Remote sensing of terrestrial non-photosynthetic vegetation using hyperspectral, multispectral, SAR, and LiDAR data

Quantifying non-photosynthetic vegetation (NPV) is important for ecosystem management and studies on climate change, ecology, and hydrology because it controls uptake of carbon, water, and nutrients together with frequency and intensity of natural fire, and serves as wildlife habitat. The ecological importance of NPV has driven considerable research on quantitatively estimating NPV in diverse ecosystems including croplands, forests, grasslands, savannah, and shrublands using remote sensing data. However, a comprehensive review is not available. This review highlights the theoretical bases and the critical elements of remote sensing for NPV estimation, and summarizes research on estimating fractional cover of NPV (NPV cover) and biomass using passive optical hyperspectral and multispectral remote sensing data, active synthetic aperture radar (SAR) and light detection and ranging (LiDAR), and integrated multi-sensorial data. We also discuss advantages and disadvantages of optical, LiDAR, and SAR data and pinpoint future direction on NPV estimation using remote sensing data. Currently, most NPV research has been mainly focused on NPV cover, not NPV biomass, using passive optical data, while a few studies have used LiDAR data to quantify NPV biomass in forests and SAR data on NPV estimation in croplands and grasslands. In the future, more efforts should be made to estimate NPV biomass and to investigate the best use of hyperspectral, LiDAR, SAR data, and their integration. The upcoming new optical sensor on Sentinel-2 satellites, Radarsat-2 constellation and NovaSAR, technological innovation in hyperspectral, LiDAR, and SAR, and improvements on methodology for information extraction and combining multi-sensorial data will provide more opportunities for NPV estimation.

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Mapping the Urban Atmospheric Carbon Stock by LiDAR and WorldView-3 Data

Forests ◽

10.3390/f12060692 ◽

2021 ◽

Vol 12 (6) ◽

pp. 692

Author(s):

MD Abdul Mueed Choudhury ◽

Ernesto Marcheggiani ◽

Andrea Galli ◽

Giuseppe Modica ◽

Ben Somers

Keyword(s):

Remote Sensing ◽

Urban Area ◽

Tree Species ◽

Carbon Stock ◽

Dominant Species ◽

Remote Sensing Data ◽

Data Sources ◽

Lidar Data ◽

Urban Tree ◽

Sensing Data

Currently, the worsening impacts of urbanizations have been impelled to the importance of monitoring and management of existing urban trees, securing sustainable use of the available green spaces. Urban tree species identification and evaluation of their roles in atmospheric Carbon Stock (CS) are still among the prime concerns for city planners regarding initiating a convenient and easily adaptive urban green planning and management system. A detailed methodology on the urban tree carbon stock calibration and mapping was conducted in the urban area of Brussels, Belgium. A comparative analysis of the mapping outcomes was assessed to define the convenience and efficiency of two different remote sensing data sources, Light Detection and Ranging (LiDAR) and WorldView-3 (WV-3), in a unique urban area. The mapping results were validated against field estimated carbon stocks. At the initial stage, dominant tree species were identified and classified using the high-resolution WorldView3 image, leading to the final carbon stock mapping based on the dominant species. An object-based image analysis approach was employed to attain an overall accuracy (OA) of 71% during the classification of the dominant species. The field estimations of carbon stock for each plot were done utilizing an allometric model based on the field tree dendrometric data. Later based on the correlation among the field data and the variables (i.e., Normalized Difference Vegetation Index, NDVI and Crown Height Model, CHM) extracted from the available remote sensing data, the carbon stock mapping and validation had been done in a GIS environment. The calibrated NDVI and CHM had been used to compute possible carbon stock in either case of the WV-3 image and LiDAR data, respectively. A comparative discussion has been introduced to bring out the issues, especially for the developing countries, where WV-3 data could be a better solution over the hardly available LiDAR data. This study could assist city planners in understanding and deciding the applicability of remote sensing data sources based on their availability and the level of expediency, ensuring a sustainable urban green management system.

