied annually, with the model performing best in years with higher seagrass coverage. While the deep learning method successfully identified seagrass, it also revealed that recent seagrass coverage is almost non-existent. This monitoring approach could aid in ecosystem recovery if coupled with appropriate policies for Mosquito Lagoon's restoration.
{"title":"Monitoring an Ecosystem in Crisis: Measuring Seagrass Meadow Loss Using Deep Learning in Mosquito Lagoon, Florida","authors":"Stephanie A. Insalaco, Hannah V. Herrero, Russ Limber, Clancy Oliver, William B. Wolfson","doi":"10.14358/pers.24-00001r2","DOIUrl":"https://doi.org/10.14358/pers.24-00001r2","url":null,"abstract":"The ecosystem of Mosquito Lagoon, Florida, has been rapidly deteriorating since the 2010s, with a notable decline in keystone seagrass species. Seagrass is vital for many species in the lagoon, but nutrient overloading, algal blooms, boating, manatee grazing, and other factors have\u0000 led to its loss. To understand this decline, a deep neural network analyzed Landsat imagery from 2000 to 2020. Results showed significant seagrass loss post-2013, coinciding with the 2011–2013 super algal bloom. Seagrass abundance varied annually, with the model performing best in years\u0000 with higher seagrass coverage. While the deep learning method successfully identified seagrass, it also revealed that recent seagrass coverage is almost non-existent. This monitoring approach could aid in ecosystem recovery if coupled with appropriate policies for Mosquito Lagoon's restoration.","PeriodicalId":211256,"journal":{"name":"Photogrammetric Engineering & Remote Sensing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141233468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.14358/pers.22-00132r3
Haotian Zheng, Fan Yu, Huawei Wan, Peirong Shi, Haonan Wang
The key data for accurate prediction is of great significance to accurately carry out the next step of sustainable land use development plan according to the demand of China. Consequently, the main purposes of our study are: (1) to delineate the characteristics of land use transitions within the Yangtze River Economic Belt; (2) to use the Markov model and the autoregressive integrated moving average (ARIMA) model for comparative analysis and prediction of land use distribution. This study analyzes land use/cover change (LUCC) data from 2010 and 2020 using the land use transition matrix, dynamic degree, and comprehensive index model and predicts 2025 land use by the Markov model. The study identifies a reduction in land usage over 11 years, particularly in grassland. The Markov and ARIMA models' significance is 0.002 (P < 0.01), showing arable land and woodland dominance, with varying changes in other land types.
{"title":"Land Use Change in the Yangtze River Economic Belt during 2010 to 2020 and Future Comprehensive Prediction Based on Markov and ARIMA Models","authors":"Haotian Zheng, Fan Yu, Huawei Wan, Peirong Shi, Haonan Wang","doi":"10.14358/pers.22-00132r3","DOIUrl":"https://doi.org/10.14358/pers.22-00132r3","url":null,"abstract":"The key data for accurate prediction is of great significance to accurately carry out the next step of sustainable land use development plan according to the demand of China. Consequently, the main purposes of our study are: (1) to delineate the characteristics of land use transitions\u0000 within the Yangtze River Economic Belt; (2) to use the Markov model and the autoregressive integrated moving average (ARIMA) model for comparative analysis and prediction of land use distribution. This study analyzes land use/cover change (LUCC) data from 2010 and 2020 using the land use transition\u0000 matrix, dynamic degree, and comprehensive index model and predicts 2025 land use by the Markov model. The study identifies a reduction in land usage over 11 years, particularly in grassland. The Markov and ARIMA models' significance is 0.002 (P < 0.01), showing arable land and woodland\u0000 dominance, with varying changes in other land types.","PeriodicalId":211256,"journal":{"name":"Photogrammetric Engineering & Remote Sensing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141278088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.14358/pers.23-00074r2
Fangrong Zhou, Lifeng Liu, Hao Hu, Weishi Jin, Zezhong Zheng, Zhongnian Li, Yi Ma, Qun Wang
The power grid plays a vital role in the construction of livelihood projects by transmitting electrical energy. In the event of insulator explosions on power grid towers, these insulators may detach, presenting potential safety risks to transmission lines. The identification of such failures relies on the examination of images captured by unmanned aerial vehicles (UAVs). However, accurately detecting insulator defects remains challenging, particularly when dealing with variations in size. Existing methods exhibit limited accuracy in detecting small objects. In this paper, we propose a novel detection method that incorporates the convolutional block attention module (CBAM) as an attention mechanism into the backbone of the "you only look once" version 5 (YOLOv5) model. Additionally, we integrate a residual structure into the model to learn additional information and features related to insulators, thereby enhancing detection efficiency. Experimental results demonstrate that our proposed method achieved F1 scores of 0.87 for insulator detection and 0.89 for insulator defect detection. The improved YOLOv5 network shows promise in detecting insulators and their defects in UAV images.
