Comparative Analysis of Fractional Vegetation Cover Estimation Based on Multi-sensor Data in a Semi-arid Sandy Area

The estimation of fractional vegetation cover(FVC) is important for identifying and monitoring desertification, especially in arid and semiarid regions. By using regression and pixel dichotomy models, we present the comparison of Sentinel-2A(S2) multispectral instrument(MSI) and Landsat 8(L8) operational land imager(OLI) data regarding the retrieval of FVC in a semi-arid sandy area(Mu Us Sandland, China, in August 2016). A combination of unmanned aerial vehicle(UAV) high-spatial-resolution images and field plots were used to produce verified data. Based on a normalized difference vegetation index(NDVI) regression model, the results showed that, compared with that of L8, the coefficient of determination(R2) of S2 increased by 26.0%, and the root mean square error(RMSE) and the sum of absolute error(SAE) decreased by 3.0% and 11.4%, respectively. For the ratio vegetation index(RVI) regression model, compared with that of L8, the R2 of S2 increased by 26.0%, and the RMSE and SAE decreased by 8.0% and 20.0%, respectively. When the pixel dichotomy model was used, compared with that of L8, the RMSE of S2 decreased by 21.3%, and the SAE decreased by 26.9%. Overall, S2 performed better than L8 in terms of FVC inversion. Additionally, in this paper, we develop a verified scheme based on UAV data in combination with the object-based classification method. This scheme is feasible and sufficiently robust for building relationships between field data and inversion results from satellite data. Further, the synergy of multi-source sensors(especially UAVs and satellites) is a potential effective way to estimate and evaluate regional ecological environmental parameters(FVC).

Dynamic change of net primary productivity and fractional vegetation cover in the Yellow River Basin using multi-temporal AVHRR NDVI Data

An exponential relationship between net primary productivity (NPP) and integrated NDVI has been found in this paper. Based on the relationship and using multi-temporal 8 km resolution NOAA AVHRR-NDVI data, the spatial distribution and dynamic change of NPP and fractional vegetation cover in the Yellow River Basin from 1982 to 1999 are analyzed. Finally, the effect of rainfall on NDVI is examined. Results show that mean NPP and fractional vegetation cover have an inclining trend for the whole basin, and rainfall in flood season influences vegetation cover most.

Estimating wheat fractional vegetation cover using a density peak k-means algorithm based on hyperspectral image data

Fractional vegetation cover(FVC)is an important parameter to measure crop growth.In studies of crop growth monitoring,it is very important to extract FVC quickly and accurately.As the most widely used FVC extraction method,the photographic method has the advantages of simple operation and high extraction accuracy.However,when soil moisture and acquisition times vary,the extraction results are less accurate.To accommodate various conditions of FVC extraction,this study proposes a new FVC extraction method that extracts FVC from a normalized difference vegetation index(NDVI)greyscale image of wheat by using a density peak k-means(DPK-means)algorithm.In this study,Yangfumai 4(YF4)planted in pots and Yangmai 16(Y16)planted in the field were used as the research materials.With a hyperspectral imaging camera mounted on a tripod,ground hyperspectral images of winter wheat under different soil conditions(dry and wet)were collected at 1 m above the potted wheat canopy.Unmanned aerial vehicle(UAV)hyperspectral images of winter wheat at various stages were collected at 50 m above the field wheat canopy by a UAV equipped with a hyperspectral camera.The pixel dichotomy method and DPK-means algorithm were used to classify vegetation pixels and non-vegetation pixels in NDVI greyscale images of wheat,and the extraction effects of the two methods were compared and analysed.The results showed that extraction by pixel dichotomy was influenced by the acquisition conditions and its error distribution was relatively scattered,while the extraction effect of the DPK-means algorithm was less affected by the acquisition conditions and its error distribution was concentrated.The absolute values of error were 0.042 and 0.044,the root mean square errors(RMSE)were 0.028 and 0.030,and the fitting accuracy R2 of the FVC was 0.87 and 0.93,under dry and wet soil conditions and under various time conditions,respectively.This study found that the DPK-means algorithm was capable of achieving more accurate results than the pixel dichotomy method in various soil and time conditions and was an accurate and robust method for FVC extraction.

