基于GIS和RS的呼和浩特市近百年城市空间扩展及其演变趋势

以历史悠久的草原文化名城-呼和浩特市为研究区,利用丰富的历史文献资料和地图资料,结合现代空间信息源,运用GIS技术和城市空间扩展测度的定量研究方法,具体选用扩展强度指数、紧凑度和等扇区方法,对研究区近百年的城市空间扩展过程进行定量分析。结果显示:百年尺度的城市空间扩展以东北-西北-再西北-东北方向扩展为主,扩展强度经过了上升-下降-再上升过程,紧凑度逐渐下降;各指标的变化反映,城市空间扩展经历了四次明显的演变过程,即1912-1951年低速扩展、1952-1973年中速扩展、1974-2000年快速扩展和2001-2009年高速扩展。总之,城市空间扩展具有时代特征和东西延伸的空间分异特征,反映城市空间扩展受到自然地理条件的限制之外,还有显著的社会经济发展的时代烙印。     

库尔勒市土地利用变化的时空特征分析

土地利用变化是全球变化中的重要组成部分,是短期内人类活动对自然环境施加影响的显著表现形式。本文基于遥感和地理信息系统技术,利用LandsatTM图像的解译成果,分析了库尔勒市1996~2004年土地利用变化的空间分布特征。研究表明:在这8年的时间里,库尔勒市裸岩石砾地、沙地和园地的转移趋势明显,三者的转移率分别达到32.28%、20.16%和14.98%,主要以裸岩石砾地内部向工矿用地、居民点用地、水利设施用地、交通用地和沙地转移,牧草地和荒草地内部向耕地和沙地转移,耕地内部向园地和盐碱地转移等类型为主。同时,各类土地利用的净变化呈现出明显的区域差异。     

长春市近50年城市扩展的遥感监测及时空过程分析

城市空间扩展是城市化现象在空间上的表现,因此城市空间扩展是衡量城市化水平的主要测度指标之一。我国现在正处在城市化进程快速发展时期,及时掌握城市空间扩展的信息,对于城市规划、城市建设、管理及决策具有重要的现实意义。本文结合地形图与遥感数据对长春市近50年来的城市扩展时空过程及其发展机制进行了分析与研究。在近50年来,长春市的城市空间扩展较快,但总体上是有序的、速度适中的和可持续性的。在新的多中心和组团式的城市空间快速扩展之时,贯彻新的城市总体规划和保护基本农田是当务之急。     

基于遥感与GIS的扎鲁特旗土地盐渍化动态监测

以扎鲁特旗80年代末和2000年TM影像作为基本信息源,经合成、增强、几何纠正、镶嵌,并结合野外考察进行人机交互判读获取土地盐渍化空间数据,建立数据库。结果显示:在研究期内盐渍化土地面积增加不明显,但是程度恶化严重。从1988年到2000年研究区盐渍化土地面积由63919.5hm2增加到67949.4 hm2,净增4029.9hm2;年平均增长率为0.53%,占国土面积的比例由7.8%提高到8.3%;轻度盐渍化土地面积减少,而中度和强烈盐渍化土地以及盐土面积均增加,变化幅度分别为-58.8%,19.7%、117.6%和120.4%;中度以上盐渍化土地占全部盐渍化土地比例由52%提高到81%,强烈以上比例由25%提高到51%。空间上主要分布在乌力吉木仁河西北岸。土地盐渍化与研究区低洼的地形、地表水系特征、地下水以及干旱的气候条件密切相关,人类经济活动起到了促进作用。不断退化的盐渍化土壤结构恶化,有机质含量下降,土地生产力下降明显,严重制约了农牧业生产。为了控制因盐渍化而造成的土地退化,必须控制人口密度,加强土地管理,采取有效措施降低地下水位,恢复原生植被,对土地盐渍化进行综合防治。     

Land degradation sensitivity assessment and convergence analysis in Korla of Xinjiang,China

