Band Selection for Forest Stock Volume Estimation in Southern China

2012 ◽  
Vol 500 ◽  
pp. 616-622
Author(s):  
Chen Xi Song ◽  
Zhong Sheng Xia ◽  
Yun Shao ◽  
Feng Li Zhang ◽  
Kun Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Southern China ◽  
Remote Sensing Data ◽  
Volume Estimation ◽  
Guizhou Province ◽  
Optical Data ◽  
Optical Remote Sensing ◽  
Optimal Ratio ◽  
Stock Volume ◽  
The Impact

When forest stock volume is quantitative estimated using SPOT-5, QuickBird and ALOS optical data with linear regression model, the optimal ratio of remote sensing band is chosen from the above three types of optical remote sensing data respectively, which is a significant part. In this study, the experiments are taken in Zhazuo Forest of Xiuwen County of Guizhou Province. Comprehensive utilization of the three optical data, the selected ratio of band is confirmed according with characteristics of the forest region. Optimization of the ratio of band remote sensing method used is the criteria of mean residual sum of square called RMSq. In this paper the multicollinearity which commonly exsits between ratio of the original band is analyzed and studied to get rid of its unfavorable influence in this paper. By means of the criteria of mean residual sum of square, the ratio of remote sensing band which determines the impact of forest stock volume estimation is confirmed finally. Conclusions are as follows: Compared with the selected band, multiple-correlation has been greatly reduced. The optimal ratio of remote sensing band such as SP4, SP2-3/2+3, SP 1-4/1+4, SP1*3/2 has an important role on the interpretation of forest stock volume estimation.

scholarly journals An Improved Segmentation Method for Automatic Mapping of Cone Karst from Remote Sensing Data Based on DeepLab V3+ Model

2021 ◽  
Vol 13 (3) ◽  
pp. 441
Author(s):  
Han Fu ◽  
Bihong Fu ◽  
Pilong Shi
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Guizhou Province ◽  
Training Dataset ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Karst Landscape ◽  
Heritage Site ◽  
Training Samples ◽  
Input Module

The South China Karst, a United Nations Educational, Scientific and Cultural Organization (UNESCO) natural heritage site, is one of the world’s most spectacular examples of humid tropical to subtropical karst landscapes. The Libo cone karst in the southern Guizhou Province is considered as the world reference site for these types of karst, forming a distinctive and beautiful landscape. Geomorphic information and spatial distribution of cone karst is essential for conservation and management for Libo heritage site. In this study, a deep learning (DL) method based on DeepLab V3+ network was proposed to document the cone karst landscape in Libo by multi-source data, including optical remote sensing images and digital elevation model (DEM) data. The training samples were generated by using Landsat remote sensing images and their combination with satellite derived DEM data. Each group of training dataset contains 898 samples. The input module of DeepLab V3+ network was improved to accept four-channel input data, i.e., combination of Landsat RGB images and DEM data. Our results suggest that the mean intersection over union (MIoU) using the four-channel data as training samples by a new DL-based pixel-level image segmentation approach is the highest, which can reach 95.5%. The proposed method can accomplish automatic extraction of cone karst landscape by self-learning of deep neural network, and therefore it can also provide a powerful and automatic tool for documenting other type of geological landscapes worldwide.

scholarly journals ESTIMATION OF PHYSICAL PARAMETERS OF A MULTILAYERED MULTI-SCALE VEGETATED SURFACE

2016 ◽  
Vol XLI-B1 ◽  
pp. 453-460
Author(s):  
I. Hosni ◽  
L. Bennaceur Farah ◽  
M. S. Naceur ◽  
I. R. Farah
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Cloud Model ◽  
Optical Data ◽  
Vegetation Coverage ◽  
Physical Parameters ◽  
Atmospheric Effects ◽  
Optical Remote Sensing ◽  
Statistical Parameters ◽  
The Impact

