Estimating tree canopy cover using harmonic regression coefficients derived from multitemporal Landsat data

The goal of this study was to evaluate whether harmonic regression coefficients derived using all available cloud-free observations in a given Landsat pixel for a three-year period can be used to estimate tree canopy cover (TCC), and whether models developed using harmonic regression coefficients as predictor variables are better than models developed using median composite predictor variables, the previous operational standard for the National Land Cover Database (NLCD). The two study areas in the conterminous USA were as follows: West (Oregon), bounded by Landsat Worldwide Reference System 2 (WRS-2) paths/rows 43/30, 44/30, and 45/30; and South (Georgia/South Carolina), bounded by WRS-2 paths/rows 16/37, 17/37, and 18/37. Plot-specific tree canopy cover (the response variable) was collected by experienced interpreters using a dot grid overlaid on 1 m spatial resolution National Agricultural Imagery Program (NAIP) images at two different times per region, circa 2010 and circa 2014. Random forest model comparisons (using 500 independent model runs for each comparison) revealed the following (1) harmonic regression coefficients (one harmonic) are better predictors for every time/region of TCC than median composite focal means and standard deviations (across times/regions, mean increase in pseudo R² of 6.7% and mean decrease in RMSE of 1.7% TCC) and (2) harmonic regression coefficients (one harmonic, from NDVI, SWIR1, and SWIR2), when added to the full suite of median composite and terrain variables used for the NLCD 2011 product, improve the quality of TCC models for every time/region (mean increase in pseudo R² of 3.6% and mean decrease in RMSE of 1.0% TCC). The harmonic regression NDVI constant was always one of the top four most important predictors across times/regions, and is more correlated with TCC than the NDVI median composite focal mean. Eigen analysis revealed that there is little to no additional information in the full suite of predictor variables (47 bands) when compared to the harmonic regression coefficients alone (using NDVI, SWIR1, and SWIR2; 9 bands), a finding echoed by both model fit statistics and the resulting maps. We conclude that harmonic regression coefficients derived from Landsat (or, by extension, other comparable earth resource satellite data) can be used to map TCC, either alone or in combination with other TCC-related variables.     

Woody vegetation cover,height and biomass at 25-m resolution across Australia derived from multiple site,airborne and satellite observations

Detailed spatial information on the presence and properties of woody vegetation serves many purposes, including carbon accounting, environmental reporting and land management. Here, we investigated whether machine learning can be used to combine multiple spatial observations and training data to estimate woody vegetation canopy cover fraction (‘cover’), vegetation height (‘height’) and woody above-ground biomass dry matter (‘biomass’) at 25-m resolution across the Australian continent, where possible on an annual basis. We trained a Random Forest algorithm on cover and height estimates derived from airborne LiDAR over 11 regions and inventory-based biomass estimates for many thousands of plots across Australia. As predictors, we used annual geomedian Landsat surface reflectance, ALOS/PALSAR L-band radar backscatter mosaics, spatial vegetation structure data derived primarily from ICESat/GLAS satellite altimetry, and spatial climate data. Cross-validation experiments were undertaken to optimize the selection of predictors and the configuration of the algorithm. The resulting estimation errors were 0.07 for cover, 3.4 m for height, and 80 t dry matter ha^-1 for biomass. A large fraction (89–94 %) of the observed variance was explained in each case. Priorities for future research include validation of the LiDAR-derived cover training data and the use of new satellite vegetation height data from the GEDI mission. Annual cover mapping for 2000–2018 provided detailed insight in woody vegetation dynamics. Continentally, woody vegetation change was primarily driven by water availability and its effect on bushfire and mortality, particularly in the drier interior. Changes in woody vegetation made a substantial contribution to Australia’s total carbon emissions since 2000. Whether these ecosystems will recover biomass in future remains to be seen, given the persistent pressures of climate change and land use.     

基于IDL语言的植被指数UNVI软件插件

植被指数是地球陆表植被覆盖度和植被活力的指示因子,对环境监测、植被理化参量估算等应用研究有重要的意义。基于植被的反射光谱特征,通过遥感数据波段的组合,可以计算得到遥感植被指数。传统的植被指数如NDVI、EVI等仅利用有限波段信息的线性或非线性组合构建而成,没有充分利用遥感传感器所提供的多波段遥感信息,通用归一化植被指数UNVI(Universal Normalized Vegetation Index)充分利用了遥感传感器提供的多波段植被光谱信息,因此在反演植被叶绿素、生物量等植被理化参量上较其他传统植被指数更具优势。为方便UNVI指数的计算,本文基于IDL语言开发了UNVI软件插件,可直接作为ENVI商业遥感软件进行调用,并可满足多个传感器的UNVI计算需求。为了验证UNVI的应用效果,以植被总初级生产力GPP(Gross Primary Productivity)估算为例,比较了不同植被指数估算GPP的效果,结果表明:基于UNVI估算的GPP与通量站点获得的GPP具有较高的相关性(相关系数R2为0.79),验证了UNVI在GPP估算方面的优势。本文提供的UNVI软件插件可为遥感研究和应用人员提供便捷的计算工具。     

