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1.
土壤颗粒大小差异使土壤反射光谱产生相应变化,影响土壤有机质含量等属性的光谱预测精度。本研究准备了颗粒粒径分别为2、0.25和0.15mm的土样,测定土壤有机质(Soil Organic Matter,SOM)含量,并于室内模拟条件下测定其反射光谱。通过分析不同粒径土样的原始(Raw)、多次散射校正(Multiple scattering correction, Msc)、一阶微分(First derivative, Fd)、连续统去除(Continuum removal, Cr)光谱与SOM含量之间的关系,筛选出与SOM含量相关性最强的Fd光谱单波段(2250nm, r=0.82, P<0.01),并建立线性回归模型;利用全波段光谱反射率,以偏最小二乘回归(Partial least square regression, PLSR)方法,确立2mm土样Msc处理光谱的PLSR模型为最优模型(RPD=3.56、R2=0.90、RMSEP=1.96g/kg)。土壤颗粒粒径对土壤光谱反射率变化有明显影响,但二者之间并非简单的线性关系,可能存在一个转折点;单变量(单波段光谱反射率)线性回归模型的预测能力,明显低于全波段反射光谱(Msc处理)-PLSR模型;土样样本容量对SOM含量预测精度有显著影响。因此,根据样本容量大小,选择合适的土壤颗粒粒径与光谱预处理方法组合可以提高预测精度。 相似文献
2.
《中国地理科学(英文版)》2019,(5)
Timely monitoring and early warning of soil salinity are crucial for saline soil management. Environmental variables are commonly used to build soil salinity prediction model. However, few researches have been done to summarize the environmental sensitive variables for soil electrical conductivity(EC) estimation systematically. Additionally, the performance of Multiple Linear Regression(MLR), Geographically Weighted Regression(GWR), and Random Forest regression(RFR) model, the representative of current main methods for soil EC prediction, has not been explored. Taking the north of Yinchuan plain irrigation oasis as the study area, the feasibility and potential of 64 environmental variables, extracted from the Landsat 8 remote sensed images in dry season and wet season, the digital elevation model, and other data, were assessed through the correlation analysis and the performance of MLR, GWR, and RFR model on soil salinity estimation was compared. The results showed that: 1) 10 of 15 imagery texture and spectral band reflectivity environmental variables extracted from Landsat 8 image in dry season were significantly correlated with soil EC, while only 3 of these indices extracted from Landsat 8 image in wet season have significant correlation with soil EC. Channel network base level, one of the terrain attributes, had the largest absolute correlation coefficient of 0.47 and all spatial location factors had significant correlation with soil EC. 2) Prediction accuracy of RFR model was slightly higher than that of the GWR model, while MLR model produced the largest error. 3) In general, the soil salinization level in the study area gradually increased from south to north. In conclusion, the remote sensed imagery scanned in dry season was more suitable for soil EC estimation, and topographic factors and spatial location also play a key role. This study can contribute to the research on model construction and variables selection for soil salinity estimation in arid and semiarid regions. 相似文献
3.