电网通过传输电能在民生项目建设中发挥着至关重要的作用。如果电网塔上的绝缘子发生爆炸，这些绝缘子可能会脱落，给输电线路带来潜在的安全风险。此类故障的识别有赖于对无人驾驶飞行器（UAV）拍摄的图像进行检查。然而，准确检测绝缘体缺陷仍然具有挑战性，尤其是在处理尺寸变化时。现有方法在检测小物体时表现出有限的准确性。在本文中，我们提出了一种新型检测方法，将卷积块注意力模块（CBAM）作为一种注意力机制纳入 "你只看一次 "第 5 版（YOLOv5）模型的主干。此外，我们还在模型中加入了残差结构，以学习与绝缘体相关的额外信息和特征，从而提高检测效率。实验结果表明，我们提出的方法在绝缘体检测方面取得了 0.87 的 F1 分数，在绝缘体缺陷检测方面取得了 0.89 的 F1 分数。改进后的 YOLOv5 网络有望检测无人机图像中的绝缘体及其缺陷。
{"title":"An Improved YOLO Network for Insulator and Insulator Defect Detection in UAV Images","authors":"Fangrong Zhou, Lifeng Liu, Hao Hu, Weishi Jin, Zezhong Zheng, Zhongnian Li, Yi Ma, Qun Wang","doi":"10.14358/pers.23-00074r2","DOIUrl":"https://doi.org/10.14358/pers.23-00074r2","url":null,"abstract":"The power grid plays a vital role in the construction of livelihood projects by transmitting electrical energy. In the event of insulator explosions on power grid towers, these insulators may detach, presenting potential safety risks to transmission lines. The identification of such\u0000 failures relies on the examination of images captured by unmanned aerial vehicles (UAVs). However, accurately detecting insulator defects remains challenging, particularly when dealing with variations in size. Existing methods exhibit limited accuracy in detecting small objects. In this paper,\u0000 we propose a novel detection method that incorporates the convolutional block attention module (CBAM) as an attention mechanism into the backbone of the \"you only look once\" version 5 (YOLOv5) model. Additionally, we integrate a residual structure into the model to learn additional information\u0000 and features related to insulators, thereby enhancing detection efficiency. Experimental results demonstrate that our proposed method achieved F1 scores of 0.87 for insulator detection and 0.89 for insulator defect detection. The improved YOLOv5 network shows promise in detecting insulators\u0000 and their defects in UAV images.","PeriodicalId":211256,"journal":{"name":"Photogrammetric Engineering & Remote Sensing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141275391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-01DOI: 10.14358/pers.23-00078r2
Xu Wang, Baoyun Wang, Ruohao Yuan, Yumeng Luo, Cunxi Liu
Debris flow susceptibility evaluation plays a crucial role in the prevention and control of debris flow disasters. Therefore, this article proposes a convolutional neural network model named multi-level feature extraction network (MFENet). First, a dual-channel CNN architecture incorporating the Embedding Channel Attention mechanism is used to extract shallow features from both digital elevation model images and multispectral images. Subsequently, channel shuffle and feature concatenation are applied to the features from the two channels to obtain fused feature sets. Following this, a deep feature extraction is performed on the fused feature sets using a residual module improved by maximum pooling. Finally, the susceptibility index of gullies to debris flows is calculated based on the similarity scores.
