森林扰动遥感监测研究进展

作为陆地生态系统的主体,森林的碳循环与碳蓄积对研究陆地生态系统起着重要作用,但目前森林扰动资料的缺乏在很大程度上影响着区域森林碳通量的估算精度。在对森林扰动监测方法和监测指数进行总结的基础上,对几种森林扰动监测指数进行了比较研究。鉴于当前基于长时间序列的森林扰动研究主要集中在北美国家,国内鲜有系统报道,因此,针对我国森林变化特点,结合长时间序列扰动分析方法和适宜的扰动监测指数,研究适用于我国森林的扰动监测模型具有重要的理论意义和应用价值。     

面向碳收支服务的HJ数据图像变换方法研究

为了突出遥感影像中的陆地生态系统固碳主体,增强图像的解译性,提高分类精度,从而准确评估陆地生态系统的碳收支情况,针对碳收支遥感监测所采用的国产HJ图像的变换方法展开研究,对HJ数据分别进行LBV变换、KL变换和KT变换实验,同时采用面向对象分类法评价了图像变换对碳收支土地覆盖分类的影响。实验结果证明,与KL、KT变换方法相比,LBV变换后的图像具有更好的目视解译效果,颜色更鲜艳、地物类别更易区分,能有效提高固碳主体的分类精度。     

基于多时相Landsat-8的森林生态系统树种精细分类

实时、准确地获取森林生态系统树种类型对森林资源调查与监测有重要意义.本文以吉林省抚松县为中心研究区,利用不同时相Landsat-8影像,讨论森林生态系统各地类在不同时相上光谱特征的差异,结合多时相影像的互补信息并使用支撑向量机(support vector machine,SVM)分类方法对森林树种类型进行分类.结果表明,总体分类精度达到73.67％,Kappa系数为0.65,可以满足实际应用需求.     

基于遥感的某市城市圈森林碳储量估算与空间分布特征

森林生态系统碳储量占有整个陆地生态系统碳储量约50%,利用遥感数据进行森林碳储量估算对加快实现“碳达峰”和“碳中和”具有重要意义。本研究利用Landsat 8 OLI遥感影像和DEM数据提取植被指数和地形因子,转换净初级生产力数据为生物量数据,并利用多元逐步回归分析法建立武汉城市圈森林植被碳储量遥感估算模型。根据统计数据和估算模型得出,武汉城市圈碳储量空间分布表现为东北部和南部山脉区域的碳储量和碳密度较高,而中东部武汉市和黄石市中心区域相对较低,且植被碳密度主要集中在中海拔地区。     

森林生态系统服务价值的遥感估算——以杭州市余杭区为例

森林生态系统作为陆地生态系统的主体,其服务功能价值的评估对于整个地球的生态系统都具有重要的现实意义。遥感技术的发展,为研究森林生态系统服务价值提供了有力的手段和支持。本文采用了以CASA模型为基础的光能利用率模型,基于MODIS数据,估算了2005年杭州市余杭区森林的净第一性生产力(NPP),并计算得出研究区森林生态系统六项服务功能的总价值为13.8467亿元,其中直接经济价值和间接经济价值分别为2.5215亿元和11.3252亿元,各项服务功能价值的贡献大小顺序依次为:固碳释氧>涵养水源>有机物质生产>营养物质循环>水土保持>净化作用。本文旨在探讨一个进行森林生态系统服务功能估算与评价的方法,为区域生态系统的和谐发展提供理论依据和数据支持。     

基于遥感的区域尺度森林地上生物量估算研究

森林是陆地生态系统最大的碳库,精确估算森林生物量是陆地碳循环研究的关键。首先从机载LiDAR数据中提取高度和密度统计量,采用逐步回归模型进行典型样区生物量估算;然后利用机载LiDAR数据估算的生物量作为样本数据,与多光谱遥感数据Landsat8 OLI的波段反射率及植被指数建立回归模型,实现区域尺度森林地上生物量估算。实验结果显示,机载LiDAR数据估算的鼎湖山样区生物量与地面实测生物量的相关性R2达0.81,生物量RMSE为40.85 t/ha,说明机载LiDAR点云数据的高度和密度统计量与生物量存在较高的相关性。以机载LiDAR数据估算的生物量为样本数据,结合多光谱遥感数据Landsat8 OLI估算粤西北地区的森林地上生物量,精度验证结果为:R2为0.58,RMSE为36.9 t/ha;针叶林、阔叶林和针阔叶混交林等3种不同森林类型生物量的估算结果为:R2分别为0.51(n=251)、0.58(n=235)和0.56(n=241),生物量RMSE分别为24.1 t/ha、31.3 t/ha和29.9 t/ha,估算精度相差不大。总体上看,利用遥感数据可以开展区域尺度的森林地上生物量估算,为森林固碳监测提供有力的参考数据。     

