Monitoring and identification of spatiotemporal landscape changes in multiple remote sensing images by using a stratified conditional Latin hypercube sampling approach and geostatistical simulation

This study develops a stratified conditional Latin hypercube sampling (scLHS) approach for multiple, remotely sensed, normalized difference vegetation index (NDVI) images. The objective is to sample, monitor, and delineate spatiotemporal landscape changes, including spatial heterogeneity and variability, in a given area. The scLHS approach, which is based on the variance quadtree technique (VQT) and the conditional Latin hypercube sampling (cLHS) method, selects samples in order to delineate landscape changes from multiple NDVI images. The images are then mapped for calibration and validation by using sequential Gaussian simulation (SGS) with the scLHS selected samples. Spatial statistical results indicate that in terms of their statistical distribution, spatial distribution, and spatial variation, the statistics and variograms of the scLHS samples resemble those of multiple NDVI images more closely than those of cLHS and VQT samples. Moreover, the accuracy of simulated NDVI images based on SGS with scLHS samples is significantly better than that of simulated NDVI images based on SGS with cLHS samples and VQT samples, respectively. However, the proposed approach efficiently monitors the spatial characteristics of landscape changes, including the statistics, spatial variability, and heterogeneity of NDVI images. In addition, SGS with the scLHS samples effectively reproduces spatial patterns and landscape changes in multiple NDVI images.     

Pattern of NDVI-based vegetation greening along an altitudinal gradient in the eastern Himalayas and its response to global warming

The eastern Himalayas, especially the Yarlung Zangbo Grand Canyon Nature Reserve (YNR), is a global hotspot of biodiversity because of a wide variety of climatic conditions and elevations ranging from 500 to > 7000 m above sea level (a.s.l.). The mountain ecosystems at different elevations are vulnerable to climate change; however, there has been little research into the patterns of vegetation greening and their response to global warming. The objective of this paper is to examine the pattern of vegetation greening in different altitudinal zones in the YNR and its relationship with vegetation types and climatic factors. Specifically, the inter-annual change of the normalized difference vegetation index (NDVI) and its variation along altitudinal gradient between 1999 and 2013 was investigated using SPOT-VGT NDVI data and ASTER global digital elevation model (GDEM) data. We found that annual NDVI increased by 17.58 % in the YNR from 1999 to 2013, especially in regions dominated by broad-leaved and coniferous forests at lower elevations. The vegetation greening rate decreased significantly as elevation increased, with a threshold elevation of approximately 3000 m. Rising temperature played a dominant role in driving the increase in NDVI, while precipitation has no statistical relationship with changes in NDVI in this region. This study provides useful information to develop an integrated management and conservation plan for climate change adaptation and promote biodiversity conservation in the YNR.     

Vegetation NDVI Linked to Temperature and Precipitation in the Upper Catchments of Yellow River

Vegetation in the upper catchment of Yellow River is critical for the ecological stability of the whole watershed. The dominant vegetation cover types in this region are grassland and forest, which can strongly influence the eco-environmental status of the whole watershed. The normalized difference vegetation index (NDVI) for grassland and forest has been calculated and its daily correlation models were deduced by Moderate Resolution Imaging Spectroradiometer products on 12 dates in 2000, 2003, and 2006. The responses of the NDVI values with the inter-annual grassland and forest to three climatic indices (i.e., yearly precipitation and highest and lowest temperature) were analyzed showing that, except for the lowest temperature, the yearly precipitation and highest temperature had close correlations with the NDVI values of the two vegetation communities. The value of correlation coefficients ranged from 0.815 to 0.951 (p?<?0.01). Furthermore, the interactions of NDVI values of vegetation with the climatic indicators at monthly interval were analyzed. The NDVI of vegetation and three climatic indices had strong positive correlations (larger than 0.733, p?<?0.01). The monthly correlations also provided the threshold values for the three climatic indictors, to be used for simulating vegetation growth grassland under different climate features, which is essential for the assessment of the vegetation growth and for regional environmental management.     