{"title":"Debris Flow Susceptibility Evaluation Based on Multi-level Feature Extraction CNN Model: A Case Study of Nujiang Prefecture, China","authors":"Xu Wang, Baoyun Wang, Ruohao Yuan, Yumeng Luo, Cunxi Liu","doi":"10.14358/pers.23-00078r2","DOIUrl":"https://doi.org/10.14358/pers.23-00078r2","url":null,"abstract":"Debris flow susceptibility evaluation plays a crucial role in the prevention and control of debris flow disasters. Therefore, this article proposes a convolutional neural network model named multi-level feature extraction network (MFENet). First, a dual-channel CNN architecture incorporating\u0000 the Embedding Channel Attention mechanism is used to extract shallow features from both digital elevation model images and multispectral images. Subsequently, channel shuffle and feature concatenation are applied to the features from the two channels to obtain fused feature sets. Following\u0000 this, a deep feature extraction is performed on the fused feature sets using a residual module improved by maximum pooling. Finally, the susceptibility index of gullies to debris flows is calculated based on the similarity scores.","PeriodicalId":211256,"journal":{"name":"Photogrammetric Engineering & Remote Sensing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141033073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Geoprocessing tools are the nuts of bolts of GIS processing. An “off-the-shelf” GIS software package could come with several hundred standard tools. But what are the options for a beginning or intermediate GIS analyst when you face a GIS question that requires a new or different tool. Well??? there are actually multiple options available, some easier to access than others. Below are a few “tips” for finding tools not included with the off-the-shelf GIS products. Please note that these are options, and not endorsements or recommendations.
{"title":"GIS Tips & Tricks ‐ Need More Tools? Try These...","authors":"Alma M. Karlin","doi":"10.14358/pers.90.5.273","DOIUrl":"https://doi.org/10.14358/pers.90.5.273","url":null,"abstract":"Geoprocessing tools are the nuts of bolts of GIS processing. An “off-the-shelf” GIS software package could come with several hundred standard tools. But what are the options for a beginning or intermediate GIS analyst when you face a GIS question that requires a new or different\u0000 tool. Well??? there are actually multiple options available, some easier to access than others. Below are a few “tips” for finding tools not included with the off-the-shelf GIS products. Please note that these are options, and not endorsements or recommendations.","PeriodicalId":211256,"journal":{"name":"Photogrammetric Engineering & Remote Sensing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141046574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Best Practices in Evaluating Geospatial Mapping Accuracy according to the New ASPRS Accuracy Standards","authors":"Qassim Abdullah","doi":"10.14358/pers.90.5.265","DOIUrl":"https://doi.org/10.14358/pers.90.5.265","url":null,"abstract":"","PeriodicalId":211256,"journal":{"name":"Photogrammetric Engineering & Remote Sensing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141035203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-01DOI: 10.14358/pers.23-00051r2
Xiaodan Sun, Xiaofang Sun
Image segmentation is essential for object-oriented analysis, and classification is a critical parameter influencing analysis accuracy. However, image classification and segmentation based on spectral features are easily perturbed by the high-frequency information of a high spatial resolution remotely sensed (HSRRS) image, degrading its classification and segmentation quality. This article first presents a pixel texture index (PTI) by describing the texture and edge in a local area surrounding a pixel. Indeed.. The experimental results highlight that the HSRRS image classification and segmentation quality can be effectively improved by combining it with the PTI image. Indeed, the overall accuracy improved from 7% to 14%, and the kappa can be increased from 11% to 24%, respectively.
