Annual development of subalpine grassland observed with UAV: how NDVI evolution is controlled by snow melting

Global discrimination of land cover types from metrics derived from AVHRR pathfinder data

An investigation into the impact of the maximum Normalized Difference Vegetation Index (NDVI) and the maximum surface temperature (Ts) compositing procedures (MaN and MaT respectively) upon retrieved NDVI and Ts values extracted from forested areas located across eight months of cloud screened European AVHRR data is described. NDVI values are found to be significantly higher and generally less variable when they are extracted from MaN rather than from MaT composites and Ts values are found to be significantly higher and generally less variable when they are extracted from MaT rather than from MaN composites. The impact of these differences is illustrated within the context of a European forest/non-forest classification that uses both NDVI and Ts data. Higher potential forest/non-forest classification accuracies are found using NDVI data extracted from the MaN composites and Ts data extracted from the MaT composites than from any other combination of composited data. The findings indicate that inappropriate selection of a compositing procedure may have a significant impact upon the subsequent application of NDVI and/or Ts data.

The use of the National Oceanic and Atmospheric Administration (NOAA) satellites, and the conventional Normalised Difference Vegetation Index (NDVI) model have shown promise as a large scale monitoring tool to understand the vegetation dynamics of the sparsely vegetated Sahelian grasslands. One of the assumptions of the NDVI model is that the soil background is spectrally homogeneous, which is not the case. Twelve sites, within two Système Probatoire d'Observation de Terre (SPOT) satellite imageries, corresponding to NOAA Advanced Very High Resolution Radiometer (AVHRR) Local Area Coverage (LAC) pixel resolution, were assigned representative soil NDVI values for both dry and wet conditions. These soil NDVI values, together with herbaceous above-ground biomass production estimates, were used in a multiple correlation and regression analysis to assess statistically the soil impact on integrated NDVI values, i.e. values supposed only to express the total amount of vegetation in the end of the rainy season. The analysis showed that soil influence varied significantly with different soil types and moisture content, and should therefore not be ignored in satellite based vegetation monitoring.

Intensive forest afforestation with native pine species was developed in the 1960s on degraded and deforested lands in the region of the Eastern Rhodopes (south-eastern Bulgaria). Severe damage by wet snow was registered in the coniferous forests of the Rhodopes in March 2015. In the following years, bark beetle attacks were registered on the broken and felled fresh wood. As a result, bark beetle infestation spots appeared in the pine plantations. In the period 2019–2021, damage caused by bark beetles was assessed in the region of State Forestry Kirkovo (the Eastern Rhodopes, south-eastern Bulgaria). An integrated approach using the data of the information system of the Executive Forest Agency (ISEFA), remote sensing data obtained by an “eBee SQ” unmanned aerial vehicle (UAV) equipped with a “Parrot Sequoia” multispectral camera, and subsequent terrestrial observations, was applied. ISEFA data showed that there was no serious damage caused by abiotic and biotic factors in the pine forests of SF Kirkovo until 2014. Snow damage in 2015 affected 513 ha of pine plantations, and bark beetle infestations reached up to 1316 ha in 2016. In 2019, a total of 226.87 ha of pine plantations were captured in three localities—Fotinovo, Kirkovo, and Kremen. The relative share of damage caused by bark beetles was greater in P. sylvestris plantations (15.3–23.0%), compared to damage in P. nigra (2.3%). Four different categories of normalised difference vegetation index (NDVI) were separated in bark beetle infestation spots—living trees, dead trees, grass and shrub vegetation, stones and rocks. The NDVI values in locations with living trees varied between 0.500 (spaces between tree crowns) and 0.700 (central part of the crown projection) (an average of 0.617). In the locations with dead trees, the average values of NDVI of lying trees was 0.273, and in standing trees, NDVI varied between 0.275 (central part of crown projections) and 0.424 (spaces between tree crowns). In the locations with grass and shrub vegetation, stones and rocks, the average NDVI was 0.436 and 0.329, respectively. In the field study, average defoliation of 31.2–32.3% was registered in P. sylvestris plantations, and 47.4% in P. nigra plantations. Defoliations mainly were caused by pine processionary moth (Thaumetopoea pityocampa) and fungal pathogens (Dothistroma septosporum and Lecanosticta acicola). The damage was caused by Ips acuminatus (in P. sylvestris only), and I. sexdentatus, Tomicus piniperda and T. minor (in P. sylvestris and P. nigra). Infestations by other xylophages, such as Phaenops cyanea, Rhagium inquisitor, and Pissodes spp., were also found on pine stems.