《山地科学学报》2020,17(7):1636-1651
The soil carbon pool which is the sum of soil organic carbon(SOC) and soil inorganic carbon(SIC) is the second largest active store of carbon after the oceans and it is an important component of the global carbon cycle. Hence, accurate estimation of SOC and SIC as important carbon reservoirs in terrestrial ecosystems using fast, inexpensive and non-destructive methods is crucial for planning different climate change policies. The aim of the current research was to examine the effectiveness of Vis-NIR(visible and near-infrared spectroscopy: 350-2500 nm) and MIR(mid-infrared spectroscopy: 4000-400 cm~(-1)) to characterize and estimate soil organic matter(SOM) and carbonates as main components of soil carbon stocks in Juneqan, Charmahal va Bakhtiari, Iran. To do so, a total of 548 soil samples from this area were collected(October 2015) and analyzed in laboratory(August 2017). In order to develop models capable of predicting SOM and carbonates content, seven spectral preprocessing methods comprising Absorbance(Abs), De-trending(Det), Continuum removal(CR), Savitzky-Golay derivatives(SGD), standard normal variate transformation(SNV), multiplicative scatter correction(MSC) and Normalization by range(NBR) were conducted along with five multivariate methods including Random Forest(RF), Partial Least-Squares Regression(PLSR), Artificial Neural Network(ANN), Support Vector Machine(SVM) and Gaussian Process Regression(GPR). The content of carbonates caused spectral reflectance intensity to augment on several ranges of spectrum and strong absorption feature at 2338 nm in the Vis-NIR and 714, 850, 870, 1796, 2150 and 2510 cm~(-1) in the MIR spectra range. SOM absorbed energy in several ranges, but also showed specific peaks in MIR. Both facts are associated with the structure of carbonates and SOM and its interaction with energy. The best combination of preprocessing and calibration models for carbonates quantification in Vis-NIR spectra was Det/PLSR(R~2= 0.74, RPD= 2.19, RMSE= 6.45). For SOM, it was Det/PLSR(R~2= 0.82, RPD= 2.41, RMSE= 0.75). The Det/RF(R~2= 0.87, RPD= 2.44, RMSE= 0.66) for the quantification of SOM and MSC/RF(R~2= 0.84, RPD= 2.84, RMSE= 5.50) for carbonates in MIR spectra range showed the greatest results. The stronger occurrence of spectral bands in MIR as well as the specificity of the absorption features indicated that this range produced better predictions. The obtained results highlighted the significant role of soil spectroscopy technique in predicting SOC and soil carbonates as key components of soil carbon stocks in the study area. Therefore, this technique can be used as a more cost-effective, time saving and nondestructive alternative to traditional methods of soil analysis. 相似文献
4.
《山地科学学报》2020,17(9):2214-2223
Soil moisture is essential for plant growth in terrestrial ecosystems. This study investigated the visible-near infrared(Vis-NIR) spectra of three subgroups of purple soils(calcareous, neutral, and acidic) from western Chongqing, China, containing different water contents. The relationship between soil moisture and spectral reflectivity(R) was analyzed using four spectral transformations, and estimation models were established for estimating the soil moisture content(SMC) of purple soil based on stepwise multiple linear regression(SMLR) and partial least squares regression(PLSR). We found that soil spectra were similar for different moisture contents, with reflectivity decreasing with increasing moisture content and following the order neutral calcareous acidic purple soil(at constant moisture content). Three of the four spectral transformations can highlight spectral sensitivity to SMC and significantly improve the correlation between the reflectance spectra and SMC. SMLR and PLSRmethods provide similar prediction accuracy. The PLSR-based model using a first-order reflectivity differential(R ?) is more effective for estimating the SMC, and gave coefficient of determination(v2), root mean square errors of validation(RMSEV), and ratio of performance to inter-quartile distance(RPIQ)values of 0.946, 1.347, and 6.328, respectively, for the calcareous purple soil, and 0.944, 1.818, and 6.569,respectively, for the acidic purple soil. For neutral purple soil, the best prediction was obtained using the SMLR method with R ? transformation, yieldingv2,RMSEV and RPIQ values of 0.973, 0.888 and 8.791,respectively. In general, PLSR is more suitable than SMLR for estimating the SMC of purple soil. 相似文献
5.
Soil macronutrients(i.e. nitrogen(N), phosphorus(P), and potassium(K)) are important soils components and knowing the spatial distribution of these parameters are necessary at precision agriculture. The purpose of this study was to evaluate the feasibility of different methods such as artificial neural networks(ANN) and two geostatistical methods(geographically weighted regression(GWR) and cokriging(CK)) to estimate N, P and K contents. For this purpose, soil samples were taken from topsoil(0–30 cm) at 106 points and analyzed for their chemical and physical parameters. These data were divided into calibration(n = 84) and validation(n = 22). Chemical and physical variables including clay, p H and organic carbon(OC) were used as auxiliary soil variables to estimate the N, P and K contents. Results showed that the ANN model(with coefficient of determination R~2 = 0.922 and root mean square error RMSE = 0.0079%) was more accurate compared to the CK model(with R~2 = 0.612 and RMSE = 0.0094%), and the GWR model(with R~2 = 0.872 and RMSE = 0.0089%) to estimate the N variable. The ANN model estimated the P with the RMSE of 3.630 ppm, which was respectively 28.93% and 20.00% less than the RMSE of 4.680 ppm and 4.357 ppm from the CK and GWR models. The estimated K by CK, GWR and ANN models have the RMSE of 76.794 ppm, 75.790 ppm and 52.484 ppm. Results indicated that the performance of the CK model for estimation of macro nutrients(N, P and K) was slightly lower than the GWR model. Also, the accuracy of the ANN model was higher than CK and GWR models, which proved to be more effective and reliable methods for estimating macro nutrients. 相似文献
6.
