Evaluation of dynamic linkages between evapotranspiration and land-use/land-cover changes with Landsat TM and ETM+ data

Complexity embedded in coastal management leads to numerous questions as to how inherent spatial and temporal linkages among evapotranspiration (ET), depth to groundwater and land-use/land-cover change (LUCC) could affect the dynamics among these seemingly unrelated events. This article aims to address such unique dynamics in the nexus of physical geography and ecohydrology. To understand such dynamic linkages, a case study was carried out in a fast growing coastal region – the southern Laizhou Bay in Shandong Province, China – by identifying the coastal LUCC at the decadal scale in association with the variations of ET with the aid of Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) data. In such a coastal landscape evolutionary assessment, findings show that the major patterns of land use and land cover (LULC) in the study area are farmland, saline-alkali land, developed land, salt land and beach land. Over a 20-year time frame, declining groundwater trends were observed, while ET increased gradually with changing LULC. By using the surface energy balance algorithm for land (SEBAL) with Landsat TM/ETM+ images and additional environmental data, the concomitant response of ET variations due to LUCC becomes lucid among three significantly correlated pairs including fractional vegetation cover (FVC), land surface temperature (LST) and soil heat flux. The dynamic linkages between ET and LULC were finally confirmed with such a pair-wise analysis.     

Land-use and land-cover change detection in a semi-arid area of Niger using multi-temporal analysis of Landsat images

Recent studies using low-resolution satellite time series show that the Sahelian belt of West Africa is witnessing an increase in vegetation cover/biomass, called re-greening. However, detailed information on local processing and changes is rare or lacking. A multi-temporal set of Landsat images was used to produce land-cover maps for the years 2000 and 2007 in a semi-arid region of Niger, where an anomalous vegetation trend was previously detected. Several supervised classification approaches were tested: spectral classification of single Landsat data, temporal classification of normalized difference vegetation index time series from Landsat images, and two-step classification integrating both these approaches. The accuracy of the land-cover maps obtained ranges between 80% and 90% overall for the two-step classification approach. Comparison of the maps between the two years indicates a stable semi-arid region, where some change in hot spots exists despite a generally constant level of rainfall in the area during this period. In particular, the Dallol Bosso fossil valley highlights an increase in cultivated land, while a decrease in herbaceous vegetation was observed outside the valley where rangeland is the predominant natural landscape.     

Detection of vegetation abundance change in the alpine tree line using multitemporal Landsat Thematic Mapper imagery

Detection of alpine tree line change using pixel-based approaches on medium spatial resolution imagery is challenging because of very slow tree sprawl without obvious boundaries. However, vegetation abundance or density in the tree line zones may change over time and such a change may be detected using subpixel-based approaches. In this research, a linear spectral mixture analysis (LSMA)-based approach was used to examine alpine tree line change in the Northern Tianshan Mountains located in Northwestern China. Landsat Thematic Mapper (TM) imagery was unmixed into three fraction images (i.e. green vegetation – GV, shade, and soil) using the LSMA approach. The GV and soil fractions at different years were used to examine vegetation abundance change based on samples in the alpine tree line. The results show that Picea schrenkiana abundance around the top of the forested area increased approximately by 18.6% between 1990 and 2010, but remained stable in the central forest region over this period. Juniperus sabina abundance around the top of the forested area, in the central scrub region, and at the top of the scrub region increased approximately by 19.3%, 8.2%, and 15.6%, respectively. The increased vegetation abundance and decreased soil abundance of both P. schrenkiana and J. sabina indicate vegetation sprawl in the alpine tree line between 1990 and 2010. This research will be valuable for better understanding the impacts of climate change on vegetation change in the alpine tree line of central Asia.     