基于NDVI指数的骆马湖水生植被分级研究

基于典型时段骆马湖LandsatTM遥感影像,利用归一化植被指数（NDVI）法对骆马湖水生植被进行分级研究.采用标准差分级方法,将骆马湖水生植被分为5个等级.研究结果表明,利用这种分级方法处理的图像能够较准确地反映骆马湖水生植被区域分布和等级分区,能够更准确地给出采砂区和生态保护区域的位置,增强湖区水生态环境保护工作的有效性和针对性.     

Monitoring the urban green spaces and landscape fragmentation using remote sensing: a case study in Osmaniye,Turkey

Urban green spaces play a significant role in management of physical activity, psychological well-being, and public health of urban residents. With the expansion of urban areas in Turkey during the past decades, urban green spaces have been fragmented and dispersed causing impairment and environmental degradation. The purpose of this study is to model urban green space distribution by focusing on the landscape fragmentation in city of Osmaniye using remote sensing and geographic information system technology. Normalized difference vegetation index (NDVI) and urban landscape ratio (ULR) were used to assess the proximity and spatial arrangement of urban green spaces within the neighbor landscapes to quantify the urban land use effect. The geospatial analysis results showed that increase in total built-up area and population has significantly decreased the urban green space cover because of high levels of landscape fragmentation in urban city center. Also, due to high levels of landscape fragmentation, approximately 45% of the Osmaniye city is estimated to become urbanized by 2030. This study demonstrated the benefits of directional vegetation index application with geospatial analyses in characterizing the environmental quality for planning and management of urban green spaces. This approach could be used for determining the future urban land development scenarios correlating with regional planning procedures.     

Analysis of agricultural drought using vegetation temperature condition index (VTCI) from Terra/MODIS satellite data

The most commonly used normalized difference vegetation index (NDVI) from remote sensing often fall short in real-time drought monitoring due to a lagged vegetation response to drought. Therefore, research recently emphasized on the use of combination of surface temperature and NDVI which provides vegetation and moisture conditions simultaneously. Since drought stress effects on agriculture are closely linked to actual evapotranspiration, we used a vegetation temperature condition index (VTCI) which is more closely related to crop water status and holds a key place in real-time drought monitoring and assessment. In this study, NDVI and land surface temperature (T _s) from MODIS 8-day composite data during cloud-free period (September–October) were adopted to construct an NDVI–T _s space, from which the VTCI was computed. The crop moisture index (based on estimates of potential evapotranspiration and soil moisture depletion) was calculated to represent soil moisture stress on weekly basis for 20 weather monitoring stations. Correlation and regression analysis were attempted to relate VTCI with crop moisture status and crop performance. VTCI was found to accurately access the degree and spatial extent of drought stress in all years (2000, 2002, and 2004). The temporal variation of VTCI also provides drought pattern changes over space and time. Results showed significant and positive relations between CMI (crop moisture index) and VTCI observed particularly during prominent drought periods which proved VTCI as an ideal index to monitor terminal drought at regional scale. VTCI had significant positive relationship with yield but weakly related to crop anomalies. Duration of terminal drought stress derived from VTCI has a significant negative relationship with yields of major grain and oilseeds crops, particularly, groundnut.     

Evaluating the utility and seasonality of NDVI values for assessing post-disturbance recovery in a subalpine forest

Forest disturbances around the world have the potential to alter forest type and cover, with impacts on diversity, carbon storage, and landscape composition. These disturbances, especially fire, are common and often large, making ground investigation of forest recovery difficult. Remote sensing offers a means to monitor forest recovery in real time, over the entire landscape. Typically, recovery monitoring via remote sensing consists of measuring vegetation indices (e.g., NDVI) or index-derived metrics, with the assumption that recovery in NDVI (for example) is a meaningful measure of ecosystem recovery. This study tests that assumption using MODIS 16-day imagery from 2000 to 2010 in the area of the Colorado’s Routt National Forest Hinman burn (2002) and seedling density counts taken in the same area. Results indicate that NDVI is rarely correlated with forest recovery, and is dominated by annual and perennial forb cover, although topography complicates analysis. Utility of NDVI as a means to delineate areas of recovery or non-recovery are in doubt, as bootstrapped analysis indicates distinguishing power only slightly better than random. NDVI in revegetation analyses should carefully consider the ecology and seasonal patterns of the system in question.     