Land degradation has a major impact on environmental and socio-economic sustainability. Scientific methods are necessary to monitor the risk of land degradation. In this study, the environmental sensitive area index (ESAI) was utilized to assess land degradation sensitivity and convergence analysis in Korla, a typical oasis city in Xinjiang of China, which is located on the northeast border of the Tarim Basin. A total of 18 indicators depicting soil, climate, vegetation, and management qualities were used to illustrate spatial-temporal patterns of land degradation sensitivity from 1994 to 2018. We investigated the causes of spatial convergence and divergence based on the Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models. The results show that the branch of the Tianshan Mountains and oasis plain had a low sensitivity to land degradation, while the Tarim Basin had a high risk of land degradation. More than two-thirds of the study area can be categorized as critical sensitivity classes. The largest percentage (32.6%) of fragile classes was observed for 2006. There was no significant change in insensitive or low-sensitivity areas, which accounted for less than 0.4% of the entire observation period. The ESAI of the four time periods (1994-1998, 1998-2006, 2006-2010, and 2010-2018) formed a series of convergence patterns. The convergence patterns of 1994-1998 and 1998-2006 can be explained by the government's efforts to Returning Farmland to Forests and other governance projects. In 2006-2010, the construction of afforested work intensified, but industrial development and human activities affected the convergence pattern. The pattern of convergence in most regions between 2010 and 2018 can be attributed to the government's implementation of a series of key ecological protection projects, which led to a decrease in sensitivity to land degradation. The results of this study altogether suggest that the ESAI convergence analysis is an effective early warning method for land degradation sensitivity.     

正蓝旗沙漠化草地分布格局及2002年-2011年动态变化的遥感监测

沙漠化草地分布格局及其动态监测是北方重要生态功能区防风固沙功能基线评估的基础数据。为开展防风固沙功能基线评估,以正蓝旗为典型研究区,地面调查与遥感解译相结合,以2002年和2011年TM影像为信息源,建立目视解译标志,利用ERDAS的图像和数据处理功能,获得正蓝旗2002-2011年草地沙漠化动态变化图,计算不同程度沙漠化草地之间的转换面积。探讨了北方防风固沙重要区近10年的沙漠化草地分布格局及动态变化。结果表明:(1)2011年正蓝旗沙漠化草地总面积为5246.62km2,其中轻度沙漠化草地面积所占比例最大,为49.15%,沙漠化草地主要分布在北部地区。(2)2002-2011年间,正蓝旗沙漠化草地面积减少了16.05%。(3)沙漠化逆转面积是沙漠化发展面积的1.5倍,草原沙漠化趋势得到了一定程度的逆转,但仍有20.1%的地区沙漠化程度加重。沙漠化发展的动态趋势是整体逆转,局部扩张。     

Development of a large-scale remote sensing ecological index in arid areas and its application in the Aral Sea Basin

The Aral Sea Basin in Central Asia is an important geographical environment unit in the center of Eurasia.It is of great significance to the ecological protection and sustainable development of Central Asia to carry out dynamic monitoring and effective evaluation of the eco-environmental quality of the Aral Sea Basin.In this study,the arid remote sensing ecological index(ARSEI)for large-scale arid areas was developed,which coupled the information of the greenness index,the salinity index,the humidity index,the heat index,and the land degradation index of arid areas.The ARSEI was used to monitor and evaluate the eco-environmental quality of the Aral Sea Basin from 2000 to 2019.The results show that the greenness index,the humidity index and the land degradation index had a positive impact on the quality of the ecological environment in the Aral Sea Basin,while the salinity index and the heat index exerted a negative impact on the quality of the ecological environment.The eco-environmental quality of the Aral Sea Basin demonstrated a trend of initial improvement,followed by deterioration,and finally further improvement.The spatial variation of these changes was significant.From 2000 to 2019,grassland and wasteland(saline alkali land and sandy land)in the central and western parts of the basin had the worst ecological environment quality.The areas with poor ecological environment quality are mainly distributed in rivers,wetlands,and cultivated land around lakes.During the period from 2000 to 2019,except for the surrounding areas of the Aral Sea,the ecological environment quality in other areas of the Aral Sea Basin has been improved in general.The correlation coefficients between the change in the eco-environmental quality and the heat index and between the change in the eco-environmental quality and the humidity index were–0.593 and 0.524,respectively.Climate conditions and human activities have led to different combinations of heat and humidity changes in the eco-environmental quality of the Aral Sea Basin.However,human activities had a greater impact.The ARSEI can quantitatively and intuitively reflect the scale and causes of large-scale and long-time period changes of the eco-environmental quality in arid areas;it is very suitable for the study of the eco-environmental quality in arid areas.     

