scholarly journals Investigating Banksia Coastal Woodland Decline Using Multi-Temporal Remote Sensing and Field-Based Monitoring Techniques

2020 ◽  
Vol 12 (4) ◽  
pp. 669 ◽  
Author(s):  
Rose-Anne Bell ◽  
J. Nikolaus Callow
Keyword(s):  
Remote Sensing ◽  
Tree Mortality ◽  
Spectral Characteristics ◽  
Sensing Applications ◽  
Abiotic Stressors ◽  
Object Based ◽  
Multi Temporal ◽  
Primary Driver ◽  
Vegetation Surveys ◽  
Closed Canopy

Coastal woodlands, notable for their floristic diversity and ecosystem service values, are increasingly under threat from a range of interacting biotic and abiotic stressors. Monitoring these complex ecosystems has traditionally been confined to field-scale vegetation surveys; however, remote sensing applications are increasingly becoming more viable. This study reports on the application of field-based monitoring and remote sensing/(Geographic Information System) GIS to interrogate trends in Banksia coastal woodland decline (Kings Park, Perth and Western Australia) and documents the patterns, and potential drivers, of tree mortality over the period 2012–2016. Application of geographic object-based image analysis (GEOBIA) at a park scale was of limited benefit within the closed-canopy ecosystem, with manual digitisation methods feasible only at the smaller transect scale. Analysis of field-based identification of tree mortality, crown-specific spectral characteristics and park-scale change detection imagery identified climate-driven stressors as the likely primary driver of tree mortality in the woodland, with vegetation decline exacerbated by secondary factors, including water stress and low system resilience occasioned by the inability to access the water table and competition between tree species. The results from this paper provide a platform to inform monitoring efforts using airborne remote sensing within coastal woodlands.

scholarly journals Remote Sensing in Urban Forestry: Recent Applications and Future Directions

2019 ◽  
Vol 11 (10) ◽  
pp. 1144 ◽  
Author(s):  
Xun Li ◽  
Wendy Y. Chen ◽  
Giovanni Sanesi ◽  
Raffaele Lafortezza
Keyword(s):  
Remote Sensing ◽  
Data Processing ◽  
Urban Forest ◽  
Scale Effects ◽  
Urban Forestry ◽  
Urban Forests ◽  
Accurate Information ◽  
Multi Scale ◽  
Sensing Applications ◽  
Multi Temporal

Increasing recognition of the importance of urban forest ecosystem services calls for the sustainable management of urban forests, which requires timely and accurate information on the status, trends and interactions between socioeconomic and ecological processes pertaining to urban forests. In this regard, remote sensing, especially with its recent advances in sensors and data processing methods, has emerged as a premier and useful observational and analytical tool. This study summarises recent remote sensing applications in urban forestry from the perspective of three distinctive themes: multi-source, multi-temporal and multi-scale inputs. It reviews how different sources of remotely sensed data offer a fast, replicable and scalable way to quantify urban forest dynamics at varying spatiotemporal scales on a case-by-case basis. Combined optical imagery and LiDAR data results as the most promising among multi-source inputs; in addition, future efforts should focus on enhancing data processing efficiency. For long-term multi-temporal inputs, in the event satellite imagery is the only available data source, future work should improve haze-/cloud-removal techniques for enhancing image quality. Current attention given to multi-scale inputs remains limited; hence, future studies should be more aware of scale effects and cautiously draw conclusions.

scholarly journals A Soft Shadow Detection Method Based on MRF for Remote Sensing Images

2015 ◽  
Vol 2015 ◽  
pp. 1-11
Author(s):  
Pengwei Li ◽  
Wenying Ge
Keyword(s):  
Remote Sensing ◽  
Spatial Information ◽  
Detection Method ◽  
Spectral Characteristics ◽  
Shadow Detection ◽  
Detection Methods ◽  
Remote Sensing Images ◽  
Sensing Applications ◽  
Soft Shadow ◽  
Remote Sensing Applications