A new multiscale approach for monitoring vegetation using remote sensing-based indicators in laboratory, field, and landscape

Remote sensing is an important tool for studying patterns in surface processes on different spatiotemporal scales. However, differences in the spatiospectral and temporal resolution of remote sensing data as well as sensor-specific surveying characteristics very often hinder comparative analyses and effective up- and downscaling analyses. This paper presents a new methodical framework for combining hyperspectral remote sensing data on different spatial and temporal scales. We demonstrate the potential of using the “One Sensor at Different Scales” (OSADIS) approach for the laboratory (plot), field (local), and landscape (regional) scales. By implementing the OSADIS approach, we are able (1) to develop suitable stress-controlled vegetation indices for selected variables such as the Leaf Area Index (LAI), chlorophyll, photosynthesis, water content, nutrient content, etc. over a whole vegetation period. Focused laboratory monitoring can help to document additive and counteractive factors and processes of the vegetation and to correctly interpret their spectral response; (2) to transfer the models obtained to the landscape level; (3) to record imaging hyperspectral information on different spatial scales, achieving a true comparison of the structure and process results; (4) to minimize existing errors from geometrical, spectral, and temporal effects due to sensor- and time-specific differences; and (5) to carry out a realistic top- and downscaling by determining scale-dependent correction factors and transfer functions. The first results of OSADIS experiments are provided by controlled whole vegetation experiments on barley under water stress on the plot scale to model LAI using the vegetation indices Normalized Difference Vegetation Index (NDVI) and green NDVI (GNDVI). The regression model ascertained from imaging hyperspectral AISA-EAGLE/HAWK (DUAL) data was used to model LAI. This was done by using the vegetation index GNDVI with an R ² of 0.83, which was transferred to airborne hyperspectral data on the local and regional scales. For this purpose, hyperspectral imagery was collected at three altitudes over a land cover gradient of 25 km within a timeframe of a few minutes, yielding a spatial resolution from 1 to 3 m. For all recorded spatial scales, both the LAI and the NDVI were determined. The spatial properties of LAI and NDVI of all recorded hyperspectral images were compared using semivariance metrics derived from the variogram. The first results show spatial differences in the heterogeneity of LAI and NDVI from 1 to 3 m with the recorded hyperspectral data. That means that differently recorded data on different scales might not sufficiently maintain the spatial properties of high spatial resolution hyperspectral images.     

Using two classification schemes to develop vegetation indices of biological integrity for wetlands in West Virginia, USA

Bioassessment methods for wetlands, and other bodies of water, have been developed worldwide to measure and quantify changes in “biological integrity.” These assessments are based on a classification system, meant to ensure appropriate comparisons between wetland types. Using a local site-specific disturbance gradient, we built vegetation indices of biological integrity (Veg-IBIs) based on two commonly used wetland classification systems in the USA: One based on vegetative structure and the other based on a wetland’s position in a landscape and sources of water. The resulting class-specific Veg-IBIs were comprised of 1–5 metrics that varied in their sensitivity to the disturbance gradient (R ²?=?0.14???0.65). Moreover, the sensitivity to the disturbance gradient increased as metrics from each of the two classification schemes were combined (added). Using this information to monitor natural and created wetlands will help natural resource managers track changes in biological integrity of wetlands in response to anthropogenic disturbance and allows the use of vegetative communities to set ecological performance standards for mitigation banks.     

