Assessment of vegetation cover changes and the contributing factors in the Al-Ahsa Oasis using Normalized Difference Vegetation Index(NDVI)

The abandonment of date palm grove of the former Al-Ahsa Oasis in the eastern region of Saudi Arabia has resulted in the conversion of delicate agricultural area into urban area.The current state of the oasis is influenced by both expansion and degradation factors.Therefore,it is important to study the spatiotemporal variation of vegetation cover for the sustainable management of oasis resources.This study used Landsat satellite images in 1987,2002,and 2021 to monitor the spatiotemporal variation of vegetation cover in the Al-Ahsa Oasis,applied multi-temporal Normalized Difference Vegetation Index(NDVI)data spanning from 1987 to 2021 to assess environmental and spatiotemporal variations that have occurred in the Al-Ahsa Oasis,and investigated the factors influencing these variation.This study reveals that there is a significant improvement in the ecological environment of the oasis during 1987–2021,with increase of NDVI values being higher than 0.10.In 2021,the highest NDVI value is generally above 0.70,while the lowest value remains largely unchanged.However,there is a remarkable increase in NDVI values between 0.20 and 0.30.The area of low NDVI values(0.00–0.20)has remained almost stable,but the region with high NDVI values(above 0.70)expands during 1987–2021.Furthermore,this study finds that in 1987–2002,the increase of vegetation cover is most notable in the northern region of the study area,whereas from 2002 to 2021,the increase of vegetation cover is mainly concentrated in the northern and southern regions of the study area.From 1987 to 2021,NDVI values exhibit the most pronounced variation,with a significant increase in the“green”zone(characterized by NDVI values exceeding 0.40),indicating a substantial enhancement in the ecological environment of the oasis.The NDVI classification is validated through 50 ground validation points in the study area,demonstrating a mean accuracy of 92.00%in the detection of vegetation cover.In general,both the user’s and producer’s accuracies of NDVI classification are extremely high in 1987,2002,and 2021.Finally,this study suggests that environmental authorities should strengthen their overall forestry project arrangements to combat sand encroachment and enhance the ecological environment of the Al-Ahsa Oasis.

Interannual Variability of the Normalized Difference Vegetation Index on the Tibetan Plateau and Its Relationship with Climate Change

The Qinghai-Xizang Plateau, or Tibetan Plateau, is a sensitive region for climate change, where the manifestation of global warming is particularly noticeable. The wide climate variability in this region significantly affects the local land ecosystem and could consequently lead to notable vegetation changes. In this paper, the interannual variations of the plateau vegetation are investigated using a 21-year normalized difference vegetation index （NDVI） dataset to quantify the consequences of climate warming for the regional ecosystem and its interactions. The results show that vegetation coverage is best in the eastern and southern plateau regions and deteriorates toward the west and north. On the whole, vegetation activity demonstrates a gradual enhancement in an oscillatory manner during 1982-2002. The temporal variation also exhibits striking regional differences： an increasing trend is most apparent in the west, south, north and southeast, whereas a decreasing trend is present along the southern plateau boundary and in the central-east region. Covariance analysis between the NDVI and surface temperature/precipitation suggests that vegetation change is closely related to climate change. However, the controlling physical processes vary geographically. In the west and east, vegetation variability is found to be driven predominantly by temperature, with the impact of precipitation being of secondary importance. In the central plateau, however, temperature and precipitation factors are equally important in modulating the interannual vegetation variability.

A second modified normalized difference water index(SMNDWI) in the case of extracting the shoreline

Due to the fast development of industrialization and urbanization, shorelineextraction is necessary for the sustainable development and environment protection inmany countries. This study focused on the accurate methods of extracting theinstantaneous waterline —shoreline obtained as the same instant as the satellite imageis acquired. Based on NDWI (Normalized Difference Water Index) and MNDWI(Modified Normalized Difference Water Index), the study changed the bandcombination and proposed a second modified normalized water index (SMNDWI) toextract the waterline. And, this new index is applied to three types of coast to evaluatethe performance of this method with traditional ones. Results show that SNDWI isbetter than NDWI and suitable for applying to the waterline extraction.

