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.

A phenology-based vegetation index for improving ratoon rice mapping using harmonized Landsat and Sentinel-2 data

Ratoon rice,which refers to a second harvest of rice obtained from the regenerated tillers originating from the stubble of the first harvested crop,plays an important role in both food security and agroecology while requiring minimal agricultural inputs.However,accurately identifying ratoon rice crops is challenging due to the similarity of its spectral features with other rice cropping systems(e.g.,double rice).Moreover,images with a high spatiotemporal resolution are essential since ratoon rice is generally cultivated in fragmented croplands within regions that frequently exhibit cloudy and rainy weather.In this study,taking Qichun County in Hubei Province,China as an example,we developed a new phenology-based ratoon rice vegetation index(PRVI)for the purpose of ratoon rice mapping at a 30 m spatial resolution using a robust time series generated from Harmonized Landsat and Sentinel-2(HLS)images.The PRVI that incorporated the red,near-infrared,and shortwave infrared 1 bands was developed based on the analysis of spectro-phenological separability and feature selection.Based on actual field samples,the performance of the PRVI for ratoon rice mapping was carefully evaluated by comparing it to several vegetation indices,including normalized difference vegetation index(NDVI),enhanced vegetation index(EVI)and land surface water index(LSWI).The results suggested that the PRVI could sufficiently capture the specific characteristics of ratoon rice,leading to a favorable separability between ratoon rice and other land cover types.Furthermore,the PRVI showed the best performance for identifying ratoon rice in the phenological phases characterized by grain filling and harvesting to tillering of the ratoon crop(GHS-TS2),indicating that only several images are required to obtain an accurate ratoon rice map.Finally,the PRVI performed better than NDVI,EVI,LSWI and their combination at the GHS-TS2 stages,with producer's accuracy and user's accuracy of 92.22 and 89.30%,respectively.These results demonstrate that the proposed PRVI based on HLS data can effectively identify ratoon rice in fragmented croplands at crucial phenological stages,which is promising for identifying the earliest timing of ratoon rice planting and can provide a fundamental dataset for crop management activities.

Impact of climate and human activity on NDVI of various vegetation types in the Three-River Source Region, China

The Three-River Source Region(TRSR)in China holds a vital position and exhibits an irreplaceable strategic importance in ecological preservation at the national level.On the basis of an in-depth study of the vegetation evolution in the TRSR from 2000 to 2022,we conducted a detailed analysis of the feedback mechanism of vegetation growth to climate change and human activity for different vegetation types.During the growing season,the spatiotemporal variations of normalized difference vegetation index(NDVI)for different vegetation types in the TRSR were analyzed using the Moderate Resolution Imaging Spectroradiometer(MODIS)-NDVI data and meteorological data from 2000 to 2022.In addition,the response characteristics of vegetation to temperature,precipitation,and human activity were assessed using trend analysis,partial correlation analysis,and residual analysis.Results indicated that,after in-depth research,from 2000 to 2022,the TRSR's average NDVI during the growing season was 0.3482.The preliminary ranking of the average NDVI for different vegetation types was as follows:shrubland(0.5762)>forest(0.5443)>meadow(0.4219)>highland vegetation(0.2223)>steppe(0.2159).The NDVI during the growing season exhibited a fluctuating growth trend,with an average growth rate of 0.0018/10a(P<0.01).Notably,forests displayed a significant development trend throughout the growing season,possessing the fastest rate of change in NDVI(0.0028/10a).Moreover,the upward trends in NDVI for forests and steppes exhibited extensive spatial distributions,with significant increases accounting for 95.23%and 93.80%,respectively.The sensitivity to precipitation was significantly enhanced in other vegetation types other than highland vegetation.By contrast,steppes,meadows,and highland vegetation demonstrated relatively high vulnerability to temperature fluctuations.A further detailed analysis revealed that climate change had a significant positive impact on the TRSR from 2000 to 2022,particularly in its northwestern areas,accounting for 85.05%of the total area.Meanwhile,human activity played a notable positive role in the southwestern and southeastern areas of the TRSR,covering 62.65%of the total area.Therefore,climate change had a significantly higher impact on NDVI during the growing season in the TRSR than human activity.

