Enhanced Vegetation Index Time Series Research Articles

基于MODIS数据的神农架大九湖泥炭藓沼泽植被指数变化研究

PDF HTML阅读 XML下载 导出引用 引用提醒 基于MODIS数据的神农架大九湖泥炭藓沼泽植被指数变化研究 DOI: 10.5846/stxb201806231381 作者: 作者单位: 杭州师范大学遥感与地球科学研究院,杭州师范大学遥感与地球科学研究院,浙江安吉小鲵国家级自然保护区,杭州师范大学遥感与地球科学研究院,杭州师范大学生命与环境科学研究院,杭州师范大学遥感与地球科学研究院 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金项目（41571049）；浙江省自然科学基金（LY16D010007）；杭州市科技计划项目（20170533B01） Vegetation index change of Sphagnum palustre bog in Dajiu Lake of Mt. Shennongjia based on MODIS data Author: Affiliation: Research Academy of Remote Sensing and Earth Sciences, Hangzhou Normal University,Research Academy of Remote Sensing and Earth Sciences, Hangzhou Normal University,Zhejiang Hynobius Amjiensis National Nature Reserve,Research Academy of Remote Sensing and Earth Sciences, Hangzhou Normal University,College of Life and Environmental Sciences, Hangzhou Normal University,Research Academy of Remote Sensing and Earth Sciences, Hangzhou Normal University 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:泥炭藓是陆地生态系统中最重要的固碳植物之一，固碳量约占全球土壤碳的15%。近几十年来，由全球气候变暖导致的泥炭藓沼泽水热状况变化对泥炭藓的固碳量和速率产生影响。选取我国最重要的亚高山泥炭沼泽--神农架大九湖泥炭藓沼泽为试验区，以分析中纬度地区泥炭藓沼泽植被生长状况受气候变化的影响。研究以2000-2017年MODIS植被指数NDVI和EVI为数据源，通过对比Logistic模型订正后的NDVI和EVI时间序列在泥炭藓沼泽植被生长状况监测中的优劣，选出最佳指标以获得18年来泥炭藓沼泽植被生长状况的变化趋势。研究结果表明：1）Logistic模型能够很好的消除泥炭藓沼泽植被指数时间序列的噪声；2）在季节和年际两个时间尺度上，EVI对泥炭藓沼泽植被生长状况的监测效果均优于NDVI。在季节周期上，虽然EVI和NDVI均得到泥炭藓沼泽植被生长周期规律，但EVI更灵敏。在年际分析中，EVI有更大的值域响应空间，以准确反映泥炭藓沼泽植被的年际变化规律；3）由EVI获得18年来泥炭藓沼泽植被变化趋势指出，泥炭藓沼泽植被呈显著微弱增长，年均EVI增长率为3.8‰（R2=0.45，P < 0.01）。相比于EVI年均值，EVI年内最大值（R2=0.47，P < 0.01）更敏锐的反映泥炭藓沼泽植被生长状况的动态变化。 Abstract:Sphagnum is one of the most important carbon sequestration plants in terrestrial ecosystem, which accounts for approximately 15% of global soil carbon. In recent decades, the changes in the hydrothermal status of Sphagnum bogs caused by global warming have directly affected the amount and rate of carbon fixed by Sphagnum. In this study, the most important subalpine peat bogs of China, the Mt. Shennongjia Dajiu Lake Sphagnum palustre bog, was selected as the experimental area to analyze the effect of climate change on the vegetation growth of Sphagnum bogs in the mid-latitude area. The Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) were used as data sources from 2000-2017 to compare the advantages and disadvantages of NDVI and EVI time series, revised by the logistic model in monitoring the vegetation growth status of Sphagnum bogs. The best index was selected to obtain the change trend of Sphagnum bogs vegetation growth in the past 18 years. The results showed that: (1) the logistic model can effectively eliminate the noise on the time series of Sphagnum palustre bog vegetation index. (2) in the seasonal and annual time scales, the EVI monitors the vegetation growth of Sphagnum palustre bog better than NDVI. In the seasonal scale, although both EVI and NDVI reflected the regularity of the growth cycle of Sphagnum palustre bog vegetation, EVI was more sensitive to the time period of the change. In the annual analysis, EVI had a greater range of response space to accurately reflect the annual variation of Sphagnum palustre bog vegetation. (3) the change in the trend of Sphagnum palustre bog vegetation in the past 18 years was obtained by EVI, it was found that the annual average growth rate of Sphagnum palustre bog vegetation showed a remarkably steady growth, with an average annual EVI growth rate of 3.8‰ (R2=0.45, P < 0.01). Compared with the annual average of EVI, the annual maximum (R2=0.47,P < 0.01) reflects the dynamic changes of Sphagnum palustre bog vegetation growth more sensitively. 参考文献 相似文献 引证文献

