scholarly journals A Relative Radiation Normalization Method of ISS Nighttime Light Images Based on Pseudo Invariant Features

2020 ◽  
Vol 12 (20) ◽  
pp. 3349
Author(s):  
Shengrong Wei ◽  
Weili Jiao ◽  
Tengfei Long ◽  
Huichan Liu ◽  
Lu Bi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Space Station ◽  
Remote Sensing Data ◽  
Normalization Method ◽  
Small Scale ◽  
Free Access ◽  
Invariant Features ◽  
Planning Optimization ◽  
Composite Product ◽  
Nighttime Light

The International Space Station (ISS) offers a unique view from space that provides nighttime light (NTL) images of many parts of the globe. Compared with other NTL remote sensing data, ISS NTL multispectral images taken by astronauts with commercial digital single-lens reflex (DSLR) cameras have the characteristics of free access, high spatial resolution, abundant data and no light saturation, so it plays a unique advantage in the research of small-scale urban planning, optimization of lighting resource allocation and blue light pollution. In order to improve the radiation consistency of ISS NTL images, a relative radiation normalization method of ISS NTL images is proposed in this paper. Pseudo invariant features (PIF) were identified in the cloud-free Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) time series NTL remote sensing annual composite product, and then they were used to derive the relative radiation normalization model of ISS NTL images. The results show that the radiation brightness of ISS NTL images in different regions is normalized to the same gray level with that of DMSP/OLS NTL remote sensing images in the same year, which improves the radiation brightness comparability between different regions of ISS NTL images. This method is universally applicable to all ISS NTL images, which is beneficial to the NTL comparability of ISS NTL image in the regional horizontal and temporal vertical.

Small-scale vegetation mapping of large territories: perspectives of using modern remote sensing and GIS technologies, the Indian experiences

1996 ◽  
pp. 51-54 ◽  
Author(s):  
N. V. M. Unni
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Forest Cover ◽  
Human Life ◽  
Remote Sensing Data ◽  
Modern World ◽  
Small Scale ◽  
Vegetation Map ◽  
Gis Technologies ◽  
Forest Cover Mapping

The recognition of versatile importance of vegetation for the human life resulted in the emergence of vegetation science and many its applications in the modern world. Hence a vegetation map should be versatile enough to provide the basis for these applications. Thus, a vegetation map should contain not only information on vegetation types and their derivatives but also the geospheric and climatic background. While the geospheric information could be obtained, mapped and generalized directly using satellite remote sensing, a computerized Geographic Information System can integrate it with meaningful vegetation information classes for large areas. Such aft approach was developed with respect to mapping forest vegetation in India at. 1 : 100 000 (1983) and is in progress now (forest cover mapping at 1 : 250 000). Several review works reporting the experimental and operational use of satellite remote sensing data in India were published in the last years (Unni, 1991, 1992, 1994).

scholarly journals Large-Eddy Simulations of Real-World Episodes in Complex Terrain Based on ERA-Reanalysis and Validated by Ground-Based Remote Sensing Data

2019 ◽  
Vol 147 (12) ◽  
pp. 4325-4343 ◽  
Author(s):  
Cornelius Hald ◽  
Matthias Zeeman ◽  
Patrick Laux ◽  
Matthias Mauder ◽  
Harald Kunstmann
Keyword(s):  
Remote Sensing ◽  
Boundary Conditions ◽  
Complex Terrain ◽  
Computing Time ◽  
Remote Sensing Data ◽  
Large Eddy Simulations ◽  
Small Scale ◽  
Sensing Data ◽  
Large Eddy ◽  
Local Weather

Abstract A computationally efficient and inexpensive approach for using the capabilities of large-eddy simulations (LES) to model small-scale local weather phenomena is presented. The setup uses the LES capabilities of the Weather Research and Forecasting Model (WRF-LES) on a single domain that is directly driven by reanalysis data as boundary conditions. The simulated area is an example for complex terrain, and the employed parameterizations are chosen in a way to represent realistic conditions during two 48-h periods while still keeping the required computing time around 105 CPU hours. We show by evaluating turbulence characteristics that the model results conform to results from typical LES. A comparison with ground-based remote sensing data from a triple Doppler-lidar setup, employed during the “ScaleX” campaigns, shows the grade of adherence of the results to the measured local weather conditions. The representation of mesoscale phenomena, including nocturnal low-level jets, strongly depends on the temporal and spatial resolution of the meteorological boundary conditions used to drive the model. Small-scale meteorological features that are induced by the terrain, such as katabatic flows, are present in the simulated output as well as in the measured data. This result shows that the four-dimensional output of WRF-LES simulations for a real area and real episode can be technically realized, allowing a more comprehensive and detailed view of the micrometeorological conditions than can be achieved with measurements alone.

