Investigating spatial non-stationary and scale-dependent relationships between urban surface temperature and environmental factors using geographically weighted regression

Despite growing concerns for the variation of urban thermal environments and driving factors, relatively little attention has been paid to issues of spatial non-stationarity and scale-dependence, which are intrinsic properties of the urban ecosystem. In this paper, using Shenzhen City in China as a case study, a geographically weighted regression (GWR) model is used to explore the scale-dependent and spatial non-stationary relationships between urban land surface temperature (LST) and environmental determinants. These determinants include the distance between city and highway, patch richness density of forestland, wetland, built-up land and unused land and topographic factors such as elevation and slope aspect. For reference, the ordinary least squares (OLS) model, a global regression technique, was also employed, using the same response variable and explanatory variables as in the GWR model. The results indicate that the GWR model not only provides a better fit than the traditional OLS model, but also provides local detailed information about the spatial variation of LST, which is affected by geographical and ecological factors. With the GWR model, the strength of the regression relationships increased significantly, with a mean of 59% of the changes in the LST values explained by the predictors, compared with only 43% using the OLS model. By computing a stationarity index, one finds that different predictors have different variational trends which tend towards the stationary state with the coarsening of the spatial scale. This implies that underlying natural processes affecting the land surface temperature and its spatial pattern may operate at different spatial scales. In conclusion, the GWR model is an alternative approach to addressing spatial non-stationary and scale-dependent problems in geography and ecology.     

Spatial downscaling of TRMM 3B43 precipitation considering spatial heterogeneity

The availability of accurate precipitation data with high spatial resolution is deemed necessary for many types of hydrological, meteorological, and environmental applications. The Tropical Rainfall Measuring Mission (TRMM) data sets can provide effective precipitation information, but at coarse resolution (0.25°), so it is very important to improve their resolution. There is a strong relationship between precipitation and other environment variables (e.g. vegetation and topography). The existing precipitation-downscaling methods attempt to describe this relationship by using a uniform empirical model. However, in the real world, the relationship is disturbed due to the influence of certain factors such as soil type, hydrological conditions, and human activities. In this study, a new downscaling method considering this spatial heterogeneity was proposed to downscale version 7 of the TRMM 3B43 precipitation product, which assumes that the relationship varies spatially but is the same in a local region. At a spatial resolution of 0.25°, the spatially varying relationship among TRMM, normalized difference vegetation index (NDVI), and digital elevation model (DEM) is explored by using a local regression analysis approach known as geographically weighted regression (GWR), but this relationship is the same in a pixel of 0.25° × 0.25°. The derived relationship is used to construct the precipitation downscaling model, which then produces 1 km downscaled precipitation data. The existing and proposed downscaling methods were both tested in North China for 2008–2011. The accuracy of the downscaled precipitation was validated by comparing it with observed precipitation data from 49 meteorological stations located in the study area. The results show that GWR is more suitable to capture the relationship among TRMM, DEM, and NDVI (minimum R² = 0.93). Compared with the existing downscaling method, the proposed method, which consistently showed increased R² (e.g. from 0.80 to 0.82 in 2011) and reduced RMSE (e.g. from 125.4 mm to 91 mm in 2011) in all four years, can more accurately produce downscaled precipitation data.     

Spatial non-stationarity and scale-dependency of prediction accuracy in the remote estimation of LAI over a tropical rainforest in Sulawesi, Indonesia

The technique of Geographically Weighted Regression (GWR) was used for estimation of Leaf Area Index (LAI) from remote sensing-based multi-spectral vegetation indices (VI) such as Normalized Difference Vegetation Index (NDVI), the mid-infrared corrected Normalized Difference Vegetation Index (NDVIc), Simple Ratio (SR), Soil-Adjusted Vegetation Index (SAVI) and Reduced Simple Ratio (RSR) in a region of equatorial rainforest in Central Sulavesi, Indonesia. The linear regressions between NDVI, NDVIc, SR, SAVI and RSR as explanatory variables and ground measurements of LAI at 166 plots as a dependent variable were produced using common modelling approach — Ordinary Least Squares (OLS) regression fitted to all data points, as well as GWR. Accuracy and precision statistics indicate that the GWR method made significantly better predictions of LAI in all simulations than OLS did. The relationships between LAI and the explanatory variables were found to be significantly spatially variable and scale-dependent. GWR has the potential to reveal local patterns in the spatial distribution of parameter estimates, it demonstrated sensitivity of the model's accuracy and performance to scale variation. The GWR approach enables finding the most appropriate scale for data analysis. This scale was different for each VI. The results suggest that spatial non-stationarity and scale-dependency in the relationship between LAI and remote sensing data has important implications for estimations of LAI based on empirical transfer functions.     

