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.     

Drought impact assessment from monitoring the seasonality of vegetation condition using long-term time-series satellite images: a case study of Mt. Kenya region

Drought-induced anomalies in vegetation condition over wide areas can be observed by using time-series satellite remote sensing data. Previous methods to assess the anomalies may include limitations in considering (1) the seasonality in terms of each vegetation-cover type, (2) cumulative damage during the drought event, and (3) the application to various types of land cover. This study proposed an improved methodology to assess drought impact from the annual vegetation responses, and discussed the result in terms of diverse landscape mosaics in the Mt. Kenya region (0.4° N 35.8° E?~?1.6° S 38.4° E). From the 30-year annual rainfall records at the six meteorological stations in the study area, we identified 2000 as the drought year and 2001, 2004, and 2007 as the normal precipitation years. The time-series profiles of vegetation condition in the drought and normal precipitation years were obtained from the values of Enhanced Vegetation Index (EVI; Huete et al. 2002), which were acquired from Terra MODIS remote sensing dataset (MOD13Q1) taken every 16 days at the scale of 250-m spatial resolution. The drought impact was determined by integrating the annual differences in EVI profiles between drought and normal conditions, per pixel based on nearly same day of year. As a result, we successfully described the distribution of landscape vulnerability to drought, considering the seasonality of each vegetation-cover type at every MODIS pixel. This result will contribute to the large-scale landscape management of Mt. Kenya region. Future study should improve this method by considering land-use change occurred during the long-term monitoring period.     

Yield and leaf area index estimations for sunflower plants using unmanned aerial vehicle images

Vegetation is commonly monitored to improve efficiency of various agricultural practices. Spatial and temporal changes in plant growth and development can be monitored with the aid of remote sensing techniques employing ground, aerial, and satellite platforms. Unmanned aerial vehicles (UAV) and multi-spectral cameras developed for UAVs have an important potential for agricultural management activities with high-resolution spatial and temporal images. However, UAV images should be assessed based on ground measurements for using these images as a decision-support tool in agriculture. This study was conducted to estimate sunflower leaf area index (LAI) and yield with the aid of Normalized Difference Vegetation Index (NDVI) images generated from raw UAV images. Furthermore, UAV-based NDVI values were compared with NDVI values calculated by using hyper-spectral measurements carried out with a ground-based spectroradiometer. Between July and August of 2017, six flight missions were conducted and spectral measurements were made simultaneously. A significant correlation (R²?=?0.77) was determined between NDVI values that belong to UAV platform and spectroradiometer. Also, regression models developed for sunflower LAI and yield estimation depending UAV-based NDVI have R² values of 0.88 and 0.91, respectively.     

Evaluating Change in Rangeland Condition using Multitemporal AVHRR Data and Geographic Information System Analysis

Coarse-scale, multitemporal satellite image data were evaluated as a tool for detecting variation in vegetation productivity, as a potential indicator of change in rangeland condition in the western U.S. The conterminous U.S. Advanced Very High Resolution Radiometer (AVHRR) biweekly composite data set was employed using the six-year time series 1989–1994. Normalized Difference Vegetation Index (NDVI) image bands for the state of New Mexico were imported into a Geographic Information System (GIS) for analysis with other spatial data sets. Averaged NDVI was calculated for each year, and a series of regression analyses were performed using one year as the baseline. Residuals from the regression line indicated 14 significant areas of NDVI change: two with lower NDVI, and 11 with higher NDVI. Rangeland management changes, cross-country military training activities, and increases in irrigated cropland were among the identified causes of change.     

