Thematic Mapper Images Research Articles

基于RS和GIS的玛曲高原土地沙漠化时空演变研究

PDF HTML阅读 XML下载 导出引用 引用提醒 基于RS和GIS的玛曲高原土地沙漠化时空演变研究 DOI: 10.5846/stxb201509111881 作者: 作者单位: 西北师范大学地理与环境科学学院,西北师范大学地理与环境科学学院,西北师范大学地理与环境科学学院,西北师范大学地理与环境科学学院 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金项目（41161036，41171018）；甘肃省自然科学基金项目（1010RJZA016）；西北师范大学青年教师科研能力提升计划（NWNU-LKON-10-34） Spatio-temporal change in desertification of the Maqu Plateau based on RS and GIS Author: Affiliation: College of Geography and Environment Science, Northwest Normal University,,, Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:基于RS和GIS空间分析技术，以玛曲高原多期Landsat遥感数据为主要信息源，监测分析了1977-2014年玛曲高原沙漠化土地的时空演变规律。结果表明：近40年来玛曲高原的土地沙漠化经历了迅速发展-缓慢发展-急速逆转-缓慢逆转-快速逆转的变化过程。1977-1995年玛曲高原沙漠化处于快速扩张阶段，年均扩张速率为3.01%；1995-2014年沙漠化扩张趋势出现逆转，年均逆转速率为4.14%。沙漠化土地的发展和逆转区域主要分布在东北部欧拉秀玛乡和尼玛乡黄河沿岸、东南部采日玛乡和曼日玛乡沼泽区、西南部齐哈玛乡和中部阿万仓乡黄河支流两岸。玛曲高原沙漠化土地的发展和逆转过程并非逐级递增或递减，而是在15-20年内交替出现，不同等级沙漠化土地之间的转移也存在明显的跃变。从整体来看，玛曲高原土地沙漠化呈现逆转趋势，区域生态环境得到明显改善。 Abstract:The Maqu plateau, an important part of the Qinghai-Tibet plateau, is the main water conservation area in the upper reaches of the Yellow River. In recent years, under the influence of natural environmental and human activities, the ecologically sensitive Maqu plateau area has experienced environmental problems such as glacier retreat, permafrost degradation, aeolian desertification, and grassland degradation. The Maqu plateau is one of the largest pasture lands in Asia, with a rangeland area of 8.58×107hm2. In the past decade, the eco-environmental problems of the Qinghai-Tibetan plateau have received increasing attention owing to global warming and intensifying regional development. The aim of this study was to quantify the developmental processes in these region, as well as any changes in the spatial distribution of land desertification. We used remote sensing (RS) and geographic information system (GIS) technologies, and applied multi-temporal Landsat imagery to investigate the spatio-temporal development of land desertification in the Maqu Plateau during 1977-2014. The remote-sensing data used in this study include:Landsat multi-spectral scanner images from 1977; Thematic Mapper images from 1989, 1995, 2006, and 2014; and Enhanced Thematic Mapper images from 2001 and 2010. We mainly selected images recorded between June and October, because aeolian desertified lands are more easily recognized during this period of greater vegetation growth. Our results indicated that the area of desertified land in the Maqu plateau had experienced five main phases:rapid development, where the area of aeolian desertified land increased at a rate of 2004.6 hm2/a between 1977 and 1989; slow development, where it increased at a rate of 57 hm2/a between 1989 and 1995; rapid reverse, where it decreased at a rate of 3868 hm2/a between 1995 and 2006; slow reverse, where it decreased at a rate of 893.4 hm2/a between 2006 and 2010; and, fast reverse, where it decreased at a rate of 2129.3 hm2/a between 2010 and 2014. The area of desertified land expanded rapidly between 1977 and 1995, with an average growth rate of 3.01% per year. An obvious decrease in the area of desertified land was observed from 1995 to 2014, with an average reversal rate of 4.14% per year. The areas affected by the development and reversal of aeolian desertification were mainly distributed alongside the Yellow River, including near the Oulaxiuma and Nima villages of the northeastern region, the marshlands of the Cairuma and Manrima villages in the southeastern region, and the riversides of tributaries to the Yellow River near the Qihama and Awancang villages of the southwestern region. We found that the spatial distribution of aeolian desertification changed significantly over time, with fragmentation of the desertified land increasing rapidly between 1977 and 1995. Aeolian desertification in the Maqu Plateau has not been progressive, but instead, it has been an alternating process of development and reversal approximately every 15 to 20 years. There was an abrupt change in the desertification level of lands in different levels of the plateau. Overall, we found that the trend of aeolian desertification in the Maqu plateau had been reversed and the local environment had improved since 1995. 参考文献 相似文献 引证文献

