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Spatio-temporal variations of land surface temperature using Landsat and MODIS: case study of the City of Tshwane, South Africa 基于Landsat和MODIS的地表温度时空变化——以南非Tshwane市为例
IF 0.5 Q4 REMOTE SENSING Pub Date : 2020-09-14 DOI: 10.4314/sajg.v9i2.25
J. Magidi, F. Ahmed
Urbanisation is accelerating urban land use dynamics and this has a significant impact on land surface temperature (LST). Impervious surfaces and increase in air pollution has led to the increase in land surface temperature. This study reports on the use of geospatial technologies to monitor and quantify changes in LST using remotely sensed data in the City of Tshwane. Land surface temperature was retrieved using the winter and summer Landsat datasets for 1997 and 2015 and the MODIS data from 2000 to 2015. Land surface temperature was extracted using emissivity and satellite temperature as input parameters. The spatial and temporal variations in the LST were retrieved to show the effects of land cover change on LST. Change in LST was also analysed on different land cover types using transects across the study area. The study revealed an increase in land surface temperature between the years. It also showed that impervious surfaces had a higher LST compared to the non-impervious surfaces. The results revealed variations in LST between non-cropped and cropped agricultural areas, where the former had higher LST than the latter. Temporal trends revealed a notable increase in LST in the urban areas and there were some seasonal variations in LST with high LST values in summer and low values in winter. Cross-section analysis along transects revealed spatio-temporal thermal variations across different land cover types.
城市化正在加速城市土地利用动态,这对地表温度(LST)产生了重大影响。不透水的地表和空气污染的增加导致了地表温度的升高。这项研究报告了使用地理空间技术,利用茨瓦内市的遥感数据监测和量化LST的变化。使用1997年和2015年的冬季和夏季陆地卫星数据集以及2000年至2015年的MODIS数据检索地表温度。使用发射率和卫星温度作为输入参数提取地表温度。反演了地表温度的空间和时间变化,以显示土地覆盖变化对地表温度的影响。还使用整个研究区域的样带分析了不同土地覆盖类型的LST变化。这项研究显示,这些年来陆地表面温度有所上升。研究还表明,与非防渗表面相比,防渗表面具有更高的LST。结果表明,非作物和作物农业区的地表温度存在差异,前者的地表温度高于后者。时间趋势显示,城市地区的LST显著增加,并且LST存在一些季节性变化,夏季LST值高,冬季LST值低。横断面分析揭示了不同土地覆盖类型的时空热变化。
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引用次数: 1
A feature selection approach for terrestrial hyperspectral image analysis 一种用于地面高光谱图像分析的特征选择方法
IF 0.5 Q4 REMOTE SENSING Pub Date : 2020-09-14 DOI: 10.4314/sajg.v9i2.20
Kyle Loggenberg, Nitesh K. Poona
Feature selection techniques are often employed for reducing data dimensionality, improving computational efficiency, and most importantly for selecting a subset of the most important features for model building. The present study explored the utility of a Filter-Wrapper (FW) approach for feature selection using terrestrial hyperspectral remote sensing imagery. The efficacy of the FW approach was evaluated in conjunction with the Random Forest (RF) and Extreme Gradient Boosting (XGBoost) classifiers, to discriminate between water-stressed and non-stressed Shiraz vines. The proposed FW approach yielded a test accuracy of 80.0% (KHAT = 0.6) for both RF and XGBoost, outperforming the more traditional Kruskal-Wallis (KW) filter by more than 20%. The FW approach was also less computationally expensive when compared with the more commonly used Sequential Floating Forward Selection (SFFS) wrapper. Additionally, we examined the effect of hyperparameter optimisation on classification accuracy and computational expense. The results showed that RF marginally outperformed XGBoost when using all wavebands (p = 176) and optimised hyperparameter values. RF yielded a test accuracy of 83.3% (KHAT = 0.67), whereas XGBoost yielded a test accuracy of 81.7% (KHAT = 0.63). Our results further show that optimising hyperparameter values yielded an overall increase in test accuracy, ranging from 0.8% to 5.0%, for both RF and XGBoost. Overall, the results highlight the effect of feature selection and optimisation on the performance of machine learning ensembles for modelling vineyard water stress.