Simple method for extracting the seasonal signals of photochemical reflectance index and normalized difference vegetation index measured using a spectral reflectance sensor

A spectral reflectance sensor(SRS)fixed on the near-surface ground was developed to support the continuous monitoring of vegetation indices such as the normalized difference vegetation index(NDVI)and photochemical reflectance index(PRI).NDVI is useful for indicating crop growth/phenology,whereas PRI was developed for observing physiological conditions.Thus,the seasonal change patterns of NDVI and PRI are two valuable pieces of information in a crop-monitoring system.However,capturing the seasonal patterns is considered challenging because the vegetation index values estimated by the reflection from vegetation are often governed by meteorological conditions,such as solar irradiance and precipitation.Further,unlike growth/phenology,the physiological condition has diurnal changes as well as seasonal characteristics.This study proposed a novel filtering method for extracting the seasonal signals of SRS-based NDVI and PRI in paddy rice,barley,and garlic.First,the measurement accuracy of SRSs was compared with handheld spectrometers,and the R^(2)values between the two devices were 0.96 and 0.81 for NDVI and PRI,respectively.Second,the experimental study of threshold criteria with respect to meteorological variables(i.e.,insolation,cloudiness,sunshine duration,and precipitation)was conducted,and sunshine duration was the most useful one for excluding distorted values of the vegetation indices.After data processing based on sunshine duration,the R^(2)values between the measured vegetation indices and the extracted seasonal signals of vegetation indices increased by approximately 0.002–0.004(NDVI)and 0.065–0.298(PRI)on the three crops,and the seasonal signals of vegetation indices became noticeably improved.This method will contribute to an agricultural monitoring system by identifying the seasonal changes in crop growth and physiological conditions.

Drought trend analysis in a semi-arid area of Iraq based on Normalized Difference Vegetation Index, Normalized Difference Water Index and Standardized Precipitation Index

Drought was a severe recurring phenomenon in Iraq over the past two decades due to climate change despite the fact that Iraq has been one of the most water-rich countries in the Middle East in the past.The Iraqi Kurdistan Region(IKR)is located in the north of Iraq,which has also suffered from extreme drought.In this study,the drought severity status in Sulaimaniyah Province,one of four provinces of the IKR,was investigated for the years from 1998 to 2017.Thus,Landsat time series dataset,including 40 images,were downloaded and used in this study.The Normalized Difference Vegetation Index(NDVI)and the Normalized Difference Water Index(NDWI)were utilized as spectral-based drought indices and the Standardized Precipitation Index(SPI)was employed as a meteorological-based drought index,to assess the drought severity and analyse the changes of vegetative cover and water bodies.The study area experienced precipitation deficiency and severe drought in 1999,2000,2008,2009,and 2012.Study findings also revealed a drop in the vegetative cover by 33.3%in the year 2000.Furthermore,the most significant shrinkage in water bodies was observed in the Lake Darbandikhan(LDK),which lost 40.5%of its total surface area in 2009.The statistical analyses revealed that precipitation was significantly positively correlated with the SPI and the surface area of the LDK(correlation coefficients of 0.92 and 0.72,respectively).The relationship between SPI and NDVI-based vegetation cover was positive but not significant.Low precipitation did not always correspond to vegetative drought;the delay of the effect of precipitation on NDVI was one year.

Diagnostic value of FIB-4, aspartate aminotransferaseto-platelet ratio index and liver stiffness measurement in hepatitis B virus-infected patients with persistently normal alanine aminotransferase

AIM To assess the diagnostic value of FIB-4, aspartate aminotransferase-to-platelet ratio index(APRI), and liver stiffness measurement(LSM) in patients with hepatitis B virus infection who have persistently normal alanine transaminase(PNALT).METHODS We enrolled 245 patients with chronic hepatitis B: 95 in PNALT group, 86 in intermittently elevated alanine transaminase(PIALT1) group [alanine transaminase(ALT) within 1-2 × upper limit of normal value(ULN)], and 64 in PIALT2 group(ALT > 2 × ULN). All the patients received a percutaneous liver biopsy guided by ultrasonography. LSM, biochemical tests, and complete blood cell counts were performed.RESULTS The pathological examination revealed moderate inflammatory necrosis ratios of 16.81%(16/95), 32.56%(28/86), and 45.31%(28/64), and moderate liverfibrosis of 24.2%(23/95), 33.72%(29/86), and 43.75%(28/64) in the PNALT, PIALT1, and PIALT2 groups, respectively. The degrees of inflammation and liver fibrosis were significantly higher in the PIALT groups than in the PNALT group(P < 0.05). No significant difference was found in the areas under the curve(AUCs) between APRI and FIB-4 in the PNALT group; however, significant differences were found between APRI and LSM, and between FIB-4 and LSM in the PNALT group(P < 0.05 for both). In the PIALT1 and PIALT2 groups, no significant difference(P > 0.05) was found in AUCs for all comparisons(P > 0.05 for all). In the overall patients, a significant difference in the AUCs was found only between LSM and APRI(P < 0.05).CONCLUSION APRI and FIB-4 are not the ideal noninvasive hepatic fibrosis markers for PNALT patients. LSM is superior to APRI and FIB-4 in PNALT patients because of the influence of liver inflammation and necrosis.