1990年—2020年广西北仑河口红树林扰动研究

监测国家级自然保护区内红树林的扰动,可为滨海湿地生态系统的管理和保护提供数据支撑和决策支持。本研究使用谷歌云平台GEE（Google Earth Engine）建立Landsat长时间序列卫星数据集,结合LandTrendr算法研究了广西北仑河口红树林自然保护区1990年—2020年红树林的扰动情况。结果表明：（1）1990年—2020年,共有45.94 ha的红树林发生了扰动,其中2001年保护区内红树林扰动面积最大,为12.91 ha;（2）1990年—2020年,轻微扰动和中度扰动所占比例较大,分别为57.5%和29.17%,严重扰动所占比例最少,只有13.33%;（3）红树林变化像元的总体识别精度达到88.56%,对扰动年份检测的总体精度达到87%,Kappa系数为0.76。本研究基于LandTrendr算法解析了30年间北仑河口保护区内红树林发生扰动的年份、频率和面积,结合实际情况分析了导致扰动的因素,认为人类活动是北仑河口红树林扰动的次要原因,自然因素,如虫灾,台风等是导致扰动的主要原因。本研究的结论和方法可为红树林保护区管理处制定科学合理的保护和恢复政策提供重要的决策支持。     

精密三角高程实施沉降监测方法研究及精度分析

针对沉降监测中普通水准观测方法无法实施的问题,本文通过对三角高程测量原理与误差来源的分析,研究了中间设站式三角高程测量实施沉降监测的方法,分析了观测精度及可行性。实验表明,该方法可以方便快捷地获取待监测体的沉降数据,提高了工作效率,测量精度达到了建筑变形测量等级二级精度要求。     

机器学习算法在森林地上生物量估算中的应用

森林地上生物量是森林生产力的重要评价指标,对其进行高效监测对维持全球碳平衡和保护生态系统具有重要意义。本文首先基于冠层高度模型数据,通过分水岭分割算法得到单木冠幅边界;然后在单木冠幅范围内提取23个LiDAR变量,结合佩诺布斯科特试验森林的87组实测数据,利用随机森林和支持向量机建立森林地上生物量估算模型;最后对样地模型估算的结果进行了比较,讨论了预测结果及其精度。结果表明:本文选用的随机森林模型和支持向量机模型在估算森林地上生物量的应用中获得了较高的精度;并且,随机森林模型在基于机载雷达数据估测森林地上生物量中的估算精度更高,模型泛化能力更强,制图精度也更好,具有更好的适用性。     

基于气象数据的地基雷达大气扰动校正方法研究

大气扰动是影响地基雷达(Ground-based SAR,GBSAR)系统监测精度的主要因素,文中研究大气扰动对GBSAR相位的影响及相应的补偿方法。实验以自制的角反射器作为监测目标,利用气象数据补偿法改正角反射器相位,用观测区域的温度、气压和湿度建立大气折射模型,估算出大气折射率的变化来校正大气扰动误差。结果表明,文中方法能有效地剔除大气扰动相位,达到改善监测精度的效果。     

基于时间序列统计特性的森林变化监测

森林动态变化分析对揭示生态系统环境变化及植被恢复和布局重建等具有重要意义,时间序列的遥感数据为森林监测提供了基础数据。本文根据森林植被的统计学特性,在暗目标法的基础上,利用归一化植被指数NDVI实现森林样本自动选择;并融合NDVI构建了新的综合森林特征指数(Integrated Forest Z-Score,IFZ);以时间序列的IFZ分析森林动态信息,实现森林变化动态监测。以三峡大坝及周边区域森林为研究区,利用2001年至2012年每年生长季节(5月—10月)的Landsat TM影像检验本文算法。基于2002年、2006年和2010年三期7月—9月的Quick Bird影像的精度分析结果发现:研究区森林变化检测的总体精度可达96.53%,Kappa系数为0.9512。在添加NDVI指数后构建的IFZ提高了总体监测精度。其中,毁林类别的检测精度提高显著,漏检率和误检率分别为2.74%和3.64%;干扰后重建的森林类别的检测精度有一定提高,其漏检率和误检率分别为10.79%和10.51%。研究结果表明,改进暗目标法能提高森林样本的选样效率,添加NDVI的IFZ能提高森林动态变化的识别度。此外,本算法不仅能定性识别森林变化,而且能定量提供森林干扰发生时间和干扰强度。     