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.

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.

The aim of this study was to use small unmanned aerial vehicles (UAVs) for determining high-resolution normalized difference vegetation index (NDVI) values. Subsequently, these results were used to assess their correlations with fertilizer application levels and the yields of rice and wheat crops. For multispectral sensing, we flew two types of small UAVs (DJI Phantom 4 and DJI Phantom 4 Pro)—each equipped with a compact multispectral sensor (Parrot Sequoia). The information collected was composed of numerous RGB orthomosaic images as well as reflectance maps with spatial resolution greater than a ground sampling distance of 10.5 cm. From 223 UAV flight campaigns over 120 fields with a total area coverage of 77.48 ha, we determined that the highest efficiency for the UAV-based remote sensing measurement was approximately 19.8 ha per 10 min while flying 100 m above ground level. During image processing, we developed and used a batch image alignment algorithm—a program written in Python language–to calculate the NDVI values in experimental plots or fields in a batch of NDVI index maps. The color NDVI distribution maps of wide rice fields identified differences in stages of ripening and lodging-injury areas, which accorded with practical crop growth status from aboveground observation. For direct-seeded rice, variation in the grain yield was most closely related to that in the NDVI at the early reproductive and late ripening stages. For wheat, the NDVI values were highly correlated with the yield ( R 2 = 0.601–0.809) from the middle reproductive to the early ripening stages. Furthermore, using the NDVI values, it was possible to differentiate the levels of fertilizer application for both rice and wheat. These results indicate that the small UAV-derived NDVI values are effective for predicting yield and detecting fertilizer application levels during rice and wheat production.

Based on a Normalized Difference Vegetation Index (NDVI) and remote sensing data of snow cover, we analyzed the variation in NDVI in relation to trends in snow cover and vegetation of the source regions of large rivers on the Qinghai-Tibetan Plateau. We then calculated the relationship between snow cover duration, snow depth, and NDVI to reveal the effect of snow cover change on vegetation growth on a regional scale. The results show that both snow depth and duration tend to reduce gradually from northeast to southwest on the Qinghai-Tibetan Plateau. Furthermore, snow cover duration (snow depth > 0 cm) has high interannual fluctuation and generally shows an increasing trend (P < 0.01) from 1980 to 2004. The interannual fluctuations of the duration of days with snow depth ≥ 5 cm as well as the maximum and average snow depth are also quite high, but they generally show insignificant tendencies (P > 0.05) from 1980 to 2004. The snow cover characteristics (duration and depth) are insignificantly correlated to annual maximum NDVI. However, a significant positive correlation (P < 0.05) is observed between snow cover duration (snow depth > 0 cm) and the NDVI values of both April and July, and an obvious negative correlation (P < 0.05) is observed between snow depth and the NDVI value in October across all source regions from 1981 to 2004. In the study area, increasing snow depth and the prolongation of the duration of snow cover have adverse effects on vegetation growth the following year. The melting of snow brings increasing effects to the NDVI value in the spring.