Soil moisture is important to enable the growth of vegetation in the way that it also conditions the development of plant population. Additionally, its assessment is important in hydrology and agronomy, and is a warning parameter for desertification. <br><br> Furthermore, the soil moisture content affects exchanges with the atmosphere via the energy balance at the soil surface; it is significant due to its impact on soil evaporation and transpiration. Therefore, it conditions the energy transfer between Earth and atmosphere. <br><br> Many remote sensing methods were tested. For the soil moisture; the first methods relied on the optical domain (short wavelengths). Obviously, due to atmospheric effects and the presence of clouds and vegetation cover, this approach is doomed to fail in most cases. Therefore, the presence of vegetation canopy complicates the retrieval of soil moisture because the canopy contains moisture of its own. <br><br> This paper presents a synergistic methodology of SAR and optical remote sensing data, and it’s for simulation of statistical parameters of soil from C-band radar measurements. Vegetation coverage, which can be easily estimated from optical data, was combined in the backscattering model. The total backscattering was divided into the amount attributed to areas covered with vegetation and that attributed to areas of bare soil. <br><br> Backscattering coefficients were simulated using the established backscattering model. A two-dimensional multiscale SPM model has been employed to investigate the problem of electromagnetic scattering from an underlying soil. The water cloud model (WCM) is used to account for the effect of vegetation water content on radar backscatter data, whereof to eliminate the impact of vegetation layer and isolate the contributions of vegetation scattering and absorption from the total backscattering coefficient.

scholarly journals Discriminating Wet Snow and Firn for Alpine Glaciers Using Sentinel-1 Data: A Case Study at Rofental, Austria

Geosciences ◽  
2019 ◽  
Vol 9 (2) ◽  
pp. 69 ◽  
Author(s):  
Achim Heilig ◽  
Anna Wendleder ◽  
Andreas Schmitt ◽  
Christoph Mayer
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Optical Data ◽  
Optical Remote Sensing ◽  
Mountain Ranges ◽  
Snow Covered Area ◽  
Covered Area ◽  
Wet Snow ◽  
Sar Data ◽  
Sar Analysis

Continuous monitoring of glacier changes supports our understanding of climate related glacier behavior. Remote sensing data offer the unique opportunity to observe individual glaciers as well as entire mountain ranges. In this study, we used synthetic aperture radar (SAR) data to monitor the recession of wet snow area extent per season for three different glacier areas of the Rofental, Austria. For four glaciological years (GYs, 2014/2015–2017/2018), Sentinel-1 (S1) SAR data were acquired and processed. For all four GYs, the seasonal snow retreated above the elevation range of perennial firn. The described processing routine is capable of discriminating wet snow from firn areas for all GYs with sufficient accuracy. For a short in situ transect of the snow—firn boundary, SAR derived wet snow extent agreed within an accuracy of three to four pixels or 30–40 m. For entire glaciers, we used optical remote sensing imagery and field data to assess reliability of derived wet snow covered area extent. Differences in determination of snow covered area between optical data and SAR analysis did not exceed 10% on average. Offsets of SAR data to results of annual field assessments are below 10% as well. The introduced workflow for S1 data will contribute to monitoring accumulation area extent for remote and hazardous glacier areas and thus improve the data basis for such locations.

scholarly journals ASSESSMENT OF LANDSLIDE-INDUCED MORPHOLOGY CHANGES USING AN OBJECT-BASED IMAGE ANALYSIS APPROACH: A CASE STUDY OF HÍTARDALUR, ICELAND

2019 ◽  
Vol XLII-3/W8 ◽  
pp. 109-114
Author(s):  
Z. Dabiri ◽  
D. Hölbling ◽  
L. Abad ◽  
D. Tiede
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
Remote Sensing Data ◽  
Optical Data ◽  
Sensing Data ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Spatio Temporal ◽  
The Impact ◽  
Morphology Changes

<p><strong>Abstract.</strong> On July 7, 2018, a large landslide occurred at the eastern slope of the Fagraskógarfjall Mountain in Hítardalur valley in West Iceland. The landslide dammed the river, led to the formation of a lake and, consequently, to a change in the river course. The main focus of this research is to develop a knowledge-based expert system using an object-based image analysis (OBIA) approach for identifying morphology changes caused by the Hítardalur landslide. We use synthetic aperture radar (SAR) and optical remote sensing data, in particular from Sentinel-1/2 for detection of the landslide and its effects on the river system. We extracted and classified the landslide area, the landslide-dammed lake, other lakes and the river course using intensity information from S1 and spectral information from S2 in the object-based framework. Future research will focus on further developing this approach to support mapping and monitoring of the spatio-temporal dynamics of surface morphology in an object-based framework by combining SAR and optical data. The results can reveal details on the applicability of different remote sensing data for the spatio-temporal investigation of landslides, landslide-induced river course changes and lake formation and lead to a better understanding of the impact of large landslides on river systems.</p>

scholarly journals Charting Dynamic Areas in the Mackenzie River with RADARSAT-2, Simulated RADARSAT Constellation Mission and Optical Remote Sensing Data