Estimation of Fractional Vegetation Cover Based on Digital Camera Survey Data and a Remote Sensing Model

The objective of this paper is to improve the monitoring speed and precision of fractional vegetation cover (fc). It mainly focuses on fc estimation when fcmax and fcmin are not approximately equal to 100% and 0%, respectively due to using remote sensing image with medium or low spatial resolution. Meanwhile, we present a new method of fc estimation based on a random set of fc maximum and minimum values from digital camera (DC) survey data and a di- midiate pixel model. The results show that this is a convenient, efficient and accurate method for fc monitoring, with the maximum error -0.172 and correlation coefficient of 0.974 between DC survey data and the estimated value of the remote sensing model. The remaining DC survey data can be used as verification data for the precision of the fc estimation. In general, the estimation of fc based on DC survey data and a remote sensing model is a brand-new development trend and deserves further extensive utilization.

基于SPOT-VEGETATION数据的神农架林区1998—2013年植被覆盖度格局变化

基于1998—2013年的SPOT-VEGETATION归一化植被指数(normalized differential vegetation index,NDVI)数据,利用二分模型法、相关性分析和空间分析的方法,结合同期降水量和平均温度数据,估算了神农架林区及神农架国家级自然保护区的植被覆盖度,并分析了空间格局及植被覆盖度变化的影响因素。结果表明,1998—2013年间,神农架林区平均植被覆盖度为66.8%,年最大植被覆盖度为93.8%,保护区内最大植被覆盖度显著高于保护区外;林区植被覆盖度变化率为1.45%,保护区植被覆盖度变化率为2.26%,植被整体呈增加的趋势,保护区保护效果较好。温度、降水量、年最低气温、距道路和居民地距离的远近是影响植被覆盖度变化的重要因子,而海拔对植被覆盖度变化无影响。

Effects of ecological projects on vegetation in the Three Gorges Area of Chongqing, China

The construction of the Three Gorges Reservoir and the resettlement project have caused increasing contradictions between human and land,and led to the deterioration of the ecological environment. In order to ameliorate ecological environment of the Three Gorges Area, the government carried out several ecological restoration projects to improve the vegetation coverage from 1990 s. This paper aims to quantitatively analyze the impact of ecological projects on the vegetation in the Three Gorges Area of Chongqing, China. Landsat and MODIS data from 1992 to 2015 were used to estimate vegetation coverage. In addition, the land cover data of the European Space Agency(ESA) was used to explore the impact of ecological projects on land cover change. The cropland accounted for about 62% and the forestland accounted for about 34% of the total area. There was more than 90% of the study area covered with high or very high vegetation coverage.From 1992 to 2015, a total of 272.7 km;croplands were converted into forestland in the Ecological Migration Project(EMP), 795.6 km;in the Grain for Green Project(GGP), and 13.77 km;in the Ecological Restoration Zone Project(ERZP). Among the three projects, the GGP was the most powerful measure,with a contribution rate of 1.6%. The implementation of the ecological projects improved vegetation coverage, which indicated that the ecological projects measures were effective in ecological restoration.

基于Landsat时间序列遥感影像的合肥市FVC时空演化与分析

文章基于Landsat时间序列遥感影像,采用像元二分模型反演合肥市2008—2019年植被覆盖度(fractional vegetation cover,FVC),并利用马尔可夫转移矩阵与差值图像算法,定量分析2008—2019年合肥市不同等级FVC变化特征。研究结果表明:2008年、2011年、2013年、2015年、2017年、2019年合肥市平均FVC分别为60.9%、48.4%、56.0%、48.0%、63.0%、61.2%,年均FVC波动较大,但总体呈上升趋势;合肥市FVC变化以稳定区为主,主城区的外围FVC出现退化趋势,而长丰县、主城区及庐江县FVC增长明显;各等级FVC总体变化趋势为良性,未来FVC预计以增加为主;2008—2019年合肥市气温呈升高趋势,而降水量呈下降趋势,总体表现为“暖干化”,且这种趋势对合肥市FVC增加具有促进作用,其中年均气温是影响FVC演化的主要因子。