Normalized difference vegetation index (NDVI) has been correlated with various vegetation parameters using different preprocessing methods, corrections and sampling time based on the aim of the study. In yield estimation studies, maximum NDVI value of the season and the same day of the year NDVI value, which are based on chronological sampling time, are used within different techniques from statistical analysis to machine learning. However, analysis of biological systems based on their chronological timing results in an error increase at data extraction phase due to the non-linearity among phenological stages, representing plant development and its variability. In this study, a phenology based optimum NDVI sampling time is determined and proposed as a replacement of chronologically sampled NDVI time for yield estimation analysis. It may not be possible to have or acquire satellite images for the desired NDVI date due to the temporal resolution of existing remote sensing satellites and meteorological limitations. Therefore, a compensation process based on Adaptive Savitzky-Golay filter and using the existing images is proposed to constitute a new NDVI value for the desired day of the season. The study area is situated in the Southeastern Anatolia region of Turkey within the Fertile Crescent where wheat was first cultivated 10000 years ago. The region has the highest durum wheat production, supplying %46 of the whole production in Turkey. 8-day interval, Landsat-7 and Landsat-8 NDVI time-series are analyzed for seasonal vegetation development with TIMESAT software for the 2014–2016 period. Ground-based ancillary data was obtained within the Turkish Agricultural and Environmental Informatics Research and Application Center (TARBIL) project. Trend analysis of NDVI time-series was performed using Adaptive Savitzky-Golay filter, form of a moving average, adapting to the upper envelope of the data points. Two different sampling methods representing chronological and phenological approaches in addition to the max NDVI value are used to determine the optimum NDVI day. Phenological sampling is carried out as 10-day intervals starting from the emergence phase indicating the start of the season whereas 15 April, representing the long-term annual mean peak NDVI date of the study area was used for chronological sampling. Adaptive Savitzky-Golay filtering and different sampling combinations were used to perform correlation analysis with annual yield data. Best sampling method along with the optimum NDVI sampling day of the season was determined based on the correlation analysis. It is observed that the combinations with phenological sampling corresponding to the first node stage according to Food and Agriculture Organization (FAO) guidelines have the highest correlation. Regression analysis between agrometeorological data with and without compensated NDVI and yield variables showed that the usage of compensated NDVI had higher correlation for wheat yield estimation. The results showed that, in comparison with the conventional approaches, the usage of phenology based compensated NDVI, enhanced the yield estimation percentage. Along with the possibility of producing ancillary data from remote sensing images, this approach will minimize the need for ground-based observations that are time and money consuming.