Inter-sensor relationships between IKONOS and Landsat-7 ETM+ NDVI data in three ecoregions of Africa

The goal of this research was to establish inter-sensor relationships between IKONOS and Landsat-7 ETM+ data. Dry and wet season images were acquired on the same date or about the same date from IKONOS and ETM+ sensors to enable direct comparison between the two distinctly different data types. The images were from three distinct ecoregions located in African rainforests and savannas that encompass a wide range of land use/land cover classes and ecological units. The IKONOS NDVI had a high degree of correlation with ETM+ NDVI with R ² values between 0.67 and 0.72. Inter-sensor model equations relating IKONOS NDVI with ETM+ NDVI were determined. The characteristics that contribute to the increased sensitivity in dynamic ranges of IKONOS NDVI relative to ETM+ NDVI were attributed to: (1) radiometric resolution that adds more bits per data point (11-bit IKONOS data as opposed to 8-bit ETM+); and (2) spatial resolution that helped in resolving spectral characteristics at micro landscape units. Spectral bandwidths of the two sensors had no effect on the dynamic ranges of NDVIs. Overall, the IKONOS data showed greater sensitivity to landscape units and ecological characteristics when compared with Landsat-7 ETM+ data. Across ecoregions and land use/land cover classes, the IKONOS NDVI dynamic range (?0.07 to 0.71) was considerably greater than the ETM+ NDVI dynamic range (?0.24 to 0.46). IKONOS data explained greater variability (R ²=0.73) in agroforest biomass when compared with ETM+ data (R ²=0.66). The inter-sensor relationships presented in this paper are expected to facilitate better understanding and proper interpretation of terrestrial characteristics studied using multiple sensors over time periods.     

The influence of sampling density on geographically weighted regression: a case study using forest canopy height and optical data

Geographically weighted regression (GWR) extends the conventional ordinary least squares (OLS) regression technique by considering spatial nonstationarity in variable relationships and allowing the use of spatially varying coefficients in linear models. Previous forest studies have demonstrated the better performance of GWR compared to OLS when calibrated and validated at sampled locations where field measurements are collected. However, the use of GWR for remote-sensing applications requires generating estimates and evaluating the model performance for the large image scene, not just for sampled locations. In this study, we introduce GWR to estimate forest canopy height using high spatial resolution Quickbird (QB) imagery and evaluate the influence of sampling density on GWR. We also examine four commonly used spatial analysis techniques – OLS, inverse distance weighting (IDW), ordinary kriging (OK) and cokriging (COK) – and compare their performance with that using GWR. Results show that (i) GWR outperformed OLS at all sampling densities; however, they produced similar results at low sampling densities, suggesting that GWR may not produce significantly better results than OLS in remote-sensing operational applications where only a small number of field data are collected. (ii) The performance of GWR was better than those of IDW, OK and COK at most sampling densities. Among the spatial interpolation techniques we examined, IDW was the best to estimate the canopy height at most densities, while COK outperformed OK only marginally and produced larger canopy height estimation errors than both IDW and GWR. (iii) GWR had the advantage of generating canopy height estimation maps with more accurate estimates than OLS, and it preserved patterns of geographic features better than IDW, OK or COK.     

A ground spectral model for estimating biomass at the peak of the growing season in Hulunbeier grassland,Inner Mongolia,China

To investigate the application of hyperspectral remote sensing to estimate grassland biomass at the peak of the growing season, hyperspectral data were measured with an analytical spectral device (ASD) Fieldspec3 spectroradiometer, and harvested aboveground net primary productivity (ANPP) was recorded simultaneously in Hulunbeier grassland, Inner Mongolia, China. Ground spectral models were developed to estimate ANPP from the normalized difference vegetation index (NDVI) measured in the field following the same method as that of the National Aeronautic and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS-NDVI). Regression analysis was used to assess the relationship between ANPP and NDVI. Based on coefficients of determination (R ²) and error analysis, we determined that each vegetation type and the entire study area had unique optimal regression models. A linear equation best fit the arid steppe data, an exponential equation was best suited to wetland vegetation and power equations were optimal for meadow steppe and sand vegetation. After considering all factors, an exponential model between ANPP and NDVI (ANPP = 20.1921e^3.2154(NDVI); standard error (SE) = 62.50 g m^–2, R ² = 0.7445, p < 0.001) was selected for the entire Hulunbeier grassland study area. Ground spectral models could become the foundation for yield estimation over large areas of Hulunbeier grassland.     

