决策树结合混合像元分解的中国竹林遥感信息提取

竹林是中国亚热带地区特殊而重要的森林资源,现有方法难以实现全国范围竹林时空分布信息快速准确提取。针对此问题,本研究利用2003年、2008年、2014年MODIS NDVI、反射率产品数据和省域Landsat分类数据,提出了基于决策树结合混合像元分解的全国竹林信息提取方法。首先,通过最大似然法获取中国林地分布信息;然后,在林地信息的基础上,构建决策树模型提取中国竹林分布信息;最后,采用线性最小二乘法混合像元分解得到中国竹林丰度图,并计算竹林面积。研究结果表明:(1)最大似然法提取的3个时期中国林地信息的生产者与用户精度均在90%以上,Kappa系数均值为0.93,为竹林信息提取奠定了基础。(2)C5.0算法构建的决策树模型能够很好的提取中国竹林时空分布信息,3个时期竹林分类精度均在80%左右。(3)在混合像元分解的基础上,统计得到的全国各省竹林估算面积与清查面积具有较高的相关性,R~2分别为0.98、0.97和0.95,RMSE范围为3.92万—9.58万ha,说明估算得到全国竹林面积与实际情况较为吻合。本研究所提出基于MODIS遥感数据运用C5.0算法决策树结合混合像元分解的方法,实现了全国竹林时空分布信息的准确提取,为全国竹林资源信息动态监测及管理提供了技术手段和数据支撑。     

Field-based sub-boundary extraction from remote sensing imagery using perceptual grouping

This study presents an approach for the automatic extraction of dynamic sub-boundaries within existing agricultural fields from remote sensing imagery using perceptual grouping. We define sub-boundaries as boundaries, where a change in crop type takes a place within the fixed geometry of an agricultural field. To perform field-based processing and analysis operations, the field boundary data and satellite imagery are integrated. The edge pixels are detected using the Canny edge detector. The edge pixels are then analyzed to find the connected edge chains and from these chains the line segments are detected using the graph-based vectorization method. The spurious line segments are eliminated through a line simplification process. The perceptual grouping of the line segments is employed for detecting sub-boundaries and constructing sub-fields within the fixed geometry of agricultural fields. Our strategy for perceptual grouping involves the Gestalt laws of proximity, continuation, symmetry and closure. The processing and analysis operations are carried out on field-by-field basis. For each field, the geometries of sub-boundaries are determined through analyzing the line segments that fall within the field and the extracted sub-boundaries are integrated with the fixed geometry of the field.The experimental validation of the approach was carried out on the SPOT4 multispectral (XS) and SPOT5 XS images that cover an agricultural area located in the north-west section of Turkey. The overall matching percentages between the reference data and the automatically extracted sub-boundaries were computed to be 82.6% and 76.2% for the SPOT5 and SPOT4 images respectively. The higher matching percentage of the SPOT5 image is due to the fact that some of the boundaries present in the SPOT5 image were not detected in the coarser resolution SPOT4 image. For the SPOT5 image, of the total 292 fields processed, 177 showed a total agreement between the detected segments and the reference data. For the SPOT4 image, 154 fields showed a total agreement between the detected segments and the reference data.     

Night on Earth: Mapping decadal changes of anthropogenic night light in Asia

The defense meteorological satellite program (DMSP) operational linescan system (OLS) sensors have imaged emitted light from Earth's surface since the 1970s. Temporal overlap in the missions of 5 OLS sensors allows for intercalibration of the annual composites over the past 19 years (Elvidge et al., 2009). The resulting image time series captures a spatiotemporal signature of the growth and evolution of lighted human settlements and development. We use empirical orthogonal function (EOF) analysis and the temporal feature space to characterize and quantify patterns of temporal change in stable night light brightness and spatial extent since 1992. Temporal EOF analysis provides a statistical basis for representing spatially abundant temporal patterns in the image time series as uncorrelated vectors of brightness as a function of time from 1992 to 2009. The variance partition of the eigenvalue spectrum combined with temporal structure of the EOFs and spatial structure of the PCs provides a basis for distinguishing between deterministic multi-year trends and stochastic year-to-year variance. The low order EOFs and principal components (PC) space together discriminate both earlier (1990s) and later (2000s) increases and decreases in brightness. Inverse transformation of these low order dimensions reduces stochastic variance sufficiently so that tri-temporal composites depict potentially deterministic decadal trends. The most pronounced changes occur in Asia. At critical brightness threshold we find an 18% increase in the number of spatially distinct lights and an 80% increase in lighted area in southern and eastern Asia between 1992 and 2009. During this time both China and India experienced a ∼20% increase in number of lights and a ∼270% increase in lighted area – although the timing of the increase is later in China than in India. Throughout Asia a variety of different patterns of brightness increase are apparent in tri-temporal brightness composites – as well as some conspicuous areas of apparently decreasing background luminance and, in many places, intermittent light suggesting development of infrastructure rather than persistently lighted development. Vicarious validation using higher resolution Landsat imagery verifies multiple phases of urban growth in several cities as well as the consistent presence of low DN (<∼15) background luminance for many agricultural areas. Lights also allow us to quantify changes in the size distribution and connectedness of different intensities of development. Over a wide range of brightnesses, the size distributions of spatially contiguous lighted area are consistent with power laws with exponents near −1 as predicted by Zipf's Law for cities. However, the larger lighted segments are much larger than individual cities; they correspond to vast spatial networks of contiguous development (Small et al., 2011).     