Modeling of spatial distributions of farmland density and its temporal change using geographically weighted regression model 总被引：1，自引：1，他引：0
Haitao Zhang Long Guo Jiaying Chen Peihong Fu Jianli Gu Guangyu Liao 《中国地理科学(英文版)》2014,24(2):191-204
This study used spatial autoregression（SAR） model and geographically weighted regression（GWR） model to model the spatial patterns of farmland density and its temporal change in Gucheng County,Hubei Province,China in 1999 and 2009,and discussed the difference between global and local spatial autocorrelations in terms of spatial heterogeneity and non-stationarity. Results showed that strong spatial positive correlations existed in the spatial distributions of farmland density,its temporal change and the driving factors,and the coefficients of spatial autocorrelations decreased as the spatial lag distance increased. SAR models revealed the global spatial relations between dependent and independent variables,while the GWR model showed the spatially varying fitting degree and local weighting coefficients of driving factors and farmland indices（i.e.,farmland density and temporal change）. The GWR model has smooth process when constructing the farmland spatial model. The coefficients of GWR model can show the accurate influence degrees of different driving factors on the farmland at different geographical locations. The performance indices of GWR model showed that GWR model produced more accurate simulation results than other models at different times,and the improvement precision of GWR model was obvious. The global and local farmland models used in this study showed different characteristics in the spatial distributions of farmland indices at different scales,which may provide the theoretical basis for farmland protection from the influence of different driving factors. 相似文献
7.
光谱数据变换对消除背景、噪音影响以及提取光谱特征有重要的作用,是光谱数据分析过程中的必要步骤。为了研究光谱变换处理对土壤氮素PLSR模型的影响精度,并选择最佳光谱变换处理方法,本文对原始光谱数据进行了15种典型光谱变换,通过比较不同变换光谱与土壤氮素的相关性,实现土壤氮素的PLSR精确诊断,并综合评定最佳光谱数据变换方法。结果表明,涉及微分处理后的光谱变换,尤其是先进行开方（T8、T11）、对数（T6、T12）等变换后再进行微分处理,可提高其与土壤氮素的相关性。在引入较少因子变量个数的条件下,该方法使因变量解释量达到了98%。综合考虑模型的校正、验证效果及模型复杂度（模型最佳因子变量个数）,可得出光谱平方根的一阶微分变换处理（T8）为最佳的土壤光谱变换算法。该条件下的土壤氮素的校正模型表现为R2=0.985、RMSEC=0.000132、Fn=6,验证模型的表现为R2=0.9853、RMSEV=0.000162,结果表明基于T8的光谱数据变换可实现本试验条件下土壤氮素的光谱估算。另外,可以考虑将原始光谱的一阶微分（T9）、对数和对数倒数的一阶微分（T6、T7）以及平方根和对数的二阶微分（T11、T12）作为光谱数据变换方法。本文研究结果可为土壤氮素估算和光谱数据预处理提供技术参考。 相似文献
8.
