基于BP神经网络与ETM+遥感数据的盐城滨海自然湿地覆被分类

高效而精确的湿地遥感分类是大范围湿地资源动态监测与管理的必要保障。本研究使用ETM 遥感数据,借助Matlab神经网络工具箱,构建了基于BP神经网络的滨海湿地覆被分类模型,并将其应用于江苏盐城沿海湿地珍禽国家级自然保护区的核心区的自然湿地覆被分类研究中。本研究选择3、4、7、8波段作为输入层变量,单隐藏层设为10个节点,输出层变量对应待划分的8种覆被类型,构建三层式BP神经网络滨海湿地覆被分类模型。结果显示,BP分类总精度为85.91%,Kappa系数为0.8328,与最小距离法和极大似然法的分类总精度相比,分别提高了7.99%和6.08%,Kappa系数也相比提高。研究结果表明,BP神经网络分类法是一种较为有效的湿地遥感影像分类技术,能够提高分类精度。     

基于TM/ETM~+数据的沈阳市各区城市热岛特征

应用2001年8月和2010年8月Landsat TM/ETM+数据,计算沈阳市区及三环内各区的地表热岛强度(SUHI),根据土地利用数据和热岛源汇特征提取源汇信息,分析SUHI与土地利用类型、热岛源汇面积及边界长度、归一化植被指数(NDVI)、归一化建筑指数(NDBI)、改进的归一化差异水体指数(MNDWI)的关系。结果表明:2001年沈阳市区的中等热岛强度以上区域主要集中在三环内和苏家屯区,强热岛地区主要出现在铁西区和皇姑区;2010年中等以上热岛强度地区,与城区发展相一致,主要向西南,南面扩展,弱热岛、中等强度和强热岛地区有较大幅度的增加;城市热岛强度与NDVI存在显著的负相关,与NDBI存在显著的正相关;热岛源在区域内的面积比重与中等强度以上热岛存在较显著的相关关系,源汇边界长度与弱热岛和中等热岛存在较显著的相关关系。     

Canopy closure estimation in a temperate forest using airborne LiDAR and LANDSAT ETM+ data

Aims　Forest canopy closure is one of the essential factors in forest survey, and plays an important role in forest ecosystem management. It is of great significance to study how to apply LiDAR (light detection and ranging) data efficiently in remote sensing estimation of forest canopy closure. LiDAR can be used to obtain data fast and accurately and therefore be used as training and validation data to estimate forest canopy closure in large spatial scale. It can compensate for the insufficiency (e.g. labor-intensive, time-consuming) of conventional ground survey, and provide foundations to forest inventory.Methods　In this study, we estimated canopy closure of a temperate forest in Genhe forest of Da Hinggan Ling area, Nei Mongol, China, using LiDAR and LANDSAT ETM+ data. Firstly, we calculated the canopy closure from ALS (Airborne Laser Scanning) high density point cloud data. Then, the estimated canopy closure from ALS data was used as training and validation data to modeling and inversion from eight vegetation indices computed from LANDSAT ETM+ data. Three approaches, multi-variable stepwise regression (MSR), random forest (RF) and Cubist, were developed and tested to estimate canopy closure from these vegetation indices, respectively.Important findings The validation results showed that the Cubist model yielded the highest accuracy compared to the other two models (determination coefficient (R²) = 0.722, root mean square error (RMSE) = 0.126, relative root mean square error (rRMSE) = 0.209, estimation accuracy (EA) = 79.883%). The combination of LiDAR data and LANDSAT ETM+ showed great potential to accurately estimate the canopy closure of the temperate forest. However, the model prediction capability needs to be further improved in order to be applied in larger spatial scale. More independent variables from other remotely sensed datasets, e.g. topographic data, texture information from high-resolution imagery, should be added into the model. These variables can help to reduce the influence of optical image, vegetation indices, terrain and shadow and so on. Moreover, the accuracy of the LiDAR-derived canopy closure needs to be further validated in future studies.     