Machine learning approaches to coastal water quality monitoring using GOCI satellite data

Since coastal waters are one of the most vulnerable marine systems to environmental pollution, it is very important to operationally monitor coastal water quality. This study attempts to estimate two major water quality indicators, chlorophyll-a (chl-a) and suspended particulate matter (SPM) concentrations, in coastal environments on the west coast of South Korea using Geostationary Ocean Color Imager (GOCI) satellite data. Three machine learning approaches including random forest, Cubist, and support vector regression (SVR) were evaluated for coastal water quality estimation. In situ measurements (63 samples) collected during four days in 2011 and 2012 were used as reference data. Due to the limited number of samples, leave-one-out cross validation (CV) was used to assess the performance of the water quality estimation models. Results show that SVR outperformed the other two machine learning approaches, yielding calibration R² of 0.91 and CV root-mean-squared-error (RMSE) of 1.74 mg/m³ (40.7%) for chl-a, and calibration R² of 0.98 and CV RMSE of 11.42 g/m³ (63.1%) for SPM when using GOCI-derived radiance data. Relative importance of the predictor variables was examined. When GOCI-derived radiance data were used, the ratio of band 2 to band 4 and bands 6 and 5 were the most influential input variables in predicting chl-a and SPM concentrations, respectively. Hourly available GOCI images were useful to discuss spatiotemporal distributions of the water quality parameters with tidal phases in the west coast of Korea.     

Distribution of Errors in a Classified Map of Satellite Data

Abstract

The output from any spatial data processing method may contain some uncertainty. With the increasing use of satellite data products as a source of data for Geographical Information Systems (GIS), there have been some major concerns about the accuracy of the satellite‐based information. Due to the nature of spatial data and remotely sensed data acquisition technology, and conventional classification, any single classified image can contain a number of mis‐classified pixels. Conventional accuracy evaluation procedures can report only the number of pixels that are mis‐classified based on some sampling observation. This study investigates the spatial distribution and the amount of these pixels associated with each cover type in a product of satellite data. The study uses Thematic Mapper (TM) and SPOT multispectral data sets obtained for a study area selected in North East New South Wales, Australia. The Fuzzy c‐Means algorithm is used to identify the classified pixels that contained some uncertainty. The approach is based on evaluating the strength of class membership of pixels. This study is important as it can give an indication of the amount of error resulting from the mis‐classification of pixels of specific cover types as well as the spatial distribution of such pixels. The results show that the spatial distribution of erroneously classified pixels are not random and varies depending on the nature of cover types. The proportions of such pixels are higher in spectrally less clearly defined cover types such as grasslands.     

Mapping trees in high resolution imagery across large areas using locally variable thresholds guided by medium resolution tree maps

Large area tree maps, important for environmental monitoring and natural resource management, are often based on medium resolution satellite imagery. These data have difficulty in detecting trees in fragmented woodlands, and have significant omission errors in modified agricultural areas. High resolution imagery can better detect these trees, however, as most high resolution imagery is not normalised it is difficult to automate a tree classification method over large areas. The method developed here used an existing medium resolution map derived from either Landsat or SPOT5 satellite imagery to guide the classification of the high resolution imagery. It selected a spatially-variable threshold on the green band, calculated based on the spatially-variable percentage of trees in the existing map of tree cover. The green band proved more consistent at classifying trees across different images than several common band combinations. The method was tested on 0.5 m resolution imagery from airborne digital sensor (ADS) imagery across New South Wales (NSW), Australia using both Landsat and SPOT5 derived tree maps to guide the threshold selection. Accuracy was assessed across 6 large image mosaics revealing a more accurate result when the more accurate tree map from SPOT5 imagery was used. The resulting maps achieved an overall accuracy with 95% confidence intervals of 93% (90–95%), while the overall accuracy of the previous SPOT5 tree map was 87% (86–89%). The method reduced omission errors by mapping more scattered trees, although it did increase commission errors caused by dark pixels from water, building shadows, topographic shadows, and some soils and crops. The method allows trees to be automatically mapped at 5 m resolution from high resolution imagery, provided a medium resolution tree map already exists.     