Decreased Gastric Body Mucosa Obestatin Expression in Abdominal Obesity Patients With Normal Body Mass Index

The aim of the study was to investigate whether the expression of obestatin in gastric body mucosa in abdominal obesity patients with normal body mass index （BMI） is different compared with healthy controls. Twenty abdominal obesity patients with normal BMI and twenty healthy controls were included in the study. The number of obestatin-positive cells in gastric body mucosa was significantly lower in abdominal obesity patients with normal BMI than that in healthy subjects.

Retrospective analysis of two northern California wild-land fires via Landsat five satellite imagery and Normalized Difference Vegetation Index (NDVI)

Wild-land fires are a dynamic and destructive force in natural ecosystems. In recent decades, fire disturbances have increased concerns and awareness over significant economic loss and landscape change. The focus of this research was to study two northern California wild-land fires: Butte Humboldt Complex and Butte Lightning Complex of 2008 and assessment of vegetation recovery after the fires via ground based measurements and utilization of Landsat 5 imagery and analysis software to assess landscape change. Multi-temporal and burn severity dynamics and assessment through satellite imagery were used to visually ascertain levels of landscape change, under two temporal scales. Visual interpretation indicated noticeable levels of landscape change and relevant insight into the magnitude and impact of both wild-land fires. Normalized Burn Ratio (NBR) and delta NBR (DNBR) data allowed for quantitative analysis of burn severity levels. DNBR results indicate low severity and low re-growth for Butte Humboldt Complex “burned center” subplots. In contrast, DNBR values for Butte Lightning Complex “burned center” subplots indicated low-moderate burn severity levels.

The construction of normalized enhanced water index and the extraction of supra-glacial water based on WorldView-2 imagery

As an important part of the mass balance of the Ice Sheet,Supra-glacial Water not only reflects the diversity of polar environmental changes,but also plays an important role in the study of global climate and environmental changes.In this paper,we chose northern Greenland as the research area,and constructed a Normalized Enhanced Water Index(NEWI)based on the high-precision WorldView-2 images of different phases during the ablation period in northern Greenland,followed by a statistical analysis on the spectral characteristics of the images were for the typical features in the study area.Then the fuzzy areas with similar gray values of thin sea ice and shallow ice water bodies were located,according to the distribution rules of ground objects and histogram graphic features of the images,so as to enhance the contrast of ground objects between the regions,and finally the extraction of the fine range of water bodies on the ice surface.Experimental results showed that the proposed index effectively highlighted the ice water with the water of the reflectivity difference,compared with the commonly used water index NDWI,etc.,especially in shallow water,which contributes to differentiation from other objects.The precision evaluation showed that the applied method of extraction has higher degree of refinement compared with other methods,by which the ice water can get complete ice water effectively.

Monitoring vegetation drought in the nine major river basins of China based on a new developed Vegetation Drought Condition Index