Mapping fractional cropland covers in Brazil through integrating LSMA and SDI techniques applied to MODIS imagery

MODIS time-series imagery is promising for generating regional and global land cover products. For Brazil, however, accurate fractional cropland covers (FCC) information is difficult to obtain due to frequent cloud coverage and the mixing-pixel problem. To address these problems, this study developed an innovative approach to mapping the FCC of the Mato Grosso State, Brazil through integrating Linear Spectral Mixture Analysis (LSMA) and Seasonal Dynamic Index (SDI) models. With MOD13Q1 time-series EVI imagery, a SDI was developed to represent the phenology of croplands. Furthermore, fractional land covers (e.g., vegetation, soil, and low albedo components) were derived with the LSMA algorithms. A stepwise regression model was established to estimate the FCC at the regional scale. Finally, ground truth cropland cover information was extracted from Landsat TM imagery using a hybrid method. Results indicated that the combination of multiple feature variables produced better results when compared with individual variables. Through cross-validation and comparative analysis, the coefficient of determination (R2) between the reference and estimated FCCs reached 0.84 with a Root Mean Square Error (RMSE) of 0.13. This indicates that the proposed method effectively improved the accuracy of fractional cropland mapping. When compared to the traditional per-pixel “hard” classification, the sub-pixel level maps illustrated detailed cropland spatial distribution patterns. Keywords: fractional cropland covers (FCC), MODIS, enhanced vegetation index (EVI), subpixel mapping, remote sensing DOI: 10.25165/j.ijabe.20191201.4419 Citation: Zhu C M, Zhang X, Huang Q H. Mapping fractional cropland covers in Brazil through integrating LSMA and SDI techniques applied to MODIS imagery. Int J Agric & Biol Eng, 2019; 12(1): 192–200.

Investigation of Pangkalan Floods: Possible Reasons and Future Directions

Two flood events in Pangkalan, Lima Puluh Kota Regency on 3 March and 29 December 2017 have triggered some questions whether a dam downstream the catchment is solely responsible, or they were caused by other factors. In fact, several other major floods have been recorded after dam commission in 1998. Yet, the catchment have undergone rapid land use conversion into commercial plantation such as palm and Uncaria gambir Roxb (gambir) since mid-1990’s. To understand the flood, three contributing factors i.e. flood inundation by the dam and river tributaries, land use changes, and rainfall data were discussed and analysed, and some field works were conducted along the main river and flooded areas. Flood inundation was estimated using Digital Elevation Models (DEM) of 30 m in resolution and modeled using QGIS. Land use changes from year 1994 were analysed from Landsat and EVI (Enhanced Vegetation Index) time series. Rainfall data were collected and analysed using nearby station from year 1980. Flood inundation analysis showed that dam spillway at an elevation of 80 m is unlikely the main reason, since maximum flood elevation occurred at 92 m. Rather, the flood was likely induced by a constriction due to rock in the river at midstream catchment, since flood inundation model using this scenario exhibited similarities to the impacted areas. Furthermore, land conversion from forest to palm plantation and gambir was thought to increase runoff and also contributed to the floods. Yet, this manuscript needs to be followed by a model with reliable data such as river topography and high resolution DEM and automatic water level recorder (AWLR).

