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1.
Digital soil mapping in a Himalayan watershed using remote sensing and terrain parameters employing artificial neural network model 总被引：2，自引：0，他引：2
Digital soil mapping relies on field observations, laboratory measurements and remote sensing data, integrated with quantitative methods to map spatial patterns of soil properties. The study was undertaken in a hilly watershed in the Indian Himalayan region of Mandi district, Himachal Pradesh for mapping soil nutrients by employing artificial neural network (ANN), a potent data mining technique. Soil samples collected from the surface layer (0–15 cm) of 75 locations in the watershed, through grid sampling approach during the fallow period of November 2015, were preprocessed and analysed for various soil nutrients like soil organic carbon (SOC), nitrogen (N) and phosphorus (P). Spectral indices like Colouration Index, Brightness Index, Hue Index and Redness Index derived from Landsat 8 satellite data and terrain parameters such as Terrain Wetness Index, Stream Power Index and slope using CartoDEM (30 m) were used. Spectral and terrain indices sensitive to different nutrients were identified using correlation analysis and thereafter used for predictive modelling of nutrients using ANN technique by employing feed-forward neural network with backpropagation network architecture and Levenberg–Marquardt training algorithm. The prediction of SOC was obtained with an R2 of 0.83 and mean squared error (MSE) of 0.05, whereas for available nitrogen, it was achieved with an R2 value of 0.62 and MSE of 0.0006. The prediction accuracy for phosphorus was low, since the phosphorus content in the area was far below the normal P values of typical Indian soils and thus the R2 value observed was only 0.511. The attempts to develop prediction models for available potassium (K) and clay (%) failed to give satisfactory results. The developed models were validated using independent data sets and used for mapping the spatial distribution of SOC and N in the watershed. 相似文献
2.
Satellite remote sensing data play an important role in the improvement of climate models forcing field, relevant physical parameters and simulation accuracy. At present, there are many years of satellite remote sensing data and a variety of products about land surface attributes. However, the application of satellite remote sensing data to climate models is still very limited. Fully using satellite remote sensing data is important to improving the simulation ability. In the paper, remote sensing estimates methods of three key land surface parameters including Fractional Vegetation Coverage（FVC）, Leaf Area Index（LAI）and surface albedo（Albedo）is reviewed and up or down scaling land surface variables in validation process is analyzed. Secondly, taking WRF（Weather Research and Forecasting）model as an example, three parameters in climate model are described. Finally, the key problems of using remote sensing data in climate models are discussed, which comprise the uncertainties and scales of remote sensing estimation parameters and the future direction is prospected. 相似文献
3.
As drought occurs in different climates, assessment of drought impacts on parameters such as vegetation cover is of utmost importance. Satellite remote sensing images with various spectral and spatial resolutions represent information about different land covers such as vegetation cover. Hence, the purpose of this study was to investigate the performance of satellite vegetation indices to monitor the agricultural drought on a local scale. In this regard, satellite images including Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR) data were used to evaluate vegetation cover and their gradual changes effects on agricultural drought. Fars province in Iran with relatively low precipitation values was selected as the study area. Modified Perpendicular Drought Index (MPDI), MPDI1, Vegetation Condition Index (VCI), Normalized Difference Vegetation Index Anomalies (NDVIA), and Standardized Vegetation Index (SVI), were evaluated to select the remote sensing based index with the best performance in drought monitoring. The performance of such indices were investigated during 13 years (2000–2013) for MODIS and 29 years (1985–2013) for AVHRR. To assess the efficiency of the satellite indices in drought investigation, Standardized Precipitation Index (SPI) data of five selected stations were used for 3, 6, and 9 month periods on August. The results showed that NDVI-based vegetation indices had the highest correlation with SPI in cold climate and long-term timescale (6 and 9 month). The highest correlation values between remote sensing based indices and SPI were acquired, respectively, in 9-month and 6-month time-scales, with the values of 43.5% and 40%. Moreover, VCI showed the highest capability for agricultural drought investigating in different climate regions of the study area. Overall, the results proved that NDVI-based indices can be used for drought monitoring and assessment in a long-term timescale on a local time-scale.
相似文献4.
