Digital support of agrotechnologies in the Southern Urals’ agriculture

A methodology is presented for estimating the urban bias of surface shelter temperatures due to the effect of the urban heat island. Multiple regression techniques were used to predict surface shelter temperatures based on the time period 1986–89 using upper-air data from the European Centre for Medium-Range Weather Forecasts to represent the background climate, site-specific data to represent the local landscape, and satellite-derived data—the normalized difference vegetation index (NDVI) and the Defense Meteorological Satellite Program (DMSP) nighttime brightness data—to represent the urban and rural landscape. Local NDVI and DMSP values were calculated for each station using the mean NDVI and DMSP values from a 3 km × 3 km area centered over the given station. Regional NDVI and DMSP values were calculated to represent a typical rural value for each station using the mean NDVI and DMSP values from a 1° × 1° latitude–longitude area in which the given station was located. Models for the United States were then developed for monthly maximum, mean, and minimum temperatures using data from over 1000 stations in the U.S. Cooperative Network and for monthly mean temperatures with data from over 1150 stations in the Global Historical Climate Network. Local biases, or the differences between the model predictions using the observed NDVI and DMSP values, and the predictions using the background regional values were calculated and compared with the results of other research. The local or urban bias of U.S. temperatures, as derived from all U.S. stations (urban and rural) used in the models, averaged near 0.40°C for monthly minimum temperatures, near 0.25°C for monthly mean temperatures, and near 0.10°C for monthly maximum temperatures. The biases of monthly minimum temperatures for individual stations ranged from near −1.1°C for rural stations to 2.4°C for stations from the largest urban areas. There are some regions of the United States where a regional NDVI value based on a 1° × 1° latitude–longitude area will not represent a typical “rural” NDVI value for the given region, Thus, for some regions of the United States, the urban bias of this study may underestimate the actual current urban bias. The results of this study indicate minimal problems for global application once global NDVI and DMSP data become available. It is anticipated that results from global application will provide insights into the urban bias of the global temperature record.

Remote sensing was applied for the collection of spatio-temporal data to increase our understanding of the potential distribution of the kala-azar vector Phlebotomus argentipes in endemic areas of the Vaishali district of Bihar, India. We produced monthly distribution maps of the normalized difference vegetation index (NDVI) based on data from the thematic mapper (TM) sensor onboard the Landsat-5 satellite. Minimum, maximum and mean NDVI values were computed for each month and compared with the concurrent incidence of kala-azar and the vector density. Maximum and mean NDVI values (R2 = 0.55 and R2 = 0.60, respectively), as well as the season likelihood ratio (X2 = 17.51; P <0.001), were found to be strongly associated with kala-azar, while the correlation with between minimum NDVI values and kala-azar was weak (R2 = 0.25). Additionally, a strong association was found between the mean and maximum NDVI values with seasonal vector abundance (R2 = 0.60 and R2 = 0.55, respectively) but there was only a marginal association between minimum NDVI value and the spatial distribution of kala-azar vis-à-vis P. argentipes density.

Normalized difference vegetation index (NDVI) derived from SPOT HRV multispectral data was used to study the changing environmental quality of Hong Kong from 1987, 1991 and 1993 to 1995. Conventional change detection techniques such as image differencing or principal components analysis helped to highlight salient changes. These techniques, however, were less effective in identifying subtle changes, in particular the amount and quality of green space. Integrating the mean NDVI values at the Tertiary Planning Unit (TPU) level with census and land-cover data showed that the NDVI values were related to woodland, tall scrubland and high-density urban areas. It was also related to the level of crowding as depicted from a factor analysis of census data. Tracing the changing pattern of mean NDVI values revealed that areas with continuous increases in NDVI values are scattered around old urban districts experiencing improved landscaping. Areas of continuous decrease in NDVI values covered a large part of rural New Territories and western Hong Kong Island revealing the urban expansion process. This provided valuable information for the assessment of environmental quality for planning and management of the environment.