scholarly journals Mapping the Global Mangrove Forest Aboveground Biomass Using Multisource Remote Sensing Data

2020 ◽  
Vol 12 (10) ◽  
pp. 1690 ◽  
Author(s):  
Tianyu Hu ◽  
YingYing Zhang ◽  
Yanjun Su ◽  
Yi Zheng ◽  
Guanghui Lin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Aboveground Biomass ◽  
Mangrove Forest ◽  
Remote Sensing Data ◽  
Dimensional Structure ◽  
Coefficient Of Determination ◽  
Free Access ◽  
Mangrove Forests ◽  
Estimation Model ◽  
Mangrove Biomass

Mangrove forest ecosystems are distributed at the land–sea interface in tropical and subtropical regions and play an important role in carbon cycles and biodiversity. Accurately mapping global mangrove aboveground biomass (AGB) will help us understand how mangrove ecosystems are affected by the impacts of climatic change and human activities. Light detection and ranging (LiDAR) techniques have been proven to accurately capture the three-dimensional structure of mangroves and LiDAR can estimate forest AGB with high accuracy. In this study, we produced a global mangrove forest AGB map for 2004 at a 250-m resolution by combining ground inventory data, spaceborne LiDAR, optical imagery, climate surfaces, and topographic data with random forest, a machine learning method. From the published literature and free-access datasets of mangrove biomass, we selected 342 surface observations to train and validate the mangrove AGB estimation model. Our global mangrove AGB map showed that average global mangrove AGB density was 115.23 Mg/ha, with a standard deviation of 48.89 Mg/ha. Total global AGB storage within mangrove forests was 1.52 Pg. Cross-validation with observed data demonstrated that our mangrove AGB estimates were reliable. The adjusted coefficient of determination (R2) and root-mean-square error (RMSE) were 0.48 and 75.85 Mg/ha, respectively. Our estimated global mangrove AGB storage was similar to that predicted by previous remote sensing methods, and remote sensing approaches can overcome overestimates from climate-based models. This new biomass map provides information that can help us understand the global mangrove distribution, while also serving as a baseline to monitor trends in global mangrove biomass.

scholarly journals From small-scale forest structure to Amazon-wide carbon estimates

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Edna Rödig ◽  
Nikolai Knapp ◽  
Rico Fischer ◽  
Friedrich J. Bohn ◽  
Ralph Dubayah ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Forest Biomass ◽  
3D Structure ◽  
Basal Area ◽  
Remote Sensing Data ◽  
Small Scale ◽  
Frequency Distributions ◽  
Forest Attributes ◽  
Detailed Interpretation ◽  
Remote Sensing Techniques

Abstract Tropical forests play an important role in the global carbon cycle. High-resolution remote sensing techniques, e.g., spaceborne lidar, can measure complex tropical forest structures, but it remains a challenge how to interpret such information for the assessment of forest biomass and productivity. Here, we develop an approach to estimate basal area, aboveground biomass and productivity within Amazonia by matching 770,000 GLAS lidar (ICESat) profiles with forest simulations considering spatial heterogeneous environmental and ecological conditions. This allows for deriving frequency distributions of key forest attributes for the entire Amazon. This detailed interpretation of remote sensing data improves estimates of forest attributes by 20–43% as compared to (conventional) estimates using mean canopy height. The inclusion of forest modeling has a high potential to close a missing link between remote sensing measurements and the 3D structure of forests, and may thereby improve continent-wide estimates of biomass and productivity.

The contribution of acoustic seafloor mapping techniques to outlining coral reef geomorphology: A case study in the Republic of Maldives (Magoodhoo Reef – Maldivian Archipelago)

2021 ◽  
Author(s):  
Alessandra Savini ◽  
Fabio Marchese ◽  
Luca Fallati ◽  
Sebastian Krastel ◽  
Aaron Micallef ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Coral Reef ◽  
Morphological Changes ◽  
Remote Sensing Data ◽  
Circulation Pattern ◽  
Small Scale ◽  
Optical Remote Sensing ◽  
Patch Reefs ◽  
Reef Environments ◽  
Geomorphological Map

