Assessing Spatio-Temporal Land Cover Changes in Dhund River Basin, Eastern Rajasthan (India), Using Multi-Temporal Landsat Data

Land cover is an impression of natural cover on surface of earth such as bare soil, river, grass etc. and utilization of these natural covers for various human needs and purposes by mankind is defined as land use. Land cover identification, delineation and mapping is important for planning activities, resource management and global monitoring studies while baseline mapping and subsequent monitoring is done by application of land use to get timely information about quantity of land that has been used. The present study has been carried out in Dhund river watershed of Jaipur, Rajasthan which covers an area of about 1828 sq∙km. The minimum and maximum elevation of the area is found to be 214 m and 603 m respectively. Land use and land cover changes of three decades from 1991 to 2021 have been interpreted by using remotes sensing and GIS techniques. ArcGIS software (Arc map 10.2), SOI topographic map, Cartosat-1 DEM and satellite data of Landsat 5 and Landsat 8 have been used for interpretation of eleven classes. The study shows an increase in cultivated land, settlement, waterbody, open forest, plantation and mining due to urbanization because of increasing demands of food, shelter and water while a decrease in dense forest, river, open scrub, wasteland and uncultivated land has also been marked due to destruction of aforementioned by anthropogenic activities such as industrialization resulting in environmental degradation that leads to air, soil and water pollution.

Characterization of land cover types in Xilin River Basin using multi-temporal Landsat images

This study conducted computer-aided image analysis of land use and land cover in Xilin River Basin, Inner Mongolia, using 4 sets of Landsat TM/ETM+ images acquired on July 31, 1987, August 11, 1991, September 27, 1997 and May 23, 2000, respectively. Primarily, 17 sub-class land cover types were recognized, including nine grassland types at community level: F.sibiricum steppe, S.baicalensis steppe, A.chinensis+ forbs steppe, A.chinensis+ bunchgrass steppe, A.chinensis+ Ar.frigida steppe, S.grandis+ A.chinensis steppe, S.grandis+ bunchgrass steppe, S.krylavii steppe, Ar.frigida steppe and eight non-grassland types: active cropland, harvested cropland, urban area, wetland, desertified land, saline and alkaline land, cloud, water body + cloud shadow. To eliminate the classification error existing among different sub-types of the same gross type, the 17 sub-class land cover types were grouped into five gross types: meadow grassland, temperate grassland, desert grassland, cropland and non-grassland. The overall classification accuracy of the five land cover types was 81.0% for 1987, 81.7% for 1991, 80.1% for 1997 and 78.2% for 2000.

Study of Forest Cover Change Dynamics between 2000 and 2015 in the Ikongo District of Madagascar Using Multi-Temporal Landsat Satellite Images

Satellite images are considered reliable data that preserve land cover information. In the field of remote sensing, these images allow relevant analyses of changes in space over time through the use of computer tools. In this study, we have applied the “discriminant” change detection algorithm. In this, we have verified its effectiveness in multi-temporal studies. Also, we have determined the change in forest dynamics in the Ikongo district of Madagascar between 2000 and 2015. During the treatments, we have used the Landsat TM satellite images for the years 2000, 2005 and 2010 as well as ETM+ for 2015. Thus, analyses carried out have allowed us to note that between 2000-2005, 1.4% of natural forest disappeared. And, between 2005-2010, forests degradation was 1.8%. Also, between 2010-2015, about 0.5% of the natural forest conserved in 2010 disappeared. Furthermore, we have found that the discriminant algorithm is considerably efficient in terms of monitoring the dynamics of forest cover change.