Deep learning based multi-temporal crop classification

This study aims to develop a deep learning based classification framework for remotely sensed time series. The experiment was carried out in Yolo County, California, which has a very diverse irrigated agricultural system dominated by economic crops. For the challenging task of classifying summer crops using Landsat Enhanced Vegetation Index (EVI) time series, two types of deep learning models were designed: one is based on Long Short-Term Memory (LSTM), and the other is based on one-dimensional convolutional (Conv1D) layers. Three widely-used classifiers were also tested for comparison, including a gradient boosting machine called XGBoost, Random Forest, and Support Vector Machine. Although LSTM is widely used for sequential data representation, in this study its accuracy (82.41%) and F1 score (0.67) were the lowest among all the classifiers. Among non-deep-learning classifiers, XGBoost achieved the best result with 84.17% accuracy and an F1 score of 0.69. The highest accuracy (85.54%) and F1 score (0.73) were achieved by the Conv1D-based model, which mainly consists of a stack of Conv1D layers and an inception module. The behavior of the Conv1D-based model was inspected by visualizing the activation on different layers. The model employs EVI time series by examining shapes at various scales in a hierarchical manner. Lower Conv1D layers of the optimized model capture small scale temporal variations, while upper layers focus on overall seasonal patterns. Conv1D layers were used as an embedded multi-level feature extractor in the classification model which automatically extracts features from input time series during training. The automated feature extraction reduces the dependency on manual feature engineering and pre-defined equations of crop growing cycles. This study shows that the Conv1D-based deep learning framework provides an effective and efficient method of time series representation in multi-temporal classification tasks.

Wavelet and non-parametric statistical based approach for long term land cover trend analysis using time series EVI data

Land cover change analysis was carried out using Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) time series data for the period 2005–2014. MODIS EVI data coupled with Quality Assessment Science Data Sets (QASDS) was de-noised with Savitzky–Golay filter while enhancing quality and preserving the temporal profile of EVI. Wavelet transform (WT) based approach along with Sen slope’s method was used for land cover change and trend analysis. The WT based approach is useful for studying multiscale and non-stationary processes. Mann–Kendall test was performed to confirm the significance of the identified trends. Proposed approach identified 358 locations as change points, where 285 (79.6%) and 73 (20.4%) locations were detected as ‘Change’ and ‘False Change’ with respect to high resolution images. The proposed approach is useful for monitoring land cover changes that provide vital inputs for sustainable management of land resources.

Salinity stress detection in rice crops using time series MODIS VI data

ABSTRACTSoil salinity is one of the abiotic stresses that constrains rice crop growth in the extensive coastal regions of India. Monitoring soil condition on such a large scale is time-consuming and expensive and alternative approaches have been proposed. Crop condition can be used as a reference to detect salinity-affected soils in rice crops instead of direct detection of soil condition. Vegetation indices derived from high temporal resolution satellites like the Moderate Resolution Imaging Spectroradiometer (MODIS) have the potential to detect soil salinity in a cost- and time-efficient manner. This study analysed the temporal trend of salinity in the kharif (wet) season and the behaviour of rice crop phenological indicators under different levels of salinity. We tested the applicability of MODIS time series Enhanced Vegetation Index (EVI) data for soil salinity detection in rice crops in the coastal region of Odisha, India. We used smoothed time series profiles of MODIS EVI 8 day composite data from 2011 to 2015 to extract rice crop phenological parameters. A total of 350 soil samples were periodically collected from 80 sites during the kharif season. Results showed that soil salinity was negatively correlated with EVI (r = – 0.76). Among all the rice crop parameters, the seasonal integral (SI) and amplitude of the EVI signal were the most promising indicators of soil salinity and its intensity. We observed consistently low SI and amplitude values of EVI in homogeneous rice pixels at high saline levels from 2011 to 2015 (P > 0.050). Our study showed that the extraction of phenological parameters from MODIS EVI is a promising method for detecting soil salinity in rice growing areas at regional scales even at low salinity levels.

Framework for Mapping Integrated Crop-Livestock Systems in Mato Grosso, Brazil

Integrated crop-livestock (ICL) systems combine livestock and crop production in the same area, increasing the efficiency of land use and machinery, while mitigating greenhouse gas emissions, and reducing production risks, plant diseases and pests. ICL systems are primarily divided into annual (ICLa) and multi-annual (ICLm) systems. Projects such as the “Integrated crop-livestock-forest Network” and the “Livestock Rally” have estimated the ICL areas for Brazil on a state or regional basis. However, it remains necessary to create methods for spatial identification of ICL areas. Thus, we developed a framework for mapping ICL areas in Mato Grosso, Brazil using the Enhanced Vegetation Index time-series of Moderate Resolution Imaging Spectroradiometer and a Time-Weighted Dynamic Time Warping (TWDTW) classification method. The classification of ICL areas occurred in three phases. Phase 1 corresponded to the classification of land use from 2008 to 2016. In Phase 2, the ICLa areas were identified. Finally, Phase 3 corresponded to the ICLm identification. The framework showed overall accuracies of 86% and 92% for ICL areas. ICLm accounted for 87% of the ICL areas. Considering only agricultural areas or only pasture areas, ICL systems represented 5% and 15%, respectively.