Optimisation of building detection in satellite images by combining multispectral classification and texture filtering

Conventional multispectral classification methods show poor performance with respect to detection of urban object classes, such as buildings, in high spatial resolution satellite images. This is because objects in urban areas are very complicated with respect to both their spectral and spatial characteristics. Multispectral classification detects object classes only according to the spectral information of the individual pixels, while a large amount of spatial information is neglected. In this study, a technique is described which attempts to detect urban buildings in two stages. The first stage is a conventional multispectral classification. In the second stage, the classification of buildings is improved by means of their spatial information through a modified co-occurrence matrix based filtering. The direction dependence of the co-occurrence matrix is utilised in the filtering process. The method has been tested by using TM and SPOT Pan merged data for the whole area of the city of Shanghai, China. After the co-occurrence matrix based filtering, the average user accuracy increased by about 46% and the average Kappa statistic by about 57%. This result is about 26% better than the accuracy improvement through normal texture filtering. The method presented in this study is very useful for a rapid estimation of urban building and city development, especially in metropolitan areas of developing countries.     

Multispectral remote sensing for mapping grassland degradation using the key indicators of grass species and edaphic factors

Land degradation is believed to be one of the most severe and widespread environmental problems. In South Africa, large areas of land have been identified as degraded, as shown by the lower vegetation cover. One of the major causes of grassland degradation is change in plant species composition that leads to presence of unpalatable grass species. Some grass species have been successfully used as indicators of different levels of grassland degradation in the country. This paper, therefore explores the possibility of mapping grassland degradation in Cathedral Peak, South Africa, using indicators of grass species and edaphic factors. Multispectral SPOT 5 data were used to produce a grassland degradation map based on the spatial distribution of decreaser (Themeda triandra) and increaser (Hyparrhenia hirta) species. To improve mapping accuracy, soil samples were collected from each species site and analysed for nutrient content. A t-test and machine learning random forest classification algorithm were applied for variable selection and classification using SPOT 5 data and edaphic variables. Results indicated that the decreaser and increaser grass species can be mapped with modest accuracy using SPOT 5 data (overall accuracy of 75.30%, quantity disagreement = 2 and allocation disagreement = 23). The classification accuracy was improved to 88.60%, 1 and 11 for overall accuracy, quantity and allocation disagreements, respectively, when SPOT 5 bands and edaphic factors were combined. The study demonstrated that an approach based on the integration of multispectral data and edaphic variables, which increased the overall classification accuracy by about 13%, is a suitable when adopting remote sensing to monitor grassland degradation.     

Land use change detection based on multi‐date imagery from different satellite sensor systems

An empirical study was performed assessing the accuracy of land use change detection when using satellite image data acquired ten years apart by sensors with differing spatial resolutions. Landsat/Multi‐spectral Scanner (MSS) with Landsat/Thematic Mapper (TM) or SPOT/High Resolution Visible (HRV) multi‐spectral (XS) data were used as a multi‐data pair for detecting land use change. The primary objectives of the study were to: (1) compare standard change detection methods (e.g. multi‐date ratioing and principal components analysis) applied to image data of varying spatial resolution; (2) assess whether to transform the raster grid of the higher resolution image data to that of the lower resolution raster grid or vice‐versa in the registration process: and (3) determine if Landsat/TM or SPOT/ HRV(XS) data provides more accurate detection of land use changes when registered to historical Landsat/MSS data.