Comparison of military and nonmilitary land condition using an image derived soil erosion cover factor

Land condition of military installations varies spatially and temporally mainly due to variable intensity and frequency of military training induced disturbance. The existing ground-based methods to assess the land condition are costly and time consuming. There is thus a strong need to develop an efficient and low-cost methodology for modeling and monitoring of the land condition for military installations and compare it with nonmilitary land to assess the training induced impacts. For this purpose, in this study Fort Riley military installation (FR) and its neighboring Konza Prairie Biological Station (KPBS) were chosen. A remote sensing based linear spectral mixture (LSM) analysis method was proposed to model, monitor, and compare the land conditions of both sites by developing a soil erosion relevant and image derived cover factor (ICF) using both Landsat Thematic Mapper and Moderate Resolution Imaging Spectroradiometer (MODIS) images. The results showed that (1) the LSM based ICF was statistically significantly correlated with military training induced disturbance intensity and can be used to quantify the land conditions; (2) the ICF provided the potential to generate spatially and temporally explicit estimates of the land conditions; (3) the ICF time series revealed that the land condition of FR fluctuated with a slight decreasing trend of ICF values, but statistically the trend was not significant; and (4) small ICF values dominated both FR and KPBS, but compared to that in the nonmilitary land KPBS, military training led to significantly poorer land condition in the military land FR. Overall, the ICF method provides a potential tool to spatially and temporally assess land conditions of military installations. It is expected that this method can be applied to assess land conditions for other military and nonmilitary areas.

阿拉善马鹿(Cervus alashanicus)生境适宜性评价

PDF HTML阅读 XML下载 导出引用 引用提醒 阿拉善马鹿(Cervus alashanicus)生境适宜性评价 DOI: 10.5846/stxb201603250534 作者: 作者单位: 东北林业大学野生动物资源学院,东北林业大学野生动物资源学院,东北林业大学野生动物资源学院,宁夏贺兰山国家级自然保护区管理局,东北林业大学野生动物资源学院 作者简介: 通讯作者: 中图分类号: 基金项目: 中央高校基本科研业务费资助项目（2572014CA03，DL13EA01）；国家自然科学基金资助项目（31372221） Habitat assessment of red deer (Cervus alashanicus) in the Helan Mountains, China Author: Affiliation: College of Wildlife Resources,Northeast Forestry University,College of Wildlife Resources,Northeast Forestry University,College of Wildlife Resources,Northeast Forestry University,Helan Mountains National Nature Reserve of Ningxia,College of Wildlife Resources,Northeast Forestry University Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:阿拉善马鹿（Cervus alashanicus）目前仅分布于贺兰山地区，对该物种进行生境适宜性的评价和分析是物种有效保护的前提和基础。2013-2014年通过样线调查及巡山资料查询，确定阿拉善马鹿出现位点86个，结合13种环境变量数据，利用最大熵（maximum entropy，MaxEnt）模型，并根据最大约登指数划定适宜与不适宜生境区，对贺兰山地区阿拉善马鹿的生境适宜性进行评价。ROC曲线（receiver operating characteristic curve）检测证明模型预测精度较高，研究结果表明：阿拉善马鹿主要分布于贺兰山东坡的中部和南部，以及西坡的中北部，适宜生境面积为667.87 km2，占研究区域面积的18.2%；矿区、坡度和海拔是影响阿拉善马鹿分布的最主要环境变量，矿区对阿拉善马鹿的影响最大，建议管理部门加大对此人为干扰的管控力度，控制和减少现有矿区的规模，以促进该种群的发展。 Abstract:Cervus alashanicus only occurs in the Helan Mountains. Evaluating the habitat suitability of Cervus alashanicus is the premise and foundation of effective protection for this population. We used a Geographic Information Systems (GIS) and the Maximum Entropy (MaxEnt) model to predict habitat suitability for Cervus alashanicus in the Helan Mountains. The data needed in this research, included occurrence records of Cervus alashanicus and environmental factors. We collected 86 GPS coordinates for Cervus alashanicus occurrance obtained by line transect surveys and patrol data from the Helan Mountain National Nature Reserve from 2013 to 2014. Environmental factors were grouped into three categories, including topographic data (elevation, slope, and aspect) extracted from the Digital Elevation Model (DEM), six vegetation factors extracted from the Thematic Mapper (TM) image of the Helan Mountains (2007), and water resources and human interference factors, including roads, mining, and forest protection points extracted from the 1 : 50000 vector maps of the Helan Mountains. GIS was used to produce the data needed in the model. The distribution of Cervus alashanicus was predicted using the model of MaxEnt. The area under the curve (AUC) values of a Receiver Operating Characteristic (ROC) curve was used to assess the accuracy of the MaxEnt model, and the Youden Index was applied to determine the threshold value for habitat classification in the MaxEnt model. The simulated habitat was divided into two categories, including suitable and unsuitable habitat using a threshold value of 0.31. Finally, the distribution map of habitat suitability of Cervus alashanicus in the Helan Mountains was generated from the data and software, and the AUC value for training data and test data were 0.956 and 0.898 respectively, which indicated that the result of the assessment was excellent. The results showed that suitable habitat for Cervus alashanicus was primarily distributed in the central and southern regions of the Ningxia Helan Mountain National Nature Reserve (eastern part) and to a lesser extent in the northern region of the Inner-Mongolia Helan Mountain National Nature Reserve (western part), with a total area of 667.87 km2, accounting for 18.2% of the total assessment area. Most northern areas of Helan Mountains were unsuitable habitat for Cervus alashanicus because of lower precipitation and more mining. Jackknife tests and the contribution of environmental variables indicated that mining (30.4%), slope (26%), altitude (10.8%), and aspect (8.4%) were the main factors affecting habitat selection of Cervus alashanicus, followed by the forest protection points (5.7%), grasslands (4.2%), roads (4.2%), shrubs (4%), alpine meadows (3.8%), broad-leaved forests (1.3%), and water resources (0.9%). Coniferous forest and desert had little effect on the habitat selection of Cervus alashanicus. The response curves showed that habitat suitability increased gradually with the increase of distance from mine and road, and decreased when the slop was more than 30°. Results from our study suggested that mining was the most serious human interference for the Cervus alashanicus. Managers need to take more regulatory actions to protect the suitable habitat areas of Cervus alashanicus in order to promote the conservation of this population. 参考文献 相似文献 引证文献