特征选择技术通常用于降低数据维度、提高计算效率,最重要的是用于选择用于模型构建的最重要特征的子集。本研究探讨了滤波包装(FW)方法在利用陆地高光谱遥感图像进行特征选择中的实用性。结合随机森林(RF)和极限梯度提升(XGBoost)分类器评估FW方法的功效,以区分水分胁迫和非胁迫的设拉子葡萄藤。所提出的FW方法对RF和XGBoost的测试精度均为80.0%(KHAT=0.6),比更传统的Kruskal-Wallis(KW)滤波器高出20%以上。与更常用的顺序浮动正向选择(SFFS)包装器相比,FW方法的计算成本也更低。此外,我们还研究了超参数优化对分类精度和计算费用的影响。结果表明,当使用所有波段(p=176)和优化的超参数值时,RF略微优于XGBoost。RF的测试准确率为83.3%(KHAT=0.67),而XGBoost的测试准确度为81.7%(KHAT0.63)。我们的结果进一步表明,优化超参数值可使RF和XGBoost测试准确率总体提高,从0.8%到5.0%不等。总的来说,研究结果突出了特征选择和优化对机器学习组合性能的影响,用于模拟葡萄园水分胁迫。
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引用次数: 1
Machine learning algorithms for mapping Prosopis glandulosa and land cover change using multi-temporal Landsat products: a case study of Prieska in the Northern Cape Province, South Africa 使用多时相Landsat产品绘制Prosopis glandullosa和土地覆盖变化的机器学习算法:以南非北开普省Prieska为例
IF 0.5 Q4 REMOTE SENSING Pub Date : 2020-09-14 DOI: 10.4314/SAJG.V9I2.13
C. D. Villiers, C. Munghemezulu, G. Chirima, Philemon Tsele, Zinhle Mashaba
Invasive alien plants (IAPs) are responsible for loss in biodiversity and the depletion of water resources in natural ecosystems. Prosopis species are IAPs previously introduced by farmers to provide shade and fodder for livestock. In the Northern Cape, Prosopis spp. invasions are associated with the loss of native species resulting in overgrazing and degrading rangelands. Mapping Prosopis glandulosa is essential for management initiatives to assist the government in minimising the spread and impact of IAPs. This study aims to evaluate the performance of two machine learning algorithms i.e., Support Vector Machine (SVM) and Random Forest (RF) to map the spatial dynamics of P. glandulosa in Prieska. The spatial invasion extent of P. glandulosa was mapped using multitemporal Landsat data spanning the period from 1990 to 2018. Validation of the results was done through an estimated error matrix with the use of the proportion of area and the estimates of overall accuracy, user’s accuracy and producer’s accuracy with a 95% confidence interval. The performance of the SVM and RF classifiers showed similar results in the overall accuracy and Kappa statistics throughout the years. These methods showed an overall increase of at least 3.3% of the area invaded by P. glandulosa from 1990 to 2018. The study indicates the importance of Landsat imagery for mapping historical and current land cover change of IAPs. The spread of P. glandulosa was confirmed by an increase in the total area of invasion, which enables decision-makers to improve monitoring and eradication initiatives.