地面高光谱和低空遥感监测小麦白粉病初探

利用手持式高光谱仪和基于数字技术的低空遥感系统，对不同严重度小麦白粉病冠层光谱反射率进行了测定，同时调查病情指数，分析不同时期地面平台光谱反射率与病情指数及低空遥感平台反射率与病情指数（disease index，DI）、归一化植被指数（normalized difference vegetable index，NDVI）相关性。结果表明，地面光谱测量冠层光谱反射率和低空遥感数字图像反射率与小麦白粉病病情指数在灌浆期有显著的相关关系，就地面测量结果而言，近红外波段的相关性高于绿光波段，相关系数分别为0．79和0．54；低空遥感数字图像红、绿、蓝三波段中，相关性依次降低，相关系数分别为0．79、0．75和0．62；而且低空遥感图像与归一化植被指数也存在较好的相关关系，蓝、红、绿波段，相关系数依次为0．70、0．68和0．54。这表明利用遥感非破坏性监测小麦白粉痛有着良好的应用前景，对于小麦白粉病发生面积监测有重要意义。     

天山北坡城镇用地扩展及其空间格局分析

以1990和2007年TM/ETM遥感影像为数据源,利用人机交互解译的方法得到研究区2期土地利用现状图,然后采用GIS空间分析技术,对天山北坡绿洲城镇用地扩展及其空间格局进行了分析。结果表明：1990-2007年,土地利用动态明显;耕地、草地和沙地面积变化最大,耕地、林地和草地是建设用地的主要来源。城镇用地、农村居民点...     

Evaluating and modeling the spatiotemporal pattern of regional-scale salinized land expansion in highly sensitive shoreline landscape of southeastern Iran

Taking an area of about 2.3×10⁴ km² of southeastern Iran, this study aims to detect and predict regional-scale salt-affected lands. Three sets of Landsat images, each set containing 4 images for 1986, 2000, and 2015 were acquired as the main source of data. Radiometric, atmospheric and cutline blending methods were used to improve the quality of images and help better classify salinized land areas under the support vector machine method. A set of landscape metrics was also employed to detect the spatial pattern of salinized land expansion from 1986 to 2015. Four factors including distance to sea, distance to sea water channels, slope, and elevation were identified as the main contributing factors to land salinization. These factors were then integrated using the multi-criteria evaluation (MCE) procedure to generate land sensitivity map to salinization and also to calibrate the cellular-automata (CA) Markov chain (CA-Markov) model for simulation of salt-affected lands up to 2030, 2040 and 2050. The results of this study showed a dramatic dispersive expansion of salinized land from 7.7 % to 12.7% of the total study area from 1986 to 2015. The majority of areas prone to salinization and the highest sensitivity of land to salinization was found to be in the southeastern parts of the region. The result of the MCE-informed CA-Markov model revealed that 20.3% of the study area is likely to be converted to salinized lands by 2050. The findings of this research provided a view of the magnitude and direction of salinized land expansion in a past-to-future time period which should be considered in future land development strategies.     

卫星遥感结合地面观测估算中国西北区东部地表能量通量

采用定西麦田微气象观测站和试验区其它地区36个自动气象站、91个常规气象站资料,结合NOAA-16卫星的AVHRR资料,用地表能量平衡算法(SEBAL)推导中国西北区东部4~8月的日平均地表净辐射、感热、潜热、土壤热通量密度和波文比的区域分布特征,并按土地覆盖类型的不同分别统计沿黄灌区、冬春小麦雨养农田、草地、森林和沼泽草甸等不同地表的能量通量特征。结果显示:作物不同生育期,自南向北由湿润的常绿林区经半干旱雨养农业区直到干旱的荒漠地带,地表能量输送存在巨大的地区差异。计算结果与实测值之间的平均绝对百分误差为4%~21%,两者基本相符,该方法在算法上解决了用卫星资料反演地气温差的难点问题,提高了感热通量区域分布的计算精度。     