PDF HTML阅读 XML下载 导出引用 引用提醒 基于MODIS NDVI的广西沿海植被动态及其主要驱动因素 DOI: 10.5846/stxb201509091866 作者: 作者单位: 北京师范大学环境学院 水环境模拟国家重点实验室,北京师范大学环境学院 水环境模拟国家重点实验室,北京师范大学环境学院 水环境模拟国家重点实验室,中国科学院生态环境研究中心城市与区域生态国家重点实验室,北京师范大学环境学院 水环境模拟国家重点实验室,广西壮族自治区海洋研究院 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金资助项目（41571173）；国家科技支撑计划资助项目（2014BAK19B06）；广西壮族自治区海洋研究院自主课题资助项目 The dynamics and main driving factors of coastal vegetation in Guangxi based on MODIS NDVI Author: Affiliation: State Key Laboratory of Water Environment Simulation,School of Environment,Beijing Normal University,Beijing,State Key Laboratory of Water Environment Simulation,School of Environment,Beijing Normal University,Beijing,State Key Laboratory of Water Environment Simulation,School of Environment,Beijing Normal University,Beijing,,, 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:归一化植被指数（NDVI）可以表征区域植被状况，目前利用NDVI表征沿海区域植被动态的研究仍相对缺乏。以MODIS NDVI为源数据，分析2000-2014年间，广西沿海区域的植被动态、NDVI动态趋势和持续性，以及NDVI动态的主要驱动因素。结果表明：在海岸线10 km区域范围内，NDVI均值较高（0.71），年际间波动较小（SD为0.02）。空间上，NDVI呈现出陆地高、滨海和河口区域低的分布特征，不同类型植被NDVI差异显著，以广泛分布于陆地的林地最高（0.76），以滨海湿地植被（0.52）和其他类型植被（未利用地等）最低（0.50）。植被动态趋势（斜率k）表明，57%的林地表现为改善趋势（k≥0.002），而52%的滨海湿地则表现为退化趋势（k≤-0.002）。利用Hurst指数对生态持续性进行分析，林地、旱地表现为持续改善，滨海湿地呈现持续退化的趋势。驱动因素分析表明，气象因素对植被NDVI的影响均不显著，NDVI的动态主要受地形特征和人为因素的影响，NDVI及其动态趋势与复合地形指数和距河流的距离多呈负相关，与坡度、高程、距交通线路和城镇的距离多为正相关。总体上，区域内NDVI动态趋势以良性发展为主，但滨海湿地等呈现持续退化的区域需持续关注。 Abstract:The normalized difference vegetation index (NDVI) can be used to characterize a region's vegetation status, however, there have been few studies on the NDVI dynamics of coastal wetland areas. Using MODIS NDVI as the data source, we analyzed the vegetation dynamics, NDVI trend, and the main driving factors of NDVI in the coastal wetland areas of Guangxi from 2000 to 2014. The results showed that in the coastal wetland area with a 10-km buffer, the mean NDVI value was relatively high (0.71). However, annual fluctuations were more stable (SD=0.02). Spatially, NDVI showed a higher trend in terrestrial land and a lower trend in coastal and estuarine areas. The NDVI values of various vegetation types were significantly different, and the highest value was recorded for woodland, which is widely distributed in the terrace (0.76), and the lowest value was found for coastal wetland (0.52) and other vegetation types (e.g., bare land) (0.50). The vegetation trend (slope k) showed that 57% of the woodland was improving (k≥0.002), and 52% of the coastal wetland was degrading (k ≤-0.002). The Hurst index of the sustainability of vegetation showed that forest land and dry land have been continuously improving, while the coastal wetland showed a trend of continuous degradation. The influence of meteorological factors on NDVI dynamics was not significant, and the NDVI was mainly affected by topographic characteristics and human activities. NDVI and its trend were negatively correlated with comprehensive topographic indexes and the distance from the river, and positively correlated with slope, altitude, and the distance from roads and valleys. Altogether, most regions showed positive development, but the coastal wetland exhibited degradation and needed to be improved. 参考文献 相似文献 引证文献

Soil Impact on Satellite Based Vegetation Monitoring in Sahelian Mali

This study aims to cluster sub-districts (kelurahan) in Bogor Municipality based on greenness level. Normalized Difference Vegetation Index (NDVI) values were processed from Landsat 8 OLI recorded on 24 May 2020, downloaded from United States Geological Survey (USGS) website. NDVI values greater than 0,3 indicate that vegetation pixels are separated from overall raster maps. These NDVI values over 0,3 were extracted based on each sub-district poligon within Bogor Municipality. For sub-districts with NDVI 0,3, the percentage of the area and NDVI mean values were generated using Geographic Information System (GIS). In order to cluster 68 sub-districts in Bogor Municipality, two variables of NDVI, namely area percentage and mean NDVI values, were processed using the Fuzzy C Means (FCM) method. Greenness level clustering using the FCM method shows 14 sub-districts in high class, 28 in medium class, and 26 in low cluster class. Overlay analysis among clusters shows two sub-districts (7.69%) in the low cluster class inside the medium class, one sub-district (3.57%) in the medium class within the low class, and one sub-district (7.14%) in the high cluster class inside the medium class. There are two main indications for an overlapping sub-district located in multiple clusters, namely the sub-district that has little different values with neighbouring cluster centres, and the sub-district that has similar different values with two cluster centres.