Using MODIS time series data to estimate aboveground biomass and its spatio-temporal variation in Inner Mongolia’s grassland between 2001 and 2011

It is critical to understanding grassland biomass and its dynamics to study regional carbon cycles and the sustainable use of grassland resources. In this study, we estimated aboveground biomass (AGB) and its spatio-temporal pattern for Inner Mongolia’s grassland between 2001 and 2011 using field samples, Moderate Resolution Imaging Spectroradiometer normalized difference vegetation index (MODIS-NDVI) time series data, and statistical models based on the relationship between NDVI and AGB. We also explored possible relationships between the spatio-temporal pattern of AGB and climatic factors. The following results were obtained: (1) AGB averaged 19.1 Tg C (1 Tg = 10¹² g) over a total area of 66.01 × 10⁴ km² between 2001 and 2011 and experienced a general fluctuation (coefficient of variation = 9.43%), with no significant trend over time (R² = 0.05, p > 0.05). (2) The mean AGB density was 28.9 g C m^?2 over the whole study area during the 11 year period, and it decreased from the northeastern part of the grassland to the southwestern part, exhibiting large spatial heterogeneity. (3) The AGB variation over the 11 year period was closely coupled with the pattern of precipitation from January to July, but we did not find a significant relationship between AGB and the corresponding temperature changes. Precipitation was also an important factor in the spatial pattern of AGB over the study area (R² = 0.41, p < 0.001), while temperature seemed to be a minor factor (R² = 0.14, p < 0.001). A moisture index that combined the effects of precipitation and temperature explained more variation in AGB than did precipitation alone (R² = 0.45, p < 0.001). Our findings suggest that establishing separate statistical models for different vegetation conditions may reduce the uncertainty of AGB estimation on a large spatial scale. This study provides support for grassland administration for livestock production and the assessment of carbon storage in Inner Mongolia.     

Leaf area and clumping indices for a boreal mixed-wood forest: lidar,hyperspectral, and Landsat models

The leaf area index (LAI) and the clumping index (CI) provide valuable insight into the spatial patterns of forest canopies, the canopy light regime and forest productivity. This study examines the spatial patterns of LAI and CI in a boreal mixed-wood forest, using extensive field measurements and remote sensing analysis. The objectives of this study are to: (1) examine the utility of airborne lidar (light detection and ranging) and hyperspectral data to model LAI and clumping indices; (2) compare these results to those found from commonly used Landsat vegetation indices (i.e. the normalized difference vegetation index (NDVI) and the simple ratio (SR)); (3) determine whether the fusion of lidar data with Landsat and/or hyperspectral data will improve the ability to model clumping and LAI; and (4) assess the relationships between clumping, LAI and canopy biochemistry.

Regression models to predict CI were much stronger than those for LAI at the site. Lidar was the single best predictor of CI (r ² > 0.8). Landsat NDVI and SR also had a moderately strong predictive performance for CI (r ² > 0.68 with simple linear and non-linear regression forms), suggesting that canopy clumping can be predicted operationally from satellite platforms, at least in boreal mixed-wood environments. Foliar biochemistry, specifically canopy chlorophyll, carotenoids, magnesium, phosphorus and nitrogen, was strongly related to the clumping index. Combined, these results suggest that Landsat models of clumping could provide insight into the spatial distribution of foliar biochemistry, and thereby photosynthetic capacity, for boreal mixed-wood canopies. LAI models were weak (r ² < 0.4) unless separate models were used for deciduous and coniferous plots. Coniferous LAI was easier to model than deciduous LAI (r ² > 0.8 for several indices). Deciduous models of LAI were weaker for all remote sensing indices (r ² < 0.67). There was a strong, linear relationship between foliar biochemistry and LAI for the deciduous plots. Overall, our results suggest that broadband satellite indices have strong predictive performance for clumping, but that airborne hyperspectral or lidar data are required to develop strong models of LAI at this boreal mixed-wood site.     