Evaluating the Impact of Different Vegetation Types on NEE: A Case Study of Banni Grasslands, India

Estimation of NEE of Grasslands ecosystems becomes mandatory as these grasslands with their wide spread (almost 40% of land of the earth) and high plant diversity play a major role in global carbon balances and NEE at both local and global scale. The present study has been focused on understanding the role of different plant species responsible for variation in NEE of the Banni Grasslands of India. These grasslands form a belt of arid grassland having low growing forbs, graminoids and scattered tree cover. Due to its wide spread and inaccessibility of Banni, this study utilized spatial approach for evaluating carbon emissions and NEE. Landsat data was utilized for vegetation type classification and SMAP data for extraction of NEE values proved their potential for categorising vegetation type and generating NEE values precisely. Three major plant types were identified from the study area viz., Grasslands, Land with Acacia and Land with Prosopis. Grasses were dominant covering 77% and the rest of the area was occupied by the other two classes, i.e. Acacia and Prosopis. The NEE values were higher for the grasses when compared to the other two plant species proving to be the active sinks when compared to other plants. The differential contribution of NEE by species has been depicted in the present work.

黄土高原极端降水变化及其对植被覆盖度影响

全球变暖背景下,黄土高原极端降水事件频发,对社会经济及植被生态产生了重要影响,研究黄土高原极端降水变化特征及其对植被覆盖度(fractional vegetation cover,FVC)影响,可为生态环境保护和区域可持续发展提供科学支撑。基于2000—2020年黄土高原64个气象站点逐日降水数据和MODIS NDVI数据,采用趋势分析、相关分析等方法,分析了黄土高原地区极端降水和FVC时空变化特征及极端降水对FVC的影响。结果表明:2000—2020年黄土高原地区总降水量P_(RCPTOT)、中雨日数R_(10mm)、大雨日数R_(20mm)、强降水日数R_(25mm)和降水强度S_(DII)均呈显著上升趋势(P<0.05),区域西部和北部强降水总体增加,但区域南部呈干旱化。2000—2020年黄土高原FVC总体呈显著上升趋势(P<0.05),集中分布在区域中东部,占区域总面积的32.00%;显著下降趋势主要分布在南部少部分地区,仅占区域总面积的4.91%。2000—2020年黄土高原地区FVC与中雨日数R_(10mm)、大雨日数R_(20mm)、强降水日数R_(25mm)和降水强度S_(DII)以正相关关系为主,其中呈显著正相关的区域占比分别为22.33%、48.19%、14.36%和24.37%,主要分布在中部地区。研究认为近20年黄土高原极端降水和FVC同步增加,表现为极端降水促进植被生态改善的作用,但应注意极端降水研究中的时间尺度效应问题,即年尺度数据可能掩盖了短时间尺度上极端降水对FVC的破坏作用。

Synchronous estimation of DTM and fractional vegetation cover in forested area from airborne LIDAR height and intensity data