Monitoring global change: Comparison of forest cover estimates using remote sensing and inventory approaches

Satellite-based remote sensing offers great potential for frequent assessment of forest cover over broad spatial scales, however, calibration and validation using ground-based surveys are needed. In this study, forest cover estimates for the United States from a recently developed land surface cover map generated from satellite remote sensing data were compared to state-level inventory data from the U.S. National Resources Planning Act Timber Database. The land cover map was produced at the U.S. Geological Survey EROS Data Center and is based on imagery from the AVHRR sensor (spatial resolution 1.1 km). Vegetation type was classified using the temporal signal in the Normalized Difference Vegetation Index derived from AVHRR data. Comparisons revealed close agreement in the estimate of forest cover for extensively forested states with large polygons of relatively similar vegetation such as Oregon. Larger forest cover differences were observed in other states with some regional patterns in the level of agreement apparent.Comparisons in inventory- and remote sensing-based estimates of current forested area with potential vegetation maps indicated the magnitude of past land use change and the potential for future changes. The remote sensing approach appears to hold promise for conducting surveys of forest cover where inventory data are limited or where rates of vegetation change, due to human or climatic factors, are rapid.     

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.     

Geo-spatial analysis of the temporal trends of kharif crop phenology metrics over India and its relationships with rainfall parameters

The Global Inventory Modeling and Mapping Studies bimonthly Normalized Difference Vegetation Index (NDVI) data of 8?×?8 km spatial resolution for the period of 1982–2006 were analyzed to detect the trends of crop phenology metrics (start of the growing season (SGS), seasonal NDVI amplitude (AMP), seasonally integrated NDVI (SiNDVI)) during kharif season (June to October) and their relationships with the amount of rainfall and the number of rainy days over Indian subcontinent. Direction and magnitude of trends were analyzed at pixel level using the Mann–Kendall test and further assessed at meteorological subdivision level using field significance test (α?=?0.1). Significant pre-occurrence of the SGS was observed over northern (Punjab, Haryana) and central (Marathwada, Vidarbha and Madhya Maharashtra) parts, whereas delay was found over southern (Rayalaseema, Coastal Andhra Pradesh) and eastern (Bihar, Gangetic West Bengal and Sub-Himalayan West Bengal) parts of India. North, west, and central India showed significant increasing trends of SiNDVI, corroborating the kharif food grain production performance during the time frame. Significant temporal correlation (α?=?0.1) between the rainfall/number of rainy days and crop phenology metrics was observed over the rainfed region of India. About 35–40 % of the study area showed significant correlation between the SGS and the rainfall/number of rainy days during June to August. June month rainfall/number of rainy days was found to be the most sensitive to the SGS. The amount of rainfall and the number of rainy days during monsoon were found to have significant influence over the SiNDVI in 24–30 % of the study area. The crop phenology metrics had significant correlation with the number of rainy days over the larger areas than that of the rainfall amount.     

River pollution remediation monitored by optical and infrared high-resolution satellite images

The Bormida River Basin, located in the northwestern region of Italy, has been strongly contaminated by the ACNA chemical factory. This factory was in operation from 1892 to 1998, and contamination from the factory has had deleterious consequences on the water quality, agriculture, natural ecosystems and human health. Attempts have been made to remediate the site. The aims of this study were to use high-resolution satellite images combined with a classical remote sensing methodology to monitor vegetation conditions along the Bormida River, both upstream and downstream of the ACNA chemical factory site, and to compare the results obtained at different times before and after the remediation process. The trends of the Normalised Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) along the riverbanks are used to assess the effect of water pollution on vegetation. NDVI and EVI values show that the contamination produced by the ACNA factory had less severe effects in the year 2007, when most of the remediation activities were concluded, than in 2006 and 2003. In 2007, the contamination effects were noticeable up to 6 km downstream of the factory, whereas in 2003 and 2006 the influence range was up to about 12 km downstream of the factory. The results of this study show the effectiveness of remediation activities that have been taking place in this area. In addition, the comparison between NDVI and EVI shows that the EVI is more suitable to characterise the vegetation health and can be considered an additional tool to assess vegetation health and to monitor restoration activities.     

