THỰC TRẠNG VÀ NHỮNG THÁCH THỨC TRONG QUẢN LÝ ĐẤT NGẬP NƯỚC TẠI VÙNG CỬA SÔNG Ô LÂU, TỈNH THỪA THIÊN HUẾ

Nghiên cứu này nhằm mục đích làm rõ những đặc điểm và những thách thức trong quản lý, bảo tồn đất ngập nước (ĐNN) tại vùng cửa sông Ô Lâu (CSÔL), tỉnh Thừa Thiên Huế. Kết hợp phương pháp phỏng vấn nông hộ, phỏng vấn cán bộ với phương pháp bản đồ, GIS, viễn thám đã cho thấy, vùng CSÔL có diện tích khoảng 11.000 ha, trong đó, vùng lõi có diện tích là khoảng 433 ha. Theo tiêu chuẩn phân loại ĐNN của Việt Nam, khu vực này có 3 nhóm chính là i) nhóm ĐNN biển và ven biển; ii) nhóm ĐNN nội địa; và iii) nhóm ĐNN nhân tạo. Hiện nay, người dân vẫn đang khai thác các nguồn tài nguyên của vùng CSÔL cho các hoạt động sinh kế. Khoảng 99,6 ha cây bụi tại các bãi bồi đã bị thay thế bởi các loại cây nông nghiệp. Tài nguyên, cảnh quan ĐNN tại CSÔL đang bị biến đổi mạnh mẽ và chức năng sinh thái của khu vực này cũng đang bị suy giảm mạnh. Để phục hồi các chức năng của vùng CSÔL, cần nhiều giải pháp từ cả chính quyền địa phương, người dân và các nhà khoa học. Trong đó, quan trọng nhất là nhận thức của người dân và ý chí của các cấp quản lý trong quá trình hoạch định chiến lược phát triển của vùng. ABSTRACT This study aimed at determining the O Lau river’s wetlands (OLRW) characteristics and identifying challenges in wetland management and conservation. By using various methods such as households and local government’s staff interview, mapping, geographic information system (GIS), remote sensing, the research results showed that the OLRW was about 11.000 hectares in which its core zone was about 433 hectares. Following Vietnam’s classification of wetlands, OLRW has three main categories, namely: i) marine and coastal wetlands; ii) inland wetlands; and iii) man-made wetlands. Currently, inhabitants are exploiting OLRW’s natural resources for their livelihood activities. Approximately 99,6 hectares of shrub-dominated wetlands were replaced by agricultural crops. OLRW’s natural resources and landscape have been destroying by human’s activities. In addition, its ecological function has also been reducing. For OLRW’s ecological functional resilience, it is necessary for the local government, inhabitants and sicientists to take countermeasures. The most important keys are inhabitants’ perception and local government’s mind in deciding to make of the development of the strategic plans.

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Advanced Classification of Remote Sensing High Resolution Imagery. An Application for the Management of Natural Resources

Developments and Advances in Intelligent Systems and Applications - Studies in Computational Intelligence ◽

10.1007/978-3-319-58965-7_1 ◽

2017 ◽

pp. 1-13 ◽

Cited By ~ 1

Author(s):

Edurne Ibarrola-Ulzurrun ◽

Javier Marcello ◽

Consuelo Gonzalo-Martin

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Natural Resources ◽

High Resolution Imagery

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Remote Sensing as an Aid to the Inventory and Management of Natural Resources

The Canadian Surveyor ◽

10.1139/tcs-1978-0005 ◽

1978 ◽

Vol 32 (2) ◽

pp. 183-203

Author(s):

Robert N. Colwell

Keyword(s):

Remote Sensing ◽

Natural Resources ◽

Fossil Fuels ◽

The United States ◽

Agricultural Crops ◽

Nonrenewable Resources ◽

Remotely Sensed Data ◽

Remote Sensing Techniques ◽

Using Data ◽

Work Done

An analysis is given of the extent to which modern remote-sensing techniques might be used to facilitate the inventory and management of such renewable natural resources as timber, forage, and agricultural crops and of such nonrenewable resources as minerals and fossil fuels. The first part of the paper seeks to clarify both the terms and the concepts that are applicable to the fast growing field of remote sensing. This is followed by a discussion of the various basic considerations that enter into the acquisition and analysis of remotely sensed data. There is an analysis of both the feasibility and the desirability of using data acquired by LANDSAT and other remote-sensing vehicles in the making of globally uniform inventories of various kinds of natural resources. There follows a tabulation of recent and representative applications and the citing of various references in which additional examples are fully described and well illustrated with remote-sensing imagery. Although the paper may appear to be justifiably optimistic, it concludes with some words of caution on the difficulties that can arise whenever there is an overstatement of remote-sensing capabilities and an understatement of remote-sensing limitations. The numerous specific examples of LANDSAT applications that are given in this paper pertain primarily to work done in Canada and the United States.