Satellite Multi-Temporal Data and Cropping Pattern Approach for Green Gram Crop Management in the Lower Midland Zone IV and V in Kenya

Creation of a spectral signature reflectance data, which aids in the identification of the crops is important in determining size and location crop fields. Therefore, we developed a spectral signature reflectance for the vegetative stage of the green gram (Vigna. radiata L.) over 5 years (2020, 2018, 2017, 2015, and 2013) for agroecological zone IV and V in Kenya. The years chosen were those whose satellite resolution data was available for the vegetative stage of crop growth in the short rain season (October, November, December (OND)). We used Landsat 8 OLI satellite imagery in this study. Cropping pattern data for the study area were evaluated by calculating the Top of Atmosphere reflectance. Farms geo-referencing, along with field data collection, was undertaken to extract Top of Atmosphere reflectance for bands 2, 3, 4 and 7. We also carried a spectral similarity assessment on the various cropping patterns. The spectral reflectance ranged from 0.07696 - 0.09632, 0.07466 - 0.09467, 0.0704047 - 0.12188,0.19822 - 0.24387, 0.19269 - 0.26900, and 0.11354 - 0.20815 for bands 2, 3, 4, 5, 6, and 7 for green gram, respectively. The results showed a dissimilarity among the various cropping patterns. The lowest dissimilarity index was 0.027 for the maize (Zea mays L.) bean (Phaseolus vulgaris) versus the maize-pigeon pea (Cajanus cajan) crop, while the highest dissimilarity index was 0.443 for the maize bean versus the maize bean and cowpea cropping patterns. High crop dissimilarities experienced across the cropping pattern through these spectral reflectance values confirm that the green gram was potentially identifiable. The results can be used in crop type identification in agroecological lower midland zone IV and V for mung bean management. This study therefore suggests that use of reflectance data in remote sensing of agricultural ecosystems would aid in planning, management, and crop allocation to different ecozones.

Modeling and Mapping of Urban Sprawl Pattern in Cairo Using Multi-Temporal Landsat Images, and Shannon’s Entropy

Cairo city, being the Egypt’s industrial and cultural center, has a problem of rapid urban sprawl. The city has an extremely high population density which is continuously increasing through informal settlements that grow by sprawling due to migration from the Nile Delta villages and the high population growth rates. The present study attempts to understand, detect and quantify the spatial pattern of Cairo’s urban sprawl using Shannon’s entropy and multi-temporal Landsat TM and ETM images acquired for the period from 1984 to 2013. Supervised classification was applied to extract the built-up areas and to measure the changes in the urban land-use class among the city wards. Shannon’s entropy was applied to model the city’s urban sprawl, trend and spatial change. The entropy values for the city’s electoral wards were modeled and used in an interpolation function to create an entropy surface (index) for each acquired temporal image. Such index indicates the spatial pattern of the urban sprawl and provides a visual comparison of the entropy phenomenon in such wards. Results indicate that Shannon’s entropy index increased from (1.4615) in year 1984 to (2.1023) in year 2013, indicating more dispersed urban growth, a sign of urban sprawl. The maximum entropy values are found in the eastern wards namely El Nozha, Awal Nasr District, Thany Nasr-District, El Salam, El Marg and El Bassatein. A regression analysis was carried for the population growth rate and the built-up areas. Findings help in understanding the sprawl patterns and dynamics among Cairo’s electoral wards and provide a visual comparison. The applied methodology provides explanations and facilitates tracing and measuring the urban sprawl which is needed by decision makers and city planners of mega cities.

Spatio-Temporal Land Cover Analysis and the Impact of Land Cover Variability Indices on Land Surface Temperature in Greater Accra, Ghana Using Multi-Temporal Landsat Data