Characterizing spatiotemporal non-stationarity in vegetation dynamics in China using MODIS EVI dataset

This paper evaluated the spatiotemporal non-stationarity in the vegetation dynamic based on 1-km resolution 16-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) datasets in China during 2001–2011 through a wavelet transform method. First, it revealed from selected pixels that agricultural crops, natural forests, and meadows were characterized by their distinct intra-annual temporal variation patterns in different climate regions. The amplitude of intra-annual variability generally increased with latitude. Second, parameters calculated using a per-pixel strategy indicated that the natural forests had the strongest variation pattern from seasonal to semiannual scales, and the multiple-cropping croplands typically showed almost equal variances distributed at monthly, seasonal, and semiannual scales. Third, spatiotemporal non-stationarity induced from cloud cover was also evaluated. It revealed that the EVI temporal profiles were significantly distorted with regular summer cloud cover in tropical and subtropical regions. Nevertheless, no significant differences were observed from those statistical parameters related to the interannual and interannual components between the de-clouded and the original MODIS EVI datasets across the whole country. Finally, 12 vegetation zones were proposed based on spatiotemporal variability, as indicated by the magnitude of interannual and intra-annual dynamic components, normalized wavelet variances of detailed components from monthly to semiannual scale, and proportion of cloud cover in summer. This paper provides insightful solutions for addressing spatiotemporal non-stationarity by evaluating the magnitude and frequency of vegetation variability using monthly, seasonal, semiannual to interannual scales across the whole study area.     

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。     

The spatiotemporal dynamics of urbanisation and local climate: A case study of Islamabad,Pakistan

Rapid and unplanned urbanisation, together with climate change, are increasingly affecting the local climatic conditions of urban settlements. Spatiotemporal analysis using land use/land cover (LULC), land surface temperature (LST), and local climatic zone (LCZ) assessments have been helpful in understanding the urbanisation characteristics and morphology. Islamabad, the capital and the only planned city of Pakistan, has witnessed a consistent rise in local temperatures, increased built-up areas, and reduced vegetation cover during the past decades. This study explores the spatiotemporal dynamics of LULC, LST, and LCZ in Islamabad using satellite remote sensing data and spectral indices such as Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI). The results indicate a whopping increase in a built-up area in the city (113% during 2013 and 2019). A positive correlation between LST and NDBI, whereas a negative correlation between LST and NDVI clearly indicates how urbanisation (and reduction in vegetation cover) are impacting the local temperatures. Assessment and analysis of LCZs helped to understand the variations and deviations of current LULC from the master plan. It was observed that compact low-rise urban development is the most prevalent. The outcomes of this study are expected to inform the urban planners, climatologists, and policymakers with the knowledge helpful for devising climate-resilient development policies that could reduce thermal stresses in the capital cities.     

Monitoring and assessing of landscape heterogeneity at different scales

In numerous studies, spatial and spectral aggregations of pixel information using average values from imaging spectrometer data are suggested to derive spectral indices and the subsequent vegetation parameters that are derived from these. Currently, there are very few empirical studies that use hyperspectral data, to support the hypothesis for deriving land surface variables from different spectral and spatial scales. In the study at hand, for the first time ever, investigations were carried out on fundamental scaling issues using specific experimental test flights with a hyperspectral sensor to investigate how vegetation patterns change as an effect of (1) different spatial resolutions, (2) different spectral resolutions, (3) different spatial and spectral resolutions as well as (4) different spatial and spectral resolutions of originally recorded hyperspectral image data compared to spatial and spectral up- and downscaled image data. For these experiments, the hyperspectral sensor AISA-EAGLE/HAWK (DUAL) was mounted on an aircraft to collect spectral signatures over a very short time sequence of a particular day. In the first experiment, reflectance measurements were collected at three different spatial resolutions ranging from 1 to 3 m over a 2-h period in 1 day. In the second experiment, different spectral image data and different additional spatial data were collected over a 1-h period on a particular day from the same test area. The differently recorded hyperspectral data were then spatially and spectrally rescaled to synthesize different up- and down-rescaled images. The normalised difference vegetation index (NDVI) was determined from all image data. The NDVI heterogeneity of all images was compared based on methods of variography. The results showed that (a) the spatial NDVI patterns of up- and downscaled data do not correspond with the un-scaled image data, (b) only small differences were found between NDVI patterns determined from data recorded and resampled at different spectral resolutions and (c) the overall conclusion from the tests carried out is that the spatial resolution is more important in determining heterogeneity by means of NDVI than the depth of the spectral data. The implications behind these findings are that we need to exercise caution when interpreting and combining spatial structures and spectral indices derived from satellite images with differently recorded geometric resolutions.     