Crop monitoring with advanced technology is facilitated by high-resolution satellite imagery, allowing the analysis of vegetation at plot level. Remote sensing is key in agriculture, highlighting the use of the Normalized Difference Vegetation Index (NDVI) for crop monitoring. This thesis uses NDVI time series in three experiments: one with MODIS data (medium resolution) and two with Sentinel-2 data (high resolution). In experiment 1, rainfed crops, which occupy 76% of the cultivated area in Spain, were studied. Their yield is conditioned by meteorological variability. Monitoring and predicting their dynamics are key for sustainable management, from an environmental and socio-economic point of view. Long time series of MODIS data (2000-2022) allow monitoring of vegetation dynamics. The study had two objectives: (1) to assess the dynamics of rainfed crops in a typical area of Spain and (2) to build models to predict their evolution. NDVI time series in rainfed cereal areas were analyzed by statistical methods and predicted using the Box-Jenkins approach. The evaluation of autocorrelation functions and stationarity tests revealed that most of the series are stationary, dominated by annual seasonality. The preliminary dynamic model showed a good fit for 30% of the pixels. Experiment 2 evaluated the information content of Sentinel-2 NDVI time series (2017-2023) at high spatial resolution (10 m), in five scenarios in northern Spain. The series were interpolated and filtered using four types of filters. Time dependence was assessed with Ljung-Box Q and Fisher's Kappa tests, similarity between the series was assessed by the correlation coefficient and the mean square error. An Interpolation Efficiency Indicator (IEI) was proposed to summarize the quality of the observations. Climate type, weather disturbances and management were the main sources of variability in the scenarios. Among the results, rainfed wheat and barley showed high short-term variability due to clouds and long-term variability due to rainfall, while maize showed stable summer cycles and low annual variability. Irrigated alfalfa had high intra-annual variability due to frequent mowing, and beech showed a strong summer cycle, despite the influence of clouds. Finally, the pine forest showed variable cycles due to its rapid response to changes in temperature and precipitation. The elimination of invalid observations increased the temporal dependence in areas with low IEI values, and hidden periodicities were identified by Fisher's Kappa test. Experiment 3, based on the estimation of cereal production in rainfed crops (barley and wheat) in the provinces of Palencia, Burgos and Soria, by remote sensing using Sentinel-2 images (10 m). NDVI was used to estimate crop yields. The data obtained were compared with the Survey of Surface Areas and Crop Yields (ESYRCE) between 2018 and 2022. Three multiple linear regression models were applied, including variables such as maximum NDVI value, observations after maximum NDVI (eight) and altitude. Although the overall models did not show a significant relationship between observed and estimated yields, the MOD_3 model proved to be the most effective when applied at a smaller scale, with close plots and similar dates of maximum greenness. Palencia presented the best fit results, with a coefficient of determination (R2) of 0.95 for wheat and 0.77 for barley. The use of NDVI values combined with local data such as altitude can be a useful tool for yield estimation. RESUMEN El seguimiento de los cultivos con tecnología avanzada se facilita mediante imágenes satelitales de alta resolución, permitiendo el análisis de la vegetación a nivel de parcelas. La teledetección es clave en la agricultura, destacando el uso del Índice de Vegetación de Diferencia Normalizada (NDVI) para monitorear cultivos. Esta tesis emplea series temporales del NDVI en tres experimentos: uno con datos MODIS (resolución media) y dos con datos Sentinel-2 (alta resolución). El experimento 1, se estudió los cultivos de secano, que ocupan el 76% de la superficie cultivada en España. Su rendimiento está condicionado por la variabilidad meteorológica. El monitoreo y predicción de su dinámica es clave para la gestión sostenible, desde el punto de vista ambiental y socioeconómico. Las series temporales largas de datos MODIS (2000-2022), permiten monitorear la dinámica de la vegetación. El estudio tuvo dos objetivos: (1) Evaluar la dinámica de los cultivos de secano en una zona típica de España y (2) construir modelos para predecir su evolución. Las series temporales de NDVI en zonas de cereal de secano se analizaron mediante métodos estadísticos, y se predijeron utilizando el enfoque Box-Jenkins. La evaluación de las funciones de autocorrelación y pruebas de estacionariedad revelaron que la mayoría de las series son estacionarias, dominadas por la estacionalidad anual. El modelo dinámico preliminar mostró un buen ajuste para el 30% de los píxeles. El experimento 2, evaluó el contenido de la información en series temporales de NDVI de Sentinel-2 (2017-2023) de alta resolución espacial (10 m), en cinco escenarios del norte de España. Se interpolaron las series y filtraron usaron cuatro tipos de filtros. La dependencia temporal se evaluó con las pruebas Ljung-Box Q y Fisher Kappa, la similitud entre las series se evaluó mediante el coeficiente de correlación y el error cuadrático medio. Se propuso un Indicador de Eficiencia de Interpolación (IEI) para resumir la calidad de las observaciones. El tipo de clima, perturbaciones atmosféricas y la gestión fueron las principales fuentes de variabilidad en los escenarios. Entre los resultados, el trigo y la cebada de secano mostraron alta variabilidad a corto plazo por nubes y a largo plazo por lluvias, mientras que el maíz presentó ciclos estables en verano y baja variabilidad anual. La alfalfa de regadío tuvo una alta variabilidad intra-anual por los cortes frecuentes, y el hayedo mostró un ciclo de verano fuerte, a pesar de la influencia de las nubes. Finalmente, el pinar mostró ciclos variables por su respuesta rápida a cambios en temperatura y precipitación. La eliminación de observaciones no válidas aumentó la dependencia temporal en áreas con bajos valores de IEI, y se identificaron periodicidades ocultas mediante la prueba Fisher Kappa. El experimento 3, basado en la estimación de la producción de cereales en cultivos de secano (cebada y trigo) en las provincias de Palencia, Burgos y Soria, mediante teledetección utilizando imágenes Sentinel-2 (10 m). Se empleó el NDVI, para estimar el rendimiento de los cultivos. Los datos obtenidos fueron comparados con la Encuesta de Superficies y Rendimientos de Cultivos (ESYRCE) entre 2018 y 2022. Se aplicaron tres modelos de regresión lineal múltiple, que incluían variables como el valor máximo de NDVI, observaciones posteriores al NDVI máximo (ocho) y la altitud. Aunque los modelos generales no mostraron una relación significativa entre los rendimientos observados y estimados, el modelo MOD_3 resultó ser el más efectivo cuando se aplicó a escala más reducida, con parcelas cercanas y fechas similares del máximo verdor. Palencia presentó los mejores resultados de ajuste, con un coeficiente de determinación (R2) de 0,95 para trigo y 0,77 para cebada. El uso de valores de NDVI combinado con datos locales como la altitud, puede ser una herramienta útil para la estimación de rendimientos.