基于GIS的景观动态远程监控系统

由于人类活动和地表运动使土地覆盖变化产生很大变化,所以需要对景观动态准确绘图来进行环境监测.为此提出一种基于地理信息系统(GIS)的景观动态远程监测系统,根据遥感图像的多时相遥感光谱和表面模型数据,利用多时相决策树分类器和形态学图像处理技术,实现对土地利用/覆盖变化(LUCC)的动态监测.在Landsat 5 TM数据库中的本地图像上进行实验,结果表明,该方法能够达到90.77%的识别准确度,且具有较低的计算时间,能够很好的检测出植被分布(植物/植被恢复)、城区(城市化/拆迁)和地理形态特征(河流地貌变化/边坡失稳).     

Dynamic estimating of wetland vegetation cover based on linear spectral mixture and time phase transformation models

ABSTRACT

Globally, remote sensing is being used to monitor vegetation degradation in areas of concern. In recent years, drought and water shortages have caused significant degradation of the wetland vegetation in Zhalong Wetland of Heilongjiang province, China. This paper employed middle- and high-resolution Landsat images to construct a Linear Spectral Mixture Analysis of the wetland, with the end member extraction verified by feasibility analysis and with vegetation cover data extracted over nearly 30 years. By considering the problem of poor timing with middle- and high-resolution images, this paper proposes a phase-transform method that combines the time advantage of moderate-resolution spectroradiometer images with the spatial advantage of high-resolution Landsat imagery. Based on an intensity analysis model, the temporal and spatial characteristics of vegetation cover in the study area were analyzed using a time scale and the level of vegetation cover. The results show that (1) from 1985 to 2015, the vegetation cover showed an overall tendency to degrade, and (2) vegetation cover was extracted based on the phase transformation and linear spectral mixture models with an accuracy of 0.8628, which is higher than that of traditional remote sensing methods. Improving the prediction accuracy in vegetation transfer is of great theoretical value in relation to global climate change.     

Bias in land cover change estimates due to misregistration

Land cover change may be overestimated due to positional error in multi-temporal images. To assess the potential magnitude of this bias, we introduced random positional error to identical classified images and then subtracted them. False land cover change ranged from less than 5% for a 5-class AVHRR classification, to more than 33% for a 20-class Landsat TM classification. The potential for false change was higher with more classes. However, false change could not be reliably estimated simply by number of classes, since false change varied significantly by simulation trial when class size remained constant. Registration model root mean squared (rms) error may underestimate the actual image co-registration asccuracy. In simulations with 5 to 50 ground control locations, the mean model rms error was always less than the actual population rms error. The model rms error was especially unreliable when small sample sizes were used to develop second order rectification models. We introduce a bootstrap resampling method to estimate false land cover change due to positional error. Although the bootstrap estimates were unbiased, the precision of the estimates may be too low to be of practical value in some land cover change applications.     

Assessment of deforestation in the Lower Amazon floodplain using historical Landsat MSS/TM imagery

The floodplain forests bordering the Amazon River have outstanding ecological, economic, and social importance for the region. However, the original distribution of these forests is not well known, since they have suffered severe degradation since the 16th century. The previously published vegetation map of the Amazon River floodplain (Hess et al., 2003), based on data acquired in 1996, shows enormous difference in vegetation cover classes between the regions upstream and downstream of the city of Manaus. The upper floodplain is mostly covered by forests, while the lower floodplain is predominantly occupied by grasses and shrubs.This study assesses deforestation in the Lower Amazon floodplain over a ~ 30 year period by producing and comparing a historical vegetation map based on MSS/Landsat images acquired in the late 1970s with a recent vegetation map produced from TM/Landsat images obtained in 2008. The maps were generated through the following steps: 1) normalization and mosaicking of images for each decade; 2) application of a linear mixing model transformation to produce vegetation, soil and shade fraction-images; and 3) object-oriented image analysis and classification. For both maps, the following classes were mapped: floodplain forest, non-forest floodplain vegetation, bare soil and open water. The two maps were combined using object-level Boolean operations to identify time transitions among the mapped classes, resulting in a map of the land cover change occurred over ~ 30 years. Ground information collected at 168 ground points was used to build confusion matrices and calculate Kappa indices of agreement. A survey strategy combining field observations and interviews allowed the collection of information about both recent and historical land cover for validation purposes. Kappa values (0.77, 0.75 and 0.75) indicated the good quality of the maps, and the error estimates were used to adjust the estimated deforested area to a value of 3457 km² ± 1062 km² (95% CI) of floodplain deforestation over the ~ 30 years.     