We proposed a method to separate ground points and vegetation points from discrete return,small footprint airborne laser scanner data,called skewness change algorithm.The method,which makes use of intensity of laser scanner data,is especially applicable in steep,and forested areas.It does not take slope of forested area into account,while other algorithms consider the change of slope in steep forested area.The ground points and vegetation points can be used to estimate digital terrain model(DTM) and fractional vegetation cover,respectively.A few vegetation points which were classified into the ground points were removed as noise before the generation of DTM.This method was tested in a test area of 10000 square meters.A LiteMapper -5600 laser system was used and a flight was carried out over a ground of 700―800 m.In this tested area,a total number of 1546 field measurement ground points were measured with a total station TOPCON GTS-602 and TOPCON GTS -7002 for validation of DTM and the mean error value is -18.5 cm and the RMSE(root mean square error) is ±20.9 cm.A data trap sizes of 4m in diameter from airborne laser scanner data was selected to compute vegetation fraction cover.Validation of fractional vegetation cover was carried out using 15 hemispherical photographs,which are georeferenced to centimeter accuracy by differential GPS.The gap fraction was computed over a range of zenith angles 10° using the gap light analyzer(GLA) from each hemispherical photograph.The R2 for the regression of fractional vegetation cover from these ALS data and the respective field measurements is 0.7554.So this study presents a method for synchronous estimation of DTM and fractional vegetation cover in forested area from airborne LIDAR height and intensity data.

High Spatial Resolution and High Temporal Frequency(30-m/15-day) Fractional Vegetation Cover Estimation over China Using Multiple Remote Sensing Datasets:Method Development and Validation

High spatial resolution and high temporal frequency fractional vegetation cover(FVC) products have been increasingly in demand to monitor and research land surface processes. This paper develops an algorithm to estimate FVC at a 30-m/15-day resolution over China by taking advantage of the spatial and temporal information from different types of sensors: the 30-m resolution sensor on the Chinese environment satellite(HJ-1) and the 1-km Moderate Resolution Imaging Spectroradiometer(MODIS). The algorithm was implemented for each main vegetation class and each land cover type over China. First, the high spatial resolution and high temporal frequency normalized difference vegetation index(NDVI) was acquired by using the continuous correction(CC) data assimilation method. Then, FVC was generated with a nonlinear pixel unmixing model. Model coefficients were obtained by statistical analysis of the MODIS NDVI. The proposed method was evaluated based on in situ FVC measurements and a global FVC product(GEOV1 FVC). Direct validation using in situ measurements at 97 sampling plots per half month in 2010 showed that the annual mean errors(MEs) of forest, cropland, and grassland were-0.025, 0.133, and 0.160, respectively, indicating that the FVCs derived from the proposed algorithm were consistent with ground measurements [R2 = 0.809,root-mean-square deviation(RMSD) = 0.065]. An intercomparison between the proposed FVC and GEOV1 FVC demonstrated that the two products had good spatial–temporal consistency and similar magnitude(RMSD approximates 0.1). Overall, the approach provides a new operational way to estimate high spatial resolution and high temporal frequency FVC from multiple remote sensing datasets.

New method for cotton fractional vegetation cover extraction based on UAV RGB images

As the key principle of precision farming,the distribution of fractional vegetation cover is the basis of crop management within the field serves.To estimate crop FVC rapidly at the farm scale,high temporal-spatial resolution imagery obtained by unmanned aerial vehicle(UAV)was adopted.To verify the application potential of consumer-grade UAV RGB imagery in estimated FVC,blue-green characteristic vegetation index(TBVI)and red-green vegetation index(TRVI)were proposed in this study according to the differences of the gray value among cotton vegetation,soil and shadow in the field.First,two new constructed indices and several published indices were used to extract visible light images and generate greyscale images for each of the visible light vegetation indices.Then,the thresholds of cotton vegetation and non-vegetation pixels were established based on the vegetation index threshold method which combines support vector machine classification and vegetation index.Finally,the accuracy difference in vegetation information extraction between the newly constructed and several published indices was compared.The results show that the accuracy of the information extracted by TRVI is higher than that of subdivision index of other visible light(FVC extraction precision in the first bud stage of cotton:R2=0.832,RMSE=2.307,nRMSE=4.405%;FVC extraction precision in the bud stage of cotton:R2=0.981,RMSE=1.393,nRMSE=1.984%;FVC extraction precision in the flowering stage of cotton:R2=0.893,RMSE=2.101,nRMSE=2.422%;FVC extraction precision in the boll stage of cotton:R2=0.958,RMSE=1.850,nRMSE=2.050%).