《地学前缘(英文版)》2022,13(2):101312
Drought is a natural phenomenon posing severe implications for soil, groundwater and agricultural yield. It has been recognized as one of the most pervasive global change drivers to affect the soil. Soil being a weakly renewable resource takes a long time to form, but it takes no time to degrade. However, the response of soil to drought conditions as soil loss is not manifested in the existing literature. Thus, this study makes a concerted effort to analyze the relationship between drought conditions and soil erosion in the middle sub-basin of the Godavari River in India. MODIS remote sensing data was utilized for driving drought indices during 2000–2019. Firstly, we constricted Temperature condition index (TCI) and Vegetation Condition Index (VCI) from Land Surface Temperature (LST) and Enhanced Vegetation Index (EVI) derived from MODIS data. TCI and VCI were then integrated to determine the Vegetation Health Index (VHI). Revised Universal Soil Loss Equation (RUSLE) was utilized for estimating soil loss. The relationship between drought condition and vegetation was ascertained using the Pearson correlation. Most of the northern and southern watersheds experienced severe drought condition in the sub-basin during 2000–2019. The mean frequency of the drought occurrence was 7.95 months. The average soil erosion in the sub-basin was estimated to be 9.88 t ha?1 year?1. A positive relationship was observed between drought indices and soil erosion values (r value being 0.35). However, wide variations were observed in the distribution of spatial correlation. Among various factors, the slope length and steepness were found to be the main drivers of soil erosion in the sub-basin. Thus, the study calls for policy measures to lessen the impact of drought and soil erosion. 相似文献
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6.
南洞地下河流域南部岩溶石漠化空间分布特征分析 总被引：5，自引：3，他引：2
基于2013年9月29日GF-1号WFV遥感数据对南洞地下河流域蒙自幅、个旧幅、新安幅和鸣鹫幅进行岩溶石漠化遥感解译,通过归一化植被指数（NDVI）和植被覆盖度（FVC）提取研究区域内石漠化信息,得到其空间分布规律。研究区岩溶石漠化发生率74.55%,属石漠化高发地区,其中中度石漠化最为发育,占全区石漠化的32.63%,重度石漠化最少,占17.59%。基于岩性、高程、坡向和坡度等4个影响因素与岩溶石漠化进行相关性分析,对不同因素中石漠化发生率的变化规律进行分析,结果表明南洞地下河流域南部区域岩溶石漠化在低海拔、缓坡度和纯碳酸盐岩中较为发育,在不同坡向中岩溶石漠化发育程度则较为相似。 相似文献
7.
基于地理加权回归的青藏高原季节冻土区土壤有机碳空间分布研究 总被引：1，自引：0，他引：1
青藏高原土壤有机碳储量（soil organic carbon stocks, SOCS）对于区域生态环境演替具有重要作用, 但是其空间分布数据还比较缺乏, 特别是季节冻土区的数据较少。基于378个土壤剖面数据, 结合与土壤有机碳（soil organic carbon, SOC）相关的地形、 气候以及植被等环境因子, 使用地理加权回归（geographically weighted regression, GWR）模型模拟了青藏高原季节冻土区0 ~ 30 cm、 0 ~ 50 cm、 0 ~ 100 cm和0 ~ 200 cm深度的SOC总量和空间分布。结果表明： 青藏高原季节冻土区SOCS自东南向西北递减, 表层0 ~ 200 cm的SOC总量约15.37 Pg; 季节冻土区不同植被类型SOC从大到小依次为森林、 灌丛、 高寒草甸、 高寒草原和高寒荒漠; 各土壤类型中棕壤、 黑钙土和泥炭土的SOC最大, 而棕钙土、 棕漠土、 灰棕漠土、 风沙土、 石质土、 盐土、 冷钙土、 寒漠土以及冷漠土的SOC最小。研究结果给出了青藏高原季节冻土区SOC的总量、 空间分布及规律, 可为相关地球模式的发展提供基础数据。 相似文献
8.
联合机载PLMR微波辐射计和MODIS产品反演黑河中游张掖绿洲土壤水分研究 总被引：1，自引：0，他引：1
土壤水分是气候、水文学研究中的重要变量,微波遥感是获取区域地表土壤水分的重要手段,而L波段更是微波土壤水分反演的最优波段。依托HiWATER黑河中游绿洲试验区的地面观测及机载PLMR微波辐射计亮温数据,利用微波辐射传输模型L-MEB,并将MODIS地表温度产品(MOD11A1)和叶面积指数产品(MYD15A2)作为模型及反演中的先验辅助信息,借助LM优化算法,通过PLMR双极化多角度的亮温观测,针对土壤水分、植被含水量(VWC)和地表粗糙度这3个主要参数,分别进行土壤水分单参数反演、土壤水分与VWC或粗糙度的双参数反演以及这3个参数的同时反演。通过对不同反演方法的比较可以得出结论,多源辅助数据及PLMR双极化、多角度信息的应用可以显著降低反演的不确定性,提高土壤水分反演精度。证明在合理的模型参数和反演策略下,SMOS的L-MEB模型和产品算法可以达到0.04 cm3/cm3的反演精度,另外无线传感器网络可以在遥感产品真实性检验中起到重要作用。 相似文献
9.
