Interpretation Of Remote Sensing Data Research Articles

Lithium Concentrations in Saline Brines of the Shu–Sarysu Depression

This article presents the results of a study on lithium mineralization in salt flats and underground aquifers of the Shu–Sarysu depression. Analysis of brine samples collected from 2022 to 2023, utilizing spectroscopy and X-ray diffraction, reveals elevated concentrations that hold commercial promise. These findings may have significant implications for exploration efforts and estimation regarding the lithium resource potential, which is currently in high demand. This article examines data regarding lithium brine deposits worldwide, focusing on their geology. The research methodology involves delineating regions of salt flat distribution through remote sensing data interpretation, fieldwork, and laboratory analysis, particularly for surface brines. Underground lithium-bearing brines are detected within oil and gas structures. The article presents findings from analytical studies conducted on saline and co-produced formation brines collected during the 2022 field season, with a specific focus on the epiplatform regions of Kazakhstan, encompassing the Shu–Sarysu depression.

Multi-factor Impact Analysis of Grassland Phenology Changes on the Qinghai-Xizang Plateau Based on Interpretable Machine Learning

The vegetation phenology of the Qinghai-Xizang Plateau is changing significantly in the context of climate change. However, there are many hydrothermal factors affecting the phenology, and few studies have focused on the effects of multiple factors on the phenology of the Qinghai-Xizang Plateau, resulting in a lack of understanding of the mechanisms underlying phenological changes on the Qinghai-Xizang Plateau. In this study, we used remote sensing data interpretation to analyze the spatial and temporal variability of grassland phenology on the Qinghai-Xizang Plateau from 2002 to 2021, focusing on precipitation, temperature, altitude, soil, and other aspects to reveal the dominant factors of phenological variability using an interpretable machine learning method (SHAP) and to quantify the interactive effects of multiple factors on phenology. The results showed that:① The growing season start (SOS) of grasslands on the Qinghai-Xizang Plateau mostly ranged from 110 to 150 d, with 56.32 % of grasslands showing an early SOS trend; the growing season end (EOS) mostly ranged from 290-320 d, with 67.65 % of grasslands showing a delayed EOS trend; and the growing season length (LOS) mostly ranged from 120 to 210 d, with 65.50 % of the grasslands showing a trend towards longer growing season lengths. ② SOS in grasslands on the Qinghai-Xizang Plateau was mainly influenced by moisture conditions, in which soil moisture between 10 and 25 kg·m-2 in the 0-10 cm soil layer in March promoted the advancement of SOS and peaked at approximately 20 kg·m-2. EOS was mainly influenced by temperature, with higher temperatures in September and October having a stronger effect on EOS latency promotion and peaking at over 8 ℃ and -0.5 ℃, respectively. The main influencing factors of LOS were more consistent with SOS, in which soil moisture between 15 and 25 kg·m-2 in the 0-10 cm soil layer in March promoted the prolongation of LOS and peaked at approximately 18 kg·m-2. ③ There was an obvious interactive effect of water and heat and other factors on phenology; after soil moisture reached 20 kg·m-2 in the 0-10 cm soil layer in March, SOS was more advanced in low-precipitation and low-altitude areas. Better moisture conditions were more conducive to EOS delay at temperatures above 0 ℃ in October, and soil moisture in high precipitation areas promoted LOS prolongation more when soil moisture was between 12 and 22 kg·m-2 in 0-10 cm in March. The results also demonstrated that interpretable machine learning methods could provide a new approach to the analysis of the multifactorial effects of phenological change.