Transition Modeling of Land-Use Dynamics in the Pipestem Creek, North Dakota, USA

Significant land-use changes in North Dakota have been reported and are widespread over the entire state. Such changing patterns may portend localized impairment to agricultural watersheds. In this study, Land-use Land-cover (LULC) change was modeled using geostatistics. The study area was within the Pipestem Creek watershed, a part of the Missouri Watershed James Subregion of North Dakota, USA. Landsat Thematic mapper images from the years 2007, 2011 and 2015 were used as preliminary data. LULC information for these datasets was acquired from the Global Land-cover facility and Landsat Program. Data analysis, spectral classification and post classification techniques were applied on the datasets. A transition matrix was derived using a Markov chain Monte Carlo (MCMC) model. This study demonstrates that the integration of satellite remote sensing, GIS and statistics may be an effective approach for analyzing the direction, rate, and spatial pattern of land-use change.

Burn severity mapping from Landsat MESMA fraction images and Land Surface Temperature

Forest fires are incidents of great importance in Mediterranean environments. Landsat data have proven to be suitable for evaluating post-fire vegetation damage and determining different levels of burn severity, which is crucial for planning post-fire rehabilitation. This study assessed the utility of combined Multiple Endmember Spectral Mixture Analysis (MESMA) fraction images and Land Surface Temperature (LST) to accurately map burn severity. We studied a large convection-dominated wildfire, which occurred on 19–21 September 2012 in Spain, in a zone dominated by Pinus pinaster Ait. Burn severity degree (low, moderate, and high) was measured 2–3months after fire in 111 field plots using the Composite Burn Index (CBI). Four fraction images were generated using MESMA from the reflective bands of a post-fire Landsat 7 Enhanced Thematic Mapper (ETM+) image: 1.-char, 2.-green vegetation (GV), 3.-non-photosynthetic vegetation and soil (NPVS) and 4.-shade. The thermal band was converted to LST using a single channel algorithm. Next, Multinomial Logistic Regression (MLR) was used to obtain the probability of each burn severity level from MESMA fraction images and LST. Finally, a burn severity map was generated from the probability images and independently validated using an error matrix, producer and user accuracies per class, and κ statistic. MLR identified the char fraction image and LST as the only significant explanatory variables when burn severity acted as the response variable. Two burn severity degrees (low-moderate and high) were finally considered to build the final burn severity map. In this way, we reached a higher accuracy (κ=0.79) than using the original three burn severity levels (κ=0.66). Our study demonstrates the validity of combining fraction images and LST from Landsat data to map burn severity accurately in Mediterranean countries.