Impacts of climate warming on vegetation in Qaidam Area from 1990 to 2003

The observed warming trend in the Qaidam area, an arid basin surrounded by high mountains, has caused land surface dynamics that are detectable using remotely sensed data. In this paper, we detected land-cover changes in the Qaidam Area between 1990 and 2003 in attempt to depict its spatial variability. The land-cover changes were categorized into two trends: degradation and amelioration, and their spatial patterns were examined. Then we estimated the correlation coefficients between growing-season NDVI and several climatic factors with the consideration of duration and lagging effects. The results show that the inter-annual NDVI variations are positively correlated with May to July precipitations, but not significantly correlated with sunshine duration. We observed no obvious trend in precipitation or sunshine duration from 1990 to 2003. Thus, the authors suggest that their slight fluctuations may not be responsible to the decade-scaled land-cover changes. However, our results indicate a good positive relationship between the NDVI trend and climate warming in the ameliorated areas, but a negative one in the degraded areas. By statistical analyses, we found that degradations mainly occurred at the oasis boundaries and at lower elevations in the non-oasis regions where effective soil moisture might have been reduced by the warming-caused increase in evapotranspiration. At higher elevations where thermal condition acts as a major limiting factor, ameliorations were unequivocally detected, which is attributable to the direct facilitation by temperature increases. We suggest that the impacts of the observed climate warming on vegetation are spatially heterogeneous, depending on the combinations of thermal condition and moisture availability.     

基于RS和GIS技术的贵州省植被生态环境监测分析

为阐明贵州省植被生态环境变化的整体状况，基于RS和GIS技术，应用美国国家航空航天局最新的全球植被指数变化研究数据（GIMMS），通过计算月归一化植被指数（NDVI）变化率，并对研究区一元线性回归模拟，分析了贵州省1982年-2003年的地表植被覆盖。结果表明：22年来，研究区植被覆盖呈增加趋势，表明贵州省植被生态环境向好的方向发展；贵州省平均植被覆盖在春季和秋季呈上升趋势，夏季和冬季呈下降趋势，其中春季对植被覆盖总变化量的贡献最大；植被覆盖程度增减因区域不同而异，变化程度呈增加的区域主要位于贵,ki-I省的中部地区；变化程度呈减小的区域分布在贵州省的四周边缘。     

Analysis of the changes of vegetation coverage of western Beijing mountainous areas using remote sensing and GIS

Mentougou District acts as a crucial component in the ecological buffer in western Beijing mountainous areas, Beijing, China. Using two Landsat MSS/TM images acquired on July 14, 1979 and July 23, 2005, the vegetation coverage of Mentougou District was calculated based on normalized difference vegetation index and spectral mixture analysis (NDVI-SMA) model. Its temporal and spatial changes were analyzed according to digital elevation model (DEM) image, social and economic data. The results showed that the vegetation coverage decreased from 76.4% in 1979 to 72.7% in 2005. Vegetation degradation was probably the result of human disturbance, such as outspreading of resident areas, and coal and stone mining activities, while vegetation restoration might be contributed by the combined effects of both natural processes and ecological construction effort. Vegetation changes were closely related to topographical characteristics. Plants at high altitude were more stable and less degraded than the plants at low altitude, while the plants on steep slope or northwest aspect were more vulnerable to degradation. During the period of 26 years, landscape appeared to become more fragmental, and ecological quality of the land seemed deteriorated sharply in that highly-covered vegetation area has been decreased by 24%.     