Landslide Susceptibility Prediction Using Machine Learning Methods: A Case Study of Landslides in the Yinghu Lake Basin in Shaanxi

Landslide susceptibility prediction (LSP) is the basis for risk management and plays an important role in social sustainability. However, the modeling process of LSP is constrained by various factors. This paper approaches the effect of landslide data integrity, machine-learning (ML) models, and non-landslide sample-selection methods on the accuracy of LSP, taking the Yinghu Lake Basin in Ankang City, Shaanxi Province, as an example. First, previous landslide inventory (totaling 46) and updated landslide inventory (totaling 46 + 176) were established through data collection, remote-sensing interpretation, and field investigation. With the slope unit as the mapping unit, twelve conditioning factors, including elevation, slope, aspect, topographic relief, elevation variation coefficient, slope structure, lithology, normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), distance to road, distance to river, and rainfall were selected. Next, the initial landslide susceptibility mapping (LSM) was obtained using the K-means algorithm, and non-landslide samples were determined using two methods: random selection and semi-supervised machine learning (SSML). Finally, the random forest (RF) and artificial neural network (ANN) machine-learning methods were used for modeling. The research results showed the following: (1) The performance of supervised machine learning (SML) (RF, ANN) is generally superior to unsupervised machine learning (USML) (K-means). Specifically, RF in the SML model has the best prediction performance, followed by ANN. (2) The selection method of non-landslide samples has a significant impact on LSP, and the accuracy of the SSML-based non-landslide selection method is controlled by the ratio of the number of landslide samples to the number of mapping units. (3) The quantity of landslides has an impact on how reliably the results of LSM are obtained because fewer landslides result in a smaller sample size for LSM, which deviates from reality. Although the results in this dataset are satisfactory, the zoning results cannot reliably anticipate the recently added landslide data discovered by the interpretation of remote-sensing data and field research. We propose that the landslide inventory can be increased by remote sensing in order to achieve accurate and impartial LSM since the LSM of adequate landslide samples is more reasonable. The research results of this paper will provide a reference basis for uncertain analysis of LSP and regional landslide risk management.

Synergy and regulation of the South China Karst WH site integrity protection and the buffer zone agroforestry development

The Natural World Heritage site (NWH) has a dual role of protection and utilization. The buffer zone of the South China Karst (SCK) World Heritage (WH) site is scattered with village dwellings, which adds pressure to preserve the outstanding universal value (OUV) and integrity protection of the WH site. The development of agroforestry is an essential means to maintain rural livelihoods, protect the ecological environment, and realize the protection and development of the heritage site. Studying the synergy and regulation of the heritage site integrity protection and the buffer zone agroforestry development is essential. This study takes the Shibing and Libo–Huanjiang karst WH as the study areas and administrative villages as the evaluation unit. From 2020 to 2023, through data methods such as remote sensing data interpretation, information data survey and collection, comprehensive index analysis, coupling coordination degree model, and GIS spatial analysis, a systematic study was conducted on the basic frontier research, synergy relationship, and regulation of the heritage site integrity protection and the buffer zone agroforestry development. The results demonstrate that the Shibing and Libo–Huanjiang karst WH sites integrity protection and agroforestry development in the buffer zone have a high coupling degree and a good coupling coordination relationship. This reveals that the heritage site integrity protection and the buffer zone agroforestry development are an interconnected and mutually influential whole, which the synergistic relationship between them is developing in a positive direction; The buffer zone agroforestry development lags behind the heritage site integrity protection, based on which proposed the regulatory measures for the synergy development of the heritage site integrity protection and agroforestry development in the buffer zone. The findings in this study provide references for the synergies development of the WH karst site integrity protection and agroforestry development in the buffer zone.

A high-precision image classification network model based on a voting mechanism

ABSTRACT With the development of satellite remote sensing technology, image classification task, as the basis of remote sensing data interpretation, has received wide attention to improving accuracy and robustness. At the same time, in-depth learning technology has been widely used in remote sensing and has a far-reaching impact. Since the existing image classification methods ignore the feature that the general image semantics are the same as the semantics of a single pixel, this paper presents an algorithm that uses the semantics of an image to achieve high-precision image classification. Based on the idea of partial substitution for global, this algorithm designs a split result voting mechanism and builds a Vgg-Vote network model. This mechanism votes on the semantically segmented result of an image and uses the maximum filtering function to select the category containing the most significant number of pixels as the prediction category of the image. Experiments on UC Merced Land-User complete datasets and five types of incomplete datasets with varying degrees of interference, including noise, data occlusion and loss, show that the Vote mechanism dramatically improves the classification accuracy, robustness and anti-jamming capability of Vgg-Vote.