Review of Alternative Methods for Estimating Terrestrial Emittance and Geothermal Heat Flux for Yellowstone National Park Using Landsat Imagery

Yellowstone National Park (YNP) is legally mandated to monitor geothermal features for their future preservation, and remote sensing is a component of the current monitoring plan. Landsat imagery was explored as a tool for mapping terrestrial emittance and geothermal heat flux for this purpose. Several methods were compared to estimate terrestrial emittance and geothermal heat flux (GHF) using images from 2007 (Landsat Thematic Mapper) and 2002 (Landsat Thematic Mapper Plus). Accurate estimations were reasonable when compared to previously established values and known patterns but were likely limited due to inherent properties of Landsat data, the effects of solar radiation, and variation among geothermal areas. Landsat data can be valuable for calculation of GHF in YNP. The method suggested in this paper is not highly parameterized. Landsat data provide the means to calculate GHF for all of YNP and have the potential to enable scientists to identify locations for in-depth study.     

基于影像的Landsat TM/ETM+数据正规化技术

阐述了基于影像的LandsatTM／ETM^＋的数据正规化技术及其发展。该技术通过将Landsat影像的亮度值转换成传感器处的辐射值和反射率采对影像进行辐射校正。实例表明，使用正规化技术处理后的影像可以明显削弱日照和大气的影响，去除它们产生的噪声；其所书的传感器处的反射率与地面实测反射率的RMS值非常小。     

Analyzing Change in Yellowstone's Terrestrial Emittance with Landsat Imagery

Yellowstone National Park (YNP) contains the world's largest concentration of geothermal features and is legally mandated to protect and monitor these natural features. Remote sensing is a component of the current geothermal monitoring plan. Landsat satellite data have a substantial historical archive and will continue to be collected into the future, making it the only available thermal imagery for historical analysis and long-term monitoring of geothermal areas in the entirety of YNP. Landsat imagery from Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) sensors was used to examine change trajectories for terrestrial emittance among spatial groupings from 1986 to 2007. Trajectories of locations with known change events were also examined. Relationships between the spatial groupings and several change vectors (distance to geologic faults, distance to large water bodies, and distance to earthquake swarms) were explored. The analysis showed the strongest relationship between absolute difference in terrestrial emittance and earthquake swarms, with 34% of the variation explained. Certain known change events were reflected in the change trajectories, while the Landsat imagery was not able to detect other known events. This suggests that Landsat imagery might be a useful tool for monitoring geothermal responses in YNP, but cannot be expected to suffice as the sole monitoring tool.     

Forest cover classification using Landsat ETM+ data and time series MODIS NDVI data

Forest cover plays a key role in climate change by influencing the carbon stocks, the hydrological cycle and the energy balance. Forest cover information can be determined from fine-resolution data, such as Landsat Enhanced Thematic Mapper Plus (ETM+). However, forest cover classification with fine-resolution data usually uses only one temporal data because successive data acquirement is difficult. It may achieve mis-classification result without involving vegetation growth information, because different vegetation types may have the similar spectral features in the fine-resolution data. To overcome these issues, a forest cover classification method using Landsat ETM+ data appending with time series Moderate-resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data was proposed. The objective was to investigate the potential of temporal features extracted from coarse-resolution time series vegetation index data on improving the forest cover classification accuracy using fine-resolution remote sensing data. This method firstly fused Landsat ETM+ NDVI and MODIS NDVI data to obtain time series fine-resolution NDVI data, and then the temporal features were extracted from the fused NDVI data. Finally, temporal features combined with Landsat ETM+ spectral data was used to improve forest cover classification accuracy using supervised classifier. The study in North China region confirmed that time series NDVI features had significant effects on improving forest cover classification accuracy of fine resolution remote sensing data. The NDVI features extracted from time series fused NDVI data could improve the overall classification accuracy approximately 5% from 88.99% to 93.88% compared to only using single Landsat ETM+ data.     