Improving the Accuracy of Vegetation Index Retrieval for Biomass by Combining Ground-UAV Hyperspectral Data-A New Method for Inner Mongolia Typical Grasslands

Grassland biomass is an important parameter of grassland ecosystems.The complexity of the grassland canopy vegetation spectrum makes the long-term assessment of grassland growth a challenge.Few studies have explored the original spectral information of typical grasslands in Inner Mongolia and examined the influence of spectral information on aboveground biomass(AGB)estimation.In order to improve the accuracy of vegetation index inversion of grassland AGB,this study combined ground and Unmanned Aerial Vehicle(UAV)remote sensing technology and screened sensitive bands through ground hyperspectral data transformation and correlation analysis.The narrow band vegetation indices were calculated,and ground and airborne hyperspectral inversion models were established.Finally,the accuracy of the model was verified.The results showed that:(1)The vegetation indices constructed based on the ASD FieldSpec 4 and the UAV were significantly correlated with the dry and fresh weight of AGB.(2)The comparison between measured R^(2) with the prediction R^(2) indicated that the accuracy of the model was the best when using the Soil-Adjusted Vegetation Index(SAVI)as the independent variable in the analysis of AGB(fresh weight/dry weight)and four narrow-band vegetation indices.The SAVI vegetation index showed better applicability for biomass monitoring in typical grassland areas of Inner Mongolia.(3)The obtained ground and airborne hyperspectral data with the optimal vegetation index suggested that the dry weight of AGB has the best fitting effect with airborne hyperspectral data,where y=17.962e^(4.672x),the fitting R^(2) was 0.542,the prediction R^(2)was 0.424,and RMSE and REE were 57.03 and 0.65,respectively.Therefore,established vegetation indices by screening sensitive bands through hyperspectral feature analysis can significantly improve the inversion accuracy of typical grassland biomass in Inner Mongolia.Compared with ground monitoring,airborne hyperspectral monitoring better reflects the inversion of actual surface biomass.It provides a reliable modeling framework for grassland AGB monitoring and scientific and technological support for grazing management.

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.

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.

地理信息系统支持下Spot/vegetation NDVI影像的大尺度神经网络分类

以内蒙古地区Spot/vegetation归一化植被指数(NDVI)影像为基本信息源,综合应用地理信息系统(G IS)技术进行了大尺度神经网络分类实验研究.建立多年份高分辨影像数据库,通过G IS软件集成与遥感影像目视解译方法,在全区范围选取了“纯净”样本数据,并辅助应用DTM数据和影像化多年气像观测数据,完成土地覆盖类型的BP人工神经网络分类.结果表明,G IS技术支持下,大面积区域尺度上spot/vegetation NDVI影像的BP神经网络分类可达到较高的分类精度.

Intra-annual Vegetation Change Characteristics in the NDVI-Ts Space:Application to Farming-Pastoral Zone in North China

Land use/land cover change (LUCC) mapping and analysis using multi-temporal normalize difference vegetation index (NDVI) data have been well documented. Recent empirical studies have documented that many new methods with high accuracy of retrieved land surface temperature ( Ts) have been developed. Thus, the combination of land surface temperature and NDVI has the greatest potential to improve the surface vegetation dynamic monitoring. In this study, the following objectives are pursued to: (1) introduce the practical method to produce the Ts, NDVI and Ts/NDVI based on remotely sensed data; (2) investigate the different retrieved result of vegetation cover information from NDVI, Ts and Ts/NDVI data sets, and analyze the intra-annual time trajectories of different vegetation cover categories in the NDVI- Ts space for farming-pastoral zone in North China, and (3) quantitative analysis the difference in using NDVI, Ts and Ts/NDVI data sets to express information based on the indices (information entropy and averaged information grads), and evaluate the relative role of Ts/NDVI data set in the discrimination of different vegetation cover categories through comparison to traditional NDVI data set.

