scholarly journals QUANTIFICATION OF GLACIER DEPLETION IN THE CENTRAL TIBETAN PLATEAU BY USING INTEGRATED SATELLITE REMOTE SENSING AND GRAVIMETRY

2016 ◽  
Vol XLI-B8 ◽  
pp. 399-402
Author(s):  
K.-H. Tseng ◽  
K. T. Liu ◽  
C. K. Shum ◽  
Y. Jia ◽  
K. Shang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Tibetan Plateau ◽  
The Tibetan Plateau ◽  
Optical Remote Sensing ◽  
Exact Mass ◽  
Data Set ◽  
Glacier System ◽  
Central Tibetan Plateau ◽  
Tanggula Mountains ◽  
Storage Change

Glaciers over the Tibetan Plateau have experienced accelerated depletion in the last few decades due primarily to the global warming. The freshwater drained into brackish lakes is also observed by optical remote sensing and altimetry satellites. However, the actual water storage change is difficult to be quantified since the altimetry or remote sensing only provide data in limited dimensions. The altimetry data give an elevation change of surface while the remote sensing images provide an extent variation in horizontal plane. Hence a data set used to describe the volume change is needed to measure the exact mass transition in a time span. In this study, we utilize GRACE gravimetry mission to quantify the total column mass change in the central Tibetan Plateau, especially focused on the lakes near Tanggula Mountains. By removing these factors, the freshwater storage change of glacier system at study area can be potentially isolated.

scholarly journals QUANTIFICATION OF GLACIER DEPLETION IN THE CENTRAL TIBETAN PLATEAU BY USING INTEGRATED SATELLITE REMOTE SENSING AND GRAVIMETRY

2016 ◽  
Vol XLI-B8 ◽  
pp. 399-402
Author(s):  
K.-H. Tseng ◽  
K. T. Liu ◽  
C. K. Shum ◽  
Y. Jia ◽  
K. Shang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Tibetan Plateau ◽  
The Tibetan Plateau ◽  
Optical Remote Sensing ◽  
Exact Mass ◽  
Data Set ◽  
Glacier System ◽  
Central Tibetan Plateau ◽  
Tanggula Mountains ◽  
Storage Change

Glaciers over the Tibetan Plateau have experienced accelerated depletion in the last few decades due primarily to the global warming. The freshwater drained into brackish lakes is also observed by optical remote sensing and altimetry satellites. However, the actual water storage change is difficult to be quantified since the altimetry or remote sensing only provide data in limited dimensions. The altimetry data give an elevation change of surface while the remote sensing images provide an extent variation in horizontal plane. Hence a data set used to describe the volume change is needed to measure the exact mass transition in a time span. In this study, we utilize GRACE gravimetry mission to quantify the total column mass change in the central Tibetan Plateau, especially focused on the lakes near Tanggula Mountains. By removing these factors, the freshwater storage change of glacier system at study area can be potentially isolated.

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

scholarly journals Geometric dependency of Tibetan lakes on glacial runoff

2013 ◽  
Vol 17 (10) ◽  
pp. 4061-4077 ◽  
Author(s):  
V. H. Phan ◽  
R. C. Lindenbergh ◽  
M. Menenti
Keyword(s):  
Remote Sensing ◽  
Tibetan Plateau ◽  
Shuttle Radar Topography Mission ◽  
The Tibetan Plateau ◽  
Water Level Changes ◽  
Elevation Data ◽  
Glacial Runoff ◽  
Lake Catchment ◽  
Accurate Quantification ◽  
Do So