Estimation of forest leaf area index from SPOT imagery using NDVI distribution over forest stands

Leaf area index (LAI) is a key parameter of atmosphere–vegetation exchanges, affecting the net ecosystem exchange and the productivity. At regional or continental scales, LAI can be estimated by remotely‐sensed spectral vegetation indices (SVI). Nevertheless, relationships between LAI and SVI show saturation for LAI values greater than 3–5. This is one of the principal limitations of remote sensing of LAI in forest canopies. In this article, a new approach is developed to determine LAI from the spatial variability of radiometric data. To test this method, in situ measurements for LAI of 40 stands, with three dominant species (European beech, oak and Scots pine) were available over 5 years in the Fontainebleau forest near Paris. If all years and all species are pooled, a good linear relationship without saturation is founded between average stand LAI measurements and a model combining the logarithm of the standard deviation and the skewness of the normalized difference vegetation index (NDVI) (R ² = 0.73 rmse = 1.08). We demonstrate that this relation can be slightly improved by using different linear models for each year and each species (R ² = 0.82 rmse = 0.86), but the standard deviation is less sensitive to the species and the year effects than the mean NDVI and is therefore a performing index.     

Development of a national model of Pinus radiata stand volume from lidar metrics for New Zealand

Light detection and ranging (lidar) has been successfully used to describe a wide range of forest metrics at local scales. However, little research has tested the general applicability of this technology to describe commercially important stand dimensions, such as total stem volume (V), at national levels across broad environmental gradients.

Using an extensive national data set covering the spatial extent of Pinus radiata plantation forests in New Zealand, the key objectives of this study were to (1) develop regression models to best describe V for P. radiata from lidar metrics and (2) investigate whether these relationships could be improved using coincident environmental and stand-level information. Development of relationships between lidar metrics and forest volume are of particular importance for P. radiata, as this species constitutes approximately 90% of the 1.8 Mha plantation resource.

Using lidar mean height and the percentage of lidar ground returns, the initial model (model 1) accounted for 85% of the variance in V. Addition of stand stocking (number of stems ha^?1), measured within the plots, to the model (model 2) significantly (p < 0.001) improved predictions, with R ² increasing to 0.86 and the root mean square error declining from 80.1 m³ ha^?1 to 71.6 m³ ha^?1. For both models, partial responses show V to be most sensitive to lidar mean height, which was included in the model as a second-order polynomial.

Although environmental variables are established determinants for V, their inclusion did not significantly improve either model 1 or 2. Residual values for both models showed little apparent bias when plotted against stand-level information or a wide array of environmental variables, supporting the general applicability of these relationships.     

Evaluation of LST downscaling algorithms on seasonal thermal data in humid subtropical regions of India

Thermal image downscaling algorithms use a unique relationship between land surface temperature (LST) and vegetation indices (e.g. normalized difference vegetation index (NDVI)). The LST–NDVI correlation and regression parameters vary in different seasons depending on land-use practices. Such relationships are dynamic in humid subtropical regions due to inter-seasonal changes in biophysical parameters. The present study evaluates three downscaling algorithms, namely disaggregation of radiometric surface temperature (DisTrad), sharpening thermal imagery (TsHARP), and local model using seasonal (25 February 2010, 14 April 2010, and 26 October 2011) thermal images. The aggregated Landsat LST of 960 m resolution is downscaled to 480, 360, 240, and 120 m using DisTrad, TsHARP, and the local model and validated with aggregated Landsat LSTs of a similar resolution. The results illustrate that the seasonal variability of the LST–NDVI relationship affects the accuracy of the downscaling model. For example, the accuracy of all algorithms is higher for the growing seasons (February and October) unlike the harvesting season (April). The root mean square error of the downscaled LST increases from 480 to 120 m spatial resolution in all seasons. The models are least suitable in water body and dry-river bed sand areas. However, the downscaling accuracy is higher for NDVI > 0.3. The present study is useful to understand the applicability of the downscaling models in seasonally varied landscapes and different NDVI ranges.     