New Vegetation Index and Its Application in Estimating Leaf Area Index of Rice

Leaf area index （LAI） is an important characteristic of land surface vegetation system, and is also a key parameter for the models of global water balancing and carbon circulation. By using the reflectance values of Landsat-5 blue, green and red channels simulated from rice reflectance spectrum, the sensitivities of the bands to LAI were analyzed, and the response and capability to estimate LAI of various NDVIs （normalized difference vegetation indices）, which were established by substituting the red band of general NDVI with all possible combinations of red, green and blue bands, were assessed. Finally, the conclusion was tested by rice data at different conditions. The sensitivities of red, green and blue bands to LAI were different under various conditions. When LAI was less than 3, red and blue bands were more sensitive to LAI. Though green band in the circumstances was less sensitive to LAI than red and blue bands, it was sensitive to LAI in a wider range. When the vegetation indices were constituted by all kinds of combinations of red, green and blue bands, the premise for making the sensitivity of these vegetation indices to LAI be meaningful was that the value of one of the combinations was greater than 0.024, i.e. visible reflectance （VIS）〉0.024. Otherwise, the vegetation indices would be saturated, resulting in lower estimation accuracy of LAI. Comparison on the capabilities of the vegetation indices derived from all kinds of combinations of red, green and blue bands to LAI estimation showed that GNDVI （Green NDVI） and GBNDVI （Green-Blue NDVI） had the best relations with LAI. The capabilities of GNDVI and GBNDVI to LAI estimation were tested under different circumstances, and the same result was acquired. It suggested that GNDVI and GBNDVI performed better to predict LAI than the conventional NDVI.

Numerical Simulation of the Impact of Vegetation Index on the Interannual Variation of Summer Precipitation in the Yellow River Basin

Two sets of numerical experiments using the coupled National Center for Environmental Prediction General Circulation Model （NCEP/GCM T42L18） and the Simplified Simple Biosphere land surface scheme （SSiB） were carried out to investigate the climate impacts of fractional vegetation cover （FVC） and leaf area index （LAI） on East Asia summer precipitation, especially in the Yellow River Basin （YRB）. One set employed prescribed FVC and LAI which have no interannual variations based on the climatology of vegetation distribution; the other with FVC and LAI derived from satellite observations of the International Satellite Land Surface Climate Project （ISLSCP） for 1987 and 1988. The simulations of the two experiments were compared to study the influence of FVC, LAI on summer precipitation interannual variation in the YRB. Compared with observations and the NCEP reanalysis data, the experiment that included both the effects of satellite-derived vegetation indexes and sea surface temperature （SST） produced better seasonal and interannual precipitation variations than the experiment with SST but no interannual variations in FVC and LAI, indicating that better representations of the vegetation index and its interannual variation may be important for climate prediction. The difference between 1987 and 1988 indicated that with the increase of FVC and LAI, especially around the YRB, surface albedo decreased, net surface radiation increased, and consequently local evaporation and precipitation intensified. Further more, surface sensible heat flux, surface temperature and its diurnal variation decreased around the YRB in response to more vegetation. The decrease of surface-emitting longwave radiation due to the cooler surface outweighed the decrease of surface solar radiation income with more cloud coverage, thus maintaining the positive anomaly of net surface radiation. Further study indicated that moisture flux variations associated with changes in the general circulation also contributed to the precipitation interannual variation.

Spatial distribution of vegetation and the influencing factors in Qaidam Basin based on NDVI

The spatial distribution of vegetation in Qaidam Basin was analyzed using GIMMS（Global Inventory Modeling and Mapping Studies） /NDVI（Normalized Difference Vegetation Index） data set from January 1982 to December 2006.Based on the data of precipitation,terrain,stream systems,land use and the map of vegetation distribution in Qaidam Basin,we studied the factors influencing the spatial distribution of vegetation.The results showed that the vegetation was generally low in Qaidam Basin and there was a clear semi-ring structure from southeast to northwest.In some areas,the existence of rivers,lakes and spring belts turned this semi-ring structure into a non-continuous state and formed distinct bright spots and continuous linear features.There were four main factors that affected the spatial distribution of vegetation coverage in Qaidam Basin,i.e.,precipitation,hydrological conditions,altitude and human activities.Precipitation and altitude have a correlation and determine the basic pattern of vegetation distribution in Qaidam Basin.The impacts of hydrological conditions and human activities were mainly embodied in partial areas,and often broke the pattern of vegetation distribution dominated by precipitation and altitude.