Landscape changes of the Ejin Delta in the Heihe River Basin in Northwest China from 1930 to 2010

ABSTRACTThe Heihe River Basin is located in the arid and semi-arid region of Northwest China; during the past 80 years, this basin has experienced water resource competition between irrigation agriculture and ecological demand in its middle and lower reaches, respectively. The land cover of the Ejin Delta in the lower reaches of the Heihe River Basin was interpreted and analysed for four different periods using a map created by Dr Sven Hedin in the 1930s, Corona satellite images taken in 1961, and Landsat Thematic Mapper (TM) images taken in 2000 and 2010. Overall, the results show that (1) the coarse resolution of the 1930s map increased the uncertainty of analysis in the study area and (2) the river area in the Ejin Delta decreased by 91.0% from the 1930s to 2000. In addition, two major terminal lakes, Gaxun Nuur Lake and Sogo Nuur Lake, dried up in 1961 and 1992, respectively, and the area of Populus euphratica decreased by 76.1% from the 1930s to 2000. Most reeds were overtaken by shrubs between the 1930s and 1961, which caused the area of reeds to decrease from 3481 to 1332 km2 and the area of shrubs to increase from 805 to 2795 km2. From the 1930s to 2000, the desert and alkaline land areas increased by 42.2% and 52.4%, respectively. (3) After the water transfer project was implemented in 2000, the area of Sogo Nuur Lake recovered to 40.58 km2 by 2010. The areas of Populus euphratica, shrubland, and reedland showed a recovering trend, with increases of 4.5%, 6.5%, and 43.5%, respectively, by 2010. The desert and alkaline land areas decreased by 4.2% and 15.2%, respectively, by 2010. The area of cultivated land increased from 25 km2 in 1961 to 85 km2 in 2000 and rapidly approached 160 km2 in 2010. These changes over time indicated that the ecological habitat in the Ejin Delta deteriorated between the 1930s and 2000. However, the water transfer project effectively changed the degradation trend.

Lake water surface mapping in the Tibetan Plateau using the MODIS MOD09Q1 product

ABSTRACTThe Tibetan Plateau (TP) has the largest number of inland lakes with the highest elevation on the planet. Mapping the distribution of lake water in space and time is crucial for scientific research of interactions among the regional cryosphere, hydrosphere, and atmosphere. In this study, a lake water surface mapping algorithm is developed for Moderate Resolution Imaging Spectroradiometer (MODIS) MOD09Q1 surface reflectance images, which is used to produce the 8-day lake water surface data set (lake water surface area larger than 1 km2) of theTP (Qinghai–Tibet Plateau) for the period of 2000–2012. The accuracy analysis indicate that compared with water surface data of the 134 sample lakes extracted from the 30 m Landsat Thematic Mapper (TM) images, the average overall accuracy of the results is 91.81% with average commission and omission error of 3.26% and 5.38%; the results also show strong linear (the coefficient of determination R2 is 0.9991) correlation with the global MODIS water mask data set with overall accuracy of 86.30%; and the lake area difference between the Second National Lake Survey and this study is only 4.74%, respectively. This study provides reliable data set for the lake change research of theTP in the recent decade.

PRINCIPAL COMPONENT ANALYSIS AND MORPHOSTRUCTURAL CHARACTERIZATION OF A PORTION OF THE EASTERN CONTINENTAL SHELF OF CEARÁ, BRAZIL, USING LANDSAT 5-TM IMAGES

This study used Landsat 5 Thematic Mapper (5-TM) images in the morphostructural characterization of the shallow continental shelf of the eastern coast of Ceara (CE), in the Jaguaribe river mouth. The dam built near the river mouth to transpose the water towards Fortaleza, CE, ensures good water transparency since little of the river-carried sediment reaches the sea. The used image was captured on date and time to ensure low tide. Data using Secchi disc indicated good water transparency in the turbidity zone and seaside, coastal and marine areas. Bathymetry studies and underwater photos confirm the existence of many of the features described in this study. Digital image processing techniques have been applied in the study: colored compositions (RGB 124), 90° directional filter (band 1), and a colored composition employing the filtered band 1 and band 2 and 4. The filtered image of band 1 allowed recognizing different features when interpreted in detail scale. The principal component analysis (PCA) was used here, calculating eigenvalues and eigenvectors. When applied to bands 1 to 5 and 7, PCA generated good results in PC2, whose correlation with the blue band was 0.85 (explained cumulative variance of more than 96%). Applied to the visible bands, PCA produced very similar results in PC1, and the correlation between the blue band and PC1 was 0.86 (explained cumulative variance of 94%). These results show that band 1 is the main contributor in studies of submerged morphological features. Each process resulted in a new image, from which it was possible to produce the map of the area representing morphostructural characteristics. The use of digitally processed satellite images in the visible region greatly improves the characterization and mapping of submerged features in places with shallow and good transparency waters.