Micro hydropower plant potential study based on Landsat 8 operational land imager satellite data

Remote sensing technology has been widely applied in various fields, including oil, gas, and mineral exploration, spatial planning, and environmental monitoring. This paper describes the application of remote sensing technology for the potential study of a renewable micro hydropower plant (MHP) using Landsat 8 satellite data. The Sukaati Watershed, West Java, Indonesia, was selected as the case study area. Landsat 8 satellite data, acquired on August 21, 2020, was applied to extract information on land use, geology, and potential landslides. Drainage patterns, watershed boundaries, and head height were obtained from topographic map data. Drainage patterns, watershed boundaries, and land use are used to calculate flow rates. Geological map and landslide are the basis of layout of MHP components, such as water intake, dam, waterway, settling tank, penstock, and powerhouse. A field survey to acquire actual flow rate and head height was conducted to validate the results of the remote sensing data interpretation. Two potential sites of MHP were selected with a hydropower design of 129 kW and 5.18 MW. This study showed that remote sensing technology is beneficial for studying the potential of MHP because fieldwork can be done more quickly and efficiently.

Distributed Deep Learning for Remote Sensing Data Interpretation

As a newly emerging technology, deep learning (DL) is a very promising field in big data applications. Remote sensing often involves huge data volumes obtained daily by numerous in-orbit satellites. This makes it a perfect target area for data-driven applications. Nowadays, technological advances in terms of software and hardware have a noticeable impact on Earth observation applications, more specifically in remote sensing techniques and procedures, allowing for the acquisition of data sets with greater quality at higher acquisition ratios. This results in the collection of huge amounts of remotely sensed data, characterized by their large spatial resolution (in terms of the number of pixels per scene), and very high spectral dimensionality, with hundreds or even thousands of spectral bands. As a result, remote sensing instruments on spaceborne and airborne platforms are now generating data cubes with extremely high dimensionality, imposing several restrictions in terms of both processing runtimes and storage capacity. In this article, we provide a comprehensive review of the state of the art in DL for remote sensing data interpretation, analyzing the strengths and weaknesses of the most widely used techniques in the literature, as well as an exhaustive description of their parallel and distributed implementations (with a particular focus on those conducted using cloud computing systems). We also provide quantitative results, offering an assessment of a DL technique in a specific case study (source code available: https://github.com/mhaut/cloud-dnn-HSI). This article concludes with some remarks and hints about future challenges in the application of DL techniques to distributed remote sensing data interpretation problems. We emphasize the role of the cloud in providing a powerful architecture that is now able to manage vast amounts of remotely sensed data due to its implementation simplicity, low cost, and high efficiency compared to other parallel and distributed architectures, such as grid computing or dedicated clusters.

Evaluating the effect of stand properties and site conditions on the forest reflectance from Sentinel-2 time series.

Forest stand reflectance at the canopy level results from various factors, such as vegetation chemical properties, leaf morphology, canopy structure, and tree sizes. These factors are dependent on the species, age, and health statuses of trees, as well as the site conditions. Sentinel-2 imagery with the high spatial, spectral, and temporal resolution, has enabled analysis of the relationships between vegetation properties and their spectral responses at large spatial scales. A comprehensive study of these relationships is needed to understand the drivers of vegetation spectral patterns and is essential from the point of view of remote sensing data interpretation. Our study aimed to quantify the site and forest parameters affecting forest stands reflectance. The analysis was conducted for common beech-, silver fir- and Scots pine-dominated stands in a mountainous area of the Polish Carpathians. The effect of stands and site properties on reflectance in different parts of the growing season was captured using the dense time series provided by Sentinel-2 from 2018-2019. The results indicate that the reflectance of common beech stands is mainly influenced by elevation, particularly during spring and autumn. Other factors influencing beech stand reflectance include the share of the broadleaved understory, aspect, and, during summer, the age of stands. The reflectance of coniferous species, i.e., Scots pine and silver fir, is mainly influenced by the age and stand properties, namely the crown closure and stand density. The age is a primary driver for silver fir stands reflectance changes, while the stand properties have a large impact on Scots pine stands reflectance. Also, the understory influences Scots pine stands reflectance, while there appears to be no impact on silver fir stands. The influence of the abovementioned factors is highly diverse, depending on the used band and time of the season.