The effect of global climate change on vegetation growth is variable.Timely and effective monitoring of vegetation drought is crucial for understanding its dynamics and mitigation,and even regional protection of ecological environments.In this study,we constructed a new drought index(i.e.,Vegetation Drought Condition Index(VDCI))based on precipitation,potential evapotranspiration,soil moisture and Normalized Difference Vegetation Index(NDVI)data,to monitor vegetation drought in the nine major river basins(including the Songhua River and Liaohe River Basin,Haihe River Basin,Yellow River Basin,Huaihe River Basin,Yangtze River Basin,Southeast River Basin,Pearl River Basin,Southwest River Basin and Continental River Basin)in China at 1-month–12-month(T1–T12)time scales.We used the Pearson's correlation coefficients to assess the relationships between the drought indices(the developed VDCI and traditional drought indices including the Standardized Precipitation Evapotranspiration Index(SPEI),Standardized Soil Moisture Index(SSMI)and Self-calibrating Palmer Drought Severity Index(scPDSI))and the NDVI at T1–T12 time scales,and to estimate and compare the lag times of vegetation response to drought among different drought indices.The results showed that precipitation and potential evapotranspiration have positive and major influences on vegetation in the nine major river basins at T1–T6 time scales.Soil moisture shows a lower degree of negative influence on vegetation in different river basins at multiple time scales.Potential evapotranspiration shows a higher degree of positive influence on vegetation,and it acts as the primary influencing factor with higher area proportion at multiple time scales in different river basins.The VDCI has a stronger relationship with the NDVI in the Songhua River and Liaohe River Basin,Haihe River Basin,Yellow River Basin,Huaihe River Basin and Yangtze River Basin at T1–T4 time scales.In general,the VDCI is more sensitive(with shorter lag time of vegetation response to drought)than the traditional drought indices(SPEI,scPDSI and SSMI)in monitoring vegetation drought,and thus it could be applied to monitor short-term vegetation drought.The VDCI developed in the study can reveal the law of unclear mechanisms between vegetation and climate,and can be applied in other fields of vegetation drought monitoring with complex mechanisms.

PRECISE ASYMPTOTICS IN SELF-NORMALIZED SUMS OF ITERATED LOGARITHM FOR MULTIDIMENSIONALLY INDEXED RANDOM VARIABLES

In the case of Z＋^d（d ≥ 2）-the positive d-dimensional lattice points with partial ordering ≤, {Xk,k∈ Z＋^d} i.i.d, random variables with mean 0, Sn =∑k≤nXk and Vn^2 = ∑j≤nXj^2, the precise asymptotics for ∑n1/｜n｜（log｜n｜dP（｜Sn/Vn｜≥ε√log log｜n｜） and ∑n（logn｜）b/｜n｜（log｜n｜）^d-1P（｜Sn/Vn｜≥ε√log n）,as ε↓0,is established.

基于Sentinel-2的青铜峡灌区水稻和玉米种植分布早期识别

及时准确地掌握灌区内作物种植分布对于灌溉水资源高效配置、农田精准管理具有重要指导意义。以宁夏青铜峡灌区为研究对象,利用多时相Sentinel-2卫星数据,通过水稻和玉米早期特征分析,提取关键的“水淹”信号和“植被”信号,构建时序归一化差异水体指数(MNDWI)和归一化植被指数(NDVI)特征值数据集,并通过样本分析关键特征阈值,构建水稻和玉米早期种植分布决策树模型,提取2022年宁夏青铜峡灌区水稻和玉米种植的空间分布。结果表明(:1)玉米和水稻苗期的后半段5月15—31日,水淹信号和植被信号是区分二者关键时期。(2)基于早期作物物候特征的方法,在5月16—31日获取的水稻和玉米图像制图精度高于90%,用户精度超过91%,总体精度超过90%,Kappa系数高于0.88,明显高于同时期随机森林方法的分类精度。(3)本研究提出的方法在早期水稻和玉米种植分布提取方面具有较强的适用性,并且能够在时空尺度上以较少的实地样本进行延展,同时在时间上也更有优势。因此,该方法为青铜峡灌区水稻和玉米种植分布早期调查提供了重要的方法支撑。

基于高分一号遥感影像的水体提取方法对比分析与改进

以高分一号遥感影像为数据源,以安徽省黄山市屯溪流域内的东方红水库为研究对象,采用单波段阈值法、两波段差值法、波段比值法、归一化差分水体指数(NDWI)法、归一化差分植被指数(NDVI)法共5种水陆像元度量方法,分别应用平均值法和最大类间方差迭代法两种阈值选取方法对东方红水库进行水体提取,探索最大类间方差迭代法在水体提取变量上的改进效果,在此基础上提出了一种改进的最大类间方差联合水体提取法,并对比了改进前后的水体提取效果。结果表明:改进后的水体提取方法可以很好地降低影像提取中产生的噪点,提高水体提取的精度,提取成果的平均相对误差为4.69%,决定系数为0.8579,相比于改进前平均相对误差降低了0.68%,决定系数提高了0.0539。