Leaf area index retrieval based on canopy reflectance and vegetation index in eastern China

The aim of this paper is to investigate the feasibility of using Landsat TM data to retrieve leaf area index (LAI). To get a LAI retrieval model based ground reflectance and vegetation index, detailed field data were collected in the study area of eastern China, dominated by bamboo, tea plant and greengage. Plant canopy reflectance of Landsat TM wavelength bands has been inversed using software of 6S. LAI is an important ecological parameter. In this paper, atmospheric corrected Landsat TM imagery was utilized to calculate different vegetation indices (VI), such as simple ratio vegetation index (SR), shortwave infrared modified simple ratio (MSR), and normalized difference vegetation index (NDVI). Data of 53 samples of LAI were measured by LAI-2000 (LI-COR) in the study area. LAI was modeled based on different reflectances of bands and different vegetation indices from Landsat TM and LAI samples data. There are certainly correlations between LAI and the reflectance of TM3, TM4, TM5 and TM7. The best model through analyzing the results is LAI = 1.2097*MSR + 0.4741 using the method of regression analysis. The result shows that the correlation coefficient R2 is 0.5157, and average accuracy is 85.75%. However, whether the model of this paper is suitable for application in subtropics needs to be verified in the future.

Dynamic Drought Monitoring in Guangxi Using Revised Temperature Vegetation Dryness Index

Moderate resolution imaging spectroradiometer （MODIS） data are very suitable for vast extent, long term and dynamic drought monitoring for its high temporal resolution, high spectral resolution and moderate spatial resolution. The composite Enhanced Vegetation Index （EVI） and composite land surface temperature （Ts） obtained from MODIS data MOD11A2 and MOD13A2 were used to construct the EVI-Ts space. And Temperature Vegetation Dryness Index （TVDI） was calculated to evaluate the agriculture drought in Guangxi province, China in October of 2006. The results showed that the drought area in Guangxi was evidently increasing and continuously deteriorating from the middle of September to the middle of November. The TVDI, coming from the EVI-Ts space, could effectively indicate the spatial distribution and temporal evolution of drought, so that it could provide a strong technical support for the forecasting agricultural drought in south China.

Response of vegetation variation to climate change and human activities in the Shiyang River Basin of China during 2001-2022

Understanding the response of vegetation variation to climate change and human activities is critical for addressing future conflicts between humans and the environment,and maintaining ecosystem stability.Here,we aimed to identify the determining factors of vegetation variation and explore the sensitivity of vegetation to temperature(SVT)and the sensitivity of vegetation to precipitation(SVP)in the Shiyang River Basin(SYRB)of China during 2001-2022.The climate data from climatic research unit(CRU),vegetation index data from Moderate Resolution Imaging Spectroradiometer(MODIS),and land use data from Landsat images were used to analyze the spatial-temporal changes in vegetation indices,climate,and land use in the SYRB and its sub-basins(i.e.,upstream,midstream,and downstream basins)during 2001-2022.Linear regression analysis and correlation analysis were used to explore the SVT and SVP,revealing the driving factors of vegetation variation.Significant increasing trends(P<0.05)were detected for the enhanced vegetation index(EVI)and normalized difference vegetation index(NDVI)in the SYRB during 2001-2022,with most regions(84%)experiencing significant variation in vegetation,and land use change was determined as the dominant factor of vegetation variation.Non-significant decreasing trends were detected in the SVT and SVP of the SYRB during 2001-2022.There were spatial differences in vegetation variation,SVT,and SVP.Although NDVI and EVI exhibited increasing trends in the upstream,midstream,and downstream basins,the change slope in the downstream basin was lower than those in the upstream and midstream basins,the SVT in the upstream basin was higher than those in the midstream and downstream basins,and the SVP in the downstream basin was lower than those in the upstream and midstream basins.Temperature and precipitation changes controlled vegetation variation in the upstream and midstream basins while human activities(land use change)dominated vegetation variation in the downstream basin.We concluded that there is a spatial heterogeneity in the response of vegetation variation to climate change and human activities across different sub-basins of the SYRB.These findings can enhance our understanding of the relationship among vegetation variation,climate change,and human activities,and provide a reference for addressing future conflicts between humans and the environment in the arid inland river basins.