CBF-Net: An Adaptive Context Balancing and Feature Filtering Network for Point Cloud Classification

Point cloud classification is regarded as a critical task in remote sensing data interpretation, which is widely used in many fields. Recently, many proposed methods tend to develop an end-to-end network to directly operate on the raw point cloud, which has shown great power. However, most of these methods abstract local features by equally considering the neighboring points. The features learned may neglect to distinguish contributions of different points especially the edge points and outliers, leading to a coarse classification result especially for boundaries. Moreover, the extracted features are high redundant and intercorrelated with similar categories, posing difficulty in identifying classes sharing similar characteristics especially in complex scenes. Therefore, we propose an adaptive context balancing and feature filtering network (CBF-Net) to tackle the aforementioned problems. First, we introduce a balanced context encoding module to balance semantically the features of neighboring points, which can help the model learn more from the edge points and, therefore, contribute to a finer classification. Then, considering that the interference for similar classes probably causes confusion among them, a filtered feature aggregating module is proposed to filter the extracted features by mapping them into a cleaner subspace with a lower rank. We have conducted thorough experiments on the International Society for Photogrammetry and Remote Sensing 3-D labeling dataset. Experimental results show that our CBF-Net can obtain high accuracy and achieve state-of-the-art level in the categories of Powerline, Car, and Facade. In addition, we also conduct experiments on the RueMonge2014 dataset, which further reveals the strong ability of our model.

Prospecting for gold mineralization with the use of remote sensing and GIS technology in North Kordofan State, central Sudan

The main objective of this study is to carry out regional geological mapping and gold prospecting in the poor savannah Northern Kordofan State, central Sudan. The selection of the study area was based on its favorable gold mineralization geological setting. Syntheses of the various forms of imagery, digital image processing, spectral analysis and office mapping were conducted before going into the field. Field observations, chemical analysis and petrographic investigations have all been integrated with the help of Geographic Information Systems (GIS) to come out with reliable interpretation that can be implemented in similar geologic setting and climatic conditions. A package of digital image processing techniques was applied to Landsat7 Enhanced Thematic Mapper Plus (ETM+) image in order to increase the visual interpretability of the various lithological units for the purpose of geological mapping and delineation of alteration zones, which represent possible target zones for gold mineralization. Different color composite images in addition to the result of image transformation (including Red Green Blue, RGB to Intensity Hue Saturation, IHS; Principal Component Analysis, PCA) were prepared to facilitate the delineation of the main rock groups. Several ratio images were prepared, combined together and displayed in RGB color composites. The outcome of the image processing is the discovery of many alteration and shear zones which trend north-northeast to south-southwest affecting most of the rock types. 139 chip samples were collected from the alteration and shear zones, which were delineated by remote sensing data interpretation and actual field work. They include samples from quartz veins and rock alteration zones which were analyzed by fire assay. All the collected samples proved to be gold-bearing. The Au values range from 0.1 to 84.9 g/ton. Artisanal mining activity in the study area coincided with the remotely sensed data interpretation results conducted in this work, i.e. during the field checkup artisanal miners were found in the places where anomalous signals appeared in the satellite image, thus reinforcing the reliability of the employed approach of remote sensing and GIS for gold prospecting in poor savannah zones.