Shadows limit many remote sensing applications such as classification, target detection, and change detection. Most current shadow detection methods utilize the histogram threshold of spectral characteristics to distinguish the shadows and nonshadows directly, called “hard binary shadow.” Obviously, the performance of threshold-based methods heavily rely on the selected threshold. Simultaneously, these threshold-based methods do not take any spatial information into account. To overcome these shortcomings, a soft shadow description method is developed by introducing the concept of opacity into shadow detection, and MRF-based shadow detection method is proposed in order to make use of neighborhood information. Experiments on remote sensing images have shown that the proposed method can obtain more accurate detection results.

scholarly journals Impact of the cameras radiometric resolution on the accuracy of determining spectral reflectance coefficients

2014 ◽  
Vol XL-1 ◽  
pp. 347-349 ◽  
Author(s):  
A. Orych ◽  
P. Walczykowski ◽  
A. Jenerowicz ◽  
Z. Zdunek
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Spectral Reflectance ◽  
Spectral Characteristics ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Water Contaminants ◽  
Data Registration ◽  
Sensing Applications ◽  
The Impact

Nowadays remote sensing plays a very important role in many different study fields, i.e. environmental studies, hydrology, mineralogy, ecosystem studies, etc. One of the key areas of remote sensing applications is water quality monitoring. Understanding and monitoring of the water quality parameters and detecting different water contaminants is an important issue in water management and protection of whole environment and especially the water ecosystem. There are many remote sensing methods to monitor water quality and detect water pollutants. One of the most widely used method for substance detection with remote sensing techniques is based on usage of spectral reflectance coefficients. They are usually acquired using discrete methods such as spectrometric measurements. These however can be very time consuming, therefore image-based methods are used more and more often. In order to work out the proper methodology of obtaining spectral reflectance coefficients from hyperspectral and multispectral images, it is necessary to verify the impact of cameras radiometric resolution on the accuracy of determination of them. This paper presents laboratory experiments that were conducted using two monochromatic XEVA video sensors (400–1700 nm spectral data registration) with two different radiometric resolutions (12 and 14 bits). In view of determining spectral characteristics from images, the research team used set of interferometric filters. All data collected with multispectral digital video cameras were compared with spectral reflectance coefficients obtained with spectroradiometer. The objective of this research is to find the impact of cameras radiometric resolution on reflectance values in chosen wavelength. The main topic of this study is the analysis of accuracy of spectral coefficients from sensors with different radiometric resolution. By comparing values collected from images acquired with XEVA sensors and with the curves obtained with spectroradiometer it's possible to determine accuracy of imagebased spectral reflectance coefficients and decide which sensor will be more accurate to determine them for protection of water aquatic environment purpose.

scholarly journals Remote Sensing Applications in Tobacco Yield Estimation and the Recommended Research in Zimbabwe

ISRN Agronomy ◽  
2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Ezekia Svotwa ◽  
Anxious J. Masuka ◽  
Barbara Maasdorp ◽  
Amon Murwira ◽  
Munyaradzi Shamudzarira
Keyword(s):  
Remote Sensing ◽  
Crop Yields ◽  
Spectral Characteristics ◽  
Dry Mass ◽  
Growth And Yield ◽  
Yield Response ◽  
Yield Estimation ◽  
Planting Dates ◽  
Sensing Applications ◽  
Satellite Platform

Tobacco crop area and yield forecasts are important in stabilizing tobacco prices at the auction floors. Tobacco yield estimation in Zimbabwe is currently based on statistical surveys and ground-based field reports. These methods are costly, time consuming, and are prone to large errors. Remote sensing can provide timely information on crop spectral characteristics which can be used to estimate crop yields. Remote sensing application on agriculture in Zimbabwe is still very limited. Research should focus on identifying suitable reflectance indices that are related to tobacco growth and yield. Varietal yield response to fertiliser and planting dates as well as suitable temporal windows for spectral data collection should be identified. The challenges of the different tobacco land sizes have to be overcome by identifying suitable satellite platform, with sufficient spectral resolution to separate the tobacco crop from the adjacent competing crops and noncrop vegetative surfaces. The identified suitable index should be strongly correlated with tobacco in season dry mass and yield. The suitable vegetative indices can be employed in establishing tobacco cropped area and then apply the long-term area yield relationship from government and nongovernmental statistical departments to estimate yield from remote sensing derived cropped area.

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