Ratioing multi‐sensor, multi‐date satellite image data produced higher change detection accuracies than did principal components analysis and is useful as a land use change enhancement technique. Ratioing red and near infrared bands of a Landsat/MSS‐SPOT/HRV(XS) multi‐date pair produced substantially higher change detection accuracies (～10%) than ratioing similar bands of a Landsat/MSS ‐ Landsat/TM multi‐data pair. Using a higher‐resolution raster grid of 20 meters when registering Landsat/MSS and SPOTZHRV(XS) images produced a slightly higher change detection accuracy than when both images were registered to an 80 meter raster grid. Applying a “majority”; moving window filter whose size approximated a minimum mapping unit of 1 hectare increased change detection accuracies by 1–3% and reduced commission errors by 10–25%.     

Urbanization in China from the end of 1980s until 2010 – spatial dynamics and patterns of growth using EO-data

Urbanization in China has been experiencing a remarkable dynamism in the past 40 years. The most evident implication of urbanization is the physical growth of cities. We analyze urban land growth rates and changes in spatial urban forms from the end of the 1980s to 2010 based on the authoritative National Land Use/Cover Database of China. We present new spatial measures that describe ‘urban land growth types’ and ‘fluctuations in urban land growth’ within the monitoring time span with a temporal interval of five-year steps. We evaluate the correlations between urban land growth rates and socioeconomic data. Results show that (1) distinct characteristics exist on the spatiotemporal evolutions of urban land growth rates in terms of area and perimeter, e.g. coastal areas exhibit the most dramatic growth rates; (2) the spatial distribution characteristics of ‘urban land growth types’ and ‘fluctuations in urban land growth’ follow similar spatial patterns across China, e.g. significant differences exist between the eastern region and other regions; and (3) a moderate correlation exists between urban area growth rate and urban population growth rate at an R² of 0.37. By contrast, we determine no significant correlation between urban area growth rate and tertiary industry value growth rate.     

1995—2019年北莉岛人工水产养殖遥感监测

有效监测人工水产养殖水面的分布变化对于海洋资源管理、生态环境保护、防灾减灾具有重要意义。本文以Landsat 5、SPOT 5和GF-1卫星影像为数据源,选择广东省北莉岛为研究区,使用线性光谱解混方法获取中等空间分辨率卫星影像的人工水产养殖水面面积,通过面向对象多尺度分割的方法结合支持向量机分类算法提取高空间分辨率卫星影像的人工水产养殖水面分布。研究结果表明,与单一卫星影像相比,综合多源中高空间分辨率卫星数据延长了人工水产养殖水面变化分析可追溯的时间跨度,提高了监测精度;联合光谱解混和面向对象分类方法开展人工水产养殖长时序遥感监测是可行的。近20多年来,北莉岛人工水产养殖水面的面积经历了先增加后缓慢减少的变化过程,1995—2000年平均增速为23.39 hm²/a,2000—2006年平均增速为23.95 hm²/a,2006—2019年平均减少速度为1.96 hm²/a。     

基于DMSP/OLS夜间灯光数据的长江三角洲地区城市化格局与过程研究

利用1998、2003和2008年的DMSP/OLS夜间灯光数据,结合相关统计数据,分析灯光数据与城市化进程中经济、人口和土地利用等方面的关系,构建基于灯光数据的城市化水平指数,分析长三角城市群城市化进程和空间模式;通过与统计数据对比,按照最小误差原则确定市辖区建设用地信息提取的阈值,根据各城市在1998～2003年和...     

利用GNSS水准成果融合构建中国大陆垂直运动模型

水准和全球导航卫星系统(global navigation satellite system, GNSS)成果是研究中国大陆垂直运动的重要数据,充分发挥水准点高精度和GNSS点分布均匀的优势,有利于提升垂直运动模型的可靠性。在融合过程中,针对水准和GNSS成果缺少重合点导致速度融合失效的问题,提出一种基于模型结果融合的方法,分别利用水准和GNSS成果建立垂直运动模型,并基于模型格网点精度及控制点与模型格网点的最近距离联合确定两类模型点权值,逐点加权平均,实现两类模型的融合;针对利用反距离加权方法建模过程中观测点速度精度和空间距离均影响定权结果的问题,提出了权因子相乘的方法,合理确定各因素影响程度。综合利用国家一等水准网、国家GNSS大地控制网等成果,基于双因子相乘的方式定权建立单源垂直运动模型,并按照基于结果融合的方法建立了中国大陆垂直运动模型。结果表明,华北、苏沪一带沉降严重,极个别区域沉降速度达100 mm/a;东北、西藏一带呈现隆升趋势,局部隆升速度达5 mm/a;其余地区垂直运动相对稳定。     