A Novel Rayleigh Dynamical Model for Remote Sensing Data Interpretation

This article introduces the Rayleigh autoregressive moving average (RARMA) model, which is useful to interpret multiple different sets of remotely sensed data, from wind measurements to multitemporal synthetic aperture radar (SAR) sequences. The RARMA model is indeed suitable for continuous, asymmetric, and nonnegative signals observed over time. It describes the mean of Rayleigh-distributed discrete-time signals by a dynamic structure including autoregressive (AR) and moving average (MA) terms, a set of regressors, and a link function. After presenting the conditional likelihood inference for the model parameters and the detection theory, in this article, a Monte Carlo simulation is performed to evaluate the finite signal length performance of the conditional likelihood inferences. Finally, the new model is applied first to sequences of wind speed measurements, and then to a multitemporal SAR image stack for land-use classification purposes. The results in these two test cases illustrate the usefulness of this novel dynamic model for remote sensing data interpretation.

Post–east African Orogen dilation and magmatism: development of a conjugate-style of dolerite dike swarm in the northwestern Arabian shield

The Al Madinah area bimodal dike swarms underwent detailed field mapping, measurements of dikes’ attitudes, structural analyses, remote sensing data interpretation, and petrology study. The oldest rock unit is a greenish intermediate dioritic unit that varies in its petrologic composition between microdiorite and andesite porphyry which is intruded by alkali granite. The dioritic and alkali granite units have been cut by intermediate to mafic dikes, ranging in texture and composition from micromonzodiorite and micromonzonite to microgabbro, microdolerite, and basalt or basaltic andesite, as revealed by thin-section work. A stereographic projection and rose diagrams of the mafic dikes formed a regional conjugate dike system, following probably a typical andersonian style, consisting of three major sets, namely, the WNW set which trends between N295 and N330, E-W set between N265 and N270, and finally the ENE (N065) set. The WNW set has its own local system, but it is considered as a set in the overall regional conjugate dike system. The WNW and E-W sets are the conjugate pair of the WNW Najd fault system maximum horizontal shearing, where the ENE set reflects the minimum regional horizontal shearing stress of the Najd system. The interpretation of this dike system is a WNW-ESE sinistral faulting and NE-SW dilation that formed Wadi Al-Hamd Ediacaran continental basin, bounded by two Najd faults and Thurwah-Bir Um suture. The study area underwent final thrust faulting that tilted the dikes towards the east. For the consequent magmatisms during the Neoproterozoic period, followed by Cenozoic volcanism, the Al Madinah area is considered a large igneous province.

A 3-D Spatiotemporal Model for Remote Sensing Data Cubes

Satellite images from the same scene observed over time can be composed in an image stack, which could be modeled as a 3-D cube. To handle this type of remote sensing data, on the one side, unidimensional dynamical models have been considered, modeling each pixel separately along the time (pixel-based approach), and exploring the temporal correlation. On the other side, 2-D approaches have been considered to process each image at one date, exploring the spatial correlation. In this article, we propose a new 3-D autoregressive (AR) (3-D-AR) model useful for multitemporal image interpretation exploring the correlation in three dimensions altogether. The 3-D-AR model is statistically defined, and a robust parameter estimation method is discussed. The tools for filtering, forecasting, and detecting anomalies are also introduced. A Monte Carlo simulation study is performed to evaluate the finite signal length performance of the robust estimation and its sensitivity to outliers. The proposed model is applied to a multitemporal normalized difference vegetation index (NDVI) image stack for filtering, prediction, and anomaly detection purposes. The numerical results show the importance of the proposed 3-D-AR model for spatiotemporal remote sensing data interpretation.

Revisiting the particle-size constraint of the 10-μm silicate feature

We experimentally measure the mid-IR spectra of half-millimeter-sized olivine particles with very compact morphology and smooth surfaces. We find that the 10-μm silicate feature is present in these large, irregularly shaped samples having surface roughness that is much smaller than optical wavelengths. Based on Mie theory, which assumes the particles are spherical, this feature should not exist for such large particles. As a consequence, its presence has been taken as an indicator that the particles or grains composing such particles were micron-sized or smaller. The measurement of this feature in real, irregularly shaped particles, suggests that the assumption of sphericity may severely limit our interpretations of remote-sensing data.