2019 ◽  
Vol 11 (13) ◽  
pp. 1523
Author(s):  
René Chénier ◽  
Khalid Omari ◽  
Ryan Ahola ◽  
Mesha Sagram
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Information Source ◽  
Remote Sensing Data ◽  
Time Factors ◽  
Optical Data ◽  
Observation Data ◽  
Optical Remote Sensing ◽  
Coastal Regions ◽  
Mackenzie River

Mariners navigating within Canadian waters rely on Canadian Hydrographic Service (CHS) navigational charts to safely reach their destinations. To fulfil this need, CHS charts must accurately reflect the current state of Canadian coastal regions. While many coastal regions are stable, others are dynamic and require frequent updates. In order to ensure that important and potentially dangerous changes are reflected in CHS products, the organization, in partnership with the Canadian Space Agency, is exploring coastal change detection through satellite remote sensing (SRS). In this work, CHS examined a hybrid shoreline extraction approach which uses both Synthetic Aperture Radar (SAR) and optical data. The approach was applied for a section of the Mackenzie River, one of Canada’s most dynamic river systems. The approach used RADARSAT-2 imagery as its primary information source, due to its high positioning accuracy (5 m horizontal accuracy) and ability to allow for low and high water line charting. Landsat represented the primary optical data source due to its long historical record of Earth observation data. Additional sensors, such as Sentinel-2 and WorldView, were also used where a higher resolution was required. The shoreline extraction process is based on an image segmentation approach that uses both the radar and optical data. Critical information was collected using the automated approach to support chart updates, resulting in reductions to the financial, human and time factors present within the ship-based hydrographic survey techniques traditionally used for chart improvements. The results demonstrate the potential benefit of wide area SRS change detection within dynamic waterways for navigational chart improvements. The work also demonstrates that the approach developed for RADARSAT-2 could be implemented with data from the forthcoming RADARSAT Constellation Mission (RCM), which is critical to ensure project continuity.

scholarly journals Mapping boreal peatland ecosystem types from multitemporal radar and optical satellite imagery

2017 ◽  
Vol 47 (4) ◽  
pp. 545-559 ◽  
Author(s):  
L.L. Bourgeau-Chavez ◽  
S. Endres ◽  
R. Powell ◽  
M.J. Battaglia ◽  
B. Benscoter ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Optical Data ◽  
Optical Remote Sensing ◽  
Fuel Loading ◽  
Alos Palsar ◽  
Upper Peninsula ◽  
Machine Learning Classification ◽  
Object Based ◽  
Accuracy Assessments

The ability to distinguish peatland types at the landscape scale has implications for inventory, conservation, estimation of carbon storage, fuel loading, and postfire carbon emissions, among others. This paper presents a multisensor, multiseason remote sensing approach to delineate boreal peatland types (wooded bog, open fen, shrubby fen, treed fen) using a combination of multiple dates of L-band (24 cm) synthetic aperture radar (SAR) from ALOS PALSAR, C-band (∼5.6 cm) from ERS-1 or ERS-2, and Landsat 5 TM optical remote sensing data. Imagery was first evaluated over a small test area of boreal Alberta, Canada, to determine the feasibility of using multisensor SAR and optical data to discriminate peatland types. Then object-based and (or) machine-learning classification algorithms were applied to 3.4 million ha of peatland-rich subregions of Alberta, Canada, and the 4.24 million ha region of Michigan’s Upper Peninsula where peatlands are less dominant. Accuracy assessments based on field-sampled sites show high overall map accuracies (93%–94% for Alberta and Michigan), which exceed those of previous mapping efforts.