Evaluation of Multiple Classifier Systems for Landslide Identification in LANDSAT Thematic Mapper (TM) Images

Landslide scar location is fundamental for the risk management process, e.g., it allows mitigation of these areas, decreasing the associated hazards for the population. Remote sensing data usage is an essential tool for landslide identification, mapping, and monitoring. Despite its potential use for landslide risk management, remote sensing usage does have a few drawbacks. The aforementioned events commonly occur at high steep slope regions, frequently associated with shadow occurrence in satellite images, which impairs the identification process and results in low accuracy classifications. In this sense, this paper aims to evaluate the accuracy of different ensembles of multiple classifier systems (MCSs) for landslide scar identification. A severe landslide event on a steep slope with a high rainfall rate area in the southeast region of Brazil was chosen. Ten supervised classifiers were used to identify this severe event and other possible features for the LANDSAT thematic mapper (TM) from June of 2000. The results were evaluated, and nine MCSs were constructed based on the accuracy of the classifiers. Voting was applied through the ensemble method, coupled with contextual analysis and random selection tie-breaker methods. Accuracy was evaluated for each classification ensemble, and a progressive enhancement in the ensemble accuracy was noted as the least accurate classifiers were removed. The best accuracy for landslide identification emerged from the ensemble of the three most accurate classification results. In summary, MCS application generally improved the classification quality and led to fewer omission errors, coupled with a better classification percentage for the ‘landslide’ class. However, the MCS ensemble algorithm selection must be customized to the purpose of the classification. It is crucial to assess single accuracy indicators of each algorithm to ascertain those with the most consistent performance regarding the final results.

Landsat 5 Thematic Mapper reflectance and NDVI 27-year time series inconsistencies due to satellite orbit change

Abstract The Landsat 5 Thematic Mapper (TM) sensor provided the longest single mission terrestrial remote sensing data record but temporally sparse station keeping maneuvers meant that the Landsat 5 orbit changed over the 27 year mission life. Long-term Landsat 5 TM reflectance inconsistencies may be introduced by orbit change induced solar zenith variations combined with surface reflectance anisotropy, commonly described by the Bi-directional Reflectance Distribution Function (BRDF). This study quantifies the local overpass time and observed solar zenith angle changes for all the Landsat 5 TM images available at two latitudinally separated locations along the same north-south Landsat path (27) in Minnesota (row 26) and Texas (row 42). Over the 27 years the Landsat 5 orbit changed by nearly 1 h and resulted in changes in the Landsat 5 observed solar zenith angle of > 10°. The Landsat 5 orbit was relatively stable from 1984 to 1994 and from 2007 to 2011, but changed rapidly from 1995 to 2000, and from 2003 to 2007. Rather than directly examine Landsat 5 TM reflectance time series a modelling approach was used. This was necessary because unambiguous separation of orbit change induced Landsat reflectance variations from other temporal variations is non-trivial. The impact of Landsat 5 orbit induced observed solar zenith angle variations on the red and near-infrared reflectance and derived normalized difference vegetation index (NDVI) values were modelled with respect to different Moderate-Resolution Imaging Spectroradiometer (MODIS) BRDF land cover types. Synthetic nadir BRDF-adjusted reflectance (NBAR) for the Landsat 5 TM observed and a modelled reference year 2011 solar zenith were compared over the 27 years of acquisitions. Ordinary least squares linear regression fits of the NBAR difference values as a function of the acquisition date indicated an increasing trend in red and near-infrared NBAR and a decreasing trend in NDVI NBAR due to orbit changes. The trends are statistically significant but small, no more than 0.0006 NDVI/year. Comparison of certain years of Landsat 5 data may result in significant reflectance and NDVI differences due only to Landsat 5 orbit changes and cause spurious detection of “browning” vegetation events and underestimation of greening trends. The greatest differences will occur when 1995 Landsat 5 TM data are compared with 2007 to 2011 data; NDVI values could be up to 0.11 greater in 1995 than in 2011 for anisotropic land cover types and up to 0.05 greater for average CONUS land cover types. A smaller number of Landsat 5 TM images were also examined and provide support for the modelled based findings. The paper concludes with a discussion of the implications of the research findings for Landsat 5 TM time series analyses.

Landsat Imagery Spectral Trajectories—Important Variables for Spatially Predicting the Risks of Bark Beetle Disturbance

