Maximum Value Composite Method Research Articles

Assessment of ecosystem services in new perspective: A comprehensive ecosystem service index (CESI) as a proxy to integrate multiple ecosystem services

A comprehensive understanding of multiple ecosystem services (ES) across the landscape is a key highlight of ecosystem management. There still remain a weakness to integrate multiple ESs for mirroring the capability of the ecosystems to deliver services in a broad perspective. Here, we proposed a comprehensive ecosystem services index (CESI) for integrating multiple ESs based on multiplicative method. Water supply service, carbon storage service and soil conservation service were assessed using multi-sources remote sensing datasets and InVEST models to build CESI. To examine the suitability and performance of CESI, we took the Guangdong-Hongkong-Macao Greater Bay Areas (GBA) as a test region. Meanwhile, we applied two previous methods used commonly for assessing multiple ESs: i) cumulative method and ii) maximum value composite method to construct comparative indexes for evaluating the performance of CESI with the help of spatial autocorrelation method and regression analysis method from the spatial pattern and numerical distribution perspective. The results showed that the CESI was well applied in GBA and got rid of the limitation of single ES. The spatial pattern of CESI was observed a high-value clustering in central areas and a low-value clustering in outer regions. A comparison of CESI and comparative indexes were illustrated on the results of linear regression and spatial autocorrelation, which showed a good linear correlation between each method and a similar spatial pattern in a high-value clustering, but had subtle different in low-value clustering. Specifically, the performance of CESI in expressing the low-value clustering was better than other indexes which could indicate the multiplicative method benefits to manifest the lagging in the provision of ESs better than cumulative method and maximum value composite method. This study could pave a way for a new approach to evaluate the provision of multiple ESs from the socio-ecological systems with a comprehensive perspective.

How Does Spring Phenology Respond to Climate Change in Ecologically Fragile Grassland? A Case Study from the Northeast Qinghai-Tibet Plateau

Vegetation phenology is an important indicator of global climate change, and the response of grassland phenology to climate change is particularly sensitive in ecologically fragile areas. To enhance the ecological security of the Tibetan Plateau, it is crucial to determine the relationship between fluctuations in the start of the growing season (SOS) and the response to environmental factors. We investigated the trends of the intra-annual (ten-day) and interannual spatiotemporal dynamics of the SOS on the Northeast Qinghai-Tibet Plateau (NQTP) from 2000–2020 with MOD09GA data. We identified the response relationships with environmental factors (climate, terrain) using the maximum value composite method and the Savitzky–Golay filtering and dynamic threshold method. The SOS was concentrated from the 110th to 150th days; the average annual SOS was on the 128th day, with a spatial pattern of “early in the east and late in the west”. The overall trend of the SOS was advanced (45.48%); the regions with the advanced trend were mainly distributed in the eastern part of the NQTP. The regions with a delayed SOS were mainly concentrated in the higher-altitude regions in the southwest (38.31%). The temperature, precipitation and SOS exhibited a reverse fluctuation trend around the midpoint of 2010. Precipitation affected the SOS earlier than temperature. When temperature became a limitation of the SOS, precipitation had a more significant regulatory effect on the SOS. The SOS and aspect, slope and altitude were distributed in axisymmetric, pyramidal and inverted pyramidal shapes, respectively. The SOS on shaded slopes was earlier and more intensive than that on sunny slopes. With increasing slope, the area of the SOS decreased, and it occurred later. The SOS area was largest at 4500–5000 m and decreased at lower and higher altitude intervals. The SOS occurred later as altitude increased.