极端干旱区尾闾湖生态需水估算——以东居延海为例

以东居延海为研究对象,利用遥感技术目视解译ETM影像,提取东居延海2002—2012年各月湖面面积。通过水文保证率法确定不同保证率下的湖面面积,结合额济纳旗气象站观测的风速、相对湿度、气温、水汽压、降水量等气象数据估算湖泊蒸发耗水量和湖泊降水补给量,根据湖泊渗漏系数估算湖泊渗漏量,最后运用水平衡原理构建湖泊生态需水模型,估算了东居延海在湖面面积保证率为50%、75%、95%时各月月均和年均生态需水量,其中年均生态需水量分别为1.78×108、1.60×108、1.03×108m3,约占莺落峡年均径流量的9.66%、8.66%、5.59%,约占正义峡年均径流量的16.27%、14.60%、9.42%,约占狼心山年均径流量的30.81%、27.65%、17.84%。     

Classification and change detection of built-up lands from Landsat-7 ETM+ and Landsat-8 OLI/TIRS imageries: A comparative assessment of various spectral indices

Since the mid-1990s, various spectral indices have been proposed for the rapid and accurate classification of built-up lands from satellite imageries. However, a comprehensive comparison between these indices as applied to various satellite imageries is still lacking. Hence, this study examines and compares the performance of six spectral indices in the classification and change detection of built-up lands from Landsat-7 ETM+ (Enhanced Thematic Mapper Plus) and Landsat-8 OLI/TIRS (Operational Land Imager/Thermal Infrared Sensor) imageries. It includes three mid-infrared (MIR)-based indices, i.e. the urban index (UI), the normalized difference built-up index (NDBI), and the index-based built-up index (IBI), two proposed visible (Vis)-based indices, i.e. the VrNIR-BI and VgNIR-BI or the visible red/green-based built-up indices, and one thermal infrared (TIR)-based index, i.e. the normalized difference impervious surface index (NDISI). In addition, a water index, i.e. the modified normalized difference water index (MNDWI), was also derived. Otsu's method was used to separate water from the non-water areas on the MNDWI map. Subsequently, a water mask was produced and used to mask all the built-up index maps, leaving only the non-water areas. Using the same thresholding method, the non-water areas of all the built-up index maps were classified into built-up and non-built-up classes. The classification accuracy was assessed using 5000 reference points for each image. The results show that the VrNIR-BI, with an overall accuracy of 92.50% (Landsat-7) and 92.28% (Landsat-8), and the VgNIR-BI, with 92.78% (Landsat-7) and 92.14% (Landsat-8) overall accuracy, were more robust and superior. They were more accurate than the other indices by up to 8% for the Landsat-7 ETM+ image and 10% for the Landsat-8 image OLI/TIRS image. The qualitative assessment results also supported these quantitative findings. The results also show indications that the detected spatiotemporal urban LUC changes (i.e. built-up expansions) based on the VrNIR-BI and VgNIR-BI were also the most accurate. These indices, i.e. the proposed Vis-based indices, have better potential in separating built-up lands from dry vegetation, which has been an important challenge in the application of spectral indices for classifying built-up lands from satellite imageries.     