基于时空数据融合的塔吉克斯坦中高时空分辨率NDVI数据集(2010-2020)

归一化植被指数(Normalized Difference Vegetation Index,NDVI)是研究植被最常用的遥感指数之一。NDVI长时间序列数据对于植被变化研究有着重要的意义。然而由于传感器的限制,遥感数据的时间分辨率与空间分辨率不能兼顾,因此在目前广泛使用的NDVI数据产品中,高时空分辨率的数据还较为缺乏。本产品基于Cubist模型对MODIS数据与Landsat及哨兵等遥感数据进行时空数据融合,得到了塔吉克斯坦2010–2020年中高时空分辨率Landsat-MODIS融合数据,以及2020年中高时空分辨率Sentinel-MODIS融合数据。为保证数据的准确性和可靠性,本数据集从数据源的质控,模型训练优化,以及模型独立验证三个方面对数据产品进行质量控制,且取得了较好的验证效果。本数据集可反映塔吉克斯坦2010–2020年NDVI时空变化情况,可为该地区植被变化分析、生态环境监测等提供长时间序列数据支撑。

高寒气候区生长季NDVI与昼夜不对称增温的Copula分析

利用1982—2016年的青海地区归一化植被指数和气象数据,基于马尔科夫链蒙特卡罗的Copula函数方法,深入探索昼夜增温不对称性与植被活动之间的复杂关系,揭示了昼夜增温和NDVI之间的联合概率分布及其季节性差异。研究结果表明,昼夜增温与NDVI之间的关系在不同季节呈现显著差异。尤其在秋季,昼夜增温对NDVI的影响最为显著,其次是夏季和春季。通过Copula函数模型,发现昼夜增温与NDVI在特定温度区间内呈现正相关,表明适宜的温度条件下昼夜增温对植被生长具有促进作用。然而,当昼夜增温超过某一阈值时,其对NDVI的促进作用转变为抑制作用,从而限制了植被的生长。同时,还揭示了重现期与昼夜增温及NDVI之间的关系。在较低的重现期下,昼夜增温与NDVI的联合概率较高,表明在这些条件下,植被生长良好的情况出现的频率较高。反之,较高的重现期对应于昼夜增温与NDVI较低的联合概率,表明植被生长受到抑制。本研究通过Copula函数提供了一个全新的视角来理解昼夜增温与植被动态之间的相互作用,强调了气温变化对植被生长影响的复杂性。

2000—2021年渭河流域NDVI变化及其影响因素

渭河流域是黄河中游重要的生态涵养地,同时也是黄土高原水土流失的典型区域,监测该地区植被生长变化趋势,并分析其与气候变化和人类活动的关系,对科学评估区域生态建设成效、黄土高原植被恢复和生态修复具有重要意义。基于2000—2021年归一化植被指数(NDVI)、气温、降水量、人口密度、土地利用数据,分析了渭河流域NDVI的时空变化特征,探究了气候变化和人类活动对NDVI变化趋势的影响。结果表明,2000—2021年,渭河流域植被生长季NDVI呈增加趋势,全区年平均增速为0.004。年际尺度上,NDVI与年平均降水量呈正相关关系,与年平均气温的相关性不显著;月尺度上,NDVI与4月和8月的气温、降水量均呈正相关关系,与7月气温呈弱的负相关关系。人口密度变化与NDVI变化趋势呈负相关,流域人口密度的减小有利于植被的恢复和改善。土地利用类型内部变化是植被NDVI变化的主要原因。NDVI显著减少区NDVI的减少趋势主要由关中平原耕地NDVI的减少引起,NDVI显著增加区NDVI的增加趋势主要由草地、林地以及黄土丘陵区、黄土残塬区耕地NDVI的增加引起。