Misestimation of Growing Season Length Due to Inaccurate Construction of Satellite Vegetation Index Time Series

Satellite-based vegetation index (VI) is widely used in monitoring land surface phenology (LSP). Currently, well-developed VI products utilize the maximum value composite (MVC) algorithm to produce a composite VI time series (TS). Many of these products, however, lack the actual acquisition date (AD) of the VI value. As an alternative, the median or mean date of a composite period is used to reconstruct the VI TS, which might lead to bias on LSP detection. This letter quantifies the LSP bias in the Northern Hemisphere by generating a 15-day composited normalized difference vegetation index (NDVI) TS from the land long-term data record daily NDVI products using the MVC method. The results show that the AD of the NDVI value is usually later than the mean date of a composite period in spring and earlier in fall, effectively leading to a total overestimation of the growing season length of 5.91 days on average across the Northern Hemisphere (north of 30° N). This bias has a significant spatial pattern with high values observed in Northeastern China, Central North America, and high-latitude areas. However, the temporal trend is not largely influenced overall. Accordingly, we suggest the research community using accurate temporal information, whenever possible, in extracting LSP from VI TS.

盐池县2000—2012年植被变化及其驱动力分析

PDF HTML阅读 XML下载 导出引用 引用提醒 盐池县2000-2012年植被变化及其驱动力 DOI: 10.5846/stxb201403050374 作者: 作者单位: 1. 西北土地退化与生态恢复国家重点实验室培育基地宁夏大学,1. 西北土地退化与生态恢复国家重点实验室培育基地宁夏大学 作者简介: 通讯作者: 中图分类号: 基金项目: 国家重点基础研究计划(973)前期专项(2012CB723206);国家自然科学基金项目(41201438);宁夏大学211建设项目 Vegetation dynamics over 2000-2012 and its driving factors in Yanchi County, Ningxia Province Author: Affiliation: 1.Cultivation Base for State Key Lab. of Restoration and Reconstruction of Degraded Ecosystem in Northwestern of China,Yinchuan,1.Cultivation Base for State Key Lab. of Restoration and Reconstruction of Degraded Ecosystem in Northwestern of China,Yinchuan Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:荒漠草原区的植被对防治荒漠化、维护生态屏障具有决定性作用,宁夏盐池县作为其典型代表,近13年的植被变化深受气候变化和人类活动的综合影响.基于MODIS NDVI等数据,运用趋势分析、经验模态分解和空间叠置分析等方法,对盐池县2000-2012年的植被动态变化进行研究,结果表明:(1)2000-2012年盐池县NDVI在0.2-0.4之间呈波动上升趋势,上升幅度为0.078/10 a,上升趋势显著;总体来说,植被稳定性低,年际间波动或转换频繁、幅度大;(2)NDVI的波动分量与残余分量方差贡献率各占50%,且NDVI波动呈减弱趋势.促使NDVI波动的主控因子是年降水量,但其影响在减弱;(3)推动NDVI趋势性上升的主要因素是土地利用方式改善和类型变化,但土地利用方式改善对NDVI的贡献远远大于土地利用类型变化对NDVI的贡献.因此,荒漠草原区的生态改善应以保护为主,辅之以必要的生态重建,走以适度开发带动整体保护的道路. Abstract:The vegetation of the desert steppe plays an important role in preventing desertification, maintaining ecosystem stability, and constructing "Ecological Barrier" in Northwest China. However, most desert steppe ecosystems are very fragile and constantly face the risk of degradation. Yanchi County, located in eastern Ningxia province, is a typical desert steppe, and its vegetation dynamics in the past 13 years have been affected by both climate change and human activities. In order to explore the process and driving factors based on the Normalized Difference Vegetation Index (NDVI) derived from the Moderate-resolution Imaging Spectroradiometer (MODIS), we studied the vegetation dynamics in Yanchi County from 2000 to 2012. We used 296 scenes of MODIS NDVI data, all of these were converted to an Albers conical projection system and GeoTIF format by using the MODIS Reprojection Tool, and smoothed using the Savitzky-Golay filter to reconstruct a high-quality NDVI time-series data set. Annual and quarterly NDVI were synthesized using the Maximum Value Composite (MVC) method. In addition to MODIS NDVI data, the land use data of Yanchi in 2000 and 2011, meteorological data, and social statistical data were also used in this study. Multiple methods were used to analyze the vegetation dynamics in Yanchi County. A linear regression with an F test was used to analyze the trend of NDVI and its significance. The non-parametric Mann-Kendall test was used to detect the abrupt change in the long-term NDVI from 2000 to 2012. A non-linear and non-stationary signal analysis method, Empirical Mode Decomposition (EMD), was used to isolate the amplitude-frequency determining the temporally varying trend of NDVI, and spatial overlay analysis was used to analyze the influence of land use and land cover change on vegetation dynamics. The results showed: (1) the average NDVI values of all pixels in Yanchi County, which were composited annually by the MVC method, ranged from 0.2 to 0.4 in the period 2000-2012. The vegetation index in this area was very low because there is a typical desert steppe, but significantly (P < 0.05) increased by 0.078 per 10 a, which was faster than that in the Three-North Shelter Forest Program region. Overall, the vegetation in Yanchi County lacks stability and has frequent, large-amplitude inter-annual fluctuations. (2) The Empirical Mode Decomposition found that the NDVI time-series data included two Intrinsic Mode Function (IMF) components with 4 and 9 year quasi-periodic fluctuations. The variance contribution of the first IMF component was about 50%, almost the same as that of the NDVI residue component, which increased significantly over the 13 years. However, the intensity of NDVI fluctuation decreased because the fluctuation in precipitation, one of its main driving factors, declined. (3) Improvements in land use and land cover change were the main drivers for NDVI increase. The former made a larger contribution than the latter. Therefore, protection is the first option for improving the ecological environment, and proper reconstruction can be used as a supplement in desert steppe. A proper strategy for vegetation restoration and maintenance should be adopted, and overall protection can be implemented through scientific and harmonized development. 参考文献 相似文献 引证文献