Critical applications of satellite data products include monitoring vegetation dynamics and assessing vegetation health conditions. Some indicators like normalized difference vegetation index (NDVI) and land surface temperature (LST) are used to assess the status of vegetation growth and health. But one of the major problems with passive remote sensing satellite data products is cloud and shadow cover that leads to data gaps in the images. The present study proposes temporal aggregation of images over a short time span and developing short span harmonic analysis of time series (SS-HANTS) and pixel-wise multiple linear regression (PMLR) algorithms for retrieving cloud contaminated NDVI and LST information from Landsat-8 (L8) data products, respectively. The developed algorithms were applied in the northeastern part of Thailand to recover the missing NDVI and LST values from time series L8 images acquired in 2018. The predicted NDVI and LST values at artificially clouded locations were compared with the corresponding clear pixel values. Additionally, the model predicted LST and NDVI values were also compared with MODIS LST and NDVI datasets. The calculated root mean square (RMSE) values were ranging from 0.03 to 0.11 and 1.50 to 2.98°C for NDVI and LST variables, respectively. The validation statistics show that these models can be satisfactorily applied to retrieve NDVI and LST values from cloud-contaminated pixels of L8 images. Furthermore, a vegetation health index (VHI) computed from cloud retrieved continuous NDVI and LST images at province level shows that most of the western provinces have healthy vegetation condition than other provinces in the northeast of Thailand.

PDS 70: Green space, Johan Friso Foyer, Floor 1, August 28, 2019, 1:30 PM - 3:00 PM Background/Aim: Increased air pollution and reduced greenspace have been shown to independently lead to higher mortality. Our study aimed to assess interactive effects of greenspace and air pollution on mortality among Chinese elderly. Methods: We used the 2008 wave of China Longitudinal Healthy Longevity Survey. Our participants were followed up from 2008 to 2014. We used satellite remote sensing to calculate Normalized Difference Vegetation Index (NDVI) in the 500m radius and PM 2.5 concentrations at a 1 km × 1 km grid, around participants’ residential addresses. We measured contemporaneous NDVI, which was NDVI value at the time closest to an event; and cumulative NDVI, which was mean NDVI values over the follow-up period. Cox proportional hazards models were used to estimate the effects of NDVI and PM 2.5 and their interaction on all-cause mortality, controlling for a range of potential confounders. Results and Conclusions 12,943 participants were followed up totaling 48,181 person-years. There were 7454 mortality events from 2008 to 2014. The mean age was 87 years old and 84.6% lived in rural area. The median PM 2.5 concentration was 49 μg/m3 in the 3-year window, median contemporaneous and cumulative NDVI was 0.41 and 0.45. The hazard ratio (HR) for a 10 μg/m3 increase in PM 2.5 was 1.14 (95% CI: 1.09, 1.18), and the HR for each 0.1 unit increase in contemporaneous NDVI was 0.93 (95% CI: 0.89, 0.97). Similar association was not found for cumulative NDVI (HR: 1.08, 95% CI: 1.02, 1.15). The interaction between contemporaneous NDVI and PM 2.5 was 0.99 (p value: 0.016). Our stratified analysis found that participants living in less green area were more vulnerable to lower levels of PM 2.5. Our study suggests that greenspace modifies association between PM 2.5 and mortality, which has important implications for greenness planning and air pollution control.