Urbanization, as a result of anthropogenic activities, reduces the vegetated and green spaces and thereby increases the impervious surfaces in cities. This in turn increases the surface temperature of cities when compared to the rural regions resulting in the formation of urban heat island. Especially, in under-developed countries, it is very crucial to obtain timely and accurate information on the urban trend and its development due to the higher increasing rate of population growth and lack of infrastructural facilities and regulations to mitigate the adverse consequences of urbanization. The current study analyzes the urban development of Greater Accra, Ghana using Landsat 7 dataset acquired in 2002, 2013, and 2020. Further, the influence of urban growth on the land surface temperature (LST) and land cover variability (LCV) indices including NDVI (Normalized Difference Vegetation Index), NDBI (Normalized Difference Built-Up Index), and NDWI (Normalized Difference Water Index) is identified during the study periods. Results suggest that the area of built-up quadrupled between 2002 and 2020 in the study region. Shannon’s entropy-based analysis suggests that the urban development in the region is heterogeneous or dispersive in nature. Further, the regression analysis highlights a strong positive relation between LST and NDBI (0.755) and a negative relation is found associated between LST and NDVI (0.4417) and LST and NDWI (0.76). Results of the study could be useful to design sustainable urban socio-economic and environmental planning policies in the study region.

Estimating Wheat Grain Protein Content Using Multi-Temporal Remote Sensing Data Based on Partial Least Squares Regression

Estimating wheat grain protein content by remote sensing is important for assessing wheat quality at maturity and making grains harvest and purchase policies. However, spatial variability of soil condition, temperature, and precipitation will affect grain protein contents and these factors usually cannot be monitored accurately by remote sensing data from single image. In this research, the relationships between wheat protein content at maturity and wheat agronomic parameters at different growing stages were analyzed and multi-temporal images of Landsat TM were used to estimate grain protein content by partial least squares regression. Experiment data were acquired in the suburb of Beijing during a 2-yr experiment in the period from 2003 to 2004. Determination coefficient, average deviation of self-modeling, and deviation of cross- validation were employed to assess the estimation accuracy of wheat grain protein content. Their values were 0.88, 1.30%, 3.81% and 0.72, 5.22%, 12.36% for 2003 and 2004, respectively. The research laid an agronomic foundation for GPC （grain protein content） estimation by multi-temporal remote sensing. The results showed that it is feasible to estimate GPC of wheat from multi-temporal remote sensing data in large area.

银根地区Landsat8卫星数据地下水信息反演及其对砂岩型铀矿勘查的指示

有针对性地利用遥感技术识别铀成矿环境、条件与控矿要素是开展砂岩型铀矿遥感勘查的重要内容。由于砂岩型铀矿中铀的富集过程是成矿流体在储层中迁移并与周围环境相互作用的结果,而地下水是最常见的含铀流体,因此在砂岩区开展地下水信息的遥感识别对铀矿勘查具有重要指示意义。以内蒙古银根地区为研究对象,选择Landsat8卫星遥感数据为主要数据源,以地下水活动产生的地表热异常、植被异常和湿度异常作为突破口,利用热红外波段结合大气参数反演了地表温度,同时采用多光谱波段结合归一化植被指数(NDVI)和不规则梯形模型(OPTRAM)反演了地表植被和湿度信息。反演结果表明:银根地区地表植被信息与地下水富集程度之间存在良好正相关关系,可作为识别地下水活动的首选依据,相比而言,地表温度和湿度信息则易受不同岩性和岩石蚀变作用影响,仅可作为识别地下水活动的辅助依据。利用地表植被、温度和湿度信息,在银根地区南部圈定了5处北东走向的富水带。通过将富水带与构造解译结果整合,在银根地区南部斜坡带上成功识别出泄水构造与隐伏泄水构造。结合前人钻探资料,发现所识别的泄水构造与铀矿化存在一定关系,可能促进了地下水循环并控制着铀矿的形成,由此认为下一步铀矿找矿工作可围绕泄水构造或隐伏泄水构造开展。

The Glacier Area Changes in the Qangtang Plateau Based on the Multi-temporal Grid Method and its Sensitivity to Climate Change

Glacier area changes in the Qangtang Plateau are analyzed during 1970-2000 using air photos,relevant photogrammetric maps and satellite images based on the multi-temporal grid method.The results indicate that the melting of glaciers accelerated,only a few of glaciers in an advancing state during 1970-2000 in the whole Qangtang Plateau.However,the glaciers seemed still more stable in the study area than in most areas of western China.We estimate that glacier retreat was likely due to air temperature warming during 1970-2000 in the Qangtang Plateau.Furthermore,the functional model of glacier system is applied to study climate sensitivity of glacier area changes,which indicates that glacier lifespan mainly depends on the heating rate,secondly the precipitation,and precipitation increasing can slow down glacier retreat and make glacier lifespan prolonged.

