scholarly journals Assessment of intact forest areas based on remote sensing data

2020 ◽  
Author(s):  
Ю.В. Ольхин ◽  
О.И. Гаврилова ◽  
И.В. Морозова
Keyword(s):  
Remote Sensing ◽  
Economic Activity ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Forest Area ◽  
Natural Areas ◽  
Old Growth Forest ◽  
Taxonomic Descriptions ◽  
Specially Protected Natural Areas ◽  
Protected Natural Areas

В статье рассмотрены вопросы выделения малонарушенных природных территорий (лесных массивов) с учетом данных дистанционного зондирования земли. Для оценки малонарушенности площади соотносят с определенными критериями по площади массива, по возрасту основной части насаждений, по степени фрагментированности участка, наличия антропогенно нарушенных земель. Так, площади с хозяйственной деятельностью за последние 50 лет не допускаются более 5% от общей. Участки леса, которые планировали как малонарушенные лесные массивы (МЛН), по факту на 2018 год по данным космических снимков не всегда являлись таковыми. На примере двух из планируемых территорий после совмещения границ участков по планам лесонасаждений и космических снимков и выборки выделов по таксационным описаниям сделаны выводы о несоответствии одного из них критериям выделения малонарушенного лесного массива. Как показали космические снимки 2018 года и результаты анализа фактических характеристик лесонасаждений, часть лесных площадей к категории малонарушенных лесных массивов не относится. Только один из них, расположенный в Лахколамбинском участковом лесничестве, соответствует критериям для выделения его как МЛМ. Второй участок, расположенный в Куолисмском лесничестве, не соответствует критериям, предъявляемым к МЛМ по возрастному критерию и наличию здесь в последние 20 лет хозяйственной деятельности. Для участков, планируемых как малонарушенные лесные массивы, как и категории защитных лесов и особо охраняемые природные территории, следует учитывать ограничения по ведению хозяйственной деятельности, установленные действующим законодательством РФ. The article deals with the allocation of protected natural areas (forests), taking into account the data of remote sensing of the Earth. To assess whether the area is intact, it is correlated with certain criteria for the area of the massif, the age of the main part of the plantings, the degree of fragmentation of the site, and the presence of anthropogenic disturbed land. Thus, areas with economic activity over the past 50 years are not allowed to exceed 5% of the total. Areas of forest that were planned as intact woodlands (million), in fact, for 2018, according to satellite images, were not always such. For example, two of the planned territories after alignment parcel boundaries according to the plans of forests and satellite imagery and sampling areas for taxonomic descriptions of the conclusions about the discrepancy one of these criteria for identifying s old growth forest. As shown by the satellite images of 2018 and the results of the analysis of the actual characteristics of forest plantations, part of the forest area does not belong to the category of Not destroyed forests. Only one of them, located in the Lakhkolambin district forest area, meets the criteria for allocating it as an MLM. The second section, located in the Kuolismsa forest district, does not meet the criteria for Not destroyed forests based on age and the presence of economic activity here in the last 20 years. For areas that are planned as intact forests, as well as categories of protected forests and specially protected natural areas, it is necessary to take into account the restrictions on economic activity established by the current legislation of the Russian Federation.

Cartographic rationale of protecting the transboundary territorial nature in Siberia and Far East

2020 ◽  
Vol 958 (4) ◽  
pp. 51-64
Author(s):  
A.V. Bardash ◽  
T.P. Kalihman
Keyword(s):  
Economic Activity ◽  
Far East ◽  
Natural Areas ◽  
Ecosystem Protection ◽  
Specially Protected Natural Areas ◽  
Protected Natural Areas ◽  
Sufficient Degree ◽  
Natural Diversity ◽  
Border Areas

The activities of functioning and planning new transboundary protected natural territories inevitably require the availability and quality of cartographic rationale for the countries participating in the process. In many cases, the low involvement of near-border areas into economic activity provides a sufficient degree of ecosystem protection. The possibility of organizing transboundary specially protected natural areas appears in territories most significant in the view of preserving the natural diversity, adjacent to the borders of neighboring states, where arranging specially protected natural areas is extremely actual. The authors consider the task of applying the cartographic rationale at creating transboundary specially protected natural areas by the example of five territories operating at the beginning of 2019 in accordance with the concluded interstate agreements