基于Landsat卫星的沈阳市土地覆盖变化研究

土地利用、土地覆盖变化(LUCC)是当今遥感领域的重要研究课题,也是地学界、生物学界、环境学界等关注的热点问题之一^[1]。特别是对大城市的土地类型变化研究,有利于揭示城市化进程中的空间变化规律。本文利用沈阳市1990、2000、2010年3期Landsat系列卫星数据,经过空间配准、相对辐射校正、图像增强等预处理,分别对3期遥感图像进行监督分类,提取土地类型转换信息并进行景观格局分析^[2]。结果表明,20年间沈阳市土地覆盖变化主要表现为耕地和建筑用地的转变,其中1990—2000年,耕地和建筑用地均有缓慢增加;而2000—2010年耕地面积明显减少,建筑用地显著增加,城市化进程大大加快。本文成果对沈阳市未来城市规划和可持续发展相关政策的制定有一定的参考价值。     

基于Landsat 8影像的黄河口悬浮物质量浓度遥感反演

悬浮物质量浓度是黄河口海域重要的水质和水环境监测参数之一,直接影响着水面以下光场的分布,进而影响水体的初级生产力和水域生态环境。本文基于2011年6—7月和11—12月共计89组现场实测悬浮物质量浓度和光谱数据,分析了黄河口及其附近海域不同悬浮物质量浓度的水体光谱特征,尝试利用多种波段组合建立悬浮物质量浓度遥感反演算法。结果表明865 nm波段与波段比655 nm/560 nm组合形式算法反演结果最优,算法相关系数R²为0.95,平均相对误差为25.65%。将算法应用于2014—2016年共7景Landsat 8 OLI遥感影像,分析了不同年份黄河口悬浮物质量浓度的时空分布特征,黄河口海域悬浮物质量浓度分布总体呈现近岸高,离岸低的特点,不同时期悬浮物质量浓度量值上有显著变化。     

1987 - 2018年祁连山冰川变化遥感监测及影响因子分析

祁连山冰川融水是维系我国西北地区生态平衡的重要因素。为评估祁连山冰川在全球气候变暖背景下的状态, 利用Landsat-TM、 ETM+、 OLI等遥感影像, 基于波段比值阈值法提取1987 - 2018年共计7期冰川边界进行时序变化分析。结果显示： 近31年来祁连山冰川面积从2 080.39 km²退缩到1 442.09 km², 年均退缩率达0.99%, 相比1956 - 1990年间的退缩率（0.58%）大幅增加; 近31年来冰川物质平衡线高度稳步上升; 冰川主要分布在海拔4 700 ~ 5 100 m之间, 冰川退缩随海拔降低而增加; 约93%的冰川的面积小于2.0 km², 小于0.1 km²的冰川的总数和总面积呈增加态势; 0.5 ~ 1.0 km²的冰川退缩最快, 年均退缩率达1.53%, 而大于10.0 km²的冰川退缩最慢, 年均退缩率为0.59%; 祁连山冰川退缩主要由夏季均温升高引起, 且最近十年间冰川呈现出加速退缩的态势。     

基于Landsat遥感数据的田湾核电站温排水时空特征研究

本文基于2007—2018年Landsat系列遥感卫星数据开展田湾核电站温排水时空变化特征及其影响分析。采用辐射传输方程算法和劈窗算法对核电站周围海域的海表温度进行反演,通过星星匹配对反演温度的精度进行验证。匹配验证结果表明,反演的Landsat海表温度与MODIS海表温度产品具有较好的一致性,决定系数达到0.91。基于研究海域温度反演结果分析了核电站温排水面积的季节变化、年际变化和潮周期内变化特征,并且分析了潮汐与风场对温排水扩散的影响。结果表明,核电站周边海域各季节的温升区面积存在明显差异,春季最大,可达秋季的7倍;2007—2018年,随着装机容量的扩大,温排水面积不断扩大,2018年达到峰值,瞬时最大面积可达101.7 km²;潮汐对温排水扩散有影响,涨憩时刻温升区面积较落憩时刻大;有利风会促进温排水扩散,但影响有限。     