人类活动对表层耕地土壤有机碳（Soil Organic Carbon, SOC）影响强烈,但目前大范围复杂地貌地形区的耕地SOC数字制图对人为因素的空间刻画不足。本文以福建省西部耕地为研究对象,基于Sentinel-2/MSI时间序列数据提取轮作模式分类信息（Crop Rotation, CR）,以及可反映轮作模式信息的植被特征变换变量（Harmonic Analysis of Time Series, HANTS）,分别作为农业活动定性和定量因子,将常规气候和地形因子作为自然环境因子,并对不同类型环境变量进行组合（气候+地形、气候+地形+轮作模式、气候+地形+HANTS变量、气候+地形+轮作模式+HANTS变量）。基于随机森林模型（Random Forest, RF）对不同环境变量组合驱动的耕地表层SOC空间预测精度进行对比分析,探索以轮作模式为例的农业活动因子提高耕地表层SOC数字制图精度的可能性。结果表明,同时加入两种农业活动因子的RF模型表现最佳,其模型预测精度相较于纯自然环境变量驱动的模型有明显提高（R2提高了89.47%,RMSE和MAE分别下降了10.66%和12.05%）。轮作模式类型（CR）和HANTS变量两种农业活动因子均被保留参与建模,尤其是轮作模式类型显著影响耕地SOC,在最佳模型的环境变量重要性中排序第四。由此可见,轮作模式相关农业活动因子可有效提高耕地SOC空间预测精度。在所有RF模型中,年降水量（Annual Rainfall, Rainfall）的重要性排名都是第一位。通过最佳模型反演得出该区耕地土壤有机碳均值为18.22±2.99 g/kg,范围为8.25~30.69 g/kg,双季稻和烟稻种植区域SOC含量高于稻菜种植区域。研究结果为复杂地貌地形区耕地土壤有机碳协同变量的更新提供了新的思路。 相似文献
9.
典型地物波谱库的数据体系与波谱模拟 总被引：21，自引：0，他引：21
本文讨论典型地物波谱知识库的数据体系与地物波谱模拟的相关问题 ,并给出波谱知识库支持的农业定量遥感应用示例。波谱知识库的数据体系强调波谱参数与环境参数的配套 ,波谱数据测量是在相关规范的支持下完成的 ,质量控制贯穿于数据采集的全过程。波谱模拟通过遥感物理模型完成 ,模拟波谱计算包括地表参数的时间、空间扩展与遥感物理模型运算。最后以作物生长模型结合植被组分光谱模型和冠层遥感模型为核心 ,构造了定量遥感的农业示例。 相似文献
10.
This study aims to provide a predictive vegetation mapping approach based on the spectral data, DEM and Generalized Additive Models (GAMs). GAMs were used as a prediction tool to describe the relationship between vegetation and environmental variables, as well as spectral variables. Based on the fitted GAMs model, probability map of species occurrence was generated and then vegetation type of each grid was defined according to the probability of species occurrence. Deviance analysis was employed to test the goodness of curve fitting and drop contribution calculation was used to evaluate the contribution of each predictor in the fitted GAMs models. Area under curve (AUC) of Receiver Operating Characteristic (ROC) curve was employed to assess the results maps of probability. The results showed that: 1) AUC values of the fitted GAMs models are very high which proves that integrating spectral data and environmental variables based on the GAMs is a feasible way to map the vegetation. 2) Prediction accuracy varies with plant community, and community with dense cover is better predicted than sparse plant community. 3) Both spectral variables and environmental variables play an important role in mapping the vegetation. However, the contribution of the same predictor in the GAMs models for different plant communities is different. 4) Insufficient resolution of spectral data, environmental data and confounding effects of land use and other variables which are not closely related to the environmental conditions are the major causes of imprecision. 相似文献
11.
On the basis of the soil environment investigation in Da'an City, Jilin Province, China, 40 soil samples from main land use types were obtained and tested by standard method. Soil organic matter (SOM), total N (TN), total P (TP), total K (TK), available N (AN), available P (AP) and available K (AK) were chosen as the evaluation factors. A regional soil nutrient evaluation model was developed based on the matter-element model. The results show that the soil samples with nutrient grade Ⅱ-Ⅴ respectively account for 10%, 30%, 32.5% and 27.5%, and those with grade Ⅳ and Ⅴ account for 60% in all samples. The relationship between soil nutrients and land types indicates that the nutrients of farmland are relatively good, with 41.7% of soil samples with the nutrient grade Ⅳ and Ⅴ. The nutrients of saline-alkali land and sandy land are the worst, with 100% of soil samples with the nutrient grade Ⅳ and Ⅴ. And the ratios of soil samples grade Ⅳ and Ⅴ in grassland and wasteland are respectively 62.5 % and 54.55%. Generally speaking, the soil nutrients status in Da'an City is poor, 60% of soil samples are in poor and extremely poor conditions, indicating that the soil has been severely eroded. Being a relatively superior evaluation method with more accurate resuits and spatial distribution consistency, matter-element analysis is more suitable for regional soil nutrient evaluation than previous models. 相似文献
12.