Fractional vegetation cover estimation in heterogeneous areas by combining a radiative transfer model and a dynamic vegetation model

A fractional vegetation cover(FVC)estimation method incorporating a vegetation growth model and a radiative transfer model was previously developed,which was suitable for FVC estimation in homogeneous areas because the finer-resolution pixels corresponding to one coarseresolution FVC pixel were all assumed to have the same vegetation growth model.However,this assumption does not hold over heterogeneous areas,meaning that the method cannot be applied to large regions.Therefore,this study proposes a finer spatial resolution FVC estimation method applicable to heterogeneous areas using Landsat 8 Operational Land Imager reflectance data and Global LAnd Surface Satellite(GLASS)FVC product.The FVC product was first decomposed according to the normalized difference vegetation index from the Landsat 8 OLI data.Then,independent dynamic vegetation models were built for each finer-resolution pixel.Finally,the dynamic vegetation model and a radiative transfer model were combined to estimate FVC at the Landsat 8 scale.Validation results indicated that the proposed method(R^(2)=0.7757,RMSE=0.0881)performed better than either the previous method(R^(2)=0.7038,RMSE=0.1125)or a commonly used method involving look-up table inversions of the PROSAIL model(R^(2)=0.7457,RMSE=0.1249).

青海湖流域植被动态变化驱动力及空间粒度效应

基于Landsat遥感影像分析1986~2020年青海湖流域植被覆盖度时空变化特征,结合多元线性回归和地理探测器,考虑气候、地表及人类足迹的影响,阐明时间和空间尺度下植被覆盖度变化的机制,并探讨空间粒度对驱动因素及其相对贡献的影响.结果表明:(1)近35年来青海湖流域植被状况整体改善,表现为中等、中高覆盖度植被面积增加,其中环青海湖东北部及布哈河上游植被覆盖度呈显著增加趋势;(2)时间尺度上,流域平均植被覆盖度变化受气候暖湿化和生态恢复工程驱动;(3)空间尺度上,植被覆盖度变化由气候、地形、植被和土壤控制,解释力较大因素为气温(0.41),高程(0.34)和降水(0.30).气候、地形和人为因素对植被覆盖度的影响存在交互增强效应,气温、高程对交互效用具有控制作用,其中气温与距水系距离、降水和人类足迹的交互较为显著;(4)空间粒度对植被覆盖度空间变化驱动因素的贡献具有显著影响,考虑气候、地表和人为因素交互作用时青海湖流域植被覆盖度空间变化最佳研究粒度为6km.

高寒草甸斑块化空间分异性及其地形因子影响

青藏高原高寒草甸在国家生态环境保护战略中占据重要位置,近几十年来,气候变化和人为因素导致大面积高寒草甸发生斑块化退化。为探究高寒草甸斑块化空间分异性及各斑块类型间(裸地斑块、短期恢复斑块和长期恢复斑块)的分布规律以及其地形因子的影响,本研究通过3S(RS,GIS,GNSS)技术获取位于青海省河南县和玛多县4个典型研究区的斑块类型分布及地形因子数据,利用景观生态指数探究不同斑块的空间分布差异。结果表明,研究区植被覆盖度随着海拔升高呈现先增加后减小的趋势,斑块景观的破碎化程度呈现先减小后增大的趋势。不同海拔地区斑块化程度最高的斑块类型都是长期恢复斑块。地形因子显著影响着不同斑块的分布,长期恢复斑块和健康草甸主要分布在阳坡平缓区域。

