Vegetation greenness modeling in response to climate change for Northeast Thailand

In Northeast Thailand, the climate change has resulted in erratic rainfall and tem- perature patterns. The region has experienced both periods of drought and seasonal floods with the increasing severity. This study investigated the seasonal variation of vegetation greenness based on the Normalized Difference Vegetation Index （NDVI） in major land cover types in the region. An assessment of the relationship between climate patterns and vegeta- tion conditions observed from NDVI was made. NDVI data were collected from year 2001 to 2009 using multi-temporal Terra MODIS Vegetation Indices Product （MOD13Q1）. NDVI pro- files were developed to measure vegetation dynamics and variation according to land cover types. Meteorological information, i.e. rainfall and temperature, for a 30 year time span from 1980 to 2009 was analyzed for their patterns. Furthermore, the data taken from the period of 2001-2009, were digitally encoded into GIS database and the spatial patterns of monthly rainfall and temperature maps were generated based on kriging technique. The results showed a decreasing trend in NDVI values for both deciduous and evergreen forests. The highest productivity and biomass were observed in dry evergreen forests and the lowest in paddy fields. Temperature was found to be increasing slightly from 1980 to 2009 while no significant trends in rainfall amounts were observed. In dry evergreen forest, NDVI was not correlated with rainfall but was significant negatively correlated with temperature. These re- sults indicated that the overall productivity in dry evergreen forest was affected by increasing temperatures. A vegetation greenness model was developed from correlations between NDVI and meteorological data using linear regression. The model could be used to observe the change in vegetation greenness and dynamics affected by temperature and rainfall.     

A dynamic relationship between the leaf phenology and rainfall regimes of Hawaiian tropical ecosystems: A remote sensing approach

Hypertemporal MODIS time series data provide a unique opportunity to investigate a dynamic relationship between leaf phenology and the climatic pattern of diverse, cloud‐prone Hawaiian ecosystems. Harmonic analysis summarized the complex greenness signals of Hawaiian tropical ecosystems into two main phenological wave forms – a moisture‐limited and a light‐limited type. Greenness maximums occurred during the wet season in dry and mesic ecosystems, and during the dry season in wet forests. The magnitude and periodicity of greenness fluctuations were also rainfall‐dependent. The annual greenness amplitude increased with increasing mean annual precipitation (MAP) in dry and mesic ecosystems. In wetter environments where MAP was greater than 3000 mm, however, annual greenness amplitude decreased with MAP. Annual greenness periodicity was stronger in drylands than in wet forests, and it weakened as annual precipitation increased. This result shows that rainfall is less important as a limiting factor in wet forests than it is in drylands. Therefore, leaf phenology is not governed by rainfall seasonality as forest wetness increases in the region.     

Quantifying annual land-cover change and vegetation greenness variation in a coastal ecosystem using dense time-series Landsat data

Land-cover change may affect water and carbon cycles when transitioning from one land-cover category to another (land-cover conversion, LCC) or when the characteristics of the land-cover type are altered without changing its overall category (land-cover modification, LCM). Given the increasing availability of time-series remotely sensed data for earth monitoring, there has been increased recognition of the importance of accounting for both LCC and LCM to study annual land-cover changes. In this study, we integrated 1,513 time-series Landsat images and a change-updating method to identify annual LCC and LCM during 1986–2015 in the coastal area of Zhejiang Province, China. The purpose was to quantify their contributions to land-cover changes and impacts on the amount of vegetation. The results show that LCC and LCM can be successfully distinguished with an overall accuracy of 90.0%. LCM accounted for 22% and 40.5% of the detected land-cover changes in reclaimed and inland areas, respectively, during 1986–2015. In the reclaimed area, LCC occurred mostly in muddy tidal flats, construction land, aquaculture ponds, and freshwater herbaceous land, whereas LCM occurred mostly in freshwater herbaceous land, Spartina alterniflora, and muddy tidal flats. In the inland area, both LCC and LCM were concentrated in forest and dryland. Overall, LCC had a mean magnitude of normalized difference vegetation index (NDVI) change similar to that of LCM. However, LCC had a positive effect and LCM had a negative effect on NDVI change in the reclaimed area. Both LCC and LCM in the inland area had negative impacts on vegetation greenness, but LCC resulted in larger NDVI change magnitude. Impacts of LCC and LCM on vegetation greenness were quantified for each land-cover type. This study provided a methodological framework to take both LCC and LCM into account when analyzing land-cover changes and quantified their effects on coastal ecosystem vegetation.     

基于知识与规则的红树林遥感信息提取

针对难以将红树林同陆地植被,尤其是同水体与陆地植被混合像元有效识别的现象,结合TM影像提取了能有效反映红树林湿地特征的绿度指数和湿度指数,同其他常用的NDVI、TM3/TM5、TM5/TM4等指数相比:绿度指数和湿度指数更能有效地提高红树林同陆地植被,尤其是同水体与植被混合像元的可分性.采用知识与规则方法提取红树林遥感信息,与其他学者常采用的分类特征及分类方法相比,识别精度有明显提高,Kappa系数提高0.10,错分率降低16.1个百分点.     