基于面向对象的多源卫星遥感影像玉米倒伏面积提取

风灾引起的玉米倒伏可能导致玉米大量减产,利用遥感技术准确监测玉米倒伏面积与空间分布信息对灾情的评估非常重要。利用Planet和Sentinel-2影像分别结合面向对象与基于像元方法提取研究区玉米倒伏,同时评估了不同影像特征（光谱特征、植被指数和纹理特征）与不同分类方法（支持向量机法SVM、随机森林法RF和最大似然法MLC）对玉米倒伏提取精度的影响。结果表明：(1)使用高空间分辨率的Planet影像进行玉米倒伏提取的精度普遍高于Sentinel-2影像;(2)从分类精度和面积精度来看,Planet影像的光谱特征+植被指数+均值特征结合面向对象RF分类,总体精度和Kappa系数分别为93.77%和0.87,面积的平均误差最低为4.76%;(3)采用Planet和Sentinel-2影像结合面向对象分类提取玉米倒伏精度高于基于像元分类。研究不仅分析了面向对象方法的优势,还评估了使用不用影像数据结合面向对象方法的适用性,可以为遥感提取作物倒伏相关研究提供一定的借鉴。     

基于面向对象多端元混解模型的植被覆盖度反演及其时空分布研究北大核心CSCD

近年来湿地的植被退化一直是全球关注的问题,对湿地植被覆盖度进行反演并研究其时空分布特征显得尤为重要。而为了解决植被反演中存在的混合像元问题,提出了基于面向对象的多端元光谱混合分析方法。以扎龙湿地保护区为研究对象,中高分辨率Landsat影像为数据源,从时间尺度和植被覆盖度等级变化层面,研究湿地植被时空变化特征。面向对象多端元混解模型,有效地减少了计算量和混合像元的端元变化,且反演值与检验值相关性较高,均方根误差较小,优于传统多端元混解模型方法,提高了植被覆盖度反演精度。扎龙湿地多年植被覆盖度整体呈现退化趋势,2001～2017年的平均变化速率高于1985～2000年,对于提高全球气候变化情景下植被转移预测精度具有重要理论意义。     

Large-scale monitoring of coppice forest clearcuts by multitemporal very high resolution satellite imagery. A case study from central Italy

Reliable assessment of forest resource stock, productivity and harvesting is a commonly agreed objective of environmental monitoring programs. Distinctively, the assessment of wood harvesting has become even more relevant to evaluate the sustainability of forest management and to quantify forest carbon budget. This paper presents the development and testing of procedures for assessing forest harvesting in coppice forests by very high resolution (VHR) satellite imagery. The study area is located in central Italy over approximately 34,000 km². A set of SPOT5 HRG multispectral images was acquired for the study years (2002-2007). Official administrative statistics of coppice clearcuts were also acquired. More than 9500 clearcuts were mapped and dated by on-screen interpretation of the SPOT5 images. In a subset of the study area various methods for semi-automatic clearcut mapping were tested by pixel- and object-oriented approaches. The following results are presented: (i) clearcut map developed by visual interpretation of the SPOT5 images resulted in high thematic (overall accuracy of 0.99) and geometric (root mean square error of clearcut boundary delineation of 5.3 m) reliability; (ii) object-oriented approach achieved significantly better accuracy than pixel-based methods for semi-automatic classification of the coppice clearcuts; (iii) comparison between mapped clearcut area and official forest harvesting statistics proved a significant underestimation by the latter (65% of the total mapped clearcut area). A sample-based procedure exploiting VHR satellite imagery is finally proposed to correct the official statistics of coppice clearcuts.     