Abstract. The Tibetan Plateau is an essential source of water for Southeast Asia. The runoff from its ~34 000 glaciers, which occupy an area of ~50 000 km2, feeds Tibetan lakes and major Asian rivers like the Indus and Brahmaputra. Reported glacial shrinkage likely has an impact on the runoff. Unfortunately, accurate quantification of glacial changes is difficult over the high-relief Tibetan Plateau. However, it has recently been shown that it is possible to directly assess water level changes of a significant number of the ~900 Tibetan lakes with an area over 1 km2. This paper exploits different remote sensing products to create drainage links between Tibetan glaciers, lakes and rivers. The results allow us to differentiate between lakes with and without outlet. In addition, we introduce the notion of geometric dependency of a lake on glacial runoff, defined as the ratio between the total area of glaciers draining into a lake and the total area of the lake catchment. We determined these dependencies for all ~900 sufficiently large Tibetan lakes. To do so, we combined three remote sensing products: the CAREERI glacier mask product, a lake mask product based on the MODIS MOD44W water product and the HydroSHEDS river network product derived from Shuttle Radar Topography Mission (SRTM) elevation data. Using a drainage network analysis, we determined all drainage links between glaciers and lakes. The results show that 25.3% of the total glacier area directly drains into one of 244 Tibetan lakes. The results also give the geometric dependency of each lake on glacial runoff. For example, there are ten lakes with direct glacial runoff from at least 240 km2 of glacier. Three case studies, including one of the well-studied Nam Tso Lake, demonstrate how the geometric dependency of a lake on glacial runoff can be directly linked to hydrological processes.

scholarly journals Luminescence dating of lacustrine sediments from CUOE Lake on the central Tibetan Plateau

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Yandong Hou ◽  
Hao Long ◽  
Lei Gao ◽  
Ji Shen
Keyword(s):  
Tibetan Plateau ◽  
Lacustrine Sediments ◽  
Water Reservoir ◽  
Hard Water ◽  
Luminescence Dating ◽  
Coarse Grained ◽  
The Tibetan Plateau ◽  
Sediment Layer ◽  
Central Tibetan Plateau ◽  
Dating Method

AbstractLuminescence dating technology has been used for chronological constraints on lacustrine sediments due to the ubiquitous materials (e.g., quartz and feldspar) as dosimeters, and a relatively long dating range, compared with the commonly used radiocarbon dating method. However, quartz dating on the Tibetan Plateau may suffer from dim and unstable luminescence signals. In the current study, we investigate a lake-related outcrop from the shore of Cuoe Lake on the central Tibetan Plateau. Both coarse-grained quartz and K-feldspar fractions were extracted, and OSL and post-IR IRSL signals were measured from these fractions, respectively. Combining the stratigraphy analysis and dating results, this study shows that: (1) quartz appears to be unsuitable for dating because of very dim natural signals and even anomalous fading (average g-value: 4.30 ± 2.51 %/decade). The suitability of the applied pIRIR protocol measured at 150°C (pIRIR150) for K-feldspar samples was confirmed by a set of luminescence tests; (2) compared with the luminescence-based chronology, the 14C age of shells from the same sediment layer yielded older age by ~7 ka, which is likely attributed to hard water reservoir effect in Cuoe Lake; (3) the lake level reached its peak and maintained high-stand during the early Holocene (~9.4–7.1 ka). This study highlights the applicability of K-feldspar luminescence dating when the counterpart quartz OSL is insensitive and encounters anomalous fading.

scholarly journals <sup>10</sup>Be systematics in the Tsangpo-Brahmaputra catchment: the cosmogenic nuclide legacy of the eastern Himalayan syntaxis

2017 ◽  
Author(s):  
Maarten Lupker ◽  
Jérôme Lavé ◽  
Christian France-Lanord ◽  
Marcus Christl ◽  
Didier Bourlès ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
Sediment Flux ◽  
Coarse Grained ◽  
The Tibetan Plateau ◽  
Data Set ◽  
Denudation Rate ◽  
Cosmogenic Nuclide ◽  
Denudation Rates ◽  
The Impact ◽  
Eastern Himalayan Syntaxis