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Calibration of the depth invariant algorithm to monitor the tidal action of Rabigh City at the Red Sea Coast, Saudi Arabia

Open Geosciences ◽

10.1515/geo-2020-0217 ◽

2020 ◽

Vol 12 (1) ◽

pp. 1666-1678

Author(s):

Mohammed H. Aljahdali ◽

Mohamed Elhag

Keyword(s):

Remote Sensing ◽

Saudi Arabia ◽

Wind Speed ◽

Red Sea ◽

Remote Sensing Data ◽

Microwave Remote Sensing ◽

Optical Data ◽

Mean Values ◽

Sensing Data ◽

The Mean

AbstractRabigh is a thriving coastal city located at the eastern bank of the Red Sea, Saudi Arabia. The city has suffered from shoreline destruction because of the invasive tidal action powered principally by the wind speed and direction over shallow waters. This study was carried out to calibrate the water column depth in the vicinity of Rabigh. Optical and microwave remote sensing data from the European Space Agency were collected over 2 years (2017–2018) along with the analog daily monitoring of tidal data collected from the marine station of Rabigh. Depth invariant index (DII) was implemented utilizing the optical data, while the Wind Field Estimation algorithm was implemented utilizing the microwave data. The findings of the current research emphasis on the oscillation behavior of the depth invariant mean values and the mean astronomical tides resulted in R2 of 0.75 and 0.79, respectively. Robust linear regression was established between the astronomical tide and the mean values of the normalized DII (R2 = 0.81). The findings also indicated that January had the strongest wind speed solidly correlated with the depth invariant values (R2 = 0.92). Therefore, decision-makers can depend on remote sensing data as an efficient tool to monitor natural phenomena and also to regulate human activities in fragile ecosystems.

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A Review on Remote Sensing Data Fusion with Generative Adversarial Networks (GAN)

10.36227/techrxiv.16607261 ◽

2021 ◽

Author(s):

Peng Liu

Keyword(s):

Remote Sensing ◽

Data Fusion ◽

Remote Sensing Data ◽

Generative Adversarial Networks ◽

Future Research ◽

Observation Data ◽

Sensing Data ◽

Advantages And Disadvantages ◽

Adversarial Networks ◽

Active Research

In the past decades, remote sensing (RS) data fusion has always been an active research community. A large number of algorithms and models have been developed. Generative Adversarial Networks (GAN), as an important branch of deep learning, show promising performances in variety of RS image fusions. This review provides an introduction to GAN for remote sensing data fusion. We briefly review the frequently-used architecture and characteristics of GAN in data fusion and comprehensively discuss how to use GAN to realize fusion for homogeneous RS data, heterogeneous RS data, and RS and ground observation data. We also analyzed some typical applications with GAN-based RS image fusion. This review takes insight into how to make GAN adapt to different types of fusion tasks and summarizes the advantages and disadvantages of GAN-based RS data fusion. Finally, we discuss the promising future research directions and make a prediction on its trends.

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Study on the Application and Comparative Analysis of Land Surface Temperature Retrieval Method Based on Multi-Sensor Remote Sensing Data

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1010-1012.1276 ◽

2014 ◽

Vol 1010-1012 ◽

pp. 1276-1279 ◽

Cited By ~ 1

Author(s):

Yin Tai Na

Keyword(s):

Remote Sensing ◽

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Remote Sensing Data ◽

Sensing Data ◽

Advantages And Disadvantages ◽

Urban Heat ◽

Split Window Algorithm ◽

Temperature Retrieval

The three commonly used remote sensing land surface temperature retrieval methods are described, namely single-window algorithm, split window algorithm and multi-channel algorithm, which have their advantages and disadvantages. The land surface temperature (LST) of study area was retrieved with multi-source remote sensing data. LST of study area was retrieved with the split window algorithm on January 10, 2003 and November 19, 2003 which is comparatively analyzed with the LST result of ETM+data with the single-window algorithm and the LST result of ASTER data with multi channel algorithm in the same period. The results show that land surface temperature of different land features are significantly different, where the surface temperature of urban land is the highest, and that of rivers and lakes is the lowest, followed by woodland. It is concluded that the expansion of urban green space and protection of urban water can prevent or diminish the urban heat island.

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Comparison of Machine Learning Algorithms for Wildland-Urban Interface Fuelbreak Planning Integrating ALS and UAV-Borne LiDAR Data and Multispectral Images

Drones ◽

10.3390/drones4020021 ◽

2020 ◽

Vol 4 (2) ◽

pp. 21 ◽

Cited By ~ 1

Author(s):

Francisco Rodríguez-Puerta ◽

Rafael Alonso Ponce ◽

Fernando Pérez-Rodríguez ◽

Beatriz Águeda ◽

Saray Martín-García ◽

...