外来入侵植物是造成自然生态系统生物多样性丧失和水资源枯竭的原因。Prosopis物种是农民以前引入的IAP,为牲畜提供阴凉处和饲料。在北开普省,Prosopis spp.的入侵与当地物种的丧失有关,导致过度放牧和牧场退化。绘制腺性Prosopis glandulosa地图对于协助政府将IAP的传播和影响降至最低的管理举措至关重要。本研究旨在评估两种机器学习算法(即支持向量机(SVM)和随机森林(RF))在绘制普里斯卡格兰杜洛萨P.glandulosa空间动力学图方面的性能。利用1990年至2018年期间的多时相陆地卫星数据绘制了龟头蛙的空间入侵范围。通过使用面积比例和总体准确度、用户准确度和生产商准确度的估计误差矩阵对结果进行验证,置信区间为95%。多年来,SVM和RF分类器的性能在总体准确性和Kappa统计方面显示出相似的结果。这些方法显示,从1990年到2018年,腺虫入侵的面积总体增加了至少3.3%。该研究表明了陆地卫星图像对绘制IAP历史和当前土地覆盖变化图的重要性。侵袭总面积的增加证实了腺杜洛沙的传播,这使决策者能够改进监测和根除举措。
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引用次数: 1
Object-based land use/land cover change detection of a coastal city using Multi-Source Imagery: a case study of Lagos, Nigeria 基于多源图像的沿海城市土地利用/土地覆盖变化检测:以尼日利亚拉各斯为例
IF 0.5 Q4 REMOTE SENSING Pub Date : 2020-09-14 DOI: 10.4314/sajg.v9i2.10
T. Idowu, R. Waswa, K. Lasisi, M. Nyadawa, V. Okumu
In the wake of the burgeoning population, socio-economic and environmental issues facing coastal areas, LULC change detection has become an essential tool for environmental monitoring towards achieving sustainable development. In this study, an object-based image analysis approach using post-classification comparison technique was applied for assessing the LULC of the coastal city of Lagos from 1986 to 2016. The study describes how satellite imagery from different sources (Landsat and SENTINEL 2A) can be successfully integrated for Land use Land cover change detection. The results show that between 1986 and 2016, there were net increases in bare areas, built-up areas, and shrublands and a general decline in forestlands, waterbodies and wetlands. Over 60,000ha cover (approx. 190% increase) was converted into built-up areas while 83,541ha (835.4km2) of forestland were lost, suggesting high rates of urbanization and corresponding deforestation. About 60% loss of wetlands was also observed in the same time period. The decrease in water bodies and a steady increase in bare and built-up areas are possibly due to the prevalent land reclamation activities in the study area. Higher rates of deforestation and increase in bare areas were observed from 2001 to 2016 in comparison to 1986 to 2001. The observed trends are likely to continue, and for future management actions, predictive studies are suggested to provide more empirical evidence.
随着沿海地区人口的迅速增长、社会经济和环境问题的出现,LULC变化检测已成为实现可持续发展的环境监测的重要工具。在本研究中,采用基于对象的图像分析方法,使用后分类比较技术,对1986年至2016年沿海城市拉各斯的LULC进行了评估。该研究描述了如何成功地整合来自不同来源(陆地卫星和SENTINEL 2A)的卫星图像,用于土地利用-土地覆盖变化检测。结果显示,1986年至2016年间,裸露地区、建成区和灌木林净增加,林地、水体和湿地普遍减少。超过60000公顷的覆盖面积(约增长190%)被转化为建成区,83541公顷(835.4平方公里)的林地被损失,这表明城市化率很高,相应的森林砍伐也很严重。在同一时间段内,湿地也减少了约60%。水体的减少和裸露和建成区的稳步增加可能是由于研究区域内普遍的土地复垦活动。与1986年至2001年相比,2001年至2016年的森林砍伐率和裸露地区的增加率更高。观察到的趋势可能会继续,对于未来的管理行动,建议进行预测性研究,以提供更多的经验证据。
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引用次数: 3
Investigating an agent based modelling approach for SDI planning: A case study of Tanzania NSDI development 研究基于agent的SDI规划建模方法:坦桑尼亚NSDI发展案例研究
IF 0.5 Q4 REMOTE SENSING Pub Date : 2020-09-14 DOI: 10.4314/SAJG.V9I2.14
Alex Lubida, M. Rajabi, P. Pilesjö, A. Mansourian
Spatial Data Infrastructure (SDI) provides a platform for spatial data sharing and is a key for sustainable development. Developing countries, including Tanzania, are at different stages of implementing SDIs. The importance and advantage of implementation lie in the fact that considerable funds can be saved by avoiding duplication of data, and improving quality of decisions making as well as public services. However, SDI is very complex in nature, including many influencing factors and different stakeholders. This paper investigates the possibilities of using Agent-Based Modelling (ABM) for simulating an SDI development process in Tanzania, for better understanding and making better planning. The roles and actions of organizations were identified through interviews, and the results were analysed. The behaviour of individual organizations (stakeholders) while interacting with the system were observed and analysed. The growth results in terms of data availability, standards, and data sharing for each organization were plotted and priority tables were generated. The model was evaluated for consistency and the results were judged to be within a reasonable range. The ABM simulation depicted the main attributes of agents, their roles and their interactions while pursuing SDI development in Tanzania. The results will help SDI planners and stakeholders to understand the roles of partners and prioritize activities and actions for successful SDI implementation.