A new approach for crop identification with wavelet variance and JM distance

This paper develops a new crop mapping method through combined utilization of both time and frequency information based on wavelet variance and Jeffries–Matusita (JM) distance (CIWJ for short). A two-dimensional wavelet spectrum was obtained from datasets of daily continuous vegetation indices through a continuous wavelet transform using the Mexican hat and the Morlet mother wavelets. The time-average wavelet variance (TAWV) and the scale-average wavelet variance (SAWV) were then calculated based on the wavelet spectrum of the Mexican hat and the Morlet wavelet, respectively. The class separability based on the JM distance was evaluated to discriminate the proper period or scale range applied. Finally, a procedure for criteria quantification was developed using the TAWV and SAWV as the major metrics, and the similarity between unclassified pixels and established land use/cover types was calculated. The proposed CIWJ method was applied to the middle Hexi Corridor in northwest China using 250-m 8-day composite moderate-resolution imaging spectroradiometer (MODIS) enhanced vegetation index (EVI) time series datasets in 2012. The CIWJ method was shown to be efficient in crop field mapping, with an overall accuracy of 83.6 % and kappa coefficient of 0.7009, assessed with 30 m Chinese Environmental Disaster Reduction Satellite (HJ-1)-derived data. Compared with methods utilizing information on either frequency or time, the CIWJ method demonstrates tremendous potential for efficient crop mapping and for further applications. This method could be applied to either coarse or high spatial resolution images for agricultural crop identification, as well as other more general or specific land use classifications.     

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。     

A Special Vegetation Index for the Weed Detection in Sensor Based Precision Agriculture

Many technologies in precision agriculture (PA) require image analysis and image- processing with weed and background differentiations. The detection of weeds on mulched cropland is one important image-processing task for sensor based precision herbicide applications. The article introduces a special vegetation index, the Difference Index with Red Threshold (DIRT), for the weed detection on mulched croplands. Experimental investigations in weed detection on mulched areas point out that the DIRT performs better than the Normalized Difference Vegetation Index (NDVI). The result of the evaluation with four different decision criteria indicate, that the new DIRT gives the highest reliability in weed/background differentiation on mulched areas. While using the same spectral bands (infrared and red) as the NDVI, the new DIRT is more suitable for weed detection than the other vegetation indices and requires only a small amount of additional calculation power. The new vegetation index DIRT was tested on mulched areas during automatic ratings with a special weed camera system. The test results compare the new DIRT and three other decision criteria: the difference between infrared and red intensity (Diff), the soil-adjusted quotient between infrared and red intensity (Quotient) and the NDVI. The decision criteria were compared with the definition of a worse case decision quality parameter Q, suitable for mulched croplands. Although this new index DIRT needs further testing, the index seems to be a good decision criterion for the weed detection on mulched areas and should also be useful for other image processing applications in precision agriculture. The weed detection hardware and the PC program for the weed image processing were developed with funds from the German Federal Ministry of Education and Research (BMBF).     

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.     