SPOT 5卫星影像测绘能力分析

HRS同轨立体影像获取能力是SPOT5卫星最突出的一个关于测绘制图的新特性,它确定了SPOT5卫星在测绘中的重要应用前景。本文从SPOT5HRS的立体成像方式,SPOTImage公司最新推出的Reference3D产品及陕西宝鸡地区的HRS立体影像测量试验等方面对SPOT5HRS影像测绘能力进行了分析和评价,说明了SPOT5卫星影像测绘将是解决我国西部困难地区基础测绘的一种重要手段。     

SPIRIT. SPOT 5 stereoscopic survey of Polar Ice: Reference Images and Topographies during the fourth International Polar Year (2007–2009)

Monitoring the evolution of polar glaciers, ice caps and ice streams is of utmost importance because they constitute a good indicator of global climate change and contribute significantly to ongoing sea level rise. Accurate topographic surveys are particularly relevant as they reflect the geometric evolution of ice masses. Unfortunately, the precision and/or spatial coverage of current satellite missions (radar altimetry, ICESat) or field surveys are generally insufficient. Improving our knowledge of the topography of Polar Regions is the goal of the SPIRIT (SPOT 5 stereoscopic survey of Polar Ice: Reference Images and Topographies) international polar year (IPY) project. SPIRIT will allow (1) the acquisition of a large archive of SPOT 5 stereoscopic images covering most polar ice masses and, (2) the delivery of digital terrain models (DTM) to the scientific community.Here, we present the architecture of this project and the coverage achieved over northern and southern polar areas during the first year of IPY (July 2007 to April 2008). We also provide the first accuracy assessments of the SPIRIT DTMs. Over Jakobshavn Isbrae (West Greenland), SPIRIT elevations are within ±6 m of ICESat elevations for 90% of the data. Some comparisons with ICESat profiles over Devon ice cap (Canada), St Elias Mountains (Alaska) and west Svalbard confirm the good overall quality of the SPIRIT DTMs although large errors are observed in the flat accumulation area of Devon ice cap. We then demonstrate the potential of SPIRIT DTMs for mapping glacier elevation changes. The comparison of summer-2007 SPIRIT DTMs with October-2003 ICESat profiles shows that the thinning of Jakobshavn Isbrae (by 30–40 m in 4 years) is restricted to the fast glacier trunk. The thinning of the coastal part of the ice stream (by over 100 m) and the retreat of its calving front (by up to 10 km) are clearly depicted by comparing the SPIRIT DTM to an ASTER April-2003 DTM.     

中国湿地变化的驱动力分析

在全球气候变化及中国社会经济迅速发展的背景下,为了解中国湿地分布的时空动态特征及演化规律,以4期(1978年、1990年、2000年、2008年)中国湿地遥感制图数据和3期(1990年、2000年、2005年)土地利用数据为基础,同时考虑到对湿地变化的影响程度和数据的可获取性,选取12个影响因子(平均温度、平均湿度、累计降水量、人口数量、地区生产总值、农林牧渔产值、耕地面积、粮食产量、有效灌溉面积、水库库容量、除涝面积、治碱面积)研究1978年—2008年这30年间中国湿地变化的驱动机制。考虑到地理现象的空间非平稳性,本文采用地理加权回归的方法分析驱动因子对湿地变化的影响作用。地理加权回归作为一种局部线性回归方法,能够直观地反映湿地驱动因子对湿地作用的地域差异。结果表明:不同类型的湿地变化的主要影响因素不同,内陆湿地与温度、降水以及农业耕作灌溉等密切相关;人工湿地与经济发展水平和水利设施兴建密切相关;滨海湿地与农林牧渔产业和人口等密切相关。同一类型湿地变化的主要影响因素随着时间推移也有所变化,并且影响程度在地域上也存在较为明显的南北和东西差异。本次研究结果基本反映了1978年—2008年中国湿地变化的特征规律。     

Application of frequency ratio and likelihood ratio model for geo-spatial modelling of landslide hazard vulnerability assessment and zonation: a case study from the Sikkim Himalayas in India

The Likelihood Ratio (LR) Model has been applied as an improvement upon the Frequency Ratio (FR) that computes the ratio of the percentage of the landslide pixels to the percentage of the non-landslide pixels instead of the total number of pixels used in the denominator as in case of the FR. The comparative assessment of the two techniques is made through spatial modelling of GIS vector data using the ArcGIS software. Two different Landslide Information Values were computed for each polygon element of the study area employing the two FR techniques that categorized the study area into five classes of vulnerability using natural breaks (Jenks) technique. Subsequently, vulnerability zonation maps were prepared showing the different levels of landslide vulnerability. The LR technique yielded significantly higher vulnerability assessment accuracy (77%) as compared to the standard FR (71%).     