Multi-temporal InSAR for Urban Deformation Monitoring:Progress and Challenges

Multi-temporal Interferometric Synthetic Aperture Radar(MT-InSAR) is one of the most powerful Earth observation techniques, especially useful for measuring highly detailed ground deformation over large ground areas. Much research has been carried out to apply MT-InSAR to monitor ground and infrastructure deformation in urban areas related to land reclamation, underground construction and groundwater extraction.This paper reviews the progress in the research and identifies challenges in applying the technology, including the inconsistency in coherent point identification when different approaches are used, the reliability issue in parameter estimation, difficulty in accurate geolocation of measured points, the one-dimensional line-of-sight nature of InSAR measurements, the inability of making complete measurements over an area due to geometric distortions, especially the shadowing effects, the challenges in processing large SAR datasets, the decrease of the number of coherent points with the increase of the length of SAR time series, and the difficulty in quality control of MT-InSAR results.

利用样点调查与Landsat8影像数据建立山体地表温度与坡度的关系

山区地表温度是林火预测预报的重要影响因子。山区地表温度机理的研究受稀疏气象观测站点及MODIS产品(1 km)空间尺度的限制,常以综合作用规律及宏观地形因子分析为主,缺乏微尺度地形要素的相对独立作用阐释。以我国西南纵向岭谷区为研究区,利用多期landsat8影像数据及样点实测数据,使用地理信息系统空间分析方法,通过消除或减弱海拔、坡向等地形因子的影响,定量分析坡度与地表温度相对独立关系。结果表明:(1)不同尺度下地表温度、地表温度标准差与坡度的关系受坡向影响不同。(2)30 m尺度上,西、西北、北、东北坡向的坡面上地表温度与坡度呈负向相关关系,坡度越大,地表温度空间变异越小,地表温度递减率分别为0.202、0.239、0.207、0.166℃/(°);东坡向的坡面上的地表温度与坡度呈先减小后微弱增大的二次曲线变化关系;东南、南、西南坡地表温度随着坡度增大分别以13.6、13.92、11.51℃上下波动。(3)样点尺度上,北、东北、东南、南、西南和西坡向的坡面上地表温度与坡度也呈负相关关系,地表温度递减率分别为0.106、0.396、0.168、0.111、0.235、0.077℃/(°);东、西北坡向内地表温度随着坡度的增大总体上减小,样点尺度的地表温度更易受到局地环境的影响。

Research and application of real-time monitoring and early warning thresholds for multi-temporal agricultural products information

Monitoring and early warning is an important means to effectively prevent risks in agricultural production,consumption and price.In particular,with the change of modes of national administration against the background of big data,improving the capacity to monitor agricultural products is of great significance for macroeconomic decision-making.Agricultural product information early warning thresholds are the core of agricultural product monitoring and early warning.How to appropriately determine the early warning thresholds of multi-temporal agricultural product information is a key question to realize real-time and dynamic monitoring and early warning.Based on the theory of abnormal fluctuation of agricultural product information and the research of substantive impact on the society,this paper comprehensively discussed the methods to determine the thresholds of agricultural product information fluctuation in different time dimensions.Based on the data of the National Bureau of Statistics of China(NBSC)and survey data,this paper used a variety of statistical methods to determine the early warning thresholds of the production,consumption and prices of agricultural products.Combined with Delphi expert judgment correction method,it finally determined the early warning thresholds of agricultural product information in multiple time,and carried out early warning analysis on the fluctuation of agricultural product monitoring information in 2018.The results show that:(1)the daily,weekly and monthly monitoring and early warning thresholds of agricultural products play an important early warning role in monitoring abnormal fluctuations with agricultural products;(2)the multitemporal monitoring and early warning thresholds of agricultural product information identified by the research institute can provide effective early warning on current abnormal fluctuation of agricultural product information,provide a benchmarking standard for China's agricultural production,consumption and price monitoring and early warning at the national macro level,and further improve the application of China's agricultural product monitoring and early warning.

