Applications in Remote Sensing to Forest Ecology and Management

Lechner, Alex M.; Foody, Giles M.; Boyd, Doreen S.

doi:10.1016/j.oneear.2020.05.001

Cited by 283 publications

(133 citation statements)

References 12 publications

Supporting

Mentioning

124

Contrasting

Unclassified

Order By: Relevance

“…The data processing section emphasises the differences between batch processing, which works on the expert's knowledge, processing technology and modelling such as machine learning, deep learning, Multiple Regression Analysis (MRA), Artificial Neural Network (ANN), etc. This also addresses analysis of the data in real-time as it is produced, which is especially relevant in powerful rule and model processing (Landset et al 2015;Chen, Morozov, and Chen 2019;Rossita et al 2019;Chen, Kapron, and Chen 2020;Lechner, Foody, and Boyd 2020). (3) Variety involves the issue of disparate and incompatible data formats.…”

Section: The Rs 'Big Data' Overviewmentioning

confidence: 99%

“…Firstly, in the data phase, we can choose any kind of remote sensing data as Big Data sources that are based on previous experiences and knowledge (as for CBR) as well as incorporated with forest investigation data for processing. As many studies show that satellite imagery combined with forest inventories has become one of the critical evaluation techniques for carbon sink, carbon stocks and sequestration (Muukkonen and Heiskanen 2005;Maselli et al 2006;Lechner, Foody, and Boyd 2020;Berra and Gaulton 2021), the technique of RS has many advantages, such as great area, immediacy, diverse resolution, multi-frequency, extensive manipulation, and unceasing progress of new technology; there is no doubt that it has become the mainstream measures of solution. In Taiwan, the RS data have multiple sources, except for commercial imagery such as IKONOS, QuickBird, WorldView1-4, GeoEye-1 and other high-resolution imagery or aerial photography.…”

Section: Rs-kbdss Frameworkmentioning

confidence: 99%

“…However, this study is specifically emphasised on how to incorporate the technical applications of RS with decision-making management procedures. Meanwhile, the purpose of this study is to fill this crucial knowledge gap in practice through a mix of literature review, analysis of RS technology data, and expert knowledge (Chen, Morozov, and Chen 2019;Halme, Pellikka, and Mottus 2019;Houa et al 2019;Lossou, Owusu-Prempeh, and Agyemang 2019;Chen, Morozov, and Chen 2019;Chen, Kuo, Hsieh, and Chen 2020 ;Giuntoli et al 2020;Lechner, Foody, and Boyd 2020;Aquilué et al 2021;Blondeel et al 2021). Therefore, the key purpose of this study is to utilise RS Big Data's diversified characteristics and technology to solve the long-term forest monitoring issues in Taiwan, including complicated terrain conditions, diverse and mixed plant species that led to increase the difficulty of the investigation, as well as the consumption of enormous times, manpower, and money in the survey, etc.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A decision support system for Taiwan’s forest resource management using Remote Sensing Big Data

Wang

Lin

Chu

et al. 2021

Enterprise Information Systems

View full text Add to dashboard Cite

This study aims to incorporate the application of RS technology with management strategies and proposes the Remote Sensing Knowledge-Based Decision Support System (RS-KBDSS) framework. It is not only focusing on considering RS technology for discussing the accuracy of estimation but also conducting strategic thinking that is needed for keeping the inventory and monitoring task running by obtaining knowledge and decision support. This study offers more flexible and accurate estimation models for different resolution images and different kinds of models. The results obtained by the study is satisfied with the practical application.

show abstract

Section: The Rs 'Big Data' Overviewmentioning

confidence: 99%

Section: Rs-kbdss Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A decision support system for Taiwan’s forest resource management using Remote Sensing Big Data

Wang

Lin

Chu

et al. 2021

Enterprise Information Systems

View full text Add to dashboard Cite

show abstract

“…Прогнози на майбутнє є ще гіршими [3]. З метою моніторингу та дослідження змін стану лісів створюються інформаційні технології, які використовують засоби дистанційного зондування, об-роблення зображень та прогнозування для отримання цілісної картини впливу змін клімату, а також для вирішення локальних задач лісового господарства [4].…”

Section: ʑ˔˕˖˒unclassified

Recognition of Damaged Forest With the Help of Convolutional Models in Remote Sensing