Wetlands play an important role in water conservation, environmental protection, and biodiversity conservation. Remote sensing is an economical and efficient technique for wetland monitoring which can limit disturbance in sensitive areas and support wetland conservation. In this paper, we used three phases of Thematic Mapper/Enhanced Thematic Mapper plus (TM/ETM+) remote sensing images from October 1989, October 1999, and October 2009 to study wetlands in Xingzi County. The images were segmented using the object-oriented remote sensing image interpretation software eCognition Developer 8.64, then segmented images were classified by slope, digital elevation model (DEM) data, Normalized Difference Vegetation Index (NDVI), Specific Leaf Area Vegetation Index (SLAVI), and Land and Water Masks (LWM) index to produce land type classification maps. Land use change information was obtained by analyzing the superposition of two classification maps of the wetland area from different years. The results showed that landscape patches in Xingzi County displayed fragmentation in their spatial distribution over time. Based on an index of changes in landscape patches, the fastest growing landscape type is grassland, while the fastest decreasing type is irrigated land. Dominant driving factors of changes in Xingzi County’s wetland landscape are population growth and policy changes. 相似文献
10.
Towards spatial geochemical modelling: Use of geographically weighted regression for mapping soil organic carbon contents in Ireland 总被引：6，自引：0，他引：6
It is challenging to perform spatial geochemical modelling due to the spatial heterogeneity features of geochemical variables. Meanwhile, high quality geochemical maps are needed for better environmental management. Soil organic C (SOC) distribution maps are required for improvements in soil management and for the estimation of C stocks at regional scales. This study investigates the use of a geographically weighted regression (GWR) method for the spatial modelling of SOC in Ireland. A total of 1310 samples of SOC data were extracted from the National Soil Database of Ireland. Environmental factors of rainfall, land cover and soil type were investigated and included as the independent variables to establish the GWR model. The GWR provided comparable and reasonable results with the other chosen methods of ordinary kriging (OK), inverse distance weighted (IDW) and multiple linear regression (MLR). The SOC map produced using the GWR model showed clear spatial patterns influenced by environmental factors and the smoothing effect of spatial interpolation was reduced. This study has demonstrated that GWR provides a promising method for spatial geochemical modelling of SOC and potentially other geochemical parameters. 相似文献
11.
Local-scale spatial variability of soil organic carbon and its stock in the hilly area of the Loess Plateau, China 总被引：3，自引：0，他引：3
Soil organic carbon (SOC) is one of the key components for assessing soil quality. Meanwhile, the changes in the stocks SOC may have large potential impact on global climate. It is increasingly important to estimate the SOC stock precisely and to investigate its variability. In this study, Yangjuangou watershed was selected to investigate the SOC distribution under different land uses. We found that SOC concentration decreased with increasing soil depth under all land uses and was significantly different across the vertical soil profile (P < 0.01). However, considering effect of land use on SOC, it is only significant (P < 0.01) in the topsoil (0-5 cm) layer. This indicated that land use has a large effect on the stocks of SOC in the surface soil. The stratification ratio of SOC > 1.2 may mean that soil quality is improving. The order of the SOC density (0-30 cm) under different land uses is forestland > orchard land > grassland > immature forestland > terraced cropland. The SOC stock is found to be as large as 2.67 × 103 t (0-30 cm) in this watershed. Considering time effect of restoration, the slope cropland just abandoned is more efficient for SOC accumulation than trees planted in the semi-arid hilly loess area. 相似文献
12.
Syed Muhammad Zubair Younis Muhammad Majid Ahmad Ammar 《Arabian Journal of Geosciences》2017,10(18):404
The impacts of floods and droughts are intensified by climate change, lack of preparedness, and coordination. The average rainfall in study area is ranging from 200 to 400 mm per year. Rain gauge generally provides very accurate measurement of point rain rates and the amounts of rainfall but due to scarcity of the gauge locations provides very general information of the area on regional scale. Recognizing these practical limitations, it is essential to use remote sensing techniques for measuring the quantity of rainfall in the Middle Indus. In this research, Tropical Rainfall Measuring Mission (TRMM) estimation can be used as a proxy for the magnitude of rainfall estimates from classical methods (rain gauge), quantity, and its spatial distribution for Middle Indus river basin. In order to use TRMM satellite data for discharge measurement, its accuracy is determined by statistically comparing it with in situ gauged data on daily and monthly bases. The daily R 2 value (0.42) is significantly lower than monthly R 2 value (0.82), probably due to the time of summation of TRMM 3-hourly precipitation data into daily estimates. Daily TRMM data from 2003 to 2012 was used as input forcing in Soil and Water Assessment Tool (SWAT) hydrological model along with other input parameters. The calibration and validation results of SWAT model give R 2 = 0.72 and 0.73 and Nash-Sutcliffe coefficient of efficiency = 0.69 and 0.65, respectively. Daily and monthly comparison graphs are generated on the basis of model discharge output and observed data. 相似文献
13.