Abstract. The Tsangpo-Brahmaputra River drains the eastern part of the Himalayan range, flowing from the Tibetan Plateau through the eastern Himalayan syntaxis and downstream to the Indo-Gangetic floodplain. As such it is a unique natural laboratory to study how denudation and sediment production processes are transferred to river detrital signals. In this study, we present a new 10Be data set to constrain denudation rates across the catchment and to quantify the impact of rapid erosion within the syntaxis region on cosmogenic nuclide budgets and signals. 10Be denudation rates span around two orders of magnitude across the catchments (ranging from 0.03 mm/yr to > 4 mm/yr) and sharply increase as the Tsangpo-Brahmaputra flows across the eastern Himalaya. The increase in denudation rates however occurs ~ 150 km downstream of the Namche Barwa-Gyala Peri massif (NBGPm), an area which has been previously characterized by extremely high erosion and exhumation rates. We suggest that this downstream lag is mainly due to the physical abrasion of coarse grained, low 10Be concentration, landslide material produced within the syntaxis that dilutes the upstream high concentration 10Be flux from the Tibetan Plateau only after abrasion has transferred sediment to the studied sand fraction. A simple abrasion model produces typical lag distances of 50 to 150 km compatible with our observations. Abrasion effects reduce the spatial resolution over which denudation can be constrained in the eastern Himalayan syntaxis. In addition, we also highlight that denudation rate estimates are dependent on the sediment connectivity, storage and quartz content of the upstream Tibetan Plateau part of the catchment which tends to lead to an overestimation of downstream denudations rates. Taking these effects into account we estimate a denudation rates of ca. 2 to 5 mm/yr for the entire syntaxis and ca. 4 to 28 mm/yr for the NBGPm, which is significantly higher than other to other large catchments. Overall, 10Be concentrations measured at the outlet of the Tsangpo-Brahmaputra in Bangladesh suggest a sediment flux between 780 and 1430 Mt/yr equivalent to a denudation rate between 0.7 and 1.2 mm/yr for the entire catchment.

scholarly journals Cloud Detection of SuperView-1 Remote Sensing Images Based on Genetic Reinforcement Learning

2020 ◽  
Vol 12 (19) ◽  
pp. 3190
Author(s):  
Xiaolong Li ◽  
Hong Zheng ◽  
Chuanzhao Han ◽  
Haibo Wang ◽  
Kaihan Dong ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Reinforcement Learning ◽  
False Positive Rate ◽  
Optical Remote Sensing ◽  
Cloud Detection ◽  
Remote Sensing Images ◽  
Threshold Method ◽  
Data Set ◽  
Action Strategy ◽  
Positive Rate

Cloud pixels have massively reduced the utilization of optical remote sensing images, highlighting the importance of cloud detection. According to the current remote sensing literature, methods such as the threshold method, statistical method and deep learning (DL) have been applied in cloud detection tasks. As some cloud areas are translucent, areas blurred by these clouds still retain some ground feature information, which blurs the spectral or spatial characteristics of these areas, leading to difficulty in accurate detection of cloud areas by existing methods. To solve the problem, this study presents a cloud detection method based on genetic reinforcement learning. Firstly, the factors that directly affect the classification of pixels in remote sensing images are analyzed, and the concept of pixel environmental state (PES) is proposed. Then, PES information and the algorithm’s marking action are integrated into the “PES-action” data set. Subsequently, the rule of “reward–penalty” is introduced and the “PES-action” strategy with the highest cumulative return is learned by a genetic algorithm (GA). Clouds can be detected accurately through the learned “PES-action” strategy. By virtue of the strong adaptability of reinforcement learning (RL) to the environment and the global optimization ability of the GA, cloud regions are detected accurately. In the experiment, multi-spectral remote sensing images of SuperView-1 were collected to build the data set, which was finally accurately detected. The overall accuracy (OA) of the proposed method on the test set reached 97.15%, and satisfactory cloud masks were obtained. Compared with the best DL method disclosed and the random forest (RF) method, the proposed method is superior in precision, recall, false positive rate (FPR) and OA for the detection of clouds. This study aims to improve the detection of cloud regions, providing a reference for researchers interested in cloud detection of remote sensing images.

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