Mapping Archaeological Site of Keraton Pleret Using Aerial Photograph

As a cultural heritage, Site of Keraton (Palace) Pleret becomes an essential relic in Javanese history and culture to preserve and deliver ancient information for the next generation sustainably. On the other hand, this preservation is dealing with quite rapid built-up area development. Mapping can be a significant effort as primary data in cultural preservation management. This study aims to make a detailed site map of the Keraton Pleret and identify the existing land use in each site component. Through remote sensing data interpretation and analysis, identification and mapping of archaeological sites done by matching previous archaeological documents, field conditions, digital orthophoto from aerial photography, and digital topographic data (digital surface models, DSM). Site components were identified firstly by matching the existence of the street network and river from a topographic map with orthophoto to obtain site orientation based on artificial infrastructure and existing natural features. It was followed by matching between Keraton Pleret blueprint document, previous archaeological information, and orthophoto to identify and locate each site component. Existing land use information obtained by orthophoto interpretation. Data analysis shows the compatibility between spatial data used in this research and previous documents to identify components and result in Keraton Pleret site map. At present, most of the Keraton Pleret site area has been converted to buildings and paddy fields, strengthen the need for archaeological sites management which alongside community activities.

Hydrography and geomorphology of Antananarivo High City (Madagascar)

ABSTRACT The city of Antananarivo is located in the central highlands of Madagascar, and is the largest urban center of the island. Considering the frequent heavy cyclonic rains frequently affecting the area, its geomorphological context is particularly prone to geo-hydrological hazards, such as landslides and flash floods, as recently proved during the disastrous events of the winters of 2015 and 2018. Field data and high-resolution remote sensing data interpretation (DEMs and satellite orthophotos) were combined in order to produce detailed hydrographic and geomorphological maps. The aim was to understand the processes acting in the Analamanga hill area, with special regards to the effect of human activity in modeling the natural landforms and exacerbating the geo-hydrological hazards. The obtained maps will provide management-planning tools to be used as a first step towards a risk reduction strategy in the Antananarivo historical urban center.

Using the random forest model and validated MODIS with the field spectrometer measurement promote the accuracy of estimating aboveground biomass and coverage of alpine grasslands on the Qinghai-Tibetan Plateau

Abstract Maintaining the aboveground biomass (AGB) and canopy cover (CC) of grassland is key for sustainable grassland management and rational conservation planning, especially for fragile regions of Qinghai-Tibetan Plateau (QTP). However, the estimation of AGB and CC with land use changes is often challenging due to poor availability of high-quality images of remote sensing or the large-scale field survey data. In this study, we used the transect survey method combining with quadrat measurements with 1200 plant samplings and near surface hyperspectral measurements at 180 sites to validate the MODIS data. An integrative approach combining the plant sampling, near surface hyperspectral measurements, remote sensing data interpretation, random forest modelling and Hurst phenomenon prediction was developed to accurately and efficiently estimate the CC and AGB of the alpine grasslands on the QTP from 2000 to 2017 and their future trends. The results showed: 1) estimation of AGB and CC in different land use types by integrating random forest with multiple factors (NDVI, latitude, longitude and altitude, grassland types) was more accurate; 2) the inter-annual changes of AGB and CC of the alpine grasslands presented significant fluctuations, with an increasing trend in the QTP; 3) the areas with improved vegetation conditions of AGB (66.55%) and CC (64.58%) were much larger than those with degraded vegetation conditions of AGB (33.45%) and CC (35.42%) in past 20 years. 4) there are 60.66% of the AGB and 59.87% of coverage showing sustainability and increasing in the future. Estimating the AGB and CC with multiple factors using the random forest and validated MODIS with the field spectrometer can improve the accuracy. The findings of this study can provide scientific basis for promoting the sustainability of grassland ecosystem through adaptive and rational management on the QTP and worldwide.