Land cover change detection using focused time delay neural network

The development of improved satellite technology generates a huge amount of remote sensing data, these data play the crucial role in natural resource management. The land use and land cover (LULC) change intensely affects local environment, as well as the global environment. Therefore, the quantifiable knowledge about LULC changes occur in global scale is important to make effective planning for conservation and precise use of natural resources, that has motivated the scientists to develop the various land cover change detection techniques. In this paper, we have proposed neural network-based approach, i.e., focused time delay neural network (FTDNN)-based approach for land cover change detection, which is a time series prediction-based approach and detect the sudden change in the enhanced vegetation index (EVI) time series. The performance of the proposed method has been addressed by using quantitative and qualitative analysis techniques. For the quantitative evaluation, the proposed algorithm is applied to the standard synthetic data set, which are analogous to EVI time series data set. The performance result of the proposed method compares with the four previously existing data mining-based benchmark techniques. The analysis was shown that the FTDNN-based method significantly outperforms than other techniques. For qualitative analysis, the San Francisco Bay Area data set has been used, which comprises real EVI time series. The proposed FTDNN-based method is applied to the San Francisco Bay Area data set and observe the interesting land cover changes. These outcomes indicate the effectiveness of data mining techniques for the land cover change detection problem.

Validating the use of MODIS time series for salinity assessment over agricultural soils in California, USA

Testing soil salinity assessment methodologies over different regions is important for future continental and global scale applications. A novel regional-scale soil salinity modeling approach using plant-performance metrics was proposed by Zhang et al. (2015) for farmland in the Yellow River Delta, China, a region with a humid continental/subtropical climate. The one-year integral of temporally interpolated MODIS Enhanced Vegetation Index (EVI) time series data was proposed as an explanatory variable for agricultural soil salinity modeling. Here, we test such a methodology in California’s Central Valley, USA, a region with a semi-arid Mediterranean climate. Time series of EVI, Normalized Difference Vegetation Index (NDVI), and Canopy Response Salinity Index (CRSI) were created for the 2007–2013 period. Seventy-three MODIS pixels surveyed for 0–1.2-m soil salinity in 2013 were used as the ground-truth dataset. Our results validate the tested approach: the 2013 integral of CRSI (best performing index) had a Pearson correlation coefficient (r) of −0.699 with salinity. Results obtained using temporally integrated data were almost always better than those obtained using individual data. Furthermore, we show that the methodology can be improved by the use of multi-year data. Further research is needed to improve spatial resolution and the selection of vegetation indices.

A Modified Spatiotemporal Fusion Algorithm Using Phenological Information for Predicting Reflectance of Paddy Rice in Southern China

Satellite data for studying surface dynamics in heterogeneous landscapes are missing due to frequent cloud contamination, low temporal resolution, and technological difficulties in developing satellites. A modified spatiotemporal fusion algorithm for predicting the reflectance of paddy rice is presented in this paper. The algorithm uses phenological information extracted from a moderate-resolution imaging spectroradiometer enhanced vegetation index time series to improve the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). The algorithm is tested with satellite data on Yueyang City, China. The main contribution of the modified algorithm is the selection of similar neighborhood pixels by using phenological information to improve accuracy. Results show that the modified algorithm performs better than ESTARFM in visual inspection and quantitative metrics, especially for paddy rice. This modified algorithm provides not only new ideas for the improvement of spatiotemporal data fusion method, but also technical support for the generation of remote sensing data with high spatial and temporal resolution.

Towards understanding vulnerability: Investigating disruptions in cropping schedules in irrigated rice fields in West Java