Background and Aim: Studies of greenness and health often rely on buffer-based residential measures which miss potential exposure occurring outside the home environment. We compared greenness measures obtained from traditional residence-based buffers and novel smartphone mobility-based estimates. Methods: We used data from the US-based Nurses’ Health Study 3 mHealth study, which followed 348 participants who completed four 7-day sampling periods to capture seasonal variability across the year. We used Landsat Normalized Difference Vegetation Index (NDVI) data (30mx30m resolution) for both traditional and mobility-based greenness measures. We assessed two annual average residence-based estimates: 270m and 1230m. Mobility-based greenness exposure was calculated as seasonal NDVI values at GPS points captured every 10 minutes averaged across all four seasonal sampling periods. We compared measures using descriptive statistics, Bland Altman tests, and Generalized Additive Models. Results: Mean NDVI values from traditional residential buffers (270m=0.40, SD =0.12, 1230m=0.40, SD=0.12) were higher than those obtained using mobility derived NDVI (mean = 0.32, SD=0.11). The Bland Altman agreement bias was larger by 8.0% (95% CI: 7.0%, 9.0%) using the 270m residential measure and 7.3% (95% CI: 6.0%, 8.0% ) using the 1230m residential measures compared to mobility derived NDVI. Spearman’s rank correlations comparing the mobility and residential NDVI were 0.57 and 0.55 for the 270m and 1230m buffer respectively. The two residential buffers had a Spearman’s rank correlation of 0.88. Lastly, for each 10% increase in both 270m and 1230m NDVI, was associated with 0.06 increase in mobility based NDVI (95% CI: 0.05, 0.07). Conclusions: Results from our study indicate traditional residential estimates of greenness are higher than mobility derived metrics. These findings contribute to discussions surrounding the choice of an optimal spatial scale for greenness exposure. Keywords: Wearables, Mobility, Greenspace, Exposure Validation

Unmanned aerial vehicle (UAV) remote sensing platforms allow for normalized difference vegetation index (NDVI) values to be mapped with a relatively high resolution, therefore enabling an unforeseeable ability to evaluate the influence of the operation parameters on the quality of the thus acquired data. In order to better understand the effects of these parameters, we made a comprehensive evaluation on the effects of the solar zenith angle (SZA), the time of day (TOD), the flight altitude (FA) and the growth level of paddy rice at a pixel-scale on UAV-acquired NDVI values. Our results show that: (1) there was an inverse relationship between the FA (≤100 m) and the mean NDVI values, (2) TOD and SZA had a greater impact on UAV–NDVIs than the FA and the growth level; (3) Better growth levels of rice—measured using the NDVI—could reduce the effects of the FA, TOD and SZA. We expect that our results could be used to better plan flight campaigns that aim to collect NDVI values over paddy rice fields.

ABSTRACTThe normalized difference vegetation index (NDVI) has been widely used to quantify vegetation by measuring the difference between near-infrared (NIR) and red light. Measuring NDVI throughout a growing season helps to evaluate the effect of continuous phenological and morphological changes on grain yield. A 2-year field study was conducted to characterize plant response to water stress in 20 winter wheat (Triticum aestium L.) genotypes during the season based on their NDVI values under the dryland and irrigated conditions. In addition, final biomass and yield were measured at maturity. The 2018 season was extremely dry with only 23 mm of precipitation, whereas 2016 was more favorable for wheat production with 315 mm seasonal precipitation. Except in a severe drought condition (2018, dryland), NDVI values increased from early spring to mid-season (anthesis) and decreased from mid-season to physiological maturity, indicating gradual leaf senescence. There was a significant (P = 0.05) positive correlation between NDVI and grain yield, especially for NDVI values after jointing. However, under the severe drought condition of 2018 (dryland), NDVI often did not show a strong relationship with grain yield. Even genotypes with higher NDVI at early growth stages ended up with lower yield because of the severe water stress at later growth stages. Hence, the use of NDVI is not suggested in screening genotypes for yield under extreme weather conditions.

The normalized difference vegetation index (NDVI) has been widely applied in optical remote sensing. It has been demonstrated that NDVI is still partially affected by atmospheric path scattering and bi-directional (illumination and viewing geometry) effects. We present a feature of using bi-directional NDVI. Based on the assumption that a clear day has a larger NDVI value than a dusty day (smaller atmospheric path scattering in near infrared band and larger atmospheric path scattering in red band), we used the square ratio of observed NDVI values and expected NDVI values as weights for the observations. The initial weights for each observation are calculated by the ratio of the observed NDVI value and mean NDVI value of all bi-directional observations. The inversion process will loop until all weights converge, while the expected NDVI values are calculated from the previous loop's model prediction. Our preliminary research on the early Terra/MODIS data using the semi-empirical kernel-driven bi-directional reflectance distribution function (BRDF) model (RossThick-LiTransit) shows that this new method can improve the inversion results when some of the cloudy pixels are not filtered out. As sub-pixel cloudiness is always a problem, this technique should still be very useful even as cloud detection and atmospheric correction get better.