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Random Forest ◽

Learning Algorithms ◽

Remote Sensing Data ◽

Machine Learning Algorithms ◽

Data Sources ◽

Lidar Data ◽

Sensing Data ◽

Sentinel 2

Controlling vegetation fuels around human settlements is a crucial strategy for reducing fire severity in forests, buildings and infrastructure, as well as protecting human lives. Each country has its own regulations in this respect, but they all have in common that by reducing fuel load, we in turn reduce the intensity and severity of the fire. The use of Unmanned Aerial Vehicles (UAV)-acquired data combined with other passive and active remote sensing data has the greatest performance to planning Wildland-Urban Interface (WUI) fuelbreak through machine learning algorithms. Nine remote sensing data sources (active and passive) and four supervised classification algorithms (Random Forest, Linear and Radial Support Vector Machine and Artificial Neural Networks) were tested to classify five fuel-area types. We used very high-density Light Detection and Ranging (LiDAR) data acquired by UAV (154 returns·m−2 and ortho-mosaic of 5-cm pixel), multispectral data from the satellites Pleiades-1B and Sentinel-2, and low-density LiDAR data acquired by Airborne Laser Scanning (ALS) (0.5 returns·m−2, ortho-mosaic of 25 cm pixels). Through the Variable Selection Using Random Forest (VSURF) procedure, a pre-selection of final variables was carried out to train the model. The four algorithms were compared, and it was concluded that the differences among them in overall accuracy (OA) on training datasets were negligible. Although the highest accuracy in the training step was obtained in SVML (OA=94.46%) and in testing in ANN (OA=91.91%), Random Forest was considered to be the most reliable algorithm, since it produced more consistent predictions due to the smaller differences between training and testing performance. Using a combination of Sentinel-2 and the two LiDAR data (UAV and ALS), Random Forest obtained an OA of 90.66% in training and of 91.80% in testing datasets. The differences in accuracy between the data sources used are much greater than between algorithms. LiDAR growth metrics calculated using point clouds in different dates and multispectral information from different seasons of the year are the most important variables in the classification. Our results support the essential role of UAVs in fuelbreak planning and management and thus, in the prevention of forest fires.

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Information Extraction and Metallogenic Prediction of Qiangduo Area in Tibet Based on Multi-Source Remote Sensing Data

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.962-965.127 ◽

2014 ◽

Vol 962-965 ◽

pp. 127-131

Author(s):

Xin Xing Liu

Keyword(s):

Remote Sensing ◽

Remote Sensing Data ◽

Geological Mapping ◽

Advanced Technology ◽

Data Types ◽

Sensing Data ◽

Advantages And Disadvantages ◽

Remote Sensing Technology ◽

Sensing Technology ◽

Metallogenic Prediction

Remote sensing technology as a kind of new and advanced technology has been playing an important role in geological mapping and prospecting. A single kind of remote sensing data always has both advantages and disadvantages. And with multispectral remote sensing data types increasing, the integrated application of multi-source remote sensing data will be one of the development trend of remote sensing geology. In this paper, comprehensive utilization of multi-source remote sensing data such as ETM+, ASTER, Worldview-II and DEM, lithology and geological structure of Qiangduo area in Tibet were interpreted in different levels and mineralized alteration information also was extracted. Then on the basis of modern metallogenic theory, analyzed the multiple mineralization favorite information, established the remote sensing prediction model, and on the GIS platform, carried out metallogenic prediction of the study area. The field validation shows that the results of the prediction are relatively accurate and remote sensing technology can improve the efficiency of geological work.

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

Geosciences ◽

10.3390/geosciences9020069 ◽

2019 ◽

Vol 9 (2) ◽

pp. 69 ◽

Cited By ~ 2

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.

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Hierarchical Geographic Object-Based Vegetation Type Extraction Based on Multi-Source Remote Sensing Data

Forests ◽

10.3390/f11121271 ◽

2020 ◽

Vol 11 (12) ◽

pp. 1271

Author(s):

Xuegang Mao ◽

Yueqing Deng ◽

Liang Zhu ◽

Yao Yao

Keyword(s):