集成特征分量的高分二号影像阴影检测

针对高分辨率遥感影像中阴影检测精度易受水体、植被等因素干扰的问题，通过分析高分二号影像中典型地物的光谱特征，构建了一种集成特征分量与面向对象分类相结合的阴影检测方法。构建的特征分量包括：主成分第一分量PC1、亮度分量I、归一化差分植被指数NDVI及水体指数WI。将各特征分量进行归一化处理，建立包含波段均值、标准差等特征的规则集，对影像的I和PC1分量进行多尺度分割 ，结合面向对象的方法进行阴影检测。选取不同区域遥感影像进行实验，实验结果表明:与传统基于像素的阴影提取方法相比，该方法提取出的阴影斑块完整，且能有效地减弱水体和植被的影响。     

集成特征分量的高分二号影像阴影检测

针对高分辨率遥感影像中阴影检测精度易受水体、植被等因素干扰的问题,通过分析高分二号影像中典型地物的光谱特征,构建了一种集成特征分量与面向对象分类相结合的阴影检测方法。构建的特征分量包括：主成分第一分量PC1、亮度分量I、归一化差分植被指数NDVI及水体指数WI。将各特征分量进行归一化处理,建立包含波段均值、标准差等特征的规则集,对影像的I和PC1分量进行多尺度分割 ,结合面向对象的方法进行阴影检测。选取不同区域遥感影像进行实验,实验结果表明:与传统基于像素的阴影提取方法相比,该方法提取出的阴影斑块完整,且能有效地减弱水体和植被的影响。     

Change detection of the effect of severe drought on subsistence agropastoral communities on the Bolivian Altiplano

The purpose of this study was to identify the ecological effects of drought on the vegetation resources of subsistence agropastoral communities on the Bolivian Altiplano. The objectives of the study were to (1) characterize vegetation cover response during a typical year, and (2) identify vegetation cover type's response to drought using an image differencing change detection technique. A geographical information system (GIS), which included multi-temporal (from 1972-1987) Landsat satellite imagery, was used as the assessment tool. Vegetation index difference images showed that (1) all vegetation types were impacted by drought, but the wet meadow vegetation cover type had the least response, and (2) approximately 90 per cent of vegetation cover had not changed between 1972 and 1987. Crop and animal production in agropastoral systems are dependant on the availability of vegetation resources. The wet meadow vegetation type was the most resistant to drought, which suggests that during drought periods it is a key resource upon which the livestock of the community would depend. Little change in vegetation cover over the 15-year span of the study, and the rapid recovery of this parameter following the drought of 1983-84, suggest that agricultural practices (especially livestock grazing) are not contributing to resource degradation when measured only in terms of change in vegetation cover.     

基于时序Landsat数据的三江平原植被地表类型变化遥感探测研究

Landsat 卫星遥感数据具有分辨率较高,数据积累时间长的特点,在探测地表覆盖变化和地物分类中得到广泛应用。首先,对获取的Landsat TM/ETM+时间序列数据进行了定量化处理,获取了三江平原七台河市1989~2012年时间序列Landsat地表反射率图像。其次,设计了林地指数和湿地指数,提取了三江平原七台河区域地物光谱和时序特征,同时设计构建了地表覆盖分类和植被地表类型变化探测的决策树算法,实现了1989~2012年七台河区域的植被地表覆盖变化的动态监测,提取了森林覆盖变化的空间分布与变化时间。最后,对七台河区域地表覆盖与植被地表类型变化进行了精度检验,分类总体精度达到90.04%,Kappa系数达0.88。研究结果表明:基于定量化的Landsat时间序列数据的分类算法能克服单时相影像分类的缺陷,实现区域地物自动分类和地表覆盖变化的动态监测。
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Assessing changes of urban vegetation cover and aboveground carbon stocks using LiDAR and Landsat imagery data in Auckland,New Zealand