A satellite data driven approach to monitoring and reporting fire disturbance and recovery across boreal and temperate forests

The regular and consistent measurements provided by Earth observation satellites can support the monitoring and reporting of forest indicators. Although substantial scientific literature espouses the capabilities of satellites in this area, the techniques are under-utilised in national reporting, where there is a preference for aggregating ad hoc data. In this paper, we posit that satellite information, while perhaps of low accuracy at single time steps or across small areas, can produce trends and patterns which are, in fact, more meaningful at regional and national scales. This is primarily due to data consistency over time and space. To investigate this, we use MODIS and Landsat data to explore trends associated with fire disturbance and recovery across boreal and temperate forests worldwide. Our results found that 181 million ha (9 %) of the study area (2 billion ha of forests) was burned between 2001 and 2018, as detected by MODIS satellites. World Wildlife Fund biomes were used for a detailed analysis across several countries. A significant increasing trend in area burned was observed in Mediterranean forests in Chile (8.9 % yr⁻¹), while a significant decreasing trend was found in temperate mixed forests in China (-2.2 % yr⁻¹). To explore trends and patterns in fire severity and forest recovery, we used Google Earth Engine to efficiently sample thousands of Landsat images from 1991 onwards. Fire severity, as measured by the change in the normalized burn ratio (NBR), was found to be generally stable over time; however, a slight increasing trend was observed in the Russian taiga. Our analysis of spectral recovery following wildfire indicated that it was largely dependent on location, with some biomes (particularly in the USA) showing signs that spectral recovery rates have shortened over time. This study demonstrates how satellite data and cloud-computing can be harnessed to establish baselines and reveal trends and patterns, and improve monitoring and reporting of forest indicators at national and global scales.     

Assessment of Landsat atmospheric correction methods for water color applications using global AERONET-OC data

With the longest archive of satellite remote sensing images, the Landsat series of satellites have demonstrated their great potential in aquatic environmental studies. However, although various atmospheric correction (AC) methods have been developed for Landsat observations in water color applications, a comprehensive assessment of their accuracies across different AC methods and instruments has yet to be performed. Using in situ spectral data collected by Aerosol Robotic Network-Ocean Color (AERONET-OC) sites, the performances of five types of AC methods over three different Landsat missions (i.e., Landsat 5/7/8) were evaluated. The Landsat 8 Operational Land Imager (OLI) showed more accurate AC retrievals than the other two instruments, and the results for its green and red bands appeared more reliable than those for the other wavelengths (uncertainty levels of ∼30 %). The iterative NIR algorithm with 2-bands (NIR-SWIR2) model selection embedded in SeaDAS showed the best performances for OLI in two blue bands. Moreover, larger residual errors were found for most Landsat 5/7 bands regardless of the AC methods and spectral bands employed with an uncertainty of >50 %. Interestingly, a simple aerosol subtraction method over the Rayleigh-corrected reflectance (Rrc) outperformed the exponential extrapolation (EXP) algorithms, especially for Landsat 5/7. Neither the image-based AC algorithm nor the surface reflectance (SR) products provided by the United States Geological Survey (USGS) showed acceptable performances over coastal environments. The uncertainties in the various Landsat reflectance products over water surfaces could be associated with a relatively poor signal-to-noise ratio (SNR) in addition to radiometric calibration uncertainties, imperfect aerosol removal methods. Future research is required to collect in situ data across a wider range of water optical properties (particularly more turbid inland waters) to examine the corresponding applicability of Landsat-series observations.     

Combining multisource satellite data to estimate storage variation of a lake in the Rift Valley Basin,Ethiopia

Integration of remote sensing data sets from multiple satellites is tested to simulate water storage variation of Lake Ziway, Ethiopia for the period 2009-2018. Sixty Landsat ETM+/OLI images served to trace temporal variation of lake surface area using a water extraction index. Time series of lake levels were acquired from two altimetry databases that were validated by in-situ lake level measurements. Coinciding pairs of optical satellite based lake surface area and radar altimetry based lake levels were related through regression and served for simulating lake storage variation. Indices for extracting lake surface area from images showed 91–99 % overall accuracy. Lake water levels from the altimetry products well agreed to in-situ lake level measurements with R² = 0.92 and root mean square error of 11.9 cm. Based on this study we conclude that integrating satellite imagery and radar altimetry is a viable approach for frequent and accurate monitoring of lake water volume variation and for long-term change detection. Findings indicate water level reduction (4 cm/annum), surface area shrinkage (0.08km²/annum) and water storage loss (20.4Mm³/annum) of Lake Ziway (2009–2018).