土壤有机碳的主导影响因子及其研究进展 总被引：61，自引：0，他引：61
土壤有机碳库是全球碳循环的重要组成部分，其积累和分解的变化直接影响全球的碳平衡。理解土壤有机碳蓄积过程对生物、物理和人为因素的响应，把握关键的控制因子是准确预测土壤有机碳在全球变化情景下对大气CO 2的源／汇方向及准确评估碳收支的关键。综述了土壤有机碳主导影响因子的研究进展，并针对陆地碳循环特点，提出未来土壤有机碳研究应加强土壤有机碳过程与状态的定量化、土壤有机碳分解对环境因子的敏感性、氮沉降对土壤有机碳的影响、土壤有机碳对气候变率的响应及其反馈作用，以及土壤有机碳动态的综合模拟 5个方面的研究，为准确评估陆地碳收支提供依据。 相似文献
14.
利用多元逐步回归分析法,结合Landsat8 OLI遥感数据对该地区土壤有机碳进行定量反演.试验采集了164个土壤样品,通过3倍标准差准则对样品进行奇异点去除及数据集划分,其中120个样品作为训练集,44个样品作为验证集,建立土壤有机碳的多元逐步回归预测模型.结果表明：有机碳与Landsat8各波段反射率均显著相关;黑土有机碳光谱预测最优模型以倒数为自变量模型最优,决定系数R2=0.180,均方根误差RMSE=0.558,海伦地区适于Corg含量遥感反演,预测模型稳定性好,可以用于揭示黑土典型区Corg含量的空间分布特征.同时认为在不对土壤进行地面光谱测试的情况下,直接采用化学分析数据与遥感卫星相关联的方法预测模型拟合度有限,光谱对有机碳可解释性较低. 相似文献
15.
Soil erosion assessment and its verification using the Universal Soil Loss Equation and Geographic Information System: a case study at Boun,Korea 总被引：10，自引：0，他引：10
This study is aimed at the evaluation of the hazard of soil erosion and its verification at Boun, Korea, using a Geographic Information System (GIS) and remote sensing. Precipitation, topographic, soil, and land use data were collected, processed, and constructed into a spatial database using GIS and remote sensing data. Areas that had suffered soil erosion were analysed and mapped using the Universal Soil Loss Equation (USLE). The factors that influence soil erosion are rainfall erosivitiy (R) from the precipitation database, soil erodibility (K) from the soil database, slope length and steepness (LS) from the topographic database, and crop and management (C) and conservation supporting practices (P) from the land use database. Land use was classified from Landsat Thematic Mapper satellite images. The soil erosion map verified use of the landslide location data. Landslide locations were identified in the Boun area from interpretation of aerial photographs and field surveys. 相似文献
16.
Vegetation is a natural source of Volatile Organic Compounds (VOC) that plays an important role in atmospheric chemistry. The main objective of the current study is to implement a model to quantify process-based VOC emissions from plants that focuses on the relationship between the sensitivity of VOC emission estimates to spatial resolution data, based on scientific knowledge and vegetation dynamics derived from satellite observations. The Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI) were elected to examine this issue using different resolutions of satellite-derived products: 22m from the DEIMOS-1 satellite, and 250m and 1000m provided by MODIS. The study is focused on an area of 80×80km2 in Portugal for 2011. Detailed land cover and meteorological data are also included in the emission quantification algorithm. The primary outcomes were determined using a multi-scale analysis showing spatial and temporal variations in the vegetation parameters and modeling results. The results confirm that the emissions model is highly sensitive to the spatial resolution of the satellite-derived data, resulting in about a 30% difference in total isoprene emissions for the study area. 相似文献
17.