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Land-Cover Classification of Coastal Wetlands Using the RF Algorithm for Worldview-2 and Landsat 8 Images

Remote Sensing ◽

10.3390/rs11161927 ◽

2019 ◽

Vol 11 (16) ◽

pp. 1927 ◽

Cited By ~ 17

Author(s):

Xiaoxue Wang ◽

Xiangwei Gao ◽

Yuanzhi Zhang ◽

Xianyun Fei ◽

Zhou Chen ◽

...

Keyword(s):

Remote Sensing ◽

Land Cover ◽

Image Classification ◽

Classification Accuracy ◽

Coastal Wetlands ◽

Land Cover Classification ◽

Support Vector ◽

Small Scale ◽

Landsat 8

Wetlands are one of the world’s most important ecosystems, playing an important role in regulating climate and protecting the environment. However, human activities have changed the land cover of wetlands, leading to direct destruction of the environment. If wetlands are to be protected, their land cover must be classified and changes to it monitored using remote sensing technology. The random forest (RF) machine learning algorithm, which offers clear advantages (e.g., processing feature data without feature selection and preferable classification result) for high spatial image classification, has been used in many study areas. In this research, to verify the effectiveness of this algorithm for remote sensing image classification of coastal wetlands, two types of spatial resolution images of the Linhong Estuary wetland in Lianyungang—Worldview-2 and Landsat-8 images—were used for land cover classification using the RF method. To demonstrate the preferable classification accuracy of the RF algorithm, the support vector machine (SVM) and k-nearest neighbor (k-NN) methods were also used to classify the same area of land cover for comparison with the results of RF classification. The study results showed that (1) the overall accuracy of the RF method reached 91.86%, higher than the SVM and k-NN methods by 4.68% and 4.72%, respectively, for Worldview-2 images; (2) at the same time, the classification accuracies of RF, SVM, and k-NN were 86.61%, 79.96%, and 77.23%, respectively, for Landsat-8 images; (3) for some land cover types having only a small number of samples, the RF algorithm also achieved better classification results using Worldview-2 and Landsat-8 images, and (4) the addition texture features could improve the classification accuracy of the RF method when using Worldview-2 images. Research indicated that high-resolution remote sensing images are more suitable for small-scale land cover classification image and that the RF algorithm can provide better classification accuracy and is more suitable for coastal wetland classification than the SVM and k-NN algorithms are.

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Synergetic Classification of Coastal Wetlands over the Yellow River Delta with GF-3 Full-Polarization SAR and Zhuhai-1 OHS Hyperspectral Remote Sensing

Remote Sensing ◽

10.3390/rs13214444 ◽

2021 ◽

Vol 13 (21) ◽

pp. 4444

Author(s):

Canran Tu ◽

Peng Li ◽

Zhenhong Li ◽

Houjie Wang ◽

Shuowen Yin ◽

...

Keyword(s):

Remote Sensing ◽

Spatial Distribution ◽

Coastal Wetlands ◽

Yellow River ◽

Yellow River Delta ◽

River Delta ◽

Machine Learning Algorithms ◽

Support Vector ◽

Wetland Classification

The spatial distribution of coastal wetlands affects their ecological functions. Wetland classification is a challenging task for remote sensing research due to the similarity of different wetlands. In this study, a synergetic classification method developed by fusing the 10 m Zhuhai-1 Constellation Orbita Hyperspectral Satellite (OHS) imagery with 8 m C-band Gaofen-3 (GF-3) full-polarization Synthetic Aperture Radar (SAR) imagery was proposed to offer an updated and reliable quantitative description of the spatial distribution for the entire Yellow River Delta coastal wetlands. Three classical machine learning algorithms, namely, the maximum likelihood (ML), Mahalanobis distance (MD), and support vector machine (SVM), were used for the synergetic classification of 18 spectral, index, polarization, and texture features. The results showed that the overall synergetic classification accuracy of 97% is significantly higher than that of single GF-3 or OHS classification, proving the performance of the fusion of full-polarization SAR data and hyperspectral data in wetland mapping. The synergy of polarimetric SAR (PolSAR) and hyperspectral imagery enables high-resolution classification of wetlands by capturing images throughout the year, regardless of cloud cover. The proposed method has the potential to provide wetland classification results with high accuracy and better temporal resolution in different regions. Detailed and reliable wetland classification results would provide important wetlands information for better understanding the habitat area of species, migration corridors, and the habitat change caused by natural and anthropogenic disturbances.

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