Extraction of vegetation fraction information from CBERS images based on VBSI vegetation index in Kaixian county

Based on the mixed pixel model, the vegetation fraction of Kaixian county, China, was extracted with three free CBERS images. VBSI vegetation index suitable for CBERS images constructed with FCD (forest canopy density) model principle was put forward by ITTO (International Tropical Timber Organization) was used, considering the underestimation of vegetation fraction using NDVI in low mountain-hill region influenced by soils and shadows. And vegetation fraction was divided into five categories from low to high in order to study the special variation of vegetation cover. The results show that the vegetation cover of the region is overall good, with an average of 50%. The area of vegetation fraction below 30% accounts for 11.7% of the entire studied area, mainly concentrates in central eastern Kaixian county, where is the major development zone of cities and towns; that between 30% and 60% accounts for 62%; and that higher than 60% accounts for 26%, and mostly locates in northern middle-mountain area.

Spatial and temporal variations of vegetation cover and the relationships with climate factors in Inner Mongolia based on GIMMS NDVI3g data

Variation in vegetation cover in Inner Mongolia has been previously studied by the remote sensing data spanning only one decade. However, spatial and temporal variations in vegetation cover based on the newly released GIMMS NDVI3g data spanning nearly thirty years have yet to be analyzed. In this study, we applied the methods of the maximum value composite （MVC） and Pearson＇s correlation coefficient to analyze the variations of vegetation cover in Inner Mongolia based on GIMMS NDVI3g data spanning from 1982 to 2013. Our results indicate that the normalized difference vegetation index （NDVI） increased at a rate of 0.0003/a during the growing seasons despite of the drier and hotter climate in Inner Mongolia during the past three decades. We also found that vegetation cover in the southern agro-pastoral zone significantly increased, while it significantly decreased in the central Alxa. The variations in vegetation cover were not significant in the eastern and central regions. NDVI is positively correlated with precipitation （r=0.617, P=0.000） and also with air temperature （r=0.425, P=0.015）, but the precipitation had a greater effect than the air temperature on the vegetation variations in Inner Mongolia.

Evaluation of Spectral Scale Effects in Estimation of Vegetation Leaf Area Index Using Spectral Indices Methods

Spectral index methodology has been widely used in Leaf Area Index(LAI) retrieval at different spatial scales. There are differences in the spectral response of different remote sensors and thus spectral scale effect generated during the use of spectral indices to retrieve LAI. In this study, PROSPECT, leaf optical properties model and Scattering by Arbitrarily Inclined Layers(SAIL) model, were used to simulate canopy spectral reflectance with a bandwidth of 5 nm and a Gaussian spectral response function was employed to simulate the spectral data at six bandwidths ranging from 10 to 35 nm. Additionally, for bandwidths from 5 to 35 nm, the correlation between the spectral index and LAI, and the sensitivities of the spectral index to changes in LAI and bandwidth were analyzed. Finally, the reflectance data at six bandwidths ranging from 40 to 65 nm were used to verify the spectral scale effect generated during the use of the spectral index to retrieve LAI. Results indicate that Vegetation Index of the Universal Pattern Decomposition(VIUPD) had the highest accuracy during LAI retrieval. Followed by Normalized Difference Vegetation Index(NDVI), Modified Simple Ratio Indices(MSRI) and Triangle Vegetation Index(TVI), although the coefficient of determination R^2 was higher than 0.96, the retrieved LAI values were less than the actual value and thus lacked validity. Other spectral indices were significantly affected by the spectral scale effect with poor retrieval results. In this study, VIUPD, which exhibited a relatively good correlation and sensitivity to LAI, was less affected by the spectral scale effect and had a relatively good retrieval capability. This conclusion supports a purported feature independent of the sensor of this model and also confirms the great potential of VIUPD for retrieval of physicochemical parameters of vegetation using multi-source remote sensing data.