Hyper-spectral data is widely used to determine soil properties. However, few studies have explored the soil spectral characteristics as response to soil erosion. This study analysed the spectral response of different eroded soils in subtropical China, and then identify the spectral characteristics and soil properties that better discriminate softs with different erosion degrees. Two methods were compared： direct identification by inherent spectral characteristics and indirect identification by predictions of critical soft properties. Results showed that the spectral curves for different degrees of erosion were similar in morphology, while overall reflectance and characteristics of specific absorption peaks were different. When the first method is applied, some differences among different eroded groups were found by integration of associated indicators. However, the index of such indicators showed apparent mixing and crossover among different groups, which reduced the accuracy of identification. For the second method, the correlation between critical soil properties, such as soil organic matter （SOM）, iron and aluminium oxides and reflectance spectra, was analysed. The correlation coefficients for the moderate eroded group were primarily between -0.3 to -0.5, which were worse than the other twogroups. However, the maximum value of R2 was obtained as 0.86 and 0.94 for the non-apparent eroded and the severe group. Furthermore, these two groups also showed some differences in the spectral response of iron complex state （Fep）, Aluminium amorphous state （Alo） and the modelling results for soil organic matter （SOM）. The study proved that it is feasible to identify different degrees of soil erosion by hyperspectral data, and that indirect identification by modelling critical soil properties and reflectance spectra is much better than direct identification. These results indicate that hyper-spectral data may represent a promising tool in monitoring and modelling soil erosion. 相似文献
13.
Driving factors for forest fire occurrence in Durango State of Mexico: A geospatial perspective 总被引：3，自引：2，他引：1
Diana Avila-Flores Marin Pompa-Garcia Xanat Antonio-Nemiga Dante A Rodriguez-Trejo Eduardo Vargas-Perez Javier Santillan-Perez 《中国地理科学(英文版)》2010,20(6):491-497
Forest fire is one of the major causes of forest loss and therefore one of the main constraints for sustainable forest management
worldwide. Identifying the driving factors and understanding the contribution of each factor are essential for the management
of forest fire occurrence. The objective of this study is to identify variables that are spatially related to the occurrence
and incidence of the forest fire in the State of Durango, Mexico. For this purpose, data from forest fire records for a five-year
period were analyzed. The spatial correlations between forest fire occurrence and intensity of land use, susceptibility of
vegetation, temperature, precipitation and slope were tested by Geographically Weighted Regression (GWR) method, under an
Ordinary Least Square estimator. Results show that the spatial pattern of the forest fire in the study area is closely correlated
with the intensity of land use, and land use change is one of the main explanatory variables. In addition, vegetation type
and precipitation are also the main driving factors. The fitting model indicates obvious link between the variables. Forest
fire was found to be the consequence of a particular combination of the environmental factors, and when these factors coexist
with human activities, there is high probability of forest fire occurrence. Mandatory regulation of human activities is a
key strategy for forest fire prevention. 相似文献
14.
《山地科学学报》2021,(6)
Land use intensity is a valuable concept to understand integrated land use system, which is unlike the traditional approach of analysis that often examines one or a few aspects of land use disregarding multidimensionality of the intensification process in the complex land system. Land use intensity is based on an integrative conceptual framework focusing on both inputs to and outputs from the land. Geographers' non-stationary data-analysis technique is very suitable for most of the spatial data analysis. Our study was carried out in the northeast part of the Andhikhola watershed lying in the Middle Hills of Nepal, where over the last two decades, heavy loss of labor due to outmigration of rural farmers and increasing urbanization in the relatively easy accessible lowland areas has caused agricultural land abandonment. Our intention in this study was to ascertain factors of spatial pattern of intensity dynamism between human and nature relationships in the integrated traditional agricultural system. High resolution aerial photo and multispectral satellite image were used to derive data on land use and land cover. In addition, field verification, information collected from the field and census report were other data sources. Explanatory variables were derived from those digital and analogue data. Ordinary Least Square(OLS) technique was used for filtering of the variables. Geographically Weighted Regression(GWR) model was used to identify major determining factors of land use intensity dynamics. Moran's I technique was used for model validation. GWR model was executed to identify the strength of explanatory variables explaining change of land use intensity. Accordingly, 10 variables were identified having the greatest strength to explain land use intensity change in the study area, of which physical variables such as slope gradient, temperature and solar radiation revealed the highest strength followed by variables of accessibility and natural resource. Depopulation in recent decades has been a major driver of land use intensity change but spatial variability of land use intensity was highly controlled by physical suitability, accessibility and availability of natural resources. 相似文献
15.