{"title":"A Pixel Texture Index Algorithm and Its Application","authors":"Xiaodan Sun, Xiaofang Sun","doi":"10.14358/pers.23-00051r2","DOIUrl":"https://doi.org/10.14358/pers.23-00051r2","url":null,"abstract":"Image segmentation is essential for object-oriented analysis, and classification is a critical parameter influencing analysis accuracy. However, image classification and segmentation based on spectral features are easily perturbed by the high-frequency information of a high spatial\u0000 resolution remotely sensed (HSRRS) image, degrading its classification and segmentation quality. This article first presents a pixel texture index (PTI) by describing the texture and edge in a local area surrounding a pixel. Indeed.. The experimental results highlight that the HSRRS image\u0000 classification and segmentation quality can be effectively improved by combining it with the PTI image. Indeed, the overall accuracy improved from 7% to 14%, and the kappa can be increased from 11% to 24%, respectively.","PeriodicalId":211256,"journal":{"name":"Photogrammetric Engineering & Remote Sensing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141041837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-01DOI: 10.14358/pers.23-00053r2
Qiao Zhang, Ziyi Luo, Yang Shen, Zhoufeng Wang
Accurately obtaining crop cultivation extent and estimating the cultivated area are significant for adjusting regional planting structure. This article proposes a parcel-level crop classification method using time-series, medium-resolution, remote sensing images and single-phase, high-spatial-resolution, remote sensing images. The deep learning semantic segmentation network feature pyramid network with squeeze-and-excitation network (FPN???SENet) and multi-scale segmentation were used to extract cultivated land parcels from Gaofen-2 imagery, while the pixel-level crop types were classified by using support vector machine algorithms from time-series Sentinel-2 images. Then, the parcel-level crop classification was obtained from the pixel-level crop types and land parcels.
{"title":"Parcel-Level Crop Classification in Plain Fragmented Regions Based on Multi-Source Remote Sensing Images","authors":"Qiao Zhang, Ziyi Luo, Yang Shen, Zhoufeng Wang","doi":"10.14358/pers.23-00053r2","DOIUrl":"https://doi.org/10.14358/pers.23-00053r2","url":null,"abstract":"Accurately obtaining crop cultivation extent and estimating the cultivated area are significant for adjusting regional planting structure. This article proposes a parcel-level crop classification method using time-series, medium-resolution, remote sensing images and single-phase, high-spatial-resolution,\u0000 remote sensing images. The deep learning semantic segmentation network feature pyramid network with squeeze-and-excitation network (FPN???SENet) and multi-scale segmentation were used to extract cultivated land parcels from Gaofen-2 imagery, while the pixel-level crop types were classified\u0000 by using support vector machine algorithms from time-series Sentinel-2 images. Then, the parcel-level crop classification was obtained from the pixel-level crop types and land parcels.","PeriodicalId":211256,"journal":{"name":"Photogrammetric Engineering & Remote Sensing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141027243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Drought is a devastating natural hazard and exerts profound effects on both the environment and society. Predicting drought occurrences is significant in aiding decision-making and implementing effective mitigation strategies. In regions characterized by limited data availability, such as Southern Africa, the use of satellite remote sensing data promises an excellent opportunity for achieving this predictive goal. In this article, we assess the effectiveness of Soil Moisture Active Passive (SMAP) and Cyclone Global Navigation Satellite System (CYGNSS) soil moisture data in predicting drought conditions using multiple linear regression???predicted data and Global Land Data Assimilation System (GLDAS) soil moisture data.
{"title":"Evaluation of SMAP and CYGNSS Soil Moistures in Drought Prediction Using Multiple Linear Regression and GLDAS Product","authors":"Komi Edokossi, Shuanggen Jin, Andrés Calabia, Iñigo Molina, Usman Mazhar","doi":"10.14358/pers.23-00075r2","DOIUrl":"https://doi.org/10.14358/pers.23-00075r2","url":null,"abstract":"Drought is a devastating natural hazard and exerts profound effects on both the environment and society. Predicting drought occurrences is significant in aiding decision-making and implementing effective mitigation strategies. In regions characterized by limited data availability, such\u0000 as Southern Africa, the use of satellite remote sensing data promises an excellent opportunity for achieving this predictive goal. In this article, we assess the effectiveness of Soil Moisture Active Passive (SMAP) and Cyclone Global Navigation Satellite System (CYGNSS) soil moisture data\u0000 in predicting drought conditions using multiple linear regression???predicted data and Global Land Data Assimilation System (GLDAS) soil moisture data.","PeriodicalId":211256,"journal":{"name":"Photogrammetric Engineering & Remote Sensing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141035063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Applications of Small Unmanned Aircraft Systems: Best Practices and Case Studies","authors":"C. Krampf","doi":"10.14358/pers.90.4.199","DOIUrl":"https://doi.org/10.14358/pers.90.4.199","url":null,"abstract":"","PeriodicalId":211256,"journal":{"name":"Photogrammetric Engineering & Remote Sensing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140781942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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