New Scheme for Impervious Surface Area Mapping From SAR Images With Auxiliary User-Generated Content

This article presents a new scheme to extract impervious surface area from synthetic-aperture radar (SAR) images exploiting auxiliary user-generated content (UGC). The presented scheme includes the automatic generation of training samples based on the combination of UGC and SAR data, and SAR data preprocessing, leading to impervious surface area classification through a clustering-based one-class support vector machine approach. Two areas—namely, the cities of Beijing and Taipei, have been analyzed using the Sentinel-1 SAR data to test and validate the proposed methodology. Experimental results show that the presented scheme improves the automatic selection of impervious surface training samples. Moreover, this scheme achieves a comparable classification performance to traditional methods without requiring time-consuming training point manual extraction. Results in this study will help to promote the application of UGC for urban remote sensing data interpretation.

Postglacial incision-infill cycles at the Borisoglebsk Upland: Correlations between interfluve headwaters and fluvial network

The article discusses postglacial landscape transformation in the Northern Hemisphere Middle Pleistocene glaciation area located in the center of the Russian Plain. We attempted to verify the regional paleogeographic model by reconstructing the Late Pleistocene incision-infill cycles at the Eastern Borisoglebsk Upland based on a comparison of inactive interfluves headwaters and actual fluvial network palaeoarchives. The study was also aimed to determine the past extent of fluvial systems. Interdisciplinary research of the actual and buried topography, lithology and pedogenic properties of surface deposits was carrid out with remote sensing data interpretation, DGPS survey, and detailed description of geological cores involved. The study was followed by analysis of grain size, chemical and organic contents, microstructure, and numerical dating. Integrating the available results, we propose a scenario of the fluvial network transformation at the Eastern Borisoglebsk Upland over the last 150 ka. At least four fluvial incision stages were determined while network extent has significantly changed through the Late Pleistocene. Three can be generally associated with the regional base level decrease – Late Moscow, Late Valdai and Late Holocene and accompanying isolation of the Nero Lake terraces of 130 m, 100–105 m and 95–98 m. Incision stages were separated by landscape stability or aggradation periods those were asynchronous at the middle and upper parts of the fluvial network. The main agent of initial valley infill appears to be local lacustrine sedimentation altered by alluvial and colluvial deposition towards the second half of Valdai. Revealed landscape conditions variability emphasize the importance of comprehensive local correlations for regional retrospective models.

Model Simulation and Prediction of Decadal Mountain Permafrost Distribution Based on Remote Sensing Data in the Qilian Mountains from the 1990s to the 2040s

Based on the results of remote sensing data interpretation, this paper aims to simulate and predict the mountain permafrost distribution changes affected by the mean decadal air temperature (MDAT), from the 1990s to the 2040s, in the Qilian Mountains. A bench-mark map is visually interpreted to acquire a mountain permafrost distribution from the 1990s, based on remote sensing images. Through comparison and estimation, a logistical regression model (LRM) is constructed using the bench-mark map, topographic and land coverage factors and MDAT data from the 1990s. MDAT data from the 2010s to the 2040s are predicted according to survey data from meteorological stations. Using the LRM, MDAT data and the factors, the probabilities (p) of decadal mountain permafrost distribution from the 1990s to the 2040s are simulated and predicted. According to the p value, the permafrost distribution statuses are classified as ‘permafrost probable’ (p > 0.7), ‘permafrost possible’ (0.7 ≥ p ≥ 0.3) and ‘permafrost improbable’ (p < 0.3). From the 1990s to the 2040s, the ‘permafrost probable’ type mainly degrades to that of ‘permafrost possible’, with the total area degenerating from 73.5 × 103 km2 to 66.5 × 103 km2. The ‘permafrost possible’ type mainly degrades to that of ‘permafrost impossible’, with a degradation area of 6.5 × 103 km2, which accounts for 21.3% of the total area. Meanwhile, the accuracy of the simulation results can reach about 90%, which was determined by the validation of the simulation results for the 1990s, 2000s and 2010s based on remote sensing data interpretation results. This research provides a way of understanding the mountain permafrost distribution changes affected by the rising air temperature rising over a long time, and can be used in studies of other mountains with similar topographic and climatic conditions.