Satellite detection of harmful algal bloom occurrences in Korean waters

Cochlodinium polykrikoides (p) is a planktonic dinoflagellate known to produce red tides responsible for massive fish kills and thereby serious economic loss in Korean coastal waters, particularly during summer and fall seasons. The present study involved analyzing chlorophyll-a (Chl-a) from SeaWiFS ocean color imagery collected over the period 1998–2002 to understand the spatial and temporal aspects of C. polykrikoides blooms that occurred in the enclosed and semi-enclosed bays of the Korean Southeast Sea. NOAA-AVHRR data were used to derive Sea Surface Temperature (SST) to elucidate physical factors affecting the spatial distribution and abundance of C. polykrikoides blooms. The time series of SeaWiFS-derived Chl-a gave an impression that recent red tide events with higher concentrations appeared to span more than 8 weeks during summer and fall seasons and were widespread in most of the Korean Southeast Sea coastal bays and neighboring oceanic waters. Coupled eutrophication and certain oceanic processes were thought to give rise to the formation of massive C. polykrikoides blooms with cell abundances ranging from 1000 to 30,000 cells ml⁻¹, causing heavy mortalities of aquaculture fish and other marine organisms in these areas. Our analysis indicated that Chl-a estimates from SeaWiFS ocean color imagery appeared to be useful in demarcating the locality, spatial extent and distribution of these blooms, but unique identification of C. polykrikoides from non-bloom and sediment dominated waters remains unsuccessful with this data alone. Thus, the classical spectral enhancement and classification techniques such as Forward Principal Component Analysis (FPCA) and Minimum Spectral Distance (MSD) to uniquely identify and better understand C. polykrikoides blooms characteristics from other optical water types were attempted on both low spatial resolution SeaWiFS ocean color imagery and high spatial resolution Landsat-7 ETM+ imagery. Application of these techniques could capture intricate and striking patterns of C. polykrikoides blooms from surrounding non-bloom and sediment dominated waters, providing improved capability of detecting, predicting and monitoring C. polykrikoides bloom in such optically complex waters. The result obtained from MSD classification showed that retrieval of C. polykrikoides bloom from the mixed phase of this bloom with turbid waters was not feasible with the SeaWiFS ocean color imagery, but feasible with Landsat-7 ETM+ imagery that provided more accurate and comparable spatial C. polykrikoides patterns consistent with in situ observations. The dense phase of the bloom estimated from these imageries occupied an area of more than 25 km² around the coastal bays and the mixed phase extended over several hundreds kilometers towards the Southeast Sea offshore due to exchange of water masses caused by coastal and oceanic processes. Sea surface temperature analyzed from AVHRR infrared data captured the northeastward flow of Tsushima Warm Current (TWC) waters that provided favorable environmental conditions for the rapid growth and subsequent southward initiation of C. polykrikoides blooms in hydrodynamically active regions in the Korean Southeast Sea offshore.     

Research on characteristics of biomass distribution in urban forests of Shanghai metropolis based on remote sensing and spatial analysis

Aims
Monitoring and quantifying the biomass and its distribution in urban trees and forests are crucial to understanding the role of vegetation in an urban environment. In this paper, an estimation method for biomass of urban forests was developed for the Shanghai metropolis, China, based on spatial analysis and a wide variety of data from field inventory and remote sensing.
Methods
An optimal regression model between forest biomass and auxiliary variables was established by stepwise regression analysis. The residual value of regression model was computed for each of the sites sampled and interpolated by Inverse-distance weighting (IDW) to predict residual errors of other sites not subjected to sampling. Forest biomass in the study area was estimated by combining the regression model based on remote sensing image data and residual errors of spatial distribution map. According to the distribution of plantations and management practices, a total of 93 sample plots were established between June 2011 and June 2012 in the Shanghai metropolis. To determine a suitable model, several spectral vegetation indices relating to forest biomass and structure such as normalized difference vegetation index (NDVI), ratio vegetation index (RVI), difference vegetation index (DVI), soil-adjusted vegetation index (SAVI), and modified soil-adjusted vegetation index (MSAVI), and new images synthesized through band combinations such as the sum of TM2, TM3 and TM4 (denoted Band 234), and the sum of TM3, TM4 and TM5 (denoted Band 345) were used as alternative auxiliary parameters .
Important findings
The biomass density in urban forests of the Shanghai metropolis varied from 15 to 120 t·hm^-2. The higher densities of forest biomass concentrated mostly in the urban areas, e.g. in districts of Jing’an and Huangpu, mostly ranging from 35 to 70 t·hm^-2. Suburban localities such as the districts of Jiading and Qingpu had lower biomass densities at around 15 to 50 t·hm^-2. The biomass density of Cinnamomum camphora trees across the Shanghai metropolis varied between 20 and 110 t·hm^-2. The spatial biomass distribution of urban forests displayed a tendency of higher densities in northeastern areas and lower densities in southwestern areas. The total biomass was 3.57 million tons (Tg) for urban forests and 1.33 Tg for C. camphora trees. The overall forest biomass was also found to be distributed mostly in the suburban areas with a fraction of 93.9%, whereas the urban areas shared a fraction of only 6.1%. In terms of the areas, the suburban and urban forests accounted for 95.44% and 4.56%, respectively, of the total areas in the Shanghai metropolis. Among all the administrative districts, the Chongming county and the new district of Pudong had the highest and the second highest biomass, accounting for 20.1% and 19.18% of the total forest biomass, respectively. In contrast, the Jing’an district accounted for only 0.11% of the total forest biomass. The root-mean-square error (RMSE), mean absolute error (MAE) and mean relative error (MRE) of the model for estimating urban forest biomass in this study were 8.39, 6.86 and 24.22%, respectively, decreasing by 57.69%, 55.43% and 64.00% compared to the original simple regression model and by 62.21%, 58.50%, 65.40% compared to the spatial analysis method. Our results indicated that a more efficient way to estimate urban forest biomass in the Shanghai metropolis might be achieved by combining spatial analysis with regression analysis. In fact, the estimated results based on the proposed model are also more comparable to the up-scaled forest inventory data at a city scale than the results obtained using regression analysis or spatial analysis alone.     