Integrating in-situ, Landsat, and MODIS data for mapping in Southern African savannas: experiences of LCCS-based land-cover mapping in the Kalahari in Namibia

Integrated ecosystem assessment initiatives are important steps towards a global biodiversity observing system. Reliable earth observation data are key information for tracking biodiversity change on various scales. Regarding the establishment of standardized environmental observation systems, a key question is: What can be observed on each scale and how can land cover information be transferred? In this study, a land cover map from a dry semi-arid savanna ecosystem in Namibia was obtained based on the UN LCCS, in-situ data, and MODIS and Landsat satellite imagery. In situ botanical relevé samples were used as baseline data for the definition of a standardized LCCS legend. A standard LCCS code for savanna vegetation types is introduced. An object-oriented segmentation of Landsat imagery was used as intermediate stage for downscaling in-situ training data on a coarse MODIS resolution. MODIS time series metrics of the growing season 2004/2005 were used to classify Kalahari vegetation types using a tree-based ensemble classifier (Random Forest). The prevailing Kalahari vegetation types based on LCCS was open broadleaved deciduous shrubland with an herbaceous layer which differs from the class assignments of the global and regional land-cover maps. The separability analysis based on Bhattacharya distance measurements applied on two LCCS levels indicated a relationship of spectral mapping dependencies of annual MODIS time series features due to the thematic detail of the classification scheme. The analysis of LCCS classifiers showed an increased significance of life-form composition and soil conditions to the mapping accuracy. An overall accuracy of 92.48% was achieved. Woody plant associations proved to be most stable due to small omission and commission errors. The case study comprised a first suitability assessment of the LCCS classifier approach for a southern African savanna ecosystem.     

基于火点辐射能量的2017—2021年广西秸秆露天燃烧排放时空分析

基于2017—2021年MODIS、VIIRS和Himawari-8等多套卫星的火点辐射能量(FRE)和云量反演数据,使用更高分辨率的火点替代相邻位置低分辨率火点的融合方法,利用晴空的火点分布数据对被云遮蔽的区域进行补偿,核算得到了2 km高分辨率的广西秸秆露天燃烧排放数据,并针对2017—2021年的广西秸秆露天燃烧排放量展开精细的时空分布研究。结果表明:2017—2021年广西秸秆露天燃烧的CO、NO_x、SO₂、NH₃、VOCs、PM₁₀和PM_2.5的年排放量均值分别为12.91万、0.78万、0.16万、0.17万、2.77万、2.26万、2.21万t,排放高值区域分布在广西中部及西南部。秸秆露天燃烧排放的主要时间集中在冬、春季节(10月至次年3月),时值晚稻收割期和甘蔗榨季,占全年排放量的60%以上。广西秸秆露天燃烧PM_2.5年均排放量是全广西PM_2.5人为源年排放量的8.74%,通过逐日排放贡献分析发现,秸秆露天燃烧具有短期排放量较大的特点,2017—2021年,在1—2月有34 d出现秸秆露天燃烧导致PM_2.5排放量超过人为源排放量50%的情况。     

Using NDVI to Assess Vegetative Land Cover Change in Central Puget Sound

We used the Normalized Difference Vegetation Index (NDVI) in the rapidly growing Puget Sound region over three 5-year time blocks between 1986–1999 at three spatial scales in 42 Watershed Administrative Units (WAUs) to assess changes in the amounts and patterns of green vegetation. On average, approximately 20% of the area in each WAU experienced significant NDVI change over each 5-year time block. Cumulative NDVI change over 15 years (summing change over each 5-year time block) was an average of approximately 60% of each WAU, but was as high as 100% in some. At the regional scale, seasonal weather patterns and green-up from logging were the primary drivers of observed increases in NDVI values. At the WAU scale, anthropogenic factors were important drivers of both positive and negative NDVI change. For example, population density was highly correlated with negative NDVI change over 15 years (r = 0.66, P < 0.01), as was road density (r = 0.71, P < 0.01). At the smallest scale (within 3 case study WAUs) land use differences such as preserving versus harvesting forest lands drove vegetation change. We conclude that large areas within most watersheds are continually and heavily impacted by the high levels of human use and development over short time periods. Our results indicate that varying patterns and processes can be detected at multiple scales using changes in NDVIa values.