Mapping leaf area index through spectral vegetation indices in a subtropical watershed

Leaf area index (LAI) has been associated with vegetation productivity and evapotranspiration in mathematical models. At a regional level LAI can be estimated with enough accuracy through spectral vegetation indices (SVIs), derived from remote sensing imagery. However, there are few studies showing LAI–SVI relationships in subtropical regions. The aim of this work was to examine the relationship between LAI and SVIs in a subtropical rural watershed (in Piracicaba, State of Sa?o Paulo, Brazil), for different land covers, and to use the best relationship to generate a LAI map for the watershed. LAI was measured with a LAI-2000 instrument in 32 plots on the field in areas of sugar cane, pasture, corn, eucalypt, and riparian forest. The SVIs studied were Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI), and Soil Adjusted Vegetation Index (SAVI), calculated from Landsat-7 ETM+ data. The results showed LAI values ranging from 0.47 to 4.48. LAI–SVI relationships were similar for all vegetation types, and the potential model gave the best fit. It was observed that LAI–NDVI correlation (r ²=0.72) was not statistically different from LAI–SR correlation (r ²=0.70). The worst correlation was obtained by LAI–SAVI (r ²=0.56). A map was generated for the study area using the LAI–NDVI relationship. This was the first LAI map for the region.     

Usefulness of remote sensing for the assessment of growth traits in individual cereal plants grown in the field

Biomass determination usually involves destructive and tedious measurements. This study was conducted to evaluate the usefulness of the Normalized Difference Vegetation Index (NDVI) and the Simple Ratio (SR), calculated from the spectra of individual plants, for the assessment of leaf area per plant (LAP), green area per plant (GAP) and plant dry weight (W) at different growth stages. Two varieties of four cereal species (barley, bread wheat, durum wheat and triticale) were sown in a field experiment at a density of 25 plants m^?2. The spectra were captured on three plants per plot on eight occasions from the beginning of jointing to heading using a narrow‐bandwidth visible‐near‐infrared portable field spectroradiometer adapted for measurements at plant level. Strong associations were found between NDVI and SR and growth traits, both indices being better estimators of GAP and W than of LAP. Exponential models fitted to NDVI data were more useful for a wide number of situations than the linear models fitted to SR data. However, SR was able to discriminate between genotypes within a species. The accuracy of the reflectance measurements was comparable to that obtained by destructive measurements of growth traits, in which differences between varieties of over 24% were needed to be statistically significant. However, differences in SR of only 18% were statistically significant (P<0.05). The reliability of the spectral reflectance measurements and the non‐destructive nature convert this methodology into a promising tool for the assessment of growth traits in spaced individual plants.     

A modelling methodology for the analysis of radon potential based on environmental geology and geographically weighted regression

Many countries have promoted environmental studies and established national radon programmes in order to identify those geographical areas where high indoor exposure risk of people to this radioactive gas are more likely to be found (often referred to as ‘radon-prone areas’). Traditionally, the evaluation of radon potential has been pursued by means of global inference techniques. Conversely, in this paper we present a novel modelling approach, based on well established environmental software, best suited to capture the spatial variability of local relationships between indoor radon measurements and some environmental geology-related factors. The proposed strategy consists of three stages. First, a multilevel model based standardisation of indoor radon data should be carried out in order to reduce the building related variability. Then, the global and local autocorrelation indexes have to be employed to highlight the role of the local effects. The last step implies the use of the Geographically Weighted Regression (GWR) to show the differences in associations between indoor radon and the geological factors across space. The method was tested using an available geo-referenced dataset including both radon indoor measurements and geological data related to the territory of an Italian region (Abruzzo). The results are encouraging, although there are several critical issues to be addressed.     

Assessing the response of the MODIS vegetation indices to landscape disturbance in the forested areas of the legal Brazilian Amazon

In this study we assessed the impacts of forest fragmentation on the Amazon landscape using remote sensing techniques. Landscape disturbance, obtained for an area of approximately 3.5 × 10⁶ km² through simple spatial metrics (i.e. number of fragments, mean fragment area and border size) and principal component transformation were then compared to the MODIS (Moderate Resolution Imaging Spectroradiometer) NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) seasonal responses. As expected, higher disturbance values prevail in the southern border of the Amazon, near the intensively converted deforestation arc, and close to the major roads. NDVI seasonal responses more closely follow human-induced patterns, i.e. forest remnants from areas more intensively converted were associated with higher NDVI seasonal values. The significant correlation between NDVI seasonal responses and landscape disturbances were corroborated through analysis of geographically weighted regression (GWR) parameters and predictions. On the other hand, EVI seasonal responses were more complex with significant variations found over intact, less fragmented forest patches, thus restricting its utility to assess landscape disturbance. Although further research is needed, our results suggest that the degree of fragmentation of the forest remnants can be remotely sensed with MODIS vegetation indices. Thus, it may become possible to upscale field-based data on overall canopy condition and fragmentation status for basin-wide extrapolations.     