气候变化和人类活动对环塔里木盆地植被覆盖度的影响

塔里木盆地生态系统脆弱,易受到人类活动的影响,植被对维持塔里木盆地生态系统稳定性具有极其重要的作用。以塔里木盆地边缘的绿洲区域、绿洲外围植被分布区及塔克拉玛干沙漠公路周围植被分布区作为研究区域,将植被覆盖度(FVC)作为反映植被变化的监测指标,利用归一化植被指数(NDVI)、气温、降水、植被类型数据,采用像元二分模型、一元线性回归法、残差分析等方法,分析了植被覆盖度的时空变化,以及气候变化和人类活动对环塔里木盆地、各植被类型植被覆盖度的影响。结果表明:(1)2000—2022年环塔里盆地植被覆盖度每10年增长0.008,各植被类型的植被覆盖度均呈现上升趋势,空间上植被覆盖度呈现“北高南低”的分布格局。气候变化影响的大部分区域植被覆盖度基本不变,人类活动影响的植被覆盖度以改善为主;(2)植被覆盖度的变化主要由气候变化和人类活动共同主导,但人类活动的影响大于气候变化。气候变化和人类活动共同作用时,植被覆盖度改善和退化的面积占比分别为19.79%、49.55%;(3)在植被覆盖度改善区,人类活动的相对贡献率整体较高。在植被覆盖度退化区,气候变化对绿洲区域内植被的相对贡献率更高,在绿洲区域外人类活动的相对贡献率更高。

利用FVC和DEM对中国新疆南部植被的分类研究

植被覆盖度指数FVC(FractionalVegetationCover)是从植被归一化指数中演化出来的反映植被盖度的指数,是很多干旱半干旱地区生态水文模型中的重要变量.本文通过该指数结合地貌特征对中国新疆南部植被进行了分类.首先按数字地面高程将整个研究区分为三个子研究区:(1)低海拔平原荒漠、丘陵区,海拔高度≤2000m,主要植被为绿洲、平原荒漠类植被;(2)亚高山、中低山地地区,海拔高度在2000～3900m,主要植被为森林、亚高山草原、山地草原、山地荒漠草原、山地荒漠;(3)高山区,海拔高度>3900m,主要植被高山垫状植被、高寒荒漠.然后对每一个子区的植被进行独立的分类.由于每一个子区的种类相对于整个区域种类减少,且具有特殊性,使分类方法从考虑研究区所有对象之间的均衡变为仅考虑每个子研究区内相对特殊的植被,分类条件放宽,再加之FVC指数的特点使得分类精度提高.在对三个子研究区进行相对独立的分类之后,再利用GIS方法,将三个子研究区合并为整体的分类图,完成对整个区域的植被分类.

基于FVC的新疆植被覆盖度时空变化

【目的】对新疆植被覆盖变化的空间自相关性进行分析,研究植被覆盖的时间变化特征、空间分布特征及变化趋势等,为改善区域生态环境提供参考。【方法】利用新疆1998—2012年NDVI数据,采用像元二分法获取植被覆盖指数(FVC)数据,在此基础上运用Sen+Mann-Kendall趋势分析和空间自相关分析方法,研究新疆植被覆盖的变化趋势特征和空间分布的聚集性特点。【结果】1)15年间新疆植被覆盖度略有下降趋势,山地和平原均呈下降趋势,山地较平原变化大。2)Sen+Mann-Kendall趋势分析可反映新疆FVC变化趋势的空间分布特点,全疆植被改善区域占25%,退化区域占28%,47%的区域变化不大,其中明显改善区域和严重退化区域所占比例均为10%左右。植被改善区域主要分布在天山北坡一带,退化区域主要分布在山地和平原的交错带,伊犁地区退化程度尤为严重。植被覆盖度极低地区基本不变,退化区域主要分布在植被低覆盖度区域周围。3)空间自相关分析进一步验证了Sen+Mann-Kendall趋势分析结果,新疆植被有明显的聚集现象。全局自相关性分析表明,当距离大于3 km后,空间自相关影响不大。局部相关性分析表明,新疆植被覆盖以"高-高聚集"和"低-低聚集"为主。【结论】植被盖度相对较高的地区植被覆盖越易改善,盖度较低或无覆盖的地区越难改善,而且退化越明显。根据植被盖度的聚集性可以看出,植被覆盖呈现明显的"高-高聚集"和"低-低聚集"格局,这与区域气候、水资源分布及人类活动的影响有着潜在的联系。今后可重点分析植被覆盖变化的影响因素,以了解干旱区植被覆盖变化的驱动机制。由于人类活动在短时期内对植被覆盖变化的影响比较显著,因此在空间上分析人类活动对植被覆盖变化的影响可为改善干旱区植被覆盖提供相应指导。【其他】本文从植被覆盖的空间聚集性解释了植被覆盖变化特点,一方面是对Sen+Mann-Kendall趋势分析的验证,另一方面为整体分析植被的变化特征提供了依据。