Unsafe conditions may increase the vulnerability of farmers natural hazards and reduce the capacity of farmers to prevent or recover from disaster impacts. This study aimed to investigate disruptions in cropping schedules to understand unsafe conditions that contribute to vulnerability in irrigated fields served by Ir. Djuanda (Jatiluhur) reservoir in West Java. Firstly, the deviation of ongoing cropping schedules from the official cropping calendar was evaluated using the time-series Enhanced Vegetation Index (EVI) derived from MODerate-resolution Imaging Spectroradiometer (MODIS) imageries. Secondly, reasons for disruptions in cropping schedules were explored using an in-depth interview with farmers, extension officers, and water managers and analyzed using a qualitative content analysis. Thirdly, the progression from potential causes to consequences of the disruption was identified using a Bow-Tie analysis. Unsafe conditions were identified using the result of the Bow-Tie analysis. Finally, several ways to reduce vulnerability were suggested. This study has successfully showed that cropping schedules deviate from the official cropping calendar in the study area. Reasons for disruptions in cropping schedules include economic motives, weather variabilities, geographic locations, coping strategies, farmers’ interactions, and agricultural infrastructures. The Bow-Tie analysis has visualized the progression from potential causes, disruptions in cropping schedules, to potential disaster impacts. Unsafe conditions have been identified, categorized into the dangerous locations, unsustainable farming activities, unsuitable coping strategies, fragile infrastructures, and inaccurate perceptions, have been pinpointed. Addressing unsafe conditions is likely to able to reduce vulnerability in irrigated rice fields.

Modis time series for irrigated-area mapping in hydrographic basins of the Brazilian Northeastern region

Abstract: The objective of this work was to evaluate the applicability of time series of the enhanced vegetation index (EVI), from the moderate resolution imaging spectroradiometer (Modis), in the mapping of irrigated areas in the Northeastern region of Brazil. Annual time series from 2006 to 2015 were classified with the iterative self-organizing data analysis technique (Isodata) algorithm, generating a binary map of irrigated and nonirrigated areas for each year. In the Sertão region, the classification showed an average kappa coefficient of 0.66, underestimating the irrigated area by 7.6%, compared with data of the 2006 agricultural census. In regions more humid than the Sertão, such as Agreste and Zona da Mata Nordestina, the methodology showed limitations to distinguish irrigated areas from natural vegetation, presenting average kappa coefficients of 0.26 and 0.00, respectively. The EVI time series from Modis are applicable for the mapping of irrigated areas in the Sertão of the Northeastern region of Brazil.

Vegetation Cover Dynamics and Resilience to Climatic and Hydrological Disturbances in Seasonal Floodplain: The Effects of Hydrological Connectivity.

Floodplain wetlands are valuable ecosystems for maintaining biodiversity, but are vulnerable to hydrological modification and climatic extremes. The floodplain wetlands in the middle Yangtze region are biodiversity hotspots, particularly important for wintering migratory waterbirds. In recent years, extremely low winter water level events frequently occurred in the middle Yangtze River. The hydrological droughts greatly impacted the development and distribution of the wet meadows, one of the most important ecological components in the floodplains, which is vital for the survival of many migratory waterbirds wintering in the Yangtze region. To effectively manage the wet meadows, it is critical to pinpoint the drivers for their deterioration. In this study, we assessed the effects of hydrological connectivity on the ecological stability of wet meadow in Poyang Lake for the period of 2000 to 2016. We used the time series of MODIS EVI (Enhanced Vegetation Index) as a proxy for productivity to infer the ecological stability of wet meadows in terms of resistance and resilience. Our results showed that (1) the wet meadows developed in freely connected lakes had significantly higher resilience; (2) wet meadows colonizing controlled lakes had higher resistance to water level anomalies; (3) there was no difference in the resistance to rainfall anomaly between the two types of lakes; (4) the wet meadow in freely connected lakes might approach a tipping point and a regime shift might be imminent. Our findings suggest that adaptive management at regional- (i.e., operation of Three Gorges Dam) and site-scale (e.g., regulating sand mining) are needed to safeguard the long-term ecological stability of the system, which in term has strong implications for local, regional and global biodiversity conservation.

Geo-Parcel Based Crop Identification by Integrating High Spatial-Temporal Resolution Imagery from Multi-Source Satellite Data

Geo-parcel based crop identification plays an important role in precision agriculture. It meets the needs of refined farmland management. This study presents an improved identification procedure for geo-parcel based crop identification by combining fine-resolution images and multi-source medium-resolution images. GF-2 images with fine spatial resolution of 0.8 m provided agricultural farming plot boundaries, and GF-1 (16 m) and Landsat 8 OLI data were used to transform the geo-parcel based enhanced vegetation index (EVI) time-series. In this study, we propose a piecewise EVI time-series smoothing method to fit irregular time profiles, especially for crop rotation situations. Global EVI time-series were divided into several temporal segments, from which phenological metrics could be derived. This method was applied to Lixian, where crop rotation was the common practice of growing different types of crops, in the same plot, in sequenced seasons. After collection of phenological features and multi-temporal spectral information, Random Forest (RF) was performed to classify crop types, and the overall accuracy was 93.27%. Moreover, an analysis of feature significance showed that phenological features were of greater importance for distinguishing agricultural land cover compared to temporal spectral information. The identification results indicated that the integration of high spatial-temporal resolution imagery is promising for geo-parcel based crop identification and that the newly proposed smoothing method is effective.