用NOAA/AVHRR资料动态监测小区域冬小麦长势

通过对定点监测资料和NOAA／AVHRR资料的平行分析,探讨了NOAA／AVHRR资料在小区域冬小麦长势动态监测中的应用问题,建立了农学参数与遥感绿度值之间的关系式和卫星遥感苗情分类指标动态方程,为当地冬小麦卫星遥感苗情分类提供了一套客观实用的方法。     

Developing soil indices based on brightness,darkness, and greenness to improve land surface mapping accuracy

Soil, as one of the three basic biophysical components, has been understudied using remote sensing techniques compared to vegetation and impervious surface areas (ISA). This study characterized land surfaces based on the brightness–darkness–greenness model. These three dimensions, brightness, darkness, and greenness, were represented by the first Tasseled Cap Transformation (TC1), Normalize Difference Snow Index (NDSI), and Normalized Difference Vegetation Index (NDVI), respectively. The Ratio Index for Bright Soil (RIBS) was developed based on TC1 and NDSI, and the Product Index for Dark Soil (PIDS) was established by TC1 and NDVI. Their applications to the Landsat 8 Operational Land Imager images and 500 m 8-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) in China revealed the efficiency. The two soil indices proficiently highlighted soil covers with consistently the smallest values, due to larger TC1 and smaller NDSI values in bright soil, and smaller NDVI and TC1 values in dark soil. The RIBS is capable of distinguishing bright soil from ISA without masking vegetation and water body. The spectral separability bright soil and ISA were perfect, with a Jeffries–Matusita distance of 1.916. And the PIDS was the only soil index that could discriminate dark soil from other land covers including ISA. The soil areas in China were classified using a simple threshold method based on MODIS images. An overall accuracy of 94.00% was obtained, with the kappa index of 0.8789. This study provided valuable insights into developing indices for characterizing land surfaces from different perspectives.     

1982~2011年北半球中纬度秋季植被绿度变化的主要模态

利用系统聚类方法和经验正交函数分解（EOF分析）2种方法,分别提取了北半球中纬度地区1982~2011年秋季（9~11月）归一化差值植被指数（NDVI）变化的主要模态,辨识了植被绿度变化的区域差异;并采用奇异值分解（SVD）的方法,综合时间和空间2个维度上的变异信息,揭示了植被绿度变化的气候背景。结果表明,北半球中纬度秋季植被绿度变化有2种基本模态,一种是持续增加模态（模态I）,该模态广泛分布于北美大陆、亚欧大陆的北半部（大约在55oN以北）和东西两端,NDVI平均增速为0.014/10a;另一种是趋势转折模态（模态II）, NDVI先增加,后减少,转折点大致出现在1994年,该模态主要出现在亚欧大陆中部,NDVI变化的平均速率分别是0.027/10a和-0.017/10a,其中以40oE~80oE最为典型。植被绿度变化与温度变化的时空特征基本一致。模态I区域的温度变化以持续性升高为主要特征,模态II区域的温度变化则以先增加后降低为主要特征,转折年份与NDVI变化的转折年份基本一致。SVD分析的第一模态NDVI与温度的时间系数相关系数为0.82,第二模态为0.92。由此表明,植被绿度变化主导模态可能由温度变化模态所致,在区域-大洲尺度上,温度变化的区域差异导致了秋季植被绿度变化的区域差异。     

A visible band index for remote sensing leaf chlorophyll content at the canopy scale

Leaf chlorophyll content is an important variable for agricultural remote sensing because of its close relationship to leaf nitrogen content. The triangular greenness index (TGI) was developed based on the area of a triangle surrounding the spectral features of chlorophyll with points at (670 nm, R₆₇₀), (550 nm, R₅₅₀), and (480 nm, R₄₈₀), where R_λ is the spectral reflectance at wavelengths of 670, 550 and 480, respectively. The equation is TGI = −0.5[(670 − 480)(R₆₇₀ − R₅₅₀) − (670 − 550)(R₆₇₀ − R₄₈₀)]. In 1999, investigators funded by NASA's Earth Observations Commercialization and Applications Program collaborated on a nitrogen fertilization experiment with irrigated maize in Nebraska. Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data and Landsat 5 Thematic Mapper (TM) data were acquired along with leaf chlorophyll meter and other data on three dates in July during late vegetative growth and early reproductive growth. TGI was consistently correlated with plot-averaged chlorophyll-meter values at the spectral resolutions of AVIRIS, Landsat TM, and digital cameras. Simulations using the Scattering by Arbitrarily Inclined Leaves (SAIL) canopy model indicate an interaction among TGI, leaf area index (LAI) and soil type at low crop LAI, whereas at high LAI and canopy closure, TGI was only affected by leaf chlorophyll content. Therefore, TGI may be the best spectral index to detect crop nitrogen requirements with low-cost digital cameras mounted on low-altitude airborne platforms.     

城市绿量的遥感估算与热岛效应的相关分析——以北京市五环区域为例

 本研究以遥感分析北京城市绿地对地表温度的影响,研究包括绿地提取、绿量估算、地表温度反演,地表温度和绿量相关分析。并以高精度Rapid Eye遥感影像,提取了五环内的绿地面积(197.3km²,占城区总面积的29.6%),且估算绿量总值为2450.7km²。同时用2009年7月20日的Landsat5 TM 6波段数据进行地表温度反演,低温区、中温区、次热岛和热岛区域所占的五环内城区面积的比例分别为12.3%,34.7%,40.4%和12.6%。绿量和地表温度呈负相关关系:y=-1278.7x+60650,城市绿地可以使城区平均温度降低2.6℃。     

广东大宝山矿区植被污染信息的遥感识别方法研究

为了探索遥感技术识别矿山植被污染信息的有效方法,基于ASTER和QuickBird 2种数据源,采用植被指数法和植被绿度法2种方法,对广东大宝山矿区的植被污染信息进行了识别研究。通过不同数据源、不同识别方法的对比分析,为遥感技术识别矿山植被污染信息的推广应用提供了工作思路和科学依据。