空间数据基础设施(SDI)为空间数据共享提供了一个平台,是可持续发展的关键。包括坦桑尼亚在内的发展中国家正处于实施可持续发展投资的不同阶段。实施的重要性和优势在于,通过避免数据重复、提高决策和公共服务的质量,可以节省大量资金。然而,SDI的性质非常复杂,包括许多影响因素和不同的利益相关者。本文研究了使用基于代理的建模(ABM)来模拟坦桑尼亚SDI开发过程的可能性,以更好地理解和制定更好的计划。通过访谈确定了各组织的作用和行动,并对结果进行了分析。观察和分析了各个组织(利益攸关方)在与系统互动时的行为。绘制了每个组织在数据可用性、标准和数据共享方面的增长结果,并生成了优先级表。对模型的一致性进行了评估,并判断结果在合理范围内。ABM模拟描绘了代理人的主要属性、他们的角色以及他们在坦桑尼亚进行SDI开发时的互动。研究结果将有助于SDI规划者和利益相关者了解合作伙伴的作用,并为成功实施SDI确定活动和行动的优先顺序。
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引用次数: 2
Towards Achieving Sustainability of Coastal Environments: Urban Growth Analysis and Prediction of Lagos, State Nigeria 实现沿海环境的可持续性:尼日利亚拉各斯城市增长分析与预测
IF 0.5 Q4 REMOTE SENSING Pub Date : 2020-07-23 DOI: 10.20944/preprints202007.0560.v1
T. Idowu, R. Waswa, K. Lasisi, Kenneth Mubea, M. Nyadawa, J. Kiema
The most extensive urban growths in the next 30 years are expected to occur in developing countries. Lagos, Nigeria - Africa’s second most populous megacity- is a prime example. To achieve more sustainable and resilient cities, there is a need for modeling the urban growth patterns of major cities and analyzing their implications. In this study, the urban growth of Lagos state was modeled using the Multi-Layer Perceptron (MLP) neural network for the transition modeling and the Markov Chain analysis for the change prediction, achieving a model accuracy of 81.8%. An innovative visual validation of the model results using the ArcGIS was combined with kappa correlation statistics. The results show that by 2031, built-up areas will be the most spatially extensive LULC class in the study area with percentage coverage of 34.1% as opposed to 9% in 1986. The coverage of bare areas is also expected to increase by 53% between 2016 and 2031. Conversely, 24.9% and 68.3% loss of forestlands and wetlands respectively, are expected between 2016 and 2031. In view of the 11th goal of SDGs which focuses on achieving sustainable cities and communities, the objectives of African Union’s Agenda 2063, and based on the urban growth trends observed, the study recommends a prioritization of vertical expansion as opposed to the current horizontal urban growth trends in the study area.