Ecosystem classification for EU habitat distribution assessment in sandy coastal environments: An application in central Italy

Many recent developments in coastal science have gone against the demands of European Union legislation. Coastal dune systems which cover small areas of the earth can host a high level of biodiversity. However, human pressure on coastal zones around the world has increased dramatically in the last 50 years. In addition to direct habitat loss, the rapid extinction of many species that are unique to these systems can be attributed to landscape deterioration through the lack of appropriate management. In this paper, we propose to use of an ecosystem classification technique that integrates potential natural vegetation distribution as a reference framework for coastal dune EU Habitats (92/43) distribution analysis and assessment. As an example, the present study analyses the EU Habitats distribution within a hierarchical ecosystem classification of the coastal dune systems of central Italy. In total, 24 land elements belonging to 8 land units, 5 land facets, 2 land systems and 2 land regions were identified for the coastal dunes of central Italy, based on diagnostic land attributes. In central Italy, coastal dune environments including all the beach area, mobile dunes and all the fixed-dune land elements contain or could potentially hold at least one EU habitat of interest. Almost all dune slack transitions present the potentiality for the spontaneous development of EU woodlands of interest. The precise information concerning these ecosystems distribution and ecological relationships that this method produces, makes it very effective in Natura 2000 European network assessment. This hierarchical ecosystem classification method facilitates the identification of areas to be surveyed and eventually bound, under the implementation of EU Habitat directive (92/43) including areas with highly disturbed coastal dune ecosystems.     

Impacts of ecological water conveyance on groundwater dynamics and vegetation recovery in the lower reaches of the Tarim River in northwest China

The ecological water conveyance project (EWCP) in the lower reaches of the Tarim River provided a valuable opportunity to study hydro-ecological processes of desert riparian vegetation. Ecological effects of the EWCP were assessed at large spatial and temporal scales based on 13 years of monitoring data. This study analyzed the trends in hydrological processes and the ecological effects of the EWCP. The EWCP resulted in increased groundwater storage—expressed as a general rise in the groundwater table—and improved soil moisture conditions. The change of water conditions also directly affected vegetative cover and the phenology of herbs, trees, and shrubs. Vegetative cover of herbs was most closely correlated to groundwater depth at the last year-end (R?=?0.81), and trees and shrubs were most closely correlated to annual average groundwater depth (R?=?0.79 and 0.66, respectively). The Normalized Difference Vegetation Index (NDVI) responded to groundwater depth on a 1-year time lag. Although the EWCP improved the NDVI, the study area is still sparsely vegetated. The main limitation of the EWCP is that it can only preserve the survival of existing vegetation, but it does not effectively promote the reproduction and regeneration of natural vegetation.     

20年间艾比湖流域植被覆盖度景观格局变化

选取艾比湖流域1990年、2001年、2011年同期（9月）3期I．and．satTM遥感影像，基于归一化植被指数NDVI，提取植被覆盖等级图，利用ArcGIS9．3和Fragstas3．3对该流域植被景观的变化进行了分析研究。结果表明：1990—2011年，该流域植被覆盖度变化明显，低植被覆盖区和较低植被覆盖区都有所减少，分别由1990年的34．05％和32．94％减少到2011年的32．8％和24．06％；较高植被覆盖区和高植被覆盖区有所增加，分别由8．49％和5．20％增长到15．13％和9．83％，但水域面积退化明显，由1990年的525．9765km2缩小至494．9876km2，减少了30．9889km2，退缩幅度达O．4％；最大斑块指数（LPI）由17．04上升到21．10，香农多样性指数（SHDI）和香农均势度指数（SHEI）分别由1．5387和0．8588增长到1．6395和0．9150。表明艾比湖流域景观格局混杂程度愈来愈高，空间异质性在逐年加强，总体空间格局向破碎化趋势发展。     

Mapping the Spatial Variability of Plant Diversity in a Tropical Forest: Comparison of Spatial Interpolation Methods