Spatiotemporal dynamics of vegetation response to permafrost degradation in Northeast China

Permafrost in Northeast China is undergoing extensive and rapid degradation,and it is of great importance to understand the dynamics of vegetation response to permafrost degradation during different periods in this region.Based on the meteorological station data and MODIS land surface temperature data,we mapped the distribution of permafrost using the surface frost number(SFN)model to analyze the permafrost degradation processes in Northeast China from 1981 to 2020.We investigated the spatiotemporal variation characteristics of vegetation and its response to permafrost degradation during different periods from 1982 to 2020 using the normalized difference vegetation index(NDVI).We further discussed the dominant factors influencing the vegetation dynamics in the permafrost degradation processes.Results indicated that the permafrost area in Northeast China decreased significantly by 1.01×10^(5) km^(2) in the past 40 a.The permafrost stability continued to weaken,with large areas of stable permafrost(SP)converted to semi-stable permafrost(SSP)and unstable permafrost(UP)after 2000.From 1982 to 2020,NDVI exhibited a significant decreasing trend in the seasonal frost(SF)region,while it exhibited an increasing trend in the permafrost region.NDVI in the UP and SSP regions changed from a significant increasing trend before 2000 to a nonsignificant decreasing trend after 2000.In 78.63%of the permafrost region,there was a negative correlation between the SFN and NDVI from 1982 to 2020.In the SP and SSP regions,the correlation between the SFN and NDVI was predominantly negative,while in the UP region,it was predominantly positive.Temperature was the dominant factor influencing the NDVI variations in the permafrost region from 1982 to 2020,and the impact of precipitation on NDVI variations increased after 2000.The findings elucidate the complex dynamics of vegetation in the permafrost region of Northeast China and provide deeper insights into the response mechanisms of vegetation in cold regions to permafrost degradation induced by climate change.

Vegetation/Soil Synthesis Water Index Using MODIS Data

In consideration of the spectral character of MODIS (Moderate Resolution Imaging Spectroradiometer) dataand the reflective spectrum of vegetation and soil, NDVI (Normalized Difference Vegetation Index) and NDWI (Nor-malized Difference Water Index) are deduced using one visible band (0.66μm) and two near-infrared bands (0.86μm,1.24 μm). Vegetation canopy temperature is derived using two thermal infrared bands (8.6 μm and 11μm). Then thevegetation/soil synthesis water index (VSWI) is acquired through analyzing the coupling character of three indexeswhich can reflect the water condition of vegetation. Finally, the synthesis index is verified by equivalent water contentof a single leaf. The matching results show that the synthesis index is directly proportional to the modeled data,which means that the vegetation water content can be reflected using the synthesis index effectively.

Feature extraction and analysis of reclaimed vegetation in ecological restoration area of abandoned mines based on hyperspectral remote sensing images

The vegetation growth status largely represents the ecosystem function and environmental quality.Hyperspectral remote sensing data can effectively eliminate the effects of surface spectral reflectance and atmospheric scattering and directly reflect the vegetation parameter information.In this study,the abandoned mining area in the Helan Mountains,China was taken as the study area.Based on hyperspectral remote sensing images of Zhuhai No.1 hyperspectral satellite,we used the pixel dichotomy model,which was constructed using the normalized difference vegetation index(NDVI),to estimate the vegetation coverage of the study area,and evaluated the vegetation growth status by five vegetation indices(NDVI,ratio vegetation index(RVI),photochemical vegetation index(PVI),red-green ratio index(RGI),and anthocyanin reflectance index 1(ARI1)).According to the results,the reclaimed vegetation growth status in the study area can be divided into four levels(unhealthy,low healthy,healthy,and very healthy).The overall vegetation growth status in the study area was generally at low healthy level,indicating that the vegetation growth status in the study area was not good due to short-time period restoration and harsh damaged environment such as high and steep rock slopes.Furthermore,the unhealthy areas were mainly located in Dawukougou where abandoned mines were concentrated,indicating that the original mining activities have had a large effect on vegetation ecology.After ecological restoration of abandoned mines,the vegetation coverage in the study area has increased to a certain extent,but the amplitude was not large.The situation of vegetation coverage in the northern part of the study area was worse than that in the southern part,due to abandoned mines mainly concentrating in the northern part of the Helan Mountains.The combination of hyperspectral remote sensing data and vegetation indices can comprehensively extract the characteristics of vegetation,accurately analyze the plant growth status,and provide technical support for vegetation health evaluation.