Topical issues of cartographic supporting specially protected natural areas of Russia

2019 ◽  
Vol 943 (1) ◽  
pp. 13-23
Author(s):  
N.A. Alekseenko
Keyword(s):  
Protected Areas ◽  
Annual Reports ◽  
Natural Areas ◽  
Specially Protected Natural Areas ◽  
Protected Natural Areas

In protected areas of Russia unique spatial-coordinated data on their territories on certain positions and methods is collected by local and other scientists. The data is stored in various formats (sometimes physically lost), very rarely in the form of maps, some of them in the annual reports are transferred to the MNR. Systematically arranged collecting, storage, analysis and transfer of these data could be significantly enhanced and optimized

Specially protected natural areas in the ecological structure of the mining industry of the Baikal region: cartographic representation

2018 ◽  
Vol 930 (12) ◽  
pp. 9-20 ◽  
Author(s):  
A.D. Abalakov ◽  
N.B. Basarova
Keyword(s):  
Baikal Region ◽  
Mining Industry ◽  
Natural Areas ◽  
Ecological Structure ◽  
Specially Protected Natural Areas ◽  
Protected Natural Areas

The ecological structure of the mining industry of the Baikal region is considered and the situation of specially protected natural areas of federal importance is determined there

scholarly journals Rapid Evaluation and Validation Method of Above Ground Forest Biomass Estimation Using Optical Remote Sensing in Tundi Reserved Forest Area, India

2021 ◽  
Vol 10 (1) ◽  
pp. 29
Author(s):  
Praveen Kumar ◽  
Akhouri P. Krishna ◽  
Thorkild M. Rasmussen ◽  
Mahendra K. Pal
Keyword(s):  
Remote Sensing ◽  
Vegetation Cover ◽  
Remote Sensing Data ◽  
Test Site ◽  
Forest Area ◽  
Relational Model ◽  
Biomass Estimation ◽  
Optical Remote Sensing ◽  
Ground Sample ◽  
Sensing Data

Optical remote sensing data are freely available on a global scale. However, the satellite image processing and analysis for quick, accurate, and precise forest above ground biomass (AGB) evaluation are still challenging and difficult. This paper is aimed to develop a novel method for precise, accurate, and quick evaluation of the forest AGB from optical remote sensing data. Typically, the ground forest AGB was calculated using an empirical model from ground data for biophysical parameters such as tree density, height, and diameter at breast height (DBH) collected from the field at different elevation strata. The ground fraction of vegetation cover (FVC) in each ground sample location was calculated. Then, the fraction of vegetation cover (FVC) from optical remote sensing imagery was calculated. In the first stage of method implementation, the relation model between the ground FVC and ground forest AGB was developed. In the second stage, the relational model was established between image FVC and ground FVC. Finally, both models were fused to derive the relational model between image FVC and forest AGB. The validation of the developed method was demonstrated utilizing Sentinel-2 imagery as test data and the Tundi reserved forest area located in the Dhanbad district of Jharkhand state in eastern India was used as the test site. The result from the developed model was ground validated and also compared with the result from a previously developed crown projected area (CPA)-based forest AGB estimation approach. The results from the developed approach demonstrated superior capabilities in precision compared to the CPA-based method. The average forest AGB estimation of the test site obtained by this approach revealed 463 tons per hectare, which matches the previous estimate from this test site.