ABSTRACT

Urban vegetation can help to offset carbon emissions. However, urban vegetation cover is vulnerable to urbanization. This study attempts to detect the change in vegetation cover and to quantify its impact on aboveground carbon (AGC) stocks in Auckland, New Zealand, between 1989 and 2014. Field-measured vegetation parameters were used to calculate the amount of carbon stored in plants at the plot-level. Plot-level AGC stocks were linked with vegetation spectral/structural features derived from Landsat images and Light Detection and Ranging (LiDAR) data. These data were also used to map vegetation cover and to estimate AGC stock. Vegetation cover decreased from 394.0 km² in 1989 to 379.4 km² in 2014. AGC stock in 1989 was estimated at 1,001,184 Mg C from Landsat 4 data. The total AGC in 2014 was estimated at 1,459,530 Mg C from Landsat 8 data. Thus, total AGC stock increased by 458,346 Mg C (45.8%) in spite of a 3.7% decrease in vegetation cover (14.6 km²) during the same period. The increase in AGC stock was derived partly from tree growth and tree plantings. Vegetation growth contributed more to the increase in AGC stock than its gain from non-vegetation to vegetation changes. The AGC stored in trees and shrubs estimated at 1,333,011 Mg C from the 2014 Landsat data is 5.7% lower than 1,414,607 Mg C estimated from the 2013 LiDAR data, due to the inability of optical imagery to capture the sub-canopy structure of forests and the saturation effect. Thus, LiDAR data provided a more accurate estimate of AGC stock, especially when the stock density is high (e.g. >97.9 Mg C ha^–1).     

基于Landsat TM数据的潮白河流域植被覆盖变化研究

使用经严格配准的同一时间(1991年和2002年)Landsat TM图像数据，编制归一化植被指数(NDVI)图，进而计算生成植被覆盖度图像。通过掩膜技术和变化检测等提取了北京潮白河流域中上游地区从1991～2002年的植被覆盖变化信息。研究结果表明，北京潮白河流域中上游地区11年间植被退化的总面积为1635．3km^2，占该区域总面积的30．6％；其中植被覆盖度为40％～50％的类型退化的面积最多，为411．74km^2，变化率为66．0％，覆盖度为90％～100％的类型退化的面积最少，为14km^2，变化率为4．4％；覆盖度为30-40％的类型变化率最大，为100％，覆盖度为90％～100％的类型的变化率最小。为4．4％；从植被覆盖度变化的趋势来看，随着植被覆盖度的增加，变化率在逐渐降低；流域中游、密云水库北部和东北部以及上游的河谷地带由于受人类活动干扰的强度较大，植被退化较严重；而上游的山地区域由于人类活动干扰较少，再加上近年来采取封山育林、植树造林等措施，植被覆盖程度有所改善。     

Landscape cover dynamics pattern in Meghalaya

Human interventions in natural systems have resulted in large changes in vegetation composition and distribution patterns. The Land Use Change and Climate Change (LUCC) study under the International Geosphere Biosphere Program (IGBP) is a major initiative in this regard. Changes in land use and hence in vegetation cover, due to climatic change and human activity, affect surface water and energy budgets directly through plant transpiration, surface albedo, emissivity and roughness. They also affect primary production and, therefore, the carbon cycle. Thus, there is a need for spatial and temporal characterization of vegetation cover at different scales, from the global and continental scale to the local patch scale. Satellite remote sensing provides detailed information regarding the spatial distribution and extent of land use changes in the landscape. Meghalaya, in north-east India, is one of the most important, biologically rich landscapes. Degradational activities, namely shifting cultivation, clear felling of forests for timber, and mining, have altered the natural landscape to a great extent. Because of these increased anthropogenic activities the natural landscape has been modified which has resulted in a fragmented landscape with poor species composition. These changes in the landscape were analysed using IRS 1A, 1B and Landsat Multi-Spectral Scanner (MSS) data during the period 1980-1995. The vegetation type maps were prepared by a visual interpretation technique in order to study the land cover dynamics pattern in Meghalaya.     