Strong winds uprooted more than 100 thousand m3 of coniferous trees in natural forest stands nearby the town of Smolyan (the Western Rhodopes) in January 2018. Although damaged trees were quickly removed from the stands, the European spruce bark beetle (Ips typographus) attacked the healthy Norway spruce trees near the windthrow areas in August 2020. Our hypothesis was that the trees were infested by the pest in previous years when no symptoms of attacks were observed. This study was conducted in three spruce stands, located near the windthrow areas and attacked by I. typographus, and in three control (healthy) stands located 5-10 km from the affected areas. We used satellite images captured by Sentinel-2 in September 2017-2020. It was established that in September 2017 (a year before the windthrow), the mean values of the Normalized difference vegetation index (NDVI) in the attacked stands were concentrated between 0.60 and 0.75 (with a maximum at 0.70), indicating that the trees were in good health. During the period 2018-2020 the distribution of mean values of NDVI was stretched between 0.35 and 0.75, which is an indication of evidence of pest attacks on the individual trees. The detail comparison of pixel values of the NDVI in the attacked and control sample plots was made on the base of images captured on 27.06.2020. The mean NDVI values in the three control plots (0.74-0.79) were much higher than the mean values in the sample plots attacked by the pest (0.57-0.65). These results showed that the values of NDVI based on satellite remote sensing data&amp;nbsp;of Sentinel-2 can be used for early detection of I. typographus infestations in spruce stands around the windthrows. These data are important for rapid planning and implementing the sanitary feelings that reduce the pest population.

Relating MODIS vegetation index time-series with structure, light absorption and stem production of fast-growing Eucalyptus plantations

Climatic disaster is continuously triggering environmental degradation and thermal diversification over the earth's surface. Global warming and anthropogenic activities are the triggering factors for thermal variation and ecological diversification. Saudi Arabia has also recorded precipitation, temperature, and vegetation dynamics over the past decades. Therefore, monitoring past precipitation, temperature, and vegetation condition information can help to prepare future disaster management plans and awareness strategies. The Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks - Climate Data Record (PERSIANN-CDR) from the Center for Hydrometeorology and Remote Sensing (CHRS) data portal and Moderate Resolution Imaging Spectroradiometer (MODIS) are applied for precipitation, Land Surface Temperature (LST), Enhance Vegetation Index (EVI), and Normalized Difference Vegetation Index (NDVI) from 2003 to 2021 respectively. Yearly mean LST, EVI, NDVI, and precipitation values are calculated through the Google Earth Engine (GEE) cloud computing platform. MODIS-based LST datasets recorded the highest temperatures is 43.02 &amp;#176;C (2003), 45.56 &amp;#176;C (2009), 47.83 &amp;#176;C (2015), and 49.24 &amp;#176;C (2021) respectively. In between nineteen years, the average mean LST increased by 6.22 &amp;#176;C and the most affected areas are Riyadh, Jeddah, Abha, Dammam, and Al Bahah. The mean Precipitation is recorded around 776 mm, 842 mm, 1239 mm, and 1555 mm for the four study periods, while the high precipitation area is Jazan, Asir, Baha, and Makkah provinces. In between nineteen years, 779 mm of precipitation is increasing in Saudi Arabia.&amp;#160; Similarly, the NDVI vegetation indices observed 0.885 (2003), 0.871 (2009), 0.891 (2015), and 0.943 (2021), while EVI observed 0.775 (2003), 0.776 (2009), 0.744 (2015), and 0.847 (2021). The R2 values of the LST and EVI correlation is 0.0239 (2003), 0.0336 (2009), 0.0136 (2015) and 0.0175 (2021) similarly correlation between LST and NDVI is 0.0352 (2003), 0.0265 (2009), 0.0183 (2015) and 0.0161 (2021) respectively. The vegetation indices indicate that the green space is gradually increasing in Saudi Arabia and the highly vegetated lands are Meegowa, An Nibaj, Tabuk, Wadi Al Dawasir, Al Hofuf, and part of Qaryat Al Ulya. This analysis indicates that the temperature is increasing but precipitation and green spaces are increasing because of the groundwater recharge through dam construction, precision agriculture, and planned build-up is helps to prepare Saudi Arabia as a green country. Therefore, more attention to preparing the strategic agricultural plants as well as other vegetation and artificial groundwater recharge can improve the country as a green nation. This analysis might help to prepare future planning, awareness, and disaster management teams to prepare for future disasters and strategic steps for sustainable development.