The effect of EVI time series density on crop classification accuracy

Time series remote sensing data have been found very useful in discriminating crops due to its temporal character that map the whole stages of crops. In order to analyze their performances, a range of different time series i.e. 16-day, 32-day, 48-day and 64-day interval was built from MODIS (Moderate Resolution Imaging Spectroradiometer) 250-m Enhanced Vegetation Index (EVI) data. These times series were used to discriminate five crops i-e alfalfa, corn, sorghum, soybean and winter wheat in the United State, Kansas in 2010. The time series data were used to test the discriminating ability of different classifiers like Maximum Likelihood Classifier (MLC), Minimum Distance (MD), Support Vector Machine (SVM), Neural Network (NN) and Random Forest (RF) for crop classification. The results showed that the high temporal resolution time series returned high classification accuracy and vice versa. The results comparison showed that RF classifier returned high accuracy (overall accuracy 92.61%) followed by SVM and Neural Network. However, minimum distance and MLC returned low accuracy showing their less adoptability towards different time series data.

Mapping cropping patterns in irrigated rice fields in West Java: Towards mapping vulnerability to flooding using time-series MODIS imageries

Information on the vulnerability to flooding is vital to understand the potential damages from flood events. A method to determine the vulnerability to flooding in irrigated rice fields using the Enhanced Vegetation Index (EVI) was proposed in this study. In doing so, the time-series EVI derived from time-series 8day 500m spatial resolution MODIS imageries (MOD09A1) was used to generate cropping patterns in irrigated rice fields in West Java. Cropping patterns were derived from the spatial distribution and phenology metrics so that it is possible to show the variation of vulnerability in space and time. Vulnerability curves and cropping patterns were used to determine the vulnerability to flooding in irrigated rice fields. Cropping patterns capture the shift in the vulnerability, which may lead to either an increase or decrease of the degree of damage in rice fields of origin and other rice fields. The comparison of rice field areas between MOD09A1 and ALOS PALSAR and MOD09A1 and Agricultural Statistics showed consistent results with R2=0.81 and R2=0.93, respectively. The estimated and observed DOYs showed RMSEs=9.21, 9.29, and 9.69days for the Start of Season (SOS), heading stage, and End of Season (EOS), respectively. Using the method, one can estimate the relative damage provided available information on the flood depth and velocity. The results of the study may support the efforts to reduce the potential damages from flooding in irrigated rice fields.

Analysis of Differences in Phenology Extracted from the Enhanced Vegetation Index and the Leaf Area Index.

Remote-sensing phenology detection can compensate for deficiencies in field observations and has the advantage of capturing the continuous expression of phenology on a large scale. However, there is some variability in the results of remote-sensing phenology detection derived from different vegetation parameters in satellite time-series data. Since the enhanced vegetation index (EVI) and the leaf area index (LAI) are the most widely used vegetation parameters for remote-sensing phenology extraction, this paper aims to assess the differences in phenological information extracted from EVI and LAI time series and to explore whether either index performs well for all vegetation types on a large scale. To this end, a GLASS (Global Land Surface Satellite Product)-LAI-based phenology product (GLP) was generated using the same algorithm as the MODIS (Moderate Resolution Imaging Spectroradiometer)-EVI phenology product (MLCD) over China from 2001 to 2012. The two phenology products were compared in China for different vegetation types and evaluated using ground observations. The results show that the ratio of missing data is 8.3% for the GLP, which is less than the 22.8% for the MLCD. The differences between the GLP and the MLCD become stronger as the latitude decreases, which also vary among different vegetation types. The start of the growing season (SOS) of the GLP is earlier than that of the MLCD in most vegetation types, and the end of the growing season (EOS) of the GLP is generally later than that of the MLCD. Based on ground observations, it can be suggested that the GLP performs better than the MLCD in evergreen needleleaved forests and croplands, while the MLCD performs better than the GLP in shrublands and grasslands.