预计未来30年最广泛的城市增长将发生在发展中国家。尼日利亚的拉各斯——非洲人口第二多的大城市——就是一个典型的例子。为了实现更具可持续性和弹性的城市,有必要对主要城市的城市增长模式进行建模并分析其影响。本文采用多层感知器(multilayer Perceptron, MLP)神经网络对拉各斯州的城市增长进行过渡建模,利用马尔可夫链分析对变化进行预测,模型准确率达到81.8%。结合kappa相关统计,利用ArcGIS对模型结果进行了创新性的可视化验证。结果表明,到2031年,建成区将成为研究区空间最广泛的土地利用资源类别,其覆盖率将从1986年的9%上升至34.1%。2016年至2031年间,光秃秃地区的覆盖率预计将增加53%。相反,2016年至2031年期间,预计林地和湿地的损失分别为24.9%和68.3%。鉴于可持续发展目标的第11个目标(重点是实现可持续城市和社区)、非洲联盟《2063年议程》的目标,以及根据观察到的城市增长趋势,该研究建议优先考虑垂直扩张,而不是研究区域目前的水平城市增长趋势。
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引用次数: 2
Towards development of a national human settlement layer using high resolution imagery: a contribution to SDG reporting 利用高分辨率图像开发国家人类住区层:对可持续发展目标报告的贡献
IF 0.5 Q4 REMOTE SENSING Pub Date : 2020-02-27 DOI: 10.4314/sajg.v9i1.1
N. Mudau, W. Mapurisa, Thomas Tsoeleng, Morwapula Mashalane
This study investigated the automation of the building extraction using SPOT 6 satellite imagery. The proposed methodology uses variance textural information derived from 1.5m panchromatic image to detect built-up areas from non-built-up areas. Once detected, detailed segmentation is performed on built-up class to create individual building objects. Canny edges, SAVI and spectral properties of the objects were used to classify building structures from other land use features using a thresholding technique. The methodology was tested in different areas including formal, rural village and informal and new development settlement types without modifying segmentation and classification parameters. The proposed methodology successfully detected built-up from non built-up areas in all different settlement types. The detection of individual structures achieved more than 70% in formal, rural village and new development areas while less than 50% of building structures in informal settlement were detected. The proposed method can contribute towards monitoring of human settlement developments over a larger area which is vital during spatial planning, service delivery and environmental management. This work will contribute towards the development of a National Human Settlement Layer developed and maintained by SANSA.
本研究利用spot6卫星图像对建筑物提取的自动化进行了研究。该方法利用1.5m全色图像的方差纹理信息从非建成区中检测建成区。一旦检测到,将对已构建类执行详细分割,以创建单个构建对象。使用阈值技术,利用物体的边缘、SAVI和光谱属性将建筑结构与其他土地利用特征进行分类。在不修改分割和分类参数的情况下,对该方法进行了不同领域的测试,包括正式、农村、非正式和新开发聚落类型。所提出的方法成功地检测了所有不同沉降类型的非建成区的建成区。在正式、农村和新开发地区,单个结构的检测率超过70%,而在非正式住区,检测到的建筑结构不到50%。拟议的方法有助于监测更大范围内的人类住区发展,这在空间规划、提供服务和环境管理方面是至关重要的。这项工作将有助于发展由SANSA开发和维护的国家人类住区层。
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引用次数: 2
Assessing the synergistic potential of Sentinel-2 spectral reflectance bands and derived vegetation indices for detecting and mapping invasive alien plant species Sentinel-2光谱反射带与衍生植被指数在外来入侵植物物种探测与制图中的协同潜力评估
IF 0.5 Q4 REMOTE SENSING Pub Date : 2020-02-27 DOI: 10.4314/sajg.v9i1.6
J. Odindi, O. Mutanga, Perushan Rajah
Grassland biomes are valuable socio-economic and ecological resources. However, the invasion of grasslands by alien plant species has emerged as one of the biggest threats to their sustainability, management and conservation. Timely, cost-effective and accurate determination of invasive alien plant spatial distribution is paramount for mitigating the adverse effects of alien plants on natural grasslands. Whereas literature on use of optical bands for invasive alien plants detection and mapping is abound, there is paucity in literature