淮河流域上消化道肿瘤与环境污染的模型分析 总被引：1，自引：0，他引：1
自20世纪70年代后期以来,淮河流域不断遭受工业点源污染和其他面源污染,媒体也陆续报道了淮河流域"癌症村"的出现。本文探讨了淮河流域14个监测县5810个行政村的消化道肿瘤与环境因子之间的空间分布规律。作者从流域和行政区划等多维空间角度出发,通过全局的最小二乘法线性回归和稳健回归对环境因子进行筛选分析,以局部地理加权回归方法探测各类环境因子,在不同地区对贝叶斯调整的上消化道肿瘤死亡率的影响程度,建立了消化道肿瘤死亡的风险评估模型,其中,包括地表水水质等级、浅层地下水质量分级、河网密度、土壤多环芳烃含量分级、化肥施用量和经济密度等6类环境危险因素。根据局部回归模型中各监测点环境因子的回归系数和统计学检验结果,提取出当地主要的环境影响因素。从14个监测县区总体上看,地表水水质等级和GDP与肿瘤呈负相关,其他环境因子均与肿瘤死亡存在正相关。但从局部角度看,不同地区环境影响因子种类和影响强度有较大差别。其中淮河流域江苏段以化肥施用量、土壤多环芳烃含量、GDP和河网密度为主要影响因子,安徽段以土壤多环芳烃含量和化肥为主,河南段主要是以地下水质量分级、河网密度和化肥为主,同时河南沈丘县地表水水质等级对当地影响较大。山东段虽然也探测出来部分环境危险因子的存在,但没有发现其与肿瘤死亡的关联关系,尚需进一步深化研究。 相似文献
16.
土壤粒径的光谱响应特性研究 总被引：1，自引：0，他引：1
以实验室制备的5个不同粒径水平的土壤样本和室内高光谱数据为基础,通过对光谱数据进行重采样、数学变换等预处理并进行单因素方差分析、相关性分析和回归分析,探讨土壤粒径的高光谱特性,建立了光谱数据预测土壤粒径的校正模型。结果表明,土壤粒径对反射光谱有显著的影响,波长越长影响越大;在全波段范围内土壤粒径和光谱数据都呈负相关关系,对原始光谱数据进行微分变换能增加其与土壤粒径的相关性;以反射率一阶微分建立的回归模型为反演土壤粒径的最佳模型,其建模决定系数■、预测决定系数■、预测相对偏差RPD分别为0.666,0.653,2.043,预测均方根误差RMSE为0.175。 相似文献
17.
18.