Nitrogen (N) fertilization is a key factor in turfgrass management. The Normalized Difference Vegetation Index (NDVI) is positively correlated with turfgrass health and quality and it is affected by species and fertilization rates. With the aim to test the sensitivity of remote NDVI reading methods in detecting N variation in coolseason and warmseason turfgrass species, data obtained by Satellite and Unmanned Aerial Vehicles (UAV) with a multispectral camera on board and by a ground-based instrument were related and evaluated. The relationship between NDVI value from ground-based instrument and UAV resulted very high for bermudagrass (r=0.96), knotgrass (r=0.91) and tall fescue (r=0.99). Satellite data do not allow an accurate assessment of N plant status of tall fescue (r=0.50). UAV can be used to fill the gap between ground-based measurement and remotely sensed imagery from Satellite platform.

West Java is the third province with the largest area of paddy field. On the province scale, there are five districts with more than 60,000 hectares of the paddy field area. Sukabumi is the fifth largest district, with 6.8 percent of the area covered with paddy field. Although it is not the greatest number, Sukabumi is the largest producer of paddy with more than 6 tons per hectare, especially in 2015. Rice is the primary food for most Indonesian. Therefore, monitoring the rice planting regarding the phenology, planting area, and productivity is a critical process. Information from the process is very important to address the national issues on food security. This study uses the excellence of remote sensing technology to cover a big area of paddy field in Sukabumi. The images from Sentinel 2 and Unmanned Aerial Vehicle (UAV) are utilized to generate the Normalized Difference Vegetation Index (NDVI). The objectives of this study are two folds: (i) to create NDVI map from both Sentinel 2 and UAV; and (ii) to analyze rice crop phenology from the NDVI value. With NDVI, this study can determine the growth stage of paddy by discriminating each stage based on the spectral value. The planting phases that have discovered in the area are divided into land preparation, vegetative, generative, and harvesting. Based on the NDVI value, it is known that the vegetative stage ranges from 0.18 to 0.80. The study concludes that results from both Sentinel 2 and UAV can be used to show the distribution of paddy based on different growth stages

The article used two NOAA-14 Advanced Very High Resolution Radiometer (AVHRR) datasets to assess flood damage in the middle and lower reaches of China's Changjiang River (Yangtze River) in 1998. As the AVHRR is an optical sensor, it cannot penetrate the clouds that frequently cover the land during the flood season, and this technology is greatly limited in flood monitoring. However the widely used normalized difference vegetation index (NDVI) can be used to monitor flooding, since water has a much lower NDVI value than other surface features. Though many factors other than flooding (e.g. atmospheric conditions, different sun-target-satellite angles, and cloud) can change NDVI values, inundated areas can be distinguished from other types of ground cover by changes in the NDVI value before and after the flood after eliminating the effects of other factors on NDVI. AVHRR data from 26 May and 22 August, 1998 were selected to represent the ground conditions before and after flooding. After accurate geometric correction by collecting GCPs, and atmospheric and angular corrections by using the 6S code, NDVI values for both days and their differences were calculated for cloud-free pixels. The difference in the NDVI values between these two times, together with the NDVI values and a land-use map, were used to identify inundated areas and to assess the area lost to the flood. The results show a total of 358,867 ha, with 207,556 ha of cultivated fields (paddy and non-irrigated field) inundated during the flood of 1998 in the middle and lower reaches of the Changjiang River Catchment; comparing with the reported total of 321,000 and 197,000 ha, respectively. The discrimination accuracy of this method was tested by comparing the results from two nearly simultaneous sets of remote-sensing data (NOAA's AVHRR data from 10 September, 1998, and JERS-1 synthetic aperture radar (SAR) data from 11 September, 1998, with a lag of about 18.5 hr) over a representative flooded region in the study area. The results showed that 67.26% of the total area identified as inundated using the NOAA data was also identified as inundated using the SAR data.