Lightning flash mensuration using video from the space shuttle Columbia (STS‐32)

Abstract

Environmental data are often utilized to guide interpretation of spectral information based on context, however, these are also important in deriving vegetation maps themselves, especially where ecological information can be mapped spatially. A vegetation classification procedure is presented which combines a classification of spectral data from Landsat‐5 Thematic Mapper (TM) and environmental data based on topography and fire history. These data were combined utilizing fuzzy logic where assignment of each pixel to a single vegetation category was derived comparing the partial membership of each vegetation category within spectral and environmental classes. Partial membership was assigned from canopy cover for forest types measured from field sampling. Initial classification of spectral and ecological data produced map accuracies of less than 50% due to overlap between spectrally similar vegetation and limited spatial precision for predicting local vegetation types solely from the ecological information. Combination of environmental data through fuzzy logic increased overall mapping accuracy (70%) in coniferous forest communities of northwestern Montana, USA.     

Spatial modelling of sprinkler irrigation suitability in a Central Brazilian Cerrado region

Sprinkler irrigation, an agricultural production system that is causing increasing conflict among water resource users, is expanding quickly in the Central Western Cerrado regions of Brazil. To subsidize watershed management and concession of water rights, GIS-based spatial modelling was applied to spatially predict relative likelihood of the installation of centre sprinkler irrigation systems. Interpretation of multitemporal Landsat Thematic Mapper and Enhanced Thematic Mapper imagery was conducted to map spatial distribution of centre-pivot sprinkler systems. Multi-source data layers on environmental conditions and infrastructure were elaborated to test their predictive power in an Ecological Niche Factor Analysis, a spatial modelling technique for presence-only data. Underpinned by an exploratory analysis of spatial autocorrelation of irrigation systems, suitability predictions were found to be accurate on landscape scale and improved when the model includes underlying ecogeographical factors (EGV) such as farming suitability, soil groupings and distance to the hydrographic network and a density layer of existing irrigations.     

Development of an image based integrated method for determining and mapping aerosol optical thickness (AOT) over urban areas using the darkest pixel atmospheric correction method,RT equation and GIS: A case study of the Limassol area in Cyprus

This paper presents the development of an image-based integrated method for determining and mapping aerosol optical thickness (AOT). Using the radiative transfer (RT) equation, a methodology was developed to create a Geographical Information System (GIS) model that can visually display the AOT distribution over urban areas. In this paper, the model was applied to eleven Landsat Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+) satellite images of Limassol, Cyprus during 2010 and 2011 to determine the AOT levels in Limassol Cyprus during satellite overpass. The study is innovative and unique in that the RT equation, satellite images, the darkest pixel (DP) method of atmospheric correction and GIS were integrated to derive AOT from satellite images and display the AOT distribution over an urban area without the input of any meteorological or atmospheric parameters. The accuracy of the algorithm was verified through statistical analysis by the strong agreement between the AOT values derived using the algorithm and the in situ AOT values from the ground-based sensors.     

A Neural Network Landuse Classifier for SAR Images Using Textural and Fractal Information

Abstract

Artificial neural networks (ANN) have recently been popularly used in image classification. Input features to most ANNs are extracted based on a one class per pixel basis. This requires a large number of training samples and thus a slow training rate. In this paper, we describe the use of a windowing technique to extract textural features such as average intensity, second moment of intensity histogram and fractal surface dimension from an image. This method of image characterization reduces the number of training samples efficiently, yet retains a reasonable overall classification accuracy. The ANN is trained based on the back‐error propagation algorithm. The method is applied for landuse classification of Synthetic Aperture Radar (SAR) images. An example is given for a site in Kedah State, Malaysia. The SAR images (HH,HV,VV) were taken by the Canadian Centre for Remote Sensing (CCRS) CV‐580 airborne C‐band SAR system in November 1993 during their GlobeSAR mission in Malaysia. These multi‐polarization SAR images are co‐registered with a Landsat Thematic Mapper (TM) channel 5 image from same area. An overall classification accuracy of about 86.95% is achieved using windowing technique, as compared to 68.22% based on one class per pixel approach. This shows that through fractal and textural information, the windowing technique when applied in an ANN classifier has a great potential in remote sensing applications.