Rusyn¹,

Lutsyk²,

Kosarevych³

et al. 2021

UJIT

View full text Add to dashboard Cite

The article provides a detailed review of the problem of deforestation, which in recent years has become uncontrolled. The main causes of forest damage are analyzed, among which the most well-known are climate change, diseases and pests. The losses of forestry as a result of tree diseases, which are large-scale and widespread in other countries, are given. The solution of these problems is possible under the condition of high-quality monitoring with the involvement of automated remote sensing tools and modern methods of image analysis, including artificial intelligence approaches such as neural networks and deep learning. The article proposes an approach to automatic localization and recognition of trees affected by drought, which is of great practical importance for environmental monitoring and forestry. A fully connected convolutional model of deep learning using the tensorflow and keras libraries has been developed for localization and recognition. This model consists of a detector network and a separate classifier network. To train and test the proposed network based on images obtained by remote sensing, a training database containing 8500 images was created. A comparison of the proposed model with the existing methods is based on such characteristics as accuracy and speed. The accuracy and speed of the proposed recognition system were evaluated on a validation sample of images, consisting of 1700 images. The model has been optimized for practical use with CPU and GPU due to pseudo quantization during training. This helps to distribute the values of the weights in the learning process and bring their appearance closer to a uniform distribution law, which in turn allows more efficient application of quantization to the original model. The average operating time of the algorithm is also determined. In the Visual C++ environment, based on the proposed model, an expert program has been created that allows to perform the ecological monitoring and analysis of dry forests in the field in real time. Libraries such as OpenCV and Direct were used in software development, and the code supports object-oriented programming standards. The results of the work and the developed software can be used in remote monitoring and classification systems for environmental monitoring and in applied tasks of forestry.

show abstract

“…The combination of remote sensing images and geographic information system (GIS) has been widely ap-plied in forest dynamic studies, because they can provide timely and cost-effective information and analyze the long-time processes and spatiotemporal patterns of forest changes at multiple scales (Xie et al, 2012;Song et al, 2014;Jia et al, 2015;Lindquist and D'Annunzio, 2016;Lechner et al, 2020). Landscape metrics provide new insights in characterizing the detailed patch dynamics of forest changes (Herold et al, 2002;Zengin et al, 2018;Lv et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Rapid Urbanization Induced Extensive Forest Loss to Urban Land in the Guangdong-Hong Kong-Macao Greater Bay Area, China

Yang

Liu

et al. 2021

Chin. Geogr. Sci.

View full text Add to dashboard Cite

China has experienced rapid urbanizations with dramatic land cover changes since 1978. Forest loss is one of land cover changes, and it induces various eco-environmental degradation issues. As one of China's hotspot regions, the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) has undergone a dramatic urban expansion. To better understand forest dynamics and protect forest ecosystem, revealing the processes, patterns and underlying drivers of forest loss is essential. This study focused on the spatiotemporal evolution and potential driving factors of forest loss in the GBA at regional and city level. The Landsat time-series images from 1987 to 2017 were used to derive forest, and landscape metrics and geographic information system (GIS) were applied to implement further spatial analysis. The results showed that: 1) 14.86% of the total urban growth area of the GBA was obtained from the forest loss in 1987-2017; meanwhile, the forest loss area of the GBA reached 4040.6 km 2 , of which 25.60% (1034.42 km 2 ) was converted to urban land; 2) the percentages of forest loss to urban land in Dongguan (19.14%), Guangzhou (18.35%) and Shenzhen (15.81%) were higher than those in other cities; 3) the forest became increasingly fragmented from 1987-2007, and then the fragmentation decreased from 2007 to 2017); 4) the landscape responses to forest changes varied with the scale; and 5) some forest loss to urban regions moved from low-elevation and gentle-slope terrains to higher-elevation and steep-slope terrains over time, especially in Shenzhen and Hong Kong.Urbanization and industrialization greatly drove forest loss and fragmentation, and, notably, hillside urban land expansion may have contributed to hillside forest loss. The findings will help policy makers in maintaining the stability of forest ecosystems, and provide some new insights into forest management and conservation.

show abstract

Applications in Remote Sensing to Forest Ecology and Management

Cited by 283 publications

References 12 publications

A decision support system for Taiwan’s forest resource management using Remote Sensing Big Data

A decision support system for Taiwan’s forest resource management using Remote Sensing Big Data

Recognition of Damaged Forest With the Help of Convolutional Models in Remote Sensing

Rapid Urbanization Induced Extensive Forest Loss to Urban Land in the Guangdong-Hong Kong-Macao Greater Bay Area, China

Contact Info

Product

Resources

About