Cuckoo Search Based Decision Fusion Techniques for Natural Terrain Understanding

2014 ◽  
Vol 5 (2) ◽  
pp. 1-21 ◽  
Author(s):  
Arpita Sharma ◽  
Samiksha Goel
Keyword(s):  
Remote Sensing ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Cuckoo Search ◽  
Decision Fusion ◽  
Majority Voting ◽  
Natural Terrain ◽  
Terrain Features ◽  
Level Fusion ◽  
Better Than

This paper proposes two novel nature inspired decision level fusion techniques, Cuckoo Search Decision Fusion (CSDF) and Improved Cuckoo Search Decision Fusion (ICSDF) for enhanced and refined extraction of terrain features from remote sensing data. The developed techniques derive their basis from a recently introduced bio-inspired meta-heuristic Cuckoo Search and modify it suitably to be used as a fusion technique. The algorithms are validated on remote sensing satellite images acquired by multispectral sensors namely LISS3 Sensor image of Alwar region in Rajasthan, India and LANDSAT Sensor image of Delhi region, India. Overall accuracies obtained are substantially better than those of the four individual terrain classifiers used for fusion. Results are also compared with majority voting and average weighing policy fusion strategies. A notable achievement of the proposed fusion techniques is that the two difficult to identify terrains namely barren and urban are identified with similar high accuracies as other well identified land cover types, which was not possible by single analyzers.

Study of Ancient Land Boundaries at Tauric Chersonesos Using Satellite Images

2019 ◽  
Vol 25 (1) ◽  
pp. 44-58 ◽  
Author(s):  
Edgar A. Terekhin ◽  
Tatiana N. Smekalova
Keyword(s):  
Remote Sensing ◽  
Information Systems ◽  
Geographic Information Systems ◽  
Satellite Images ◽  
Agricultural Land ◽  
Remote Sensing Data ◽  
Field Surveys ◽  
The 1960S ◽  
Better Than ◽  
Made In

Abstract The near chora (agricultural land) of Tauric Chersonesos was investigated using multiyear remote sensing data and field surveys. The boundaries of the land plots were studied with GIS (Geographic Information Systems) technology and an analysis of satellite images. Reliable reconstruction of the borders has been done for 231 plots (from a total of about 380), which is approximately 53% of the Chersonesean chora. During the last 50 years, most of the ancient land plots have been destroyed by modern buildings, roads, or forests. However, in the 1960s, a significant part of the chora was still preserved. Changes in preservation with time were studied with the aid of satellite images that were made in 1966 and 2015. During that period, it was found that the number of plots with almost-complete preservation decreased from 47 to 0. Those land plots whose preservation was better than 50% dropped from 104 to 4. A temporal map shows this decline in preservation. It was found that the areas of land plots could be determined accurately with satellite images; compared to field surveys, this accuracy was about 99%.

scholarly journals Comparing of Landsat 8 and Sentinel 2A using Water Extraction Indexes over Volta River

2017 ◽  
Vol 10 (1) ◽  
pp. 1 ◽  
Author(s):  
Clement Kwang ◽  
Edward Matthew Osei Jnr ◽  
Adwoa Sarpong Amoah
Keyword(s):  
Remote Sensing ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Unsupervised Classification ◽  
Water Bodies ◽  
Water Extraction ◽  
Landsat 8 ◽  
Sensing Data ◽  
Optimal Classifier ◽  
Sentinel 2A

Remote sensing data are most often used in water bodies’ extraction studies and the type of remote sensing data used also play a crucial role on the accuracy of the extracted water features. The performance of the proposed water indexes among the various satellite images is not well documented in literature. The proposed water indexes were initially developed with a particular type of data and with advancement and introduction of new satellite images especially Landsat 8 and Sentinel, therefore the need to test the level of performance of these water indexes as new image datasets emerged. Landsat 8 and Sentinel 2A image of part Volta River was used. The water indexes were performed and then ISODATA unsupervised classification was done. The overall accuracy and kappa coefficient values range from 98.0% to 99.8% and 0.94 to 0.98 respectively. Most of water bodies enhancement indexes work better on Sentinel 2A than on Landsat 8. Among the Landsat based water bodies enhancement ISODATA unsupervised classification, the modified normalized water difference index (MNDWI) and normalized water difference index (NDWI) were the best classifier while for Sentinel 2A, the MNDWI and the automatic water extraction index (AWEI_nsh) were the optimal classifier. The least performed classifier for both Landsat 8 and Sentinel 2A was the automatic water extraction index (AWEI_sh). The modified normalized water difference index (MNDWI) has proved to be the universal water bodies enhancement index because of its performance on both the Landsat 8 and Sentinel 2A image.

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