高原草被退化程度的遥感定量监测——以甘肃省玛曲县为例

以加权草被盖度作为草被退化的一种标识值,来标记草场沙化和毒杂草侵蚀的程度,并以机器辨识方法从TM/ETM数据中定量提取加权草被盖度现状及变迁信息。变迁研究的时段从1994年至2008年。为了使图像辨识特征量对高寒草被盖度敏感,提出了植被指数分级密度、植被相对饱和度分级密度等8个新的数学描述符,逐一进行了与加权草被盖度的相关性分析或分割实验;并通过它们的组合训练学习机和实现了对不同盖度草被的划分。通过野外采样数据检核,这种分类的准确率接近或达到80%。在草被盖度正确分类的基础上,通过调整减少不同时相图像照度差异的影响,进一步实现了加权草被盖度变迁信息的自动化提取。     

Suspended matter in the Scheldt estuary

The Scheldt estuary is characterised by a specific energy pattern resulting from the interaction of wave energy, tidal energy and river energy. It divides the estuary into three parts and governs suspended matter transport and distribution pattern. Observation of suspended matter transport shows the existence of three estuarine turbidity maxima (ETM), a marine-dominated ETM in the lower estuary at the river mouth, a river-dominated ETM in the upper estuary with suspended matter concentration reaching up to 300 mg/l, and the most important tide-dominated ETM in the middle estuary with suspended matter concentrations from several hundred milligrams per litre up to a few grams per litre. Resuspension is the dominant phenomenon in this last ETM due to the tidal related bottom scour, which is initiated when a critical erosion velocity of 0.56 m/s is exceeded. An assessment of residual current along the axis of the estuary shows distinctive pattern between the surface water flow and the near bottom water flow. Also the local morphology of the river, natural or man-made, has a prominent effect on the orientation and strength of the residual currents flowing along either side of the river or river bend. Evaluation of suspended matter concentration in relation to the current flow shows no systematic correlation either because of phenomena as scour lag and settling lag mainly in the middle estuary, or because of the current independency character of uniform-suspension mainly in the upper and lower estuary. Quantification of suspended matter load exhibits a net downstream transport from the upper estuary, a near-equilibrium sustainable status in the middle estuary and a net upstream transport of suspended matter from the lower estuary. The characteristic of suspended matter is induced by and is a function of e.g. tidal phase, spring-neap tide, longitudinal and vertical distribution mechanisms, seasons, short and long terms of anthropogenic influence and/or estuarine maintenance. Suspended matter is dominated by complex and cohesive organo-mineral aggregates. It consists of a variable amount of an inorganic fraction (average of 89%) and an organic fraction and occurs largely as flocs, the size of which is remarkably larger in the upper estuary and smallest within the ETM in the middle estuary. Independent time series measurements (1990–2000) of suspended matter property show an increasing sand fraction, a decreasing organic matter content, a rise in ¹³C as well as a decrease in water transparency. These independent measurements exhibit coherent consequences of estuarine maintenance operations. Maintenance dredging of the shipping channel and harbours and dumping operation in the Scheldt strengthen marine influence further landward, resulting in a sustained tidal range increment and upstream flow and transport of suspended matter.     