《地学前缘(英文版)》2015,6(6)
The governing factors that influence landslide occurrences are complicated by the different soil conditions at various sites.To resolve the problem,this study focused on spatial information technology to collect data and information on geology.GIS,remote sensing and digital elevation model(DEM) were used in combination to extract the attribute values of the surface material in the vast study area of SheiPa National Park,Taiwan.The factors influencing landslides were collected and quantification values computed.The major soil component of loam and gravel in the Shei-Pa area resulted in different landslide problems.The major factors were successfully extracted from the influencing factors.Finally,the discrete rough set(DRS) classifier was used as a tool to find the threshold of each attribute contributing to landslide occurrence,based upon the knowledge database.This rule-based knowledge database provides an effective and urgent system to manage landslides.NDVI(Normalized Difference Vegetation Index),VI(Vegetation Index),elevation,and distance from the road are the four major influencing factors for landslide occurrence.The landslide hazard potential diagrams(landslide susceptibility maps) were drawn and a rational accuracy rate of landslide was calculated.This study thus offers a systematic solution to the investigation of landslide disasters. 相似文献
18.
The relationship between land surface temperature and land use/land cover in Guangzhou,China 总被引：1，自引：1，他引：0
The integration of remote sensing, geographic information system, landscape ecology and statistical analysis methods was applied
to study the urban thermal environment in Guangzhou. Normalized Difference Vegetation Index (NDVI), Normalized Difference
Build-up Index (NDBI), Normalized Difference Barren Index (NDBaI) and Modified Normalized Difference Water Index (MNDWI) were
used to analyze the relationships between land surface temperature (LST) and land use/land cover (LULC) qualitatively. The
result revealed that, most urban built-up lands were located in the middle part, and high LST areas mostly and were in the
middle and southern parts. Therefore, the urbanization and thermal environment in the middle and southern parts need to be
determined. Land surface temperature increased with the density of urban built-up and barren land, but decreased with vegetation
cover. The relationship between MNDWI and LST was found to be negative, which implied that pure water would decrease the surface
temperature and the polluted water would increase the surface temperature. A multiple regression between LST and each indices
as well as the elevation was created to elevate the urban thermal environment, which showed that NDVI, NDBI, NDBaI, MNDWI
were effective indicators for quantifying LULC impacts on LST. 相似文献
19.
Identifying effective vegetation biophysical and spectral parameters for investigating light to moderate grazing effects on grasslands improves management practices on grasslands. Using mixed grasslands as a case study, this paper compares responses of vegetation biophysical properties and spectral parameters derived from satellite images to grazing intensity, and identifies the suitable biophysical and spectral parameters to detect grazing effects in these areas. Biophysical properties including cover, canopy height and Leaf area index (LAI) were measured in three sites with different grazing managements and one benchmark site in 2008 and 2009 in Grasslands PlaceTypeNational Park and surrounding provincial pastures, Canada. Thirteen vegetation spectral indices, calculated by statistically combining different spectral information, were evaluated. The results indicate that canopy height and the ratio of photosynthetically active vegetation cover to non-photosynthetically active vegetation cover (PV/NPV) showed significant differences between ungrazed and grazed sites. All spectral vegetation indices except the canopy index (CI) show significant differences between grazing treatments. Red-Near infrared (Red-NIR) based vegetation indices, such as Modified Triangular Vegetation Index 1 (MTVI1), Soil-adjusted Vegetation Index (SAVI), are significantly correlated to the PV/NPV. Green/Mid-infrared (Green/MIR) related vegetation indices, i.e. Plant Senescence Reflectance Index (PRSI) and Normalized Canopy Index (NCI), show significant correlation with canopy height. Models based on a linear combination of MTVI1 and SAVI were developed for PV/NPV and PRSI and NCI for canopy height. Models that simulated PV/NPV and canopy height show significant correlations with grazing intensity, suggesting the feasibility of remote sensing to quantify light to moderate grazing effects in mixed grasslands. 相似文献
20.
Landslides during earthquakes have led to severe casualties and have resulted in damaged structures and facilities. The goal
of the present study is to analyze the landslide problems in a remote area—Shei-Pa National Park in Taiwan. Spatial information
techniques (Remote Sensing and Geographic Information System) with an innovative data mining technique, Discrete Rough Set
(DRS) method, are incorporated to our study for analyzing landslides, their distribution, and classification. The present
study provides how to find (1) the most representative data of landslide samples from the existing database, (2) the core
attributes of the target categories: Normalized Difference Vegetation Index (NDVI) and Vegetation Index (VI), and (3) the
thresholds (segment points) of each attribute on the target categories. A conventional approach, C4.5 Decision Tree Analysis,
is used as a comparison. The methodology discussed in this study is of help to the analysis of landslide problems and thus
facilitates the informed decision-making process. 相似文献