Um Estudo do Fenômeno da Ilha de Calor Urbana na Região Metropolitana do Rio de Janeiro (RMRJ)

The main objective of this study is analyze the field of Surface Temperature (TS) of the Metropolitan Region of Rio de Janeiro (RMRJ) and its relation to land use. The analysis was based on vegetation index NDVI and TS values obtained from NOAA - 14 and 18 images covering the 2005 to 2010 years. The Maximum Value Composite method was used to generate of TS and NDVI monthly maps and also for all data period. The different statistical parameters (e.g. correlation and determination coefficients, linear regression and standard deviation) were calculated to determine the relationship between TS and NDVI. The TS monthly values for NOAA - 14 satellite shown to be high in the early morning, although slightly lower than NOAA - 18 satellite due to the different times of passage. It was observed that Urban Heat Island (ICU) phenomenon operates throughout the year, especially in the eastern portion of the city and in the Baixada Fluminense cities. The inverse relationship between TS and NDVI was observed mainly in the autumn and winter months with the highest correlation and determination coefficients. However, this is not observed in the summer months because the heating effect of macroscale (weather effects) overlaps with land use.

An automated cloud detection method for daily NOAA 16 advanced very high resolution radiometer data over Texas and Mexico

The advanced very high resolution radiometer (AVHRR) data acquired from the National Oceanic and Atmospheric Administration (NOAA) satellites have been widely applied to a variety of environmental research. A single AVHRR scene is seldom completely cloud‐free. Maximum value compositing (MVC) to create a single image from multiple orbits and dates has become the most valuable method to minimize cloud contamination. Composite images are not absolutely cloud‐free. Postcomposite cloud screening of the composite aggregates was developed to overcome the residual cloud contamination problem, but this is not possible for real‐time delivery of composite data or not suitable for compositing based on AVHRR data from multiple NOAA satellites. Another approach is to detect and remove cloud‐contaminated pixels from daily AVHRR scenes prior to applying the MVC method to provide real‐time composite images. This study developed an automated cloud detection method for daily NOAA 16 AVHRR scenes over the state of Texas and Mexico. The accuracy of the cloud detection algorithm was greater than 93% based on a random test sample from 36 images. Unidentified cloud shadow pixels as well as misidentified barren land pixels and water pixels contributed to more than 5% of the accumulated errors. The error from misidentification of water pixels can be reduced by assigning different threshold values for channel 4 brightness temperature according to the geographical latitude of the data. The resulting daily cloud‐free AVHRR data can be used to construct short‐time period composite images valuable for detecting subtle but critical environment changes. In addition, compositing methods other than MVC, such as multidate averaging or minimum value selection, can be applied for various research purposes, once the daily AVHRR data are cloud‐free.