Research on characteristics of biomass distribution in urban forests of Shanghai metropolis based on remote sensing and spatial analysis北大核心CSCD

Aims Monitoring and quantifying the biomass and its distribution in urban trees and forests are crucial to understanding the role of vegetation in an urban environment. In this paper, an estimation method for biomass of urban forests was developed for the Shanghai metropolis, China, based on spatial analysis and a wide variety of data from field inventory and remote sensing. Methods An optimal regression model between forest biomass and auxiliary variables was established by stepwise regression analysis. The residual value of regression model was computed for each of the sites sampled and interpolated by Inverse-distance weighting (IDW) to predict residual errors of other sites not subjected to sampling. Forest biomass in the study area was estimated by combining the regression model based on remote sensing image data and residual errors of spatial distribution map. According to the distribution of plantations and management practices, a total of 93 sample plots were established between June 2011 and June 2012 in the Shanghai metropolis. To determine a suitable model, several spectral vegetation indices relating to forest biomass and structure such as normalized difference vegetation index (NDVI), ratio vegetation index (RVI), difference vegetation index (DVI), soil-adjusted vegetation index (SAVI), and modified soil-adjusted vegetation index (MSAVI), and new images synthesized through band combinations such as the sum of TM2, TM3 and TM4 (denoted Band 234), and the sum of TM3, TM4 and TM5 (denoted Band 345) were used as alternative auxiliary parameters . Important findings The biomass density in urban forests of the Shanghai metropolis varied from 15 to 120 t•hm2. The higher densities of forest biomass concentrated mostly in the urban areas, e.g. in districts of Jing'an and Huangpu, mostly ranging from 35 to 70 t•hm2. Suburban localities such as the districts of Jiading and Qingpu had lower biomass densities at around 15 to 50 t•hm2. The biomass density of Cinnamomum camphora trees across the Shanghai metropolis varied between 20 and 110 t•hm2. The spatial biomass distribution of urban forests displayed a tendency of higher densities in northeastern areas and lower densities in southwestern areas. The total biomass was 3.57 million tons (Tg) for urban forests and 1.33 Tg for C. camphora trees. The overall forest biomass was also found to be distributed mostly in the suburban areas with a fraction of 93.9%, whereas the urban areas shared a fraction of only 6.1%. In terms of the areas, the suburban and urban forests accounted for 95.44% and 4.56%, respectively, of the total areas in the Shanghai metropolis. Among all the administrative districts, the Chongming county and the new district of Pudong had the highest and the second highest biomass, accounting for 20.1% and 19.18% of the total forest biomass, respectively. In contrast, the Jing'an district accounted for only 0.11% of the total forest biomass. The root-mean-square error (RMSE), mean absolute error (MAE) and mean relative error (MRE) of the model for estimating urban forest biomass in this study were 8.39, 6.86 and 24.22%, respectively, decreasing by 57.69%, 55.43% and 64.00% compared to the original simple regression model and by 62.21%, 58.50%, 65.40% compared to the spatial analysis method. Our results indicated that a more efficient way to estimate urban forest biomass in the Shanghai metropolis might be achieved by combining spatial analysis with regression analysis. In fact, the estimated results based on the proposed model are also more comparable to the up-scaled forest inventory data at a city scale than the results obtained using regression analysis or spatial analysis alone.