Remotely Monitoring Ecosystem Water Use Efficiency of Grassland and Cropland in China’s Arid and Semi-Arid Regions with MODIS Data

Scarce water resources are available in the arid and semi-arid areas of Northwest China, where significant water-related challenges will be faced in the coming decades. Quantitative evaluations of the spatio-temporal dynamics in ecosystem water use efficiency (WUE), as well as the underlying environmental controls, are crucial for predicting future climate change impacts on ecosystem carbon-water interactions and agricultural production. However, these questions remain poorly understood in this typical region. By means of continuous eddy covariance (EC) measurements and time-series MODIS data, this study revealed the distinct seasonal cycles in gross primary productivity (GPP), evapotranspiration (ET), and WUE for both grassland and cropland ecosystems, and the dominant climate factors performed jointly by temperature and precipitation. The MODIS WUE estimates from GPP and ET products can capture the broad trend in WUE variability of grassland, but with large biases for maize cropland, which was mainly ascribed to large uncertainties resulting from both GPP and ET algorithms. Given the excellent biophysical performance of the MODIS-derived enhanced vegetation index (EVI), a new greenness model (GR) was proposed to track the eight-day changes in ecosystem WUE. Seasonal variations and the scatterplots between EC-based WUE and the estimates from time-series EVI data (WUEGR) also certified its prediction accuracy with R2 and RMSE of both grassland and cropland ecosystems over 0.90 and less than 0.30 g kg−1, respectively. The application of the GR model to regional scales in the near future will provide accurate WUE information to support water resource management in dry regions around the world.

Extraction of Rice Phenological Differences under Heavy Metal Stress Using EVI Time-Series from HJ-1A/B Data.

An effective method to monitor heavy metal stress in crops is of critical importance to assure agricultural production and food security. Phenology, as a sensitive indicator of environmental change, can respond to heavy metal stress in crops and remote sensing is an effective method to detect plant phenological changes. This study focused on identifying the rice phenological differences under varied heavy metal stress using EVI (enhanced vegetation index) time-series, which was obtained from HJ-1A/B CCD images and fitted with asymmetric Gaussian model functions. We extracted three phenological periods using first derivative analysis: the tillering period, heading period, and maturation period; and constructed two kinds of metrics with phenological characteristics: date-intervals and time-integrated EVI, to explore the rice phenological differences under mild and severe stress levels. Results indicated that under severe stress the values of the metrics for presenting rice phenological differences in the experimental areas of heavy metal stress were smaller than the ones under mild stress. This finding represents a new method for monitoring heavy metal contamination through rice phenology.

The Investigation of 1997 and 2015 El Nino Events in West Sumatera, Indonesia

The 1997, 2010, and 2015 El Nino events have been recognized worldwide as a primary factor of decreasing biomass productivity. Its effects occur at farm as well as regional scale. Yet some still think that the effect is only perceived by farmers directly. We proposed a simple method to describe that its effect at catchment scale is not negligible. For this purpose, we analysed an upstream catchment in West Sumatera that normally receives high rainfall up to 5000 mm per year. This catchment is in pristine condition due to its status as national park. We used satellite and ground monitoring systems i.e . rainfall and stream water level. Satellite data such as DEM (Digital Elevation Model) used to trace river networks is acquired from ASTER GDEM in 30 m resolution. To monitor vegetation health, we used NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) on board MODIS (MODerate resolution Imaging Spectroradiometer) Terra (daytime) MOD13Q1 (250 m resolution). Catchment delineation was performed using local land use map. Time series of EVI and NDVI was processes for year 2015, comprising 23 datasets. Rainfall data from year 1980 to 2012 from 9 stations and water level at the main river were analysed. We found the trace of 1997 El Nino very clearly through rainfall anomalies at all stations. As for the 2010 event, the pattern was not consistent across all rainfall stations. The 2015 event was not imminent until late October 2015. Throughout the year, NDVI remains above 0.8. In late October, maximum NDVI dropped below 0.4. This coincides with very low water level in the stream. The same pattern was also found in two neighbouring catchments with similar land use, Kuranji and Air Dingin, and Tampo catchment. This proves that the 2015 El Nino did not only threatens farmers, but also other aspects that depend on vegetation health and stream flow. Both methods are proven to be robust and may be used as an alternative way to monitor vegetation health and the impact of global climate change for ecology and water management such as domestic water use, irrigation and flood control.