on the integration of Vegetation Indices (VIs) and optical reflectance bands in invasive species mapping. Specifically, there is need to test the efficacy of improved and freely available sensors like Sentinel-2 in understanding landscape invasion. Hence, this study sought to assess the efficacy of Sentinel-2’s optical bands and VIs for improving the mapping of American Bramble (Rubus cuneifolius) within a grassland biome. Variable Importance in the Projection (VIP) was used to identify the most influential reflectance bands and VIs, which were then fused at a feature level to determine Bramble spatial distribution. To determine the optimal season for Bramble mapping, seasonal classification accuracies were executed in Support Vector Machine (SVM) learning algorithm and accuracies for Spring, Summer, Autumn and Winter seasons compared. Results show that although the highest overall accuracy was achieved using only optical bands, fused imagery increased overall classification accuracies during spring and autumn i.e. 70% to 73% and 63% to 65%, respectively. However, the fused imagery failed to improve on the benchmark of optical imagery during summer and winter. Findings from this study underline the efficacy of complementing VIs and optical bands in determining the distribution of invasive species within grasslands at specific seasons. Furthermore, this study advocates for the adoption and fusion of freely available new generation satellite imagery such as Sentinel-2 as a cost effective option in landscape mapping.
草原生物群落是宝贵的社会经济和生态资源。然而,外来植物物种的入侵已经成为草原可持续性、管理和保护的最大威胁之一。及时、经济、准确地确定外来入侵植物的空间分布对减轻外来植物对天然草原的不利影响至关重要。利用光学波段进行外来入侵植物探测与制图的研究文献很多,但将植被指数(VIs)与光学波段相结合用于外来入侵植物制图的研究文献较少。具体来说,需要测试像Sentinel-2这样的改进的和免费的传感器在理解景观入侵方面的功效。因此,本研究试图评估Sentinel-2的光学波段和VIs在草地生物群系中改善美国黑莓(Rubus cuneifolius)定位的功效。投影中的变量重要性(VIP)用于识别最具影响力的反射带和VIs,然后在特征水平上融合以确定Bramble的空间分布。为了确定Bramble绘制的最佳季节,在支持向量机(SVM)学习算法中执行季节分类精度,并对春、夏、秋、冬四个季节的精度进行比较。结果表明,虽然仅使用光学波段获得了最高的总体精度,但融合图像在春季和秋季的总体分类精度分别提高了70%至73%和63%至65%。然而,在夏季和冬季,融合成像未能提高光学成像的基准。本研究结果强调了可见光波段和可见光波段互补在确定特定季节入侵物种在草原内分布的有效性。此外,本研究提倡采用和融合免费提供的新一代卫星图像,如Sentinel-2,作为景观制图的成本效益选择。
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引用次数: 3
Modelling forest species using LiDar-derived metrics of forest canopy gaps 利用激光雷达衍生的森林冠层间隙度量对森林物种进行建模
IF 0.5 Q4 REMOTE SENSING Pub Date : 2020-02-27 DOI: 10.4314/sajg.v9i1.3
L. Lombard, R. Ismail, Nitesh K. Poona
LiDAR intensity and texture features have reported high accuracies for discriminating forest species, particularly with the utility of the random forest (RF) algorithm. To date, limited studies has utilized LiDAR-derived forest gap information to assist in forest species discrimination. In this study, LiDAR intensity and texture features were extracted from forest canopy gaps to discriminate Eucalyptus grandis and Eucalyptus dunnii within a forest plantation. Additionally, LiDAR intensity and texture information was extracted for both canopy gaps and forest canopy and utilized for species discrimination. Using LiDAR intensity and texture information extracted for both canopy gap and forest canopy, resulted in a model accuracy of 94.74% (KHAT = 0.88). Using only canopy gap information, the RF model obtained an overall accuracy of 90.91% (KHAT = 0.81). The results highlight the potential for using canopy gap information for commercial species discrimination and mapping.