Tree mortality caused by bark beetle infestation has significant effects on the ecology and value of both natural and commercial forests. Therefore, prediction of bark beetle infestations is critical in forest management. Existing predictive models, however, rarely consider the influence of long-term stressors on forest susceptibility to bark beetle infestation. In this study we introduce pre-disturbance spectral trajectories from Landsat Thematic Mapper (TM) imagery as an indicator of long-term stress into models of bark beetle infestation together with commonly used environmental predictors. Observations for this study come from forests in the central part of the Šumava Mountains, in the border region between the Czech Republic and Germany, Central Europe. The areas of bark beetle-infested forest were delineated from aerial photographs taken in 1991 and in every year from 1994 to 2000. The environmental predictors represent forest stand attributes (e.g., tree density and distance to the infested forest from previous year) and common abiotic factors, such as topography, climate, geology, and soil. Pre-disturbance spectral trajectories were defined by the linear regression slope of Tasseled Cap components (Wetness, Brightness and Greenness) calculated from a time series of 16 Landsat TM images across years from 1984 until one year before the bark beetle infestation. Using logistic regression and multimodel inference, we calculated predictive models separately for each single year from 1994 to 2000 to account for a possible shift in importance of individual predictors during disturbance. Inclusion of two pre-disturbance spectral trajectories (Wetness slope and Brightness slope) significantly improved predictive ability of bark beetle infestation models. Wetness slope had the greatest predictive power, even relative to environmental predictors, and was relatively stable in its power over the years. Brightness slope improved the model only in the middle of the disturbance period (1996). Importantly, these pre-disturbance predictors were not correlated with other predictors, and therefore bring additional explanatory power to the model. Generally, the predictive power of most fitted model decreases as time progresses and models describing the initial phase of bark beetle outbreaks appear more reliable for conducting near-future predictions. The pre-disturbance spectral trajectories are valuable not only for assessing the risk of bark beetle infestation, but also for detection of long-term gradual changes even in non-forest ecosystems.

Estimating Soil Salinity in the Yellow River Delta, Eastern China—An Integrated Approach Using Spectral and Terrain Indices with the Generalized Additive Model

Soil salinity is one of the most severe environmental problems worldwide. It is necessary to develop a soil-salinity-estimation model to project the spatial distribution of soil salinity. The aims of this study were to use remote sensed images and digital elevation model (DEM) to develop quantitative models for estimating soil salinity and to investigate the influence of vegetation on soil salinity estimation. Digital bands of Landsat Thematic Mapper (TM) images, vegetation indices, and terrain indices were selected as predictive variables for the estimation. The generalized additive model (GAM) was used to analyze the quantitative relationship between soil salt content, spectral properties, and terrain indices. Akaike's information criterion (AIC) was used to select relevant predictive variables for fitted GAMs. A correlation analysis and root mean square error between predicted and observed soil salt contents were used to validate the fitted GAMs. A high ratio of explained deviance suggests that an integrated approach using spectral and terrain indices with GAM was practical and efficient for estimating soil salinity. The performance of the fitted GAMs varied with changes in vegetation cover. Salinity in sparsely vegetated areas was estimated better than in densely vegetated areas. Visible red and near-infrared bands, and the second and third components of the tasseled cap transformation were the most important spectral variables for the estimation. Variable combinations in the fitted GAMs and their contribution varied with changes in vegetation cover. The contribution of terrain indices was smaller than that of spectral indices, possibly due to the low spatial resolution of DEM. This research may provide some beneficial references for regional soil salinity estimation.

Flood prevention dams for arid regions at a micro-scale sub-catchment, case study: Tabuk, Saudi Arabia.

Unexpected flash flooding is one of the periodic hydrological problems affecting the city of Tabuk in Saudi Arabia. The region has high potential for floods as it suffers high rainfall intensity in a short time and also has high urbanization rates and topographic complexity. Constructing flood prevention dams is one option to solve this problem. A cost-effective design requires a detailed feasibility study and analysis for the selection of suitable sites. The aim of this study was to develop a method for selecting a suitable site for flood protection dams in the Abu Saba'a district, the most affected part of the city of Tabuk during the flash flood in January 2013. Spatial analysis was applied using Landsat Thematic Mapper images and Shuttle Radar Topography Mission digital elevation model to select a site in the Abu Saba'a area. A simple model using ArcGIS was built including all suggested parameters. The results showed the best site for a dam was 2 km distance backfrom the area, where all parameter values matched. The results showed that the dynamic properties of land cover can affect site selection. It is therefore suggested that more field and hydrological data should be gathered for greater accuracy.

Mapping population density distribution at multiple scales in Zhejiang Province using Landsat Thematic Mapper and census data