整合多源遥感数据的洪涝灾害评估恢复——以河南“7·20”暴雨灾害为例

洪涝灾害发生后通过植被指数和灯光指数定量评估灾后恢复情况,对灾区经济建设和生态恢复的评估具有重要科学意义。该文以河南“7·20”暴雨灾害区为研究区,基于日度和月度NPP-VIIRS数据、Sentinel-NDVI、MODIS-EVI数据和统计年鉴数据,构建归一化差异城市指数(normalized difference urban index,NDUI)来表征城市内部空间细节;基于回归模型模拟人口和国内生产总值的空间分布;从研究区的夜间灯光数据和植被覆盖数据2个不同的维度来评估洪涝灾害。结果表明:高危区和中危区总面积为1429.04 km^(2),占研究区总面积的6.06%,高危地区主要分布在郑州西部、新乡东部、安阳东部、鹤壁北部,其中郑州市受灾严重程度最高;从植被覆盖度恢复率(vegetation cover recovery rate,VCRR)来看,卫辉市、淇县、滑县、林州市等地区整体植被恢复情况较差,其VCRR的值大部分在0以下,植被覆盖有恶化趋势。NDUI与社会经济统计数据拟合精度高于0.8,表明NDUI可以在洪涝灾害发生后应用于精确位置救援和灾后针对性重建工作;NPP-VIIRS和MODIS-EVI评估洪涝灾害的结果具有很好的互补性,2种数据的有机结合进行洪涝灾害研究,对灾后救援和恢复评估均有较高的应用价值。

2000—2020年湖北省土地利用变化对植被覆盖度的影响

[目的]土地利用及其对陆地生态系统的影响是当前全球变化研究的重要内容。科学分析土地利用变化对植被覆盖度的影响,促进社会经济与生态环境质量的协调发展及地区经济的平衡发展,为政府部门对区域生态保护和恢复管理提供借鉴和参考。[方法]以湖北省为研究对象,利用2000,2020年2期Landsat遥感影像、土地利用遥感数据,利用遥感数据的空间处理、像元二分模型、土地利用转移矩阵等方法,分析研究湖北省植被覆盖的时空变化、土地利用类型变化特征及其对植被覆盖度的影响。[结果]①2000—2020年,湖北省耕地、草地和未利用地面积减少,水域以及建设用地面积增加,林地面积基本保持不变,其面积大小顺序为:林地>耕地>水域>建设用地>草地>未利用地。②湖北省植被覆盖度平均值上升了6.50%。林地、耕地、草地和未利用地的平均植被覆盖度均有所增加,建设用地的平均植被覆盖度有所降低。③湖北省植被覆盖度总体呈现增加的趋势。植被覆盖度增大的区域主要集中在湖北省的西部和东南部地区,局部地区也存在植被退化的区域,主要集中在湖北省中南部及襄阳北方部分区域。④不同土地利用类型FVC转移过程中,耕地较高植被覆盖与高植被覆盖之间的转移过程最为剧烈,林地不同等级植被覆盖的转移量占转移总量的47.87%,草地不同水平植被覆盖度的转移量占转移总量比例较小,仅为3.40%。[结论]2000—2020年湖北省土地利用变化较大,不同土地利用类型的植被覆盖度相互转移,尤其是林地、耕地及草地的平均植被覆盖度均有所增加,使得湖北省近20a来整体植被覆盖度呈现出上升趋势。