中亚地区2001-2020年250 m及2020年30 m分辨率植被生长季NDVI数据集

中亚地区是北半球最大的干旱和半干旱区,其生态环境十分脆弱,对全球气候变化的响应较为敏感。由于该区域的特殊地理位置,维护该区域生态系统的稳定对全球经济社会发展至关重要。植被具有重要的生态环境指示作用,其时空分布格局和变化趋势是评估区域生态状况的重要指标。归一化植被指数(Normalized Difference Vegetation Index,NDVI)作为研究植被最常用的遥感指数之一,能够表征植被的时空变化特征。本数据集利用MODIS13Q1产品生成了中亚地区2001–2020年长时间序列空间分辨率为250 m的生长季均值NDVI数据,并使用基于规则的分段回归Cubist算法,结合Landsat数据,融合得到了能够更好表征地物细节的30 m空间分辨率的2020年生长季均值NDVI数据。同时,本数据集从数据源的质控,模型训练优化,以及模型独立验证三个方面对数据产品进行质量控制,以确保数据的精度和可靠性。本数据集的生成为中亚地区植被动态变化和空间格局的分析提供了有力的数据支持。

The joint limiting distribution of maxima with random indexes and sums of normal sequence

Ｒｅｃｅｎｔｌｙ，ａｎｕｍｂｅｒｏｆｐａｐｅｒｓｈａｖｅｓｔｕｄｉｅｄｔｈｅｊｏｉｎｔａｓｙｍｐｔｏｔｉｃｉｎｄｅｐｅｎｄｅｎｔｐｒｏｐｅｒｔｉｅｓｏｆ∨ｎ１Ｘｉａｎｄｎ１Ｘｉｏｆｗｅａｋｌｙｄｅｐｅｎｄｅｎｔｓｔａｔｉｏｎａｒｙｓｅｑｕｅｎｃｅ｛...

基于主题模型的论文标准化影响力指标研究——一个新的论文标准化影响力指标GTNI

当前学界关于论文的标准化评价指标多从学科角度分类,忽视论文主题的多样性且每一主题概率不同等问题。文章在CNCI(Category Normalized Citation Impact,学科规范化影响力)指标基础上,结合主题模型、引文分析等方法提出构建可横向比较的普适性指标GTNI(General Topic Normalized Citation Impact,基于主题模型的论文标准化影响力),以提高学术论文评价的科学性。GTNI是对论文每一主题被引进行标准化并综合而获得的指标。通过理论分析、指标构建、实证研究和对比检验,表明GTNI指标在论文影响力评价方面具有较好的效果,且具有应用前景。

ZY1-02DAHSI影像归一化阴影植被指数NSVI的波段选择及其构建

高光谱影像具有连续的地物光谱信息,在阴影检测方面具有巨大的潜力,而波段冗余度高需进行波段优选。归一化阴影植被指数(NSVI)能够扩大光谱差异,在高光谱影像中应用NSVI将更有效地识别阴影。资源一号02D卫星是我国首颗自主研发并成功运行的高光谱业务卫星,数据信噪比大、覆盖能力强,对该高光谱影像进行准确的阴影检测具有重要意义。以ZY1-02DAHSI影像为试验数据,提取并分析明亮区植被、阴影区植被及水体的光谱反射率;结合竞争自适应重加权采样(CARS)和连续投影算法(SPA)筛选能够有效区分典型地物的主要波段,综合考虑算法的特性进一步选出特征波段构建NSVI;通过步长法确定最佳阈值对影像进行分类,从像元值分布情况、分类精度和光谱增强效果等对比出构建NSVI的最佳波段,并结合不同的阴影指数、波段和影像进行综合评价,验证该方法的意义及普适性。结果表明:波段32和波段73是构建NSVI的最佳波段,分别对应红光波段和近红外波段;不同波段构建的NSVI分类精度均高于90%,由最佳波段构建的NSVI分类精度为94.33%,Kappa系数为0.8328,分类效果最优;NSVI能够增强典型地物间的光谱差异并缓解归一化植被指数的“易饱和”现象,在该影像中因水体累积产生的小波峰有助于提取水体;在ZY1-02DAHSI影像中NSVI的分类效果优于归一化阴影指数和阴影指数,于另一景影像的分类精度也达到93.55%,Kappa系数为0.8167。由算法筛选出的波段具有一定的代表性,最佳波段构建的NSVI在ZY1-02DAHSI影像中具有较好的阴影检测能力,对高光谱影像阴影检测及构建植被指数具有一定的借鉴和参考意义。