Knowledge of the spatial distribution of plant species is essential to conservation and forest managers in order to identify high priority areas such as vulnerable species and habitats, and designate areas for reserves, refuges and other protected areas. A reliable map of the diversity of plant species over the landscape is an invaluable tool for such purposes. In this study, the number of species, the exponent Shannon and the reciprocal Simpson indices, calculated from 141 quadrat sites sampled in a tropical forest were used to compare the performance of several spatial interpolation techniques used to prepare a map of plant diversity, starting from sample (point) data over the landscape. Means of mapped classes, inverse distance functions, kriging and co-kriging, both, applied over the entire studied landscape and also applied within vegetation classes, were the procedures compared. Significant differences in plant diversity indices between classes demonstrated the usefulness of boundaries between vegetation types, mapped through satellite image classification, in stratifying the variability of plant diversity over the landscape. These mapped classes, improved the accuracy of the interpolation methods when they were used as prior information for stratification of the area. Spatial interpolation by co-kriging performed among the poorest interpolators due to the poor correlation between the plant diversity variables and vegetation indices computed by remote sensing and used as covariables. This indicated that the latter are not suitable covariates of plant diversity indices. Finally, a within-class kriging interpolator yielded the most accurate estimates of plant diversity values. This interpolator not only provided the most accurate estimates by accounting for the indices' intra-class variability, but also provided additional useful interpretations of the structure of spatial variability of diversity values through the interpretation of their semi-variograms. This additional role was found very useful in aiding decisions in conservation planning.     

Local versus landscape spatial influence on biodiversity: a case study across five European industrialized areas

Land use change—mostly habitat loss and fragmentation—has been recognized as one of the major drivers of biodiversity loss worldwide. According to the habitat amount hypothesis, these phenomena are mostly driven by the habitat area effect. As a result, species richness is a function of both the extent of suitable habitats and their availability in the surrounding landscape, irrespective of the dimension and isolation of patches of suitable habitat. In this context, we tested how the extent of natural areas, selected as proxies of suitable habitats for biodiversity, influences species richness in highly anthropogenic landscapes. We defined five circular sampling areas of 5 km radius, including both natural reserves and anthropogenic land uses, centred in five major industrial sites in France, Italy and Germany. We monitored different biodiversity indicators for both terrestrial and aquatic ecosystems, including breeding birds, diurnal butterflies, grassland vegetation, odonata, amphibians, aquatic plants and benthic diatoms. We studied the response of the different indicators to the extent of natural land uses in the sampling area (local effect) and in the surrounding landscape (landscape effect), identified as a peripheral ring encircling the sampling area. Results showed a positive response of five out of seven biodiversity indicators, with aquatic plants and odonata responding positively to the local effect, while birds, vegetation and diatoms showed a positive response to the landscape effect. Diatoms also showed a significant combined response to both effects. We conclude that surrounding landscapes act as important biodiversity sources, increasing the local biodiversity in highly anthropogenic contexts.     

1982—2022年黄土高原归一化植被指数变化的时空特征

长时间地表植被指数变化序列构建与分析是生态环境监测领域的重要内容。以我国生态工程建设重点地区——黄土高原为研究区，采用时间序列的方差匹配方法，融合了2套卫星遥感的归一化植被指数(NDVI)数据产品(GIMMS 3g和MODIS),建立了覆盖1982—2022年的黄土高原暖季(5—9月)NDVI数据集，揭示了其间黄土高原植被覆盖变化的时空特征。研究发现：黄土高原暖季NDVI呈现“先慢后快”的增加趋势，转折点大致出现在2002年，1982—2002年暖季NDVI增速仅为0.01/(10 a),2003—2022年增速高达0.06/(10 a),其中十八大以来增速尤为显著；暖季NDVI快速增加区域主要位于黄土高原中部，并向东北、西南方向延展，与“退耕还林(草)”重点区域范围基本一致；在黄土高原南部、东部和青海省东部一带，暖季NDVI呈缓慢下降趋势。过去40年间黄土高原NDVI增加与生态工程建设关系密切。     

高光谱数据在“十一五”期间生态环境状况监测中的应用

高光谱遥感以"图谱合一"等特点在生物多样性监测、土壤退化、植被重金属污染监测、生物量估算等方面都有广泛应用。通过长时间序列高光谱反演数据NDVI和NPP,较好的反映了"十一五"期间新疆生态环境V字型的变化趋势,基本符合5年新疆生态环境变化状况。高光谱数据反演技术是开展生态环境宏观监测的有效手段之一。