scholarly journals ESTIMATION OF PHYSICAL PARAMETERS OF A MULTILAYERED MULTI-SCALE VEGETATED SURFACE

2016 ◽  
Vol XLI-B1 ◽  
pp. 453-460
Author(s):  
I. Hosni ◽  
L. Bennaceur Farah ◽  
M. S. Naceur ◽  
I. R. Farah
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Cloud Model ◽  
Optical Data ◽  
Vegetation Coverage ◽  
Physical Parameters ◽  
Atmospheric Effects ◽  
Optical Remote Sensing ◽  
Statistical Parameters ◽  
The Impact

Soil moisture is important to enable the growth of vegetation in the way that it also conditions the development of plant population. Additionally, its assessment is important in hydrology and agronomy, and is a warning parameter for desertification. <br><br> Furthermore, the soil moisture content affects exchanges with the atmosphere via the energy balance at the soil surface; it is significant due to its impact on soil evaporation and transpiration. Therefore, it conditions the energy transfer between Earth and atmosphere. <br><br> Many remote sensing methods were tested. For the soil moisture; the first methods relied on the optical domain (short wavelengths). Obviously, due to atmospheric effects and the presence of clouds and vegetation cover, this approach is doomed to fail in most cases. Therefore, the presence of vegetation canopy complicates the retrieval of soil moisture because the canopy contains moisture of its own. <br><br> This paper presents a synergistic methodology of SAR and optical remote sensing data, and it’s for simulation of statistical parameters of soil from C-band radar measurements. Vegetation coverage, which can be easily estimated from optical data, was combined in the backscattering model. The total backscattering was divided into the amount attributed to areas covered with vegetation and that attributed to areas of bare soil. <br><br> Backscattering coefficients were simulated using the established backscattering model. A two-dimensional multiscale SPM model has been employed to investigate the problem of electromagnetic scattering from an underlying soil. The water cloud model (WCM) is used to account for the effect of vegetation water content on radar backscatter data, whereof to eliminate the impact of vegetation layer and isolate the contributions of vegetation scattering and absorption from the total backscattering coefficient.

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.

scholarly journals Analysing the Impact of Climate Change on Hydrological Ecosystem Services in Laguna del Sauce (Uruguay) Using the SWAT Model and Remote Sensing Data

2021 ◽  
Vol 13 (10) ◽  
pp. 2014
Author(s):  
Celina Aznarez ◽  
Patricia Jimeno-Sáez ◽  
Adrián López-Ballesteros ◽  
Juan Pablo Pacheco ◽  
Javier Senent-Aparicio
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Ecosystem Services ◽  
Water Resources ◽  
Swat Model ◽  
Remote Sensing Data ◽  
Erosion Control ◽  
Sensing Data ◽  
Extreme Precipitation Events ◽  
The Impact

Assessing how climate change will affect hydrological ecosystem services (HES) provision is necessary for long-term planning and requires local comprehensive climate information. In this study, we used SWAT to evaluate the impacts on four HES, natural hazard protection, erosion control regulation and water supply and flow regulation for the Laguna del Sauce catchment in Uruguay. We used downscaled CMIP-5 global climate models for Representative Concentration Pathways (RCP) 2.6, 4.5 and 8.5 projections. We calibrated and validated our SWAT model for the periods 2005–2009 and 2010–2013 based on remote sensed ET data. Monthly NSE and R2 values for calibration and validation were 0.74, 0.64 and 0.79, 0.84, respectively. Our results suggest that climate change will likely negatively affect the water resources of the Laguna del Sauce catchment, especially in the RCP 8.5 scenario. In all RCP scenarios, the catchment is likely to experience a wetting trend, higher temperatures, seasonality shifts and an increase in extreme precipitation events, particularly in frequency and magnitude. This will likely affect water quality provision through runoff and sediment yield inputs, reducing the erosion control HES and likely aggravating eutrophication. Although the amount of water will increase, changes to the hydrological cycle might jeopardize the stability of freshwater supplies and HES on which many people in the south-eastern region of Uruguay depend. Despite streamflow monitoring capacities need to be enhanced to reduce the uncertainty of model results, our findings provide valuable insights for water resources planning in the study area. Hence, water management and monitoring capacities need to be enhanced to reduce the potential negative climate change impacts on HES. The methodological approach presented here, based on satellite ET data can be replicated and adapted to any other place in the world since we employed open-access software and remote sensing data for all the phases of hydrological modelling and HES provision assessment.

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