Evaluating different NDVI composite techniques using NOAA-14 AVHRR data

Normalized Difference Vegetation Index (NDVI) data derived from Advanced Very High Resolution Radiometer (AVHRR) data are influenced by cloud contamination, which is common in individual AVHRR scenes. Maximum value compositing (MVC) of NDVI data has been employed to minimize cloud contamination. Two types of weekly NDVI composites were built for crop seasons in summer: one from all available AVHRR data (named the traditional NDVI composite) and the other from solely cloud-free AVHRR data (named the conditional NDVI composite). The MVC method was applied to both composites. The main objective of this study was to compare the two types of NDVI composites using Texas data. The NDVI seasonal profiles produced from the conditional NDVI composites agreed with the field measured leaf area index (LAI) data, reaching maximum values at similar times. However, the traditional NDVI composites showed irregular patterns, primarily due to cloud contamination. These study results suggest that cloud detection for individual AVHRR scenes should be strongly recommended before producing weekly NDVI composites. Appropriate AVHRR data pre-processing is important for composite products to be used for short-term vegetation condition and biomass studies, where the traditional NDVI composite data do not eliminate cloud-contaminated pixels. In addition, this study showed that atmosphere composition affected near-infrared reflectance more than visible reflectance. The near-infrared reflectance was increasingly adjusted through atmospheric correction.

MODIS Vegetation Index Compositing Approach: A Prototype with AVHRR Data

In this study, the 16-day MODIS (MODerate resolution Imaging Spectroradiometer) vegetation index (VI) compositing algorithm and product were described, evaluated, and compared with the current AVHRR (Advanced Very High Resolution Spectroradiometer) maximum value composite (MVC) approach. The MVC method selects the highest NDVI (normalized difference vegetation index) over a certain time interval. The MODIS VI compositing algorithm emphasizes a global and operational view angle standardization approach: a reflectance-based BRDF (Bidirectional Reflectance Distribution Function) model, succeeded by a back-up MVC algorithm that includes a view angle constraint. A year's worth of daily global AVHRR data was used to prototype the MODIS vegetation index compositing algorithm. The composite scenarios were evaluated with respect to: 1) temporal evolution of the VI for different continents and vegetation types, 2) spatial continuity of the VI, 3) quality flags related to data integrity, cloud cover, and composite method, and 4) view angle distribution of the composited data. On a continental scale, the composited NDVI values from the MODIS algorithm were as much as 30% lower than the mostly, off-nadir NDVI results based on the MVC criterion. The temporal evolution of the NDVI values derived with the MODIS algorithm were similar to the NDVI values derived from the MVC algorithm. A simple BRDF model was adequate to produce nadir equivalent reflectance values from which the NDVI could be computed. Application of the BRDF and “back-up” components in the MODIS algorithm were dependent on geographic location and season, for example, the BRDF interpolation was most frequently applied in arid and semiarid regions, and during the dry season over humid climate vegetation types. Examples of a MODIS-like global NDVI map and associated quality flags were displayed using a pseudo color bit mapping scheme.

Maximum Value Interpolated (MVI): A Maximum Value Composite method improvement in vegetation index profiles analysis

MVI (Maximum Value Interpolated) is a new proposed method in NDVI profiles maximization instead of MVC (Maximum Value Composite). The MVI method consists of recording not only the NDVI maximum value within a period (e.g., a month), but also the day when that value was recorded. Through simple linear interpolation between these points it is possible to detect, for each time period, a representative NDVI value. Through a profile simulation method it is possible to show that the new method allows a significant error reduction compared with the MVC method.