Spatiotemporal reflectance blending in a wetland environment

To understand the mechanism of wetland cover change with both moderate spatial resolution and high temporal frequency, this research evaluates the applicability of a spatiotemporal reflectance blending model in the Poyang Lake area, China, using 9 time-series Landsat-5 Thematic Mapper images and 18 time-series Terra Moderate Resolution Imaging Spectroradiometer images acquired between July 2004 and November 2005. The customized blending model was developed based on the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). Reflectance of the moderate-resolution image pixels on the target dates can be predicted more accurately by the proposed customized model than the original ESTARFM. Water level on the input image acquisition dates strongly affected the accuracy of the blended reflectance. It was found that either of the image sets used as prior or posterior inputs are required when the difference of water level between the prior or posterior date and target date at Poyang Hydrological Station is <2.68 m to achieve blending accuracy with a mean average absolute difference of 4% between the observed and blended reflectance in all spectral bands.     

Crop area mapping in West Africa using landscape stratification of MODIS time series and comparison with existing global land products

In Africa, food security early warning systems use satellite-derived data concerning crop conditions and agricultural production. Such systems can be improved if they are provided with a more reliable estimation of the cultivated area at national scale. This paper evaluates the potential of using time series from the MODerate resolution Imaging Spectroradiometer MOD13Q1 (16-day composite of normalized difference vegetation index at 250 m resolution) to extract cultivated areas in the fragmented rural landscapes of Mali. To this end, we first stratified Southern Mali into 13 rural landscapes based on the spatio-temporal variability of NDVI and textural indices, using an object-oriented classification scheme.The accuracy of the resulting map (MODIS_crop) and how it compares with existing coarse-resolution global land products (GLC2000 Africa, GLOBCOVER, MODIS V05 and ECOCLIMAP-II), was then assessed against six crop/non-crop maps derived from SPOT 2.5 m resolution images used as references. For crop areal coverage, the MODIS_crop cultivated map was successful in assessing the overall cultivated area at five out of the six validation sites (less than 6% of the absolute difference), while in terms of crop spatial distribution, the producer accuracy was between 33.1% and 80.8%. This accuracy was linearly correlated with the mean patch size index calculated on the SPOT crop maps (r² = 0.8). Using the Pareto boundary as an accuracy assessment method at the study sites, we showed that (i) 20-40% of the classification crop error was due to the spatial resolution of the MODIS sensor (250 m), and that (ii) compared to MODIS V05, which otherwise performed better than the other existing products, MODIS_crop generally minimized omission-commission errors. A spatial validation of the different products was carried out using SPOT image classifications as reference. In the corresponding error matrices, the fraction of correctly classified pixels for our product was 70%, compared to 58% for MODIS V05, while it ranged between 40% and 51% for the GLC2000, the ECOCLIMAP-II and the GLOBCOVER.     

Field spectroradiometer and simulated multispectral bands for discriminating invasive species from morphologically similar cohabitant plants

One of the challenges in fighting plant invasions is the inefficiency of identifying their distribution using field inventory techniques. Remote sensing has the potential to alleviate this problem effectively using spectral profiling for species discrimination. However, little is known about the capability of remote sensing in discriminating between shrubby invasive plants with narrow leaf structures and other cohabitants with similar ecological niche. The aims of this study were therefore to (1) assess the classification performance of field spectroradiometer data among three bushy and shruby plants (Artemesia afra, Asparagus laricinus, and Seriphium plumosum) from the coexistent plant species largely dominated by acacia and grass species, and (2) explore the performance of simulated spectral bands of five space-borne images (Landsat 8, Sentinel 2A, SPOT 6, Pleiades 1B, and WorldView-3). Two machine-learning classifiers (boosted trees classification and support vector machines) were used to classify raw hyperspectral (n = 688) and simulated multispectral wavelengths. Relatively high classification accuracies were obtained for the invasive species using the original hyperspectral bands for both classifiers (overall accuracy, OA = 83–97%). The simulated data resulted in higher accuracies for Landsat 8, Sentinel 2A, and WorldView-3 compared to those computed for bands simulated to SPOT 6 and Pleiades 1B data. These findings suggest the potential of remote-sensing techniques in the discrimination of different plant species with similar morphological characteristics occupying the same niche.