渭干河—库车河三角洲绿洲棉田土壤盐分估算及遥感反演

基于研究区的野外采样数据与Landsat 8遥感影像提取的增强型植被指数,构建渭干河—库车河三角洲绿洲棉田土壤盐分估算模型,并对土壤盐分的空间分布格局进行预测。结果表明:(1)由土壤含盐量与增强型归一化植被指数(ENDVI)构建的线性回归模型(y=-56.494x+22.687)拟合效果最好(R2=0. 886,RMSE=0. 907)。(2)通过选取的82个采样点,依据最佳遥感反演模型,预测出研究区土壤含盐量在9.33～26.99 g·kg-1之间变化,平均值为17.42 g·kg-1,标准差为2.30 g·kg-1,预测结果与土壤盐分的实测值较为一致。(3)利用地统计分析方法制作研究区棉田土壤盐分的空间分布图,分析可知土壤盐分从绿洲内部向外围呈逐渐增加的趋势。     

A spatial geostatistical analysis of impact of land use development on groundwater resources in the Sangong Oasis Region using remote sensing imagery and data

In this study, the relationship between land use and cover change (LUCC) and variation of groundwater level and quality in the Sangong Oasis Region was investigated using a spatial geostatistical approach. Specifically, interactions among groundwater, surface water, and LUCC were analyzed through the utilization of geographical information system (GIS), remote sensing (RS) Imagery processing, and geostatistics. Study outputs indicated that recharging into the ground-water did not change significantly during the period from 1978 to 1998. However, both LUCC and groundwater level changed substantially in the Sangong Oasis Region, and their variations were closely correlated to each other spatially and temporally over the past two decades. It confirmed that urbanization process and increased industrial activities were the direct reasons of groundwater table descending and the deterioration of water quality. The results of this research provided a scientific basis for understanding sustainability-related problems and solution options in the oasis areas of western China.     

虫害水平与植物影像色调的相关性研究

植物发生病虫害时,叶绿素水平会出现不同程度的下降,这种下降通常表现为近红外波段的反射率下降,在近红外遥感图像上可以探测到这种变化。通过一元回归分析证实了虫害水平与植物近红外影像色调具有显著相关性,研究了反射率测定及相关分析中采样位置与植物病虫害水平信息的关系,并对区分虫害类型是否有助于提高相关性等进行了比较研究。最后通过给出描述虫害水平与植物近红外影像色调关系的定量表达式和开发出相应的采样分析软件,使遥感技术大面积、快速监测城市绿化植物虫情成为可能。     

Check dam extraction from remote sensing images using deep learning and geospatial analysis: A case study in the Yanhe River Basin of the Loess Plateau,China

Check dams are widely used on the Loess Plateau in China to control soil and water losses, develop agricultural land, and improve watershed ecology. Detailed information on the number and spatial distribution of check dams is critical for quantitatively evaluating hydrological and ecological effects and planning the construction of new dams. Thus, this study developed a check dam detection framework for broad areas from high-resolution remote sensing images using an ensemble approach of deep learning and geospatial analysis. First, we made a sample dataset of check dams using GaoFen-2 (GF-2) and Google Earth images. Next, we evaluated five popular deep-learning-based object detectors, including Faster R-CNN, You Only Look Once (version 3) (YOLOv3), Cascade R-CNN, YOLOX, and VarifocalNet (VFNet), to identify the best one for check dam detection. Finally, we analyzed the location characteristics of the check dams and used geographical constraints to optimize the detection results. Precision, recall, average precision at intersection over union (IoU) threshold of 0.50 (AP₅₀), IoU threshold of 0.75 (AP₇₅), and average value for 10 IoU thresholds ranging from 0.50-0.95 with a 0.05 step (AP_50-95), and inference time were used to evaluate model performance. All the five deep learning networks could identify check dams quickly and accurately, with AP_50-95, AP₅₀, and AP₇₅ values higher than 60.0%, 90.0%, and 70.0%, respectively, except for YOLOv3. The VFNet had the best performance, followed by YOLOX. The proposed framework was tested in the Yanhe River Basin and yielded promising results, with a recall rate of 87.0% for 521 check dams. Furthermore, the geographic analysis deleted about 50% of the false detection boxes, increasing the identification accuracy of check dams from 78.6% to 87.6%. Simultaneously, this framework recognized 568 recently constructed check dams and small check dams not recorded in the known check dam survey datasets. The extraction results will support efficient watershed management and guide future studies on soil erosion in the Loess Plateau.