The sub-pixel impervious surface percentage(SPIS) is the fraction of impervious surface area in one pixel,and it is an important indicator of urbanization.Using remote sensing data,the spatial distribution of SPIS values over large areas can be extracted,and these data are significant for studies of urban climate,environment and hydrology.To develop a stabilized,multi-temporal SPIS estimation method suitable for typical temperate semi-arid climate zones with distinct seasons,an optimal model for estimating SPIS values within Beijing Municipality was built that is based on the classification and regression tree(CART) algorithm.First,models with different input variables for SPIS estimation were built by integrating multi-source remote sensing data with other auxiliary data.The optimal model was selected through the analysis and comparison of the assessed accuracy of these models.Subsequently,multi-temporal SPIS mapping was carried out based on the optimal model.The results are as follows:1) multi-seasonal images and nighttime light(NTL) data are the optimal input variables for SPIS estimation within Beijing Municipality,where the intra-annual variability in vegetation is distinct.The different spectral characteristics in the cultivated land caused by the different farming characteristics and vegetation phenology can be detected by the multi-seasonal images effectively.NLT data can effectively reduce the misestimation caused by the spectral similarity between bare land and impervious surfaces.After testing,the SPIS modeling correlation coefficient(r) is approximately 0.86,the average error(AE) is approximately 12.8%,and the relative error(RE) is approximately 0.39.2) The SPIS results have been divided into areas with high-density impervious cover(70%–100%),medium-density impervious cover(40%–70%),low-density impervious cover(10%–40%) and natural cover(0%–10%).The SPIS model performed better in estimating values for high-density urban areas than other categories.3) Multi-temporal SPIS mapping(1991–2016) was conducted based on the optimized SPIS results for 2005.After testing,AE ranges from 12.7% to 15.2%,RE ranges from 0.39 to 0.46,and r ranges from 0.81 to 0.86.It is demonstrated that the proposed approach for estimating sub-pixel level impervious surface by integrating the CART algorithm and multi-source remote sensing data is feasible and suitable for multi-temporal SPIS mapping of areas with distinct intra-annual variability in vegetation. 相似文献
19.
《中国地理科学(英文版)》2021,(5)
Rapid urbanization leads to dramatic changes in land use patterns, and the land use/cover change(LUCC) can reflect the spatial impact of urbanization on the ecological environment. Simulating the process of LUCC and predicting the ecological risk future changes can provide supports for urban ecological management. Taking the Yangtze River Delta Urban Agglomeration(YRDUA),China as the study area, four developmental scenarios were set on the basis of the land use data from 2005 to 2015. The temporal land use changes were predicted by the integration of the system dynamic and the future land use simulation(SD-FLUS) model, and the geographically weighted regression(GWR) model was used to identify the spatial heterogeneity and evolution characteristics between ecological risk index(ERI) and socio-economic driving forces. Results showed that: 1) From 2005 to 2015, the expansion of construction land(7670.24 km~2) mainly came from the occupation of cultivated land(7854.22 km~2). The Kappa coefficient of the SD-FLUS model was 0.886, indicating that this model could be used to predict the future land use changes in the YRDUA. 2) Gross domestic production(GDP) and population density(POP) showed a positive effect on the ERI, and the impact of POP exceeded that of GDP. The ERI showed the characteristics of zonal diffusion and a slight upward trend, and the high ecological risk region increased by 6.09%, with the largest increase. 3) Under different developmental scenarios, the land use and ecological risk patterns varied. The construction land is increased by 5.76%, 7.41%, 5.25% and 6.06%, respectively. And the high ecological risk region accounted for 12.71%, 15.06%, 11.89%,and 12.94%, correspondingly. In Scenario D, the structure of land use and ecological risk pattern was better compared with other scenarios considering the needs of rapid economic and ecological protection. This study is helpful to understand the spatio-temporal pattern and demand of land use types, grasp the ecological security pattern of large-scale areas, and provide scientific basis for the territory development of urban agglomeration in the future. 相似文献
20.
基于土地利用类型的村级人口空间分布模拟——以湖北鹤峰县为例 总被引：1，自引：0，他引：1
在人口分布及其相关研究中,常常会遇到小尺度人口数据部分缺失的问题。本文以湖北省鹤峰县为例,在分析土地利用与人口分布关系的基础上,从全局与局部、线性回归与非线性回归考虑,基于土地利用类型,分别利用地理加权回归(GWR)方法、格网方法、BP神经网络方法对缺失数据的行政村人口数据进行模拟,并进行了多角度精度对比验证。研究结果表明:(1)各种土地利用类型中,耕地、林地、城镇村及工矿用地、交通用地是影响研究区村级人口分布的主要因素;(2)30个调查村中,3种方法模拟的人口总数误差小于3%,通过每个村的模拟值与实际值相比,BP神经网络方法能更好地模拟研究区村级人口的分布,格网方法次之,GWR方法最差;(3)研究区各村人口分布呈现较高的空间正相关性,各乡镇的人口密度在空间上并不独立,而是呈现紧密的集聚特征。 相似文献