据报道,激光雷达强度和纹理特征在区分森林物种方面具有很高的准确性,特别是随机森林(RF)算法的应用。迄今为止,有限的研究利用激光雷达获得的森林间隙信息来协助森林物种识别。本研究通过提取林冠间隙的激光雷达强度和纹理特征,对人工林内的大桉(Eucalyptus grandis)和敦桉(Eucalyptus dunnii)进行区分。此外,还提取了林冠间隙和林冠的激光雷达强度和纹理信息,并将其用于物种识别。同时提取林冠间隙和林冠的激光雷达强度和纹理信息,模型精度为94.74% (KHAT = 0.88)。仅利用冠层间隙信息,RF模型的总体精度为90.91% (KHAT = 0.81)。研究结果强调了利用林隙信息进行商业物种识别和定位的潜力。
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引用次数: 0
Evaluation of effectiveness of supervised classification algorithms in land cover classification using ASTER images-A case study from the Mankweng (Turfloop) Area and its environs, Limpopo Province, South Africa 利用ASTER图像评估监督分类算法在土地覆盖分类中的有效性——以南非林波波省Mankweng(Turfloop)地区及其周边地区为例
IF 0.5 Q4 REMOTE SENSING Pub Date : 2020-02-27 DOI: 10.4314/sajg.v9i1.5
Nndanduleni Muavhi
The production of land cover maps using supervised classification algorithms is one of the most common applications of remote sensing. In this study, the effectiveness of supervised classification algorithms in land cover classification using ASTER data was evaluated in the Mankweng Area and its environs. The false colour composite image generated from combination of band 1, 2 and 3 in red, green and blue, respectively, was used to generate training classes for six land cover types (waterbody, forest, vegetation, Duiwelskloof leucogranite, Turfloop granite and built-up land). These were used to construct land cover maps using eight supervised classification algorithms: Maximum Likelihood, Minimum Distance, Support Vector Machine, Mahalanobis Distance, Parallelepiped, Neural Network, Spectral Angle Mapper and Spectral Information Divergence. To evaluate the effectiveness of the algorithms, the land cover maps were subjected to accuracy assessment to determine precision of the algorithms in accurately classifying the land cover types and level of confidence that can be attributed to the land cover maps. Most algorithms poorly performed in classifying spatially overlapping land cover types without abrupt boundaries. This indicates that the environmental conditions and distribution of land cover types can affect the performance of certain classification algorithms, and thus need to be considered prior to selection of algorithms. However, Support Vector Machine and Minimum Distance proved to be the two most effective algorithms as they provided better producer’s and user’s accuracy in the range of 80-100% for all land cover types, which represent good classification.
使用监督分类算法制作土地覆盖图是遥感最常见的应用之一。在本研究中,利用ASTER数据对Mankweng地区及其周边地区的土地覆盖分类进行了监督分类算法的有效性评估。分别由红、绿和蓝波段1、2和3组合生成的伪彩色合成图像用于生成六种土地覆盖类型(水体、森林、植被、Duiwelskloof浅色花岗岩、Turfloop花岗岩和建成区)的训练类。这些被用于使用八种监督分类算法构建土地覆盖图:最大似然、最小距离、支持向量机、马氏距离、平行六面体、神经网络、光谱角映射器和光谱信息发散。为了评估算法的有效性,对土地覆盖图进行了精度评估,以确定算法在准确分类土地覆盖类型方面的精度以及可归因于土地覆盖图的置信水平。大多数算法在没有突然边界的情况下对空间重叠的土地覆盖类型进行分类时表现不佳。这表明,环境条件和土地覆盖类型的分布会影响某些分类算法的性能,因此在选择算法之前需要加以考虑。然而,支持向量机和最小距离被证明是两种最有效的算法,因为它们在所有土地覆盖类型的80-100%范围内提供了更好的生产者和用户的准确度,这代表了良好的分类。
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引用次数: 10
期刊
South African Journal of Geomatics
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