ABSTRACTPopulation density is usually calculated from the census data, but it is dynamic over time and updating population data is often challenging because it is time-consuming and costly. Another problem is that population data for public use are often too coarse, such as at the county scale in China. Previous research on population estimation mainly focused on megacities due to their importance in socio-economic conditions, but has not paid much attention to the township or village scale because of the sparse population density and less importance in economic conditions. In reality, population density in townships and villages plays an important role in land-use/cover change and environmental conditions. It is an urgent task to timely update population density at the township and cell-size scales. Therefore, this article aims to develop an approach to estimate population density at the township scale and at a cell size of 1 km by 1 km through downscaling the population density from county to township and then to cell size. We estimated population density using Landsat Thematic Mapper (TM) and census data in Zhejiang Province, China. Landsat TM images in 2010 were used to map impervious surface area (ISA) distribution using a hybrid approach, in which a decision tree classifier was used to extract ISA data and cluster analysis was used to further modify the ISA results. A population density estimation model was developed at the county scale, and this model was then transferred to the township scale. The population density was finally redistributed to cell-size scale based on the assumption that population only occupied the sites having ISA. This research indicates that most townships have residuals within ±50 persons/km2 with a root mean squared error (RMSE) of 71.56 persons/km2, and a relative RMSE of 27.6%. The spatial patterns of population density distribution at the 1 km2 cell size are much improved compared to the township and county scales. This research indicates the importance of using the ISA for population density estimation, where ISA can be accurately extracted from remotely sensed data.

New method and error analysis of lake retrieval with MetOp-A AVHRR images on the Tibetan Plateau

ABSTRACTLand-surface water is an important factor influencing the regional environment and climate and is a key factor in the Tibetan Plateau, which is one of the most sensitive regions to global changes. Because of the high elevation, complex topography, and erratic weather of the Tibetan Plateau, direct measurement of the area of every lake is largely unfeasible. Moreover, complex natural geographic conditions increase the difficulty of image processing and information extraction with remote sensing because they enhance the uncertainty of quantitative data retrieved with satellites. Methods based on spectral features do not generate the expected results of lake area over the Tibetan Plateau due to a lack of distinction between water and other land objects, especially snow, vegetation, and low cloud cover. Therefore, a new method to extract lake area from satellite images in the Tibetan Plateau is needed. In this article, an automatic method was proposed to evaluate lake area during the wet season (from 1 September to 31 October) on the Tibetan Plateau with multi-day Advanced Very High Resolution Radiometer (AVHRR) remote-sensing images on board the Meteorological Operational satellite-A (MetOp-A) satellite. The method considers both spectral and textural features of lakes and does not need a cloud mask as an input. In addition, the Mixture Tuned Matched Filtering (MTMF) algorithm was applied to decompose the mixed pixels to better identify lakes and estimate the lake area. Based on daily lake identifications, the wet season’s lake data were composited with the maximum value composition (MVC) method to determine the lake area. A comparison of our work with the manually interpreted results from Landsat Thematic Mapper (TM) images and observational reports demonstrates the accuracy and reliability of our approach. However, certain factors, i.e. the sensor zenith angle of the polar-orbit satellite and the topography, can affect the lake area extracted from the remote-sensing images.

Object-based urban structure type pattern recognition from Landsat TM with a Support Vector Machine

ABSTRACTThis study evaluates the potential of object-based image analysis in combination with supervised machine learning to identify urban structure type patterns from Landsat Thematic Mapper (TM) images. The main aim is to assess the influence of several critical choices commonly made during the training stage of a learning machine on the classification performance and to give recommendations for classifier-dependent intelligent training. Particular emphasis is given to assess the influence of size and class distribution of the training data, the approach of training data sampling (user-guided or random) and the type of training samples (squares or segments) on the classification performance of a Support Vector Machine (SVM). Different feature selection algorithms are compared and segmentation and classifier parameters are dynamically tuned for the specific image scene, classification task, and training data. The performance of the classifier is measured against a set of reference data sets from manual image interpretation and furthermore compared on the basis of landscape metrics to a very high resolution reference classification derived from light detection and ranging (lidar) measurements. The study highlights the importance of a careful design of the training stage and dynamically tuned classifier parameters, especially when dealing with noisy data and small training data sets. For the given experimental set-up, the study concludes that given optimized feature space and classifier parameters, training an SVM with segment-shaped samples that were sampled in a guided manner and are balanced between the classes provided the best classification results. If square-shaped samples are used, a random sampling provided better results than a guided selection. Equally balanced sample distributions outperformed unbalanced training sets.

Landscape Mapping: Gaining “Sense of Place” for Conservation in the Manaslu Conservation Area, Nepal

Abstract Satellite mapping contributes significantly to ecological studies of landscape composition and structure but needs to better engage local populations in more culturally sensitive assessments of diversity patterns. This study examines how participatory mapping can contribute to land-cover analyses derived from satellite data of landscapes in the Manaslu Conservation Area (MCA), Nepal. Landscapes were mapped by local residents, first on paper and again on a geo-registered Landsat Thematic Mapper (TM) scene, for April 7, 2010. Local landscape knowledge from men and women was transferred from participatory maps to the classified satellite image to create an integrated landscape map for the MCA. Participatory maps added the significance of “place” to the evaluation of landscape diversity (n = 276 named places for women and 315 for men). The classification of the TM image resulted in seven land cover types with only 10% of the land classified as forests, which are highly fragmented. The final map combi...