Improved Yield Prediction of Ratoon Rice Using Unmanned Aerial Vehicle-Based Multi-Temporal Feature Method

Pre-harvest yield prediction of ratoon rice is critical for guiding crop interventions in precision agriculture.However,the unique agronomic practice(i.e.,varied stubble height treatment)in rice ratooning could lead to inconsistent rice phenology,which had a significant impact on yield prediction of ratoon rice.Multi-temporal unmanned aerial vehicle(UAV)-based remote sensing can likely monitor ratoon rice productivity and reflect maximum yield potential across growing seasons for improving the yield prediction compared with previous methods.Thus,in this study,we explored the performance of combination of agronomic practice information(API)and single-phase,multi-spectral features[vegetation indices(VIs)and texture(Tex)features]in predicting ratoon rice yield,and developed a new UAV-based method to retrieve yield formation process by using multi-temporal features which were effective in improving yield forecasting accuracy of ratoon rice.The results showed that the integrated use of VIs,Tex and API(VIs&Tex+API)improved the accuracy of yield prediction than single-phase UAV imagery-based feature,with the panicle initiation stage being the best period for yield prediction(R^(2) as 0.732,RMSE as 0.406,RRMSE as 0.101).More importantly,compared with previous multi-temporal UAV-based methods,our proposed multi-temporal method(multi-temporal model VIs&Tex:R^(2) as 0.795,RMSE as 0.298,RRMSE as 0.072)can increase R^(2) by 0.020-0.111 and decrease RMSE by 0.020-0.080 in crop yield forecasting.This study provides an effective method for accurate pre-harvest yield prediction of ratoon rice in precision agriculture,which is of great significance to take timely means for ensuring ratoon rice production and food security.

Detecting land subsidence near metro lines in the Baoshan district of Shanghai with multi-temporal interferometric synthetic aperture radar

Land subsidence is a major factor that affects metro line （ML） stability. In this study, an improved multi- temporal interferometric synthetic aperture radar （InSAR） （MTI） method to detect land subsidence near MLs is presented. In particular, our multi-temporal InSAR method provides surface subsidence measurements with high observation density. The MTI method tracks both point-like targets and distributed targets with temporal radar back- scattering steadiness. First, subsidence rates at the point targets with low-amplitude dispersion index （ADI） values are extracted by applying a least-squared estimator on an optimized freely connected network. Second, to reduce error propagation, the pixels with high-ADI values are classified into several groups according to ADI intervals and processed using a Pearson correlation coefficient and hierarchical analysis strategy to obtain the distributed targets. Then, nonlinear subsidence components at all point-like and distributed targets are estimated using phase unwrapping and spatiotemporal filtering on the phase residuals. The proposed MTI method was applied to detect land subsidence near MLs of No. 1 and 3 in the Baoshan district of Shanghai using 18 TerraSAR-X images acquired between April 21, 2008 and October 30, 2010. The results show that the mean subsidence rates of the stations distributed along the two MLs are -12.9 and -14.0 ram/year. Furthermore, three subsidence funnels near the MLs are discovered through the hierarchical analysis. The testing results demonstrate the satisfactory capacity of the proposed MTI method in providing detailed subsidence information near MLs.