<p>Optical remote sensing data coupled with a dense network of field surveys have historically played a crucial role in geomorphological mapping of coral reef environments. Recently this field has undergone a major upgrade thanks to the integration of new advanced methods such as LiDAR, AUV-based and close-range digital photogrammetry and acoustic remote sensing techniques, which are able to investigate the deeper components of this complex geomorphic system. The new detailed maps can produce seamless digital elevation model (DEM) of coral reef environments, by integrating the elevation datasets acquired by the combination of the mentioned survey techniques.</p><p>In our work, a harmonised geomorphological map is generated for the Magoodhoo reef, which borders the southwestern discontinuous marginal rim of a subcircular atoll (i.e. Faafu Atoll) of the Maldivian archipelago. In its north-eastern sector the reef consists of a cuspate reef joined to an almost closed ring reef to the south-west, where Magoodhoo Island is located. The map was generated from the analysis of Sentinel data, orthomosaics and 3D optical models generated by the application of SfM techniques to UAV images, as well as bathymetry and backscatter intensity measurements. The latter were collected down to a depth of up to 120 m along the oceanward margin of the atoll's rim, and to a depth of roughly 60 m along the lagoonward margin. Direct observations were also performed using an observational ROV on the forereef and within the lagoon, and video-transects on the reef flat.</p><p>The oceanward margin shows steep terraced slopes that reveal a complex history of late Pleistocene/Holocene sea level oscillations, while the backreef slopes (toward the lagoon) are generally more gentle, although at places can show abrupt escarpments and overhangs. The lagoon submarine landscape is distinctly featured by patch reefs of variable shapes (from circular to sub-elongated) and dimensions (from few meters to 30m high). Their distribution is clearly controlled by the surface circulation pattern, regulated by the pass that borders the reef to the west. Towards the deeper edge of the mapped sector of the lagoon floor, where patch reefs are totally absent, intriguing small-scale depressions have been detected instead. The regular circular and concave shape calls for their interpretation as pockmarks, but their origin is still unknow due to the  lack of core samples and geochemical analysis in the area. New data are actually needed to precisely outline the sedimentary environments that feature Faafu Atoll and its inner lagoon. Nevertheless, the obtained geomorphological map and the mapped landforms shed new light and a more complete understanding on the processes that drive morphological changes of the entire Magoodhoo reef.</p>

scholarly journals Characterizing Light Pollution Trends across Protected Areas in China Using Nighttime Light Remote Sensing Data

2018 ◽  
Vol 7 (7) ◽  
pp. 243 ◽  
Author(s):  
Wei Jiang ◽  
Guojin He ◽  
Wanchun Leng ◽  
Tengfei Long ◽  
Guizhou Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Protected Areas ◽  
Remote Sensing Data ◽  
Light Pollution ◽  
Sensing Data ◽  
Nighttime Light

scholarly journals Detection of County Economic Development Using LJ1-01 Nighttime Light Imagery: A Comparison with NPP-VIIRS Data

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6633
Author(s):  
Hongliang Liu ◽  
Nianxue Luo ◽  
Chunchun Hu
Keyword(s):  
Remote Sensing ◽  
Economic Development ◽  
Remote Sensing Data ◽  
County Level ◽  
Socioeconomic Indicators ◽  
Regression Methods ◽  
Public Resources ◽  
The Status ◽  
Nighttime Light ◽  
Nighttime Light Imagery

Nighttime light (NTL) remote sensing data have been widely used to derive socioeconomic indicators at the national and regional scales to study regional economic development. However, most previous studies only chose a single measurement indicator (such as GDP) and adopted simple regression methods to investigate the economic development of a certain area based on DMSP-OLS or NPP-VIIRS stable NTL data. The status quo shows the problems of using a single evaluation index—it has a low evaluation precision. The LJ1-01 satellite is the first dedicated NTL remote sensing satellite in the world, launched in July 2018. The data provided by LJ1-01 have a higher spatial resolution and fewer blooming phenomena. In this paper, we compared the accuracy of the LJ1-01 data and NPP-VIIRS data in detecting county-level multidimensional economic development. In three provinces in China, namely, Hubei, Hunan and Jiangxi, 20 socioeconomic parameters were selected from the following five perspectives: economic conditions, people’s livelihood, social development, public resources and natural vulnerability. Then, a County-level Economic Index (CEI) was constructed to evaluate the level of multidimensional economic development, with the spatial pattern of the multidimensional economic development also identified across the study area. The present study adopted the random forest (RF) and linear regression (LR) algorithms to establish the regression model individually, and the results were evaluated by cross-validation. The results show that the RF algorithm greatly improves the accuracy of the model compared with the LR algorithm, and thus is suitable for the study of NTL data. In addition, a better determinate coefficient (R2) based on the LJ1-01 data (0.8168) was obtained than that from the NPP-VIIRS data (0.7245) in the RF model, which reflects that the LJ1-01 data offer better potential in the evaluation of socioeconomic parameters and can be used to identify, both accurately and efficiently, multidimensional economic development at the county level.

Sign in / Sign up

Export Citation Format

Share Document