PDF HTML阅读 XML下载 导出引用 引用提醒 2000—2016年云南地区植被覆盖时空变化及其对水热因子的响应 DOI: 10.5846/stxb201806211364 作者: 作者单位: 云南大学,云南大学资源环境与地球科学学院,云南大学资源环境与地球科学学院,云南大学资源环境与地球科学学院 作者简介: 通讯作者: 中图分类号: 基金项目: 国家重点研发计划项目（2016YFC0502105）；中国科学院西部之光青年学者项目 Spatio-temporal patterns of vegetation coverage and response to hydrothermal factors in Yunnan Province, China Author: Affiliation: School of Resources Environment Earth Science,Yunnan University,School of Resources Environment Earth Science,Yunnan University,School of Resources Environment Earth Science,Yunnan 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:基于2000-2016年MODIS-NDVI数据，利用趋势分析法以及线性相关分析等方法对云南地区植被月变化趋势、年际变化趋势进行详细分析；探讨植被覆盖变化与主要气候水热因子的关系。结果表明：研究区大部分地区植被覆盖良好，年NDVI的平均值为0.55，其中NDVI较高值（> 0.8）区域主要分布于南部，而西北部和中部城市地区NDVI值较低；自2000年开始，研究区NDVI总体呈显著（P < 0.05）增加趋势，年NDVI的变化斜率为0.0036，植被覆盖呈增加趋势的区域占研究区总面积79.80%；不同季节（春、夏、秋、冬）和生长季的植被状况均呈良性发展趋势；湿润指数和水热综合因子在滇西北与NDVI多呈负相关，在滇中地区以正相关为主；春、夏、秋3个季节NDVI受降水影响较大，而冬季NDVI则受气温影响较大；受降水影响较大的区域主要分布在中部和南部，受气温影响较大区域主要分布在滇西北、滇东北地区；NDVI在不同月份对气候因子的滞后时间存在差异，NDVI与当月气温的相关性强于与当月降水的相关性，植被生长对气温的响应无明显滞后效应，对降水存在3个月的滞后期。 Abstract:The spatio-temporal patterns of vegetation coverage in Yunnan Province from 2000 to 2016 were analyzed using moderate resolution imaging spectrometer (MODIS) normalized difference vegetation index (NDVI) data. Maximum value composites, linear regression methods, and partial correlation analyses were used to investigate the monthly and annual NDVI variations, analyze the spatial distribution and changes in the NDVI, and examine the relationships between the NDVI and climatic factors. The results showed that the average NDVI value for Yunnan Province was 0.55 from 2000 to 2016. The higher NDVI values (>0.8) were mainly distributed in the southern part of Yunnan Province. In contrast, the northwest part and central urban region had lower NDVI. Annual NDVI increased significantly from 2000 to 2016 (P < 0.05), and the slope of the trend line was 0.0036. The seasonal NDVI and the growing season NDVI also showed increases. Autumn had the fastest NDVI growth rate (0.0063), followed by spring (0.0044), winter (0.0042), and summer (0.0010). The increasing rate of NDVI in the growing season (0.0025) was higher than that in summer. The vegetation improvement area was significantly greater than the degradation area, and the area of increasing trend accounted for 79.80% of the total vegetation coverage. The area of increased vegetation coverage was mainly concentrated in the northeast and east regions, and the decreased vegetation area was mainly concentrated in northwest and central regions. Precipitation had a crucial effect on the NDVI variation in spring, summer, and autumn, but temperature was the main controlling factor in winter. There was a clear difference in the relationships between NDVI and temperature and precipitation among different months of the growing season, and there was a time lag in the response of NDVI to precipitation. 参考文献 相似文献 引证文献

The solid form of oil heavy metal waste is known as acid sludge. The aim of this research is to exercise the correlation between acid sludge concentration in soil and NDVI value, and further studying the Normalized Difference Vegetation Index (NDVI) anomaly by multi-temporal Landsat satellite images. The implemented method is NDVI. In this research, NDVI is analyzed using the remote sensing data on dry season and wet season. Between 1997 to 2012, NDVI value in dry season is around – 0.007 (July 2001) to 0.386 (May 1997), meanwhile in wet season NDVI value is around – 0.005 (November 2006) to 0.381 (December 1995). The high NDVI value shows the leaf health or thickness, where the low NDVI indicates the vegetation stress and rareness which can be concluded as the evidence of contamination. The rehabilitation has been executed in the acid sludge contaminated location, where the high value of NDVI indicates the successfull land rehabilitation effort.