Landsat和GF数据面向对象土地覆盖分类研究

针对中分辨率遥感数据面向对象分类,以河北省北部山区和南部平原Landsat8 OLI,Landsat5 TM及高分一号(GF1)数据为研究对象,对支持向量机(support vector machine,SVM)、随机森林(random forest,RF)、决策树(decision tree,DT)及朴素贝叶斯(naive Bayes,NB)4种分类器的土地覆盖分类效果进行对比,并分析SVM,RF和DT中关键参数对分类结果的影响。结果表明:在2个研究区,各分类器结果略有差异,从整体上看其优劣排序为SVM,NB,RF和DT。其中SVM和DT分类精度随参数变化波动较大:对于SVM,当参数C取值不小于103且gamma不大于10^(-1)时,无论哪种情况其分类精度均优于90%;对于DT,当参数树深(Depth)大于3时,各情况下的分类精度相对较高且趋于稳定。RF分类精度随参数变化波动较小且没有明显的变化规律。研究结果可为中分辨率遥感数据面向对象土地覆盖分类研究提供参考。

基于Landsat 8的节水改造背景下盐渍化土壤含盐量反演

为探明沈乌灌域节水改造后因渠道衬砌、引排水量减少引起的土壤含盐量时空分布特征及变化规律,采用区域土壤信息定点监测,结合经典统计学、空间插值以及机器学习建模反演等技术手段,利用Landsat 8卫星获取光谱数据,通过对实测土壤含盐量、光谱指数及波段反射率进行处理,运用Adaboost回归、BP神经网络回归、梯度提升树回归、KNN回归、决策树回归、随机森林回归方法构建了沈乌灌域土壤含盐量空间反演模型。采用最优反演模型对沈乌灌域土壤含盐量空间分布特征进行了遥感反演。结果表明:通过全变量单一回归法筛选出相关系数大于0.55的9个光谱因子,使用SPSS PRO软件构建6种机器学习反演模型,对比6种反演模型精度,验证集决定系数R2由大到小依次为随机森林回归、梯度提升树回归、Adaboost回归、KNN回归、决策树回归、BP神经网络回归。其中随机森林回归模型的拟合精度最佳,训练集与验证集的决定系数R2分别为0.834和0.86,说明随机森林回归模型的反演效果较好。反演结果表明:节水改造后非盐渍土面积增加391.7 km^(2),占灌域总面积的21%,中度盐渍土面积、重度盐渍土面积、盐土面积分别减少95.61、63.37、45.7 km^(2),分别占灌域总面积的5%、3%、2%。综上所述,节水改造工程完成后,沈乌灌域土壤盐渍化程度减轻,作物生长安全区面积增加,但由于渠道衬砌以及引排水量减少,土壤盐分淋洗效果减弱,土壤盐分在灌域内部运移,整体土壤环境得到改善,局部地区出现盐分聚集。

基于多季相分形特征的Landsat 8 OLI影像耕地信息提取方法

利用遥感技术快速准确地提取耕地信息是耕地保护的关键环节。以山东省商河县为例,提出了一种基于多季相分形特征的Landsat 8 OLI影像耕地信息提取方法。首先采用毯子覆盖法计算多季相遥感影像每个像元的上分形信号和下分形信号,对比分析耕地和其他土地利用类型的分形特征,选取上分形信号的第3尺度作为特征尺度,提取商河县耕地空间分布特征;其次采用同时期的土地利用矢量数据、Esri land cover数据和统计数据进行耕地信息提取精度评价;最后分别设置多季相分形提取与单季相分形提取、现有土地利用数据产品的对比实验,并基于点位匹配度和面积匹配度进行评价。结果表明:多季相数据更能反映农作物生长的复杂性,有助于提高耕地信息的提取精度;不同土地利用类型在不同分形尺度的信号值各不相同,分形特征可以在不同尺度上清晰地刻画出不同土地利用类型的分异性;基于矢量数据和Esri land cover数据评价的多季相分形特征耕地提取点位匹配度为87.13%和89.83%,面积匹配度为99.73%和97.91%,均比单季相分形提取结果精度高;综合考虑点位匹配度、面积匹配度和空间分布特征,研发方法能有效区分耕地和其他土地利用类型,提取结果更优,且与统计数据有更高的一致性。该方法可准确提取耕地信息,为耕地的动态监测和损害评估提供技术支撑。