Deriving land surface temperature from Landsat 5 and 7 during SMEX02/SMACEX

A sequence of five high-resolution satellite-based land surface temperature (T_s) images over a watershed area in Iowa were analyzed. As a part of the SMEX02 field experiment, these land surface temperature images were extracted from Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper (ETM) thermal bands. The radiative transfer model MODTRAN 4.1 was used with atmospheric profile data to atmospherically correct the Landsat data. NDVI derived from Landsat visible and near-infrared bands was used to estimate fractional vegetation cover, which in turn was used to estimate emissivity for Landsat thermal bands. The estimated brightness temperature was compared with concurrent tower based measurements. The mean absolute difference (MAD) between the satellite-based brightness temperature estimates and the tower based brightness temperature was 0.98 °C for Landsat 7 and 1.47 °C for Landsat 5, respectively. Based on these images, the land surface temperature spatial variation and its change with scale are addressed. The scaling properties of the surface temperature are important as they have significant implications for changes in land surface flux estimation between higher-resolution Landsat and regional to global sensors such as MODIS.     

Land cover changes around a major east African wildlife reserve: The Mara Ecosystem (Kenya)

The dynamics of savannah vegetation are still poorly understood. This study aims at analysing land cover changes over the past 20 years in the rangelands area of Narok District, Kenya. To analyse the impact of inter-annual climate variability and human activities on land cover modifications in the area, change detection techniques based on remote sensing data at different spatial and temporal resolutions were used. Coarse spatial, high temporal resolution NOAA (National Oceanic and Atmospheric Administration) data were analysed to investigate the role of inter-annual climate variations on the ecosystem. A combination of time contextual and spatial contextual change detection approaches was used on a set of three high spatial resolution Landsat images to map land cover modifications over the past 20 years. Both datasets are highly complementary in the detection of land cover dynamics. On the one hand, the coarse spatial resolution data detected areas that are sensitive to inter-annual climate fluctuations, but are not subjected to land cover conversion. On the other hand, the high spatial resolution data allowed the detection of land cover conversions or modifications between two consecutive dates that have a more permanent character and are independent of climate-induced fluctuations in surface attributes.     

Estimating tree‐cover change in Australia: challenges of using the MODIS vegetation index product

Time series of the vegetation index product MOD13Q1 from the Moderate Resolution Imagery Spectroradiometer (MODIS) were assessed for estimating tree foliage projective cover (FPC) and cover change from 2000 to 2006. The MOD13Q1 product consists of the enhanced vegetation index (EVI) and the normalized difference vegetation index (NDVI). There were four challenges in using the MOD13Q1 product to derive tree FPC: assessing the impact of the sensor's varying view geometry on the vegetation index values; decoupling tree and grass cover contributions to the vegetation index signal; devising a method to relate the temporally composited vegetation index pixels to Lidar estimates of tree FPC for calibration; and estimating the accuracy of the FPC and FPC change measurements using independently derived Lidar, Landsat and MODIS cover estimates. The results show that, for complex canopies, the varying view geometry influenced the vegetation indices. The EVI was more sensitive to the view angle than the NDVI, indicating that it is sensitive to vegetation structure. An existing time series method successfully extracted the evergreen vegetation index signal while simultaneously minimizing the impact of varying view geometry. The vegetation indices were better suited to monitoring tree cover change than deriving accurate single‐date estimates of cover at regional to continental scales. The EVI was more suited to monitoring change in high‐biomass regions (cover >50%) where the NDVI begins to saturate.