Remote Sensing ◽

Forest Type ◽

Vegetation Type ◽

Remote Sensing Data ◽

Airborne Lidar ◽

Lidar Data ◽

Sensing Data ◽

Object Based ◽

Four Levels ◽

Airborne Lidar Data

Providing vegetation type information with accurate surface distribution is one of the important tasks of remote sensing of the ecological environment. Many studies have explored ecosystem structure information at specific spatial scales based on specific remote sensing data, but it is still rare to extract vegetation information at various landscape levels from a variety of remote sensing data. Based on Gaofen-1 satellite (GF-1) Wide-Field-View (WFV) data (16 m), Ziyuan-3 satellite (ZY-3) and airborne LiDAR data, this study comparatively analyzed the four levels of vegetation information by using the geographic object-based image analysis method (GEOBIA) on the typical natural secondary forest in Northeast China. The four levels of vegetation information include vegetation/non-vegetation (L1), vegetation type (L2), forest type (L3) and canopy and canopy gap (L4). The results showed that vegetation height and density provided by airborne LiDAR data could extract vegetation features and categories more effectively than the spectral information provided by GF-1 and ZY-3 images. Only 0.5 m LiDAR data can extract four levels of vegetation information (L1–L4); and from L1 to L4, the total accuracy of the classification decreased orderly 98%, 93%, 80% and 69%. Comparing with 2.1 m ZY-3, the total classification accuracy of L1, L2 and L3 extracted by 2.1 m LiDAR data increased by 3%, 17% and 43%, respectively. At the vegetation/non-vegetation level, the spatial resolution of data plays a leading role, and the data types used at the vegetation type and forest type level become the main influencing factors. This study will provide reference for data selection and mapping strategies for hierarchical multi-scale vegetation type extraction.

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Development of the preventive natural fire danger assessment algorithm using remote sensing data

Geodesy and Cartography ◽

10.22389/0016-7126-2019-943-1-102-109 ◽

2019 ◽

Vol 943 (1) ◽

pp. 102-109 ◽

Cited By ~ 1

Author(s):

A.T. Gizatullin ◽

N.A. Alekseenko ◽

V.S. Moiseeva

Keyword(s):

Remote Sensing ◽

General Structure ◽

Remote Sensing Data ◽

Fire Danger ◽

Filling Process ◽

Program Code ◽

Natural Fire ◽

Sensing Data ◽

Danger Assessment ◽

Current State

This article is devoted to the development of an algorithm for the preventive assessment of the fire danger of natural areas using remote sensing data (the preventive natural fire danger assessment algorithm). The problems of the current state of the remote sensing materials use for fires researches as a justification for the need of the algorithm are considered. A review of existing methods and algorithms of natural fire danger assessment is done. The algorithm development includes description of the general structure and the content filling process of different algorithm components. The algorithm is a stages sequence of remote sensing data processing and analysis in terms of fire danger. As a result of algorithm, the fire danger assessment of the observed territory is formed. A special feature of the algorithm is its preventiveness, universality (applicability for any territory), practical automatability (the ability to represent in the form of a program code for the processing of RSD) and flexibility (the ability to add and branch the sequence). In the end, general conclusions and recommendations on the use of the algorithm are given.

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The Influence of Widow Size on Remote Sensing-Based Prediction of Forest Structural Variables

10.21203/rs.3.rs-236373/v1 ◽

2021 ◽

Author(s):

Ulaş Yunus ÖZKAN ◽

Tufan Demirel

Keyword(s):

Remote Sensing ◽

Remote Sensing Data ◽

Window Size ◽

Aerial Image ◽

Lidar Data ◽

Black Pine ◽

Sensing Data ◽

Structural Variables ◽

Pine Stands ◽

Optimal Window

Abstract Background: Determining the appropriate window size is a critical step for estimating stand structural variables based on remote sensing data. Because the value of the reference laser and image metrics that affect the quality of the prediction model depends on window size. However, suitable window sizes are usually determined by trial and error. There are a limited number of published studies evaluating appropriate window sizes for different remote sensing data. This research investigated the effect of window size on predicting forest structural variables using airborne LiDAR data, digital aerial image and WorldView-3 satellite image.Results: In the WorldView-3 and digital aerial image, significant differences were observed in the prediction accuracies of the structural variables according to different window sizes. For the estimation based on WorldView-3 in black pine stands, the optimal window sizes for stem number (N), volume (V), basal area (BA) and mean height (H) were determined as 1000 m2, 100 m2, 100 m2 and 600 m2, respectively. In oak stands, the R2 values of each moving window size were almost identical for N and BA. The optimal window size was 400 m2 for V and 600 m2 for H. For the estimation based on aerial image in black pine stands, the 800 m2 window size is optimal for N and H, the 600 m2 window size is optimal for V and the 1000 m2 window size is optimal for BA. In the oak stands, the optimal window sizes for N, V, BA and H were determined as 1000 m2, 100 m2, 100 m2 and 600 m2, respectively. The optimal window sizes may need to be scaled up or down to match the stand canopy components. In the LiDAR data, the R2 values of each window size were almost identical for all variables of the black pine and the oak stands.Conclusion: This study illustrated that the window size has an effect on the prediction accuracy in estimating forest structural variables based on remote sensing data. Moreover, the results showed that the optimal window size for forest structural variables varies according to remote sensing data and tree species composition.

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