CLOUD DETECTION OF OPTICAL SATELLITE IMAGES USING SUPPORT VECTOR MACHINE

Cloud covers are generally present in optical remote-sensing images, which limit the usage of acquired images and increase the difficulty of data analysis, such as image compositing, correction of atmosphere effects, calculations of vegetation induces, land cover classification, and land cover change detection. In previous studies, thresholding is a common and useful method in cloud detection. However, a selected threshold is usually suitable for certain cases or local study areas, and it may be failed in other cases. In other words, thresholding-based methods are data-sensitive. Besides, there are many exceptions to control, and the environment is changed dynamically. Using the same threshold value on various data is not effective. In this study, a threshold-free method based on Support Vector Machine (SVM) is proposed, which can avoid the abovementioned problems. A statistical model is adopted to detect clouds instead of a subjective thresholding-based method, which is the main idea of this study. <br><br> The features used in a classifier is the key to a successful classification. As a result, Automatic Cloud Cover Assessment (ACCA) algorithm, which is based on physical characteristics of clouds, is used to distinguish the clouds and other objects. In the same way, the algorithm called Fmask (Zhu et al., 2012) uses a lot of thresholds and criteria to screen clouds, cloud shadows, and snow. Therefore, the algorithm of feature extraction is based on the ACCA algorithm and Fmask. Spatial and temporal information are also important for satellite images. Consequently, co-occurrence matrix and temporal variance with uniformity of the major principal axis are used in proposed method. We aim to classify images into three groups: cloud, non-cloud and the others. In experiments, images acquired by the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and images containing the landscapes of agriculture, snow area, and island are tested. Experiment results demonstrate the detection accuracy of the proposed method is better than related methods.

SPECTRAL COLOR INDICES BASED GEOSPATIAL MODELING OF SOIL ORGANIC MATTER IN CHITWAN DISTRICT, NEPAL

Abstract. Space Technology provides a resourceful-cost effective means to assess soil nutrients essential for soil management plan. Soil organic matter (SOM) is one of valuable controlling productivity of crops by providing nutrient in farming systems. Geospatial modeling of soil organic matter is essential if there is unavailability of soil test laboratories and its strong spatial correlation. In the present analysis, soil organic matter is modeled from satellite image derived spectral color indices. Brightness Index (BI), Coloration Index (CI), Hue Index (HI), Redness Index (RI) and Saturation Index (SI) were calculated by converting DN value to radiance and radiance to reflectance from Thematic Mapper image. Geospatial model was developed by regressing SOM with color indices and producing multiple regression model using stepwise regression technique. The multiple regression equation between SOM and spectral indices was significant with R = 0. 56 at 95% confidence level. The resulting MLR equation was then used for the spatial prediction for the entire study area. Redness Index was found higher significance in estimating the SOM. It was used to predict SOM as auxiliary variables using cokringing spatial interpolation technique. It was tested in seven VDCs of Chitwan district of Nepal using Thematic Mapper remotely sensed data. SOM was found to be measured ranging from 0.15% to 4.75 %, with a mean of 2.24 %. Remotely sensed data derived spectral color indices have the potential as useful auxiliary variables for estimating SOM content to generate soil fertility management plans.

SPECTRAL COLOR INDICES BASED GEOSPATIAL MODELING OF SOIL ORGANIC MATTER IN CHITWAN DISTRICT, NEPAL

Space Technology provides a resourceful-cost effective means to assess soil nutrients essential for soil management plan. Soil organic matter (SOM) is one of valuable controlling productivity of crops by providing nutrient in farming systems. Geospatial modeling of soil organic matter is essential if there is unavailability of soil test laboratories and its strong spatial correlation. In the present analysis, soil organic matter is modeled from satellite image derived spectral color indices. Brightness Index (BI), Coloration Index (CI), Hue Index (HI), Redness Index (RI) and Saturation Index (SI) were calculated by converting DN value to radiance and radiance to reflectance from Thematic Mapper image. Geospatial model was developed by regressing SOM with color indices and producing multiple regression model using stepwise regression technique. The multiple regression equation between SOM and spectral indices was significant with R = 0. 56 at 95% confidence level. The resulting MLR equation was then used for the spatial prediction for the entire study area. Redness Index was found higher significance in estimating the SOM. It was used to predict SOM as auxiliary variables using cokringing spatial interpolation technique. It was tested in seven VDCs of Chitwan district of Nepal using Thematic Mapper remotely sensed data. SOM was found to be measured ranging from 0.15% to 4.75 %, with a mean of 2.24 %. Remotely sensed data derived spectral color indices have the potential as useful auxiliary variables for estimating SOM content to generate soil fertility management plans.