基于Landsat 8 OLI的东圳水库水质参数反演研究

遥感技术是监测内陆水体水质的有效手段,东圳水库是莆田市水源地,为了对水质进行实时监测,了解其空间分布情况,本文基于Landsat 8 OLI遥感影像,结合124个采样点实测获得的Chl-a、浊度、COD浓度分别构建统计回归模型、BP神经网络模型、XGBoost模型,并采用R^(2)、MAE、RMSE进行精度检验。结果表明BP神经网络模型效果优于统计回归模型,R^(2)均大于0.9,但存在过拟合现象;XGBoost模型可以有效防止过拟合,表现出较强的拟合能力和较高的预测精度。

Human Settlement Analysis Based on Multi-temporal Remote Sensing Data： A Case Study of Xuzhou City, China

To evaluate urban human settlement, we propose a human settlement environment development index(HSEDI) model by choosing vegetation coverage, land surface temperature, impervious surfaces, slope, wetness, and water condition as the evaluation factors. We applied the proposed model to Xuzhou City, Jiangsu Province, China. Landsat-5 Thematic Mapper(TM) images from 1998 to 2010 and digital elevation model(DEM) data with a 30-m resolution were used to calculate the values of the six evaluation factors. The HSEDI value in Xuzhou City was found to be between 2.24 and 8.10 from 1998 to 2010, and it was further divided into five levels, uninhabitable, moderately uninhabitable, generally inhabitable, moderately inhabitable, and inhabitable. The best HSEDI value was in 2007. The generally inhabitable region was about 100.98 km^2, covering 30.87% of the total area in 2007; the moderately inhabitable region was about 170.58 km2 covering 52.15% of the total area; the inhabitable region was about 32.03 km^2, covering 9.79% of the total area; the percentage of the uninhabitable region was zero; and that of the moderately uninhabitable region was very small, less than 1.00%. Moreover, we analyzed the habitability in the respect of spatial patterns and change detection. Results show that the degraded regions of habitability quality are mainly located in the urban fringe and the improved regions are mainly located in the main urban and rural areas. Reason for the degraded habitability quality is the rapid progress of urbanization. However, the increase in urban green spaces and the construction of the main urban area promoted the improved habitability quality. Besides, we further analyzed socio-economic and socio-demographic data to confirm the results of the habitability analysis. The results indicate that the human settlement in Xuzhou City is in a satisfactory condition, but some efforts should be made to control the possible uninhabitable and moderately uninhabitable regions, and to improve the quality of the generally inhabitable regions.

基于Landsat影像的鄱阳湖面积监测及其与水位关系研究

湖泊是陆地生态系统的一个重要的组成部分,湖泊水域的变化对环境和人类的生产活动都有着重大的影响。鄱阳湖作为中国第一大淡水湖,近年来多次出现洪旱灾害现象,因此对鄱阳湖进行动态监测意义重大。文章以2000—2021年鄱阳湖175期Landsat影像作为数据源,对比分析了归一化差异水体指数(normalized difference water index,NDWI)、改进的归一化差异水体指数(modified normalized difference water index,MNDWI)、自动水体提取指数(automated water extraction index,AWEI)、谱间关系法(spectrum photometric method,SPM)这4种水体提取方法,优选最适宜鄱阳湖的水体提取模型;利用175期面积数据分析了鄱阳湖2000—2021年面积的年际变化趋势,分析年内季节变化特征,同时结合2009—2013年和2017—2018年同时期的50组水位数据,建立面积-水位关系模型。结果表明:①AWEI模型提取水体精度优于其他3种,该文最终选用AWEI进行鄱阳湖水体提取;②鄱阳湖面积存在明显的季节性变化,且丰水季面积年际波动大,枯水季较平缓;③棠荫水位站湖泊面积-水位分段线性模型为最佳模型,从而可以根据鄱阳湖区域的实时水位观测值对水体覆盖面积进行预测,以弥补云雨天气时利用可见光遥感手段难以监测到湖泊水体淹没情况的不足。