Correlated change in normalized difference vegetation index and the seasonal trajectory of photosynthetic capacity in a conifer stand

Seasonal changes in canopy photosynthetic activity play an important role in carbon assimilation. However, few simulation models for estimating carbon balances have included them due to scarcity in quality data. This paper investigates some important aspects of the relationship between the seasonal trajectory of photosynthetic capacity and the time series of a common vegetation index (normalized difference vegetation index, NDVI), which was derived from on site micrometeorological measurements or smoothed and downscaled from satellite‐borne NDVI sensors. A parameter indicating the seasonality of canopy physiological activity, P _E, was retrieved through fitting a half‐hour step process model, PROXEL_NEE, to gross primary production (GPP) estimates by inversion for carboxylation and light utilization efficiencies. The relative maximum rate of carboxylation (V _rm), a parameter that indicates the seasonality of CO₂ uptake potential under prevailing temperature, was then calculated from P _E and daily average air temperature. Statistical analysis revealed that there were obvious exponential relationships between NDVI and the seasonal courses for both canopy physiological activities P _E and V _rm. Among them, the on‐site broadband NDVI provided a robust and consistent relationship with canopy physiological activities (R ² = 0.84). The relationships between satellite‐borne NDVI time series with instantaneous canopy physiological activities at the time of satellite passing were also checked. The results indicate that daily step NDVI time series (data downscaled from composite temporal resolution NDVI) better represent the daily average activity of the canopy. These findings may enable us to retrieve the seasonal course of canopy physiological activity from widely available NDVI data series and, thus, to include it into carbon assimilation models. However, both smoothing methods for satellite‐borne NDVI time series may generate incorrect estimates and must be treated with care.     

Relations between AVHRR NDVI and ecoclimatic parameters in China

Based on monthly AVHRR NDVI data and weather records collected from 160 stations throughout China for 10 years, the relationships between NDVI and two ecoclimatic parameters (growing degree-days (GDD) and rainfall) were analysed. The results indicate that a significant correlation exists between NDVI and the two ecoclimatic parameters; the NDVI/GDD correlation was stronger than the NDVI/rainfall correlation. The NDVI/rainfall correlation coefficient exhibits a clear structure in terms of spatial distribution. Further, the results indicate that for natural vegetation, the NDVI/rainfall correlation coefficient increases in order from evergreen forest, to deciduous forest, to shrubs and desert vegetation, to steppe and savanna. The correlation coefficients associated with cultural vegetation type depend on a number of factors including annual rainfall, seasonal variation in precipitation, type and intensity of irrigation practice and other environmental factors.     

Leaf Area Index derivation from hyperspectral vegetation indicesand the red edge position

The aim of this study was to compare the performance of various narrowband vegetation indices in estimating Leaf Area Index (LAI) of structurally different plant species having different soil backgrounds and leaf optical properties. The study uses a dataset collected during a controlled laboratory experiment. Leaf area indices were destructively acquired for four species with different leaf size and shape. Six widely used vegetation indices were investigated. Narrowband vegetation indices involved all possible two band combinations which were used for calculating RVI, NDVI, PVI, TSAVI and SAVI2. The red edge inflection point (REIP) was computed using three different techniques. Linear regression models as well as an exponential model were used to establish relationships. REIP determined using any of the three methods was generally not sensitive to variations in LAI (R ² < 0.1). However, LAI was estimated with reasonable accuracy from red/near-infrared based narrowband indices. We observed a significant relationship between LAI and SAVI2 (R ² = 0.77, RMSE = 0.59 (cross validated)). Our results confirmed that bands from the SWIR region contain relevant information for LAI estimation. The study verified that within the range of LAI studied (0.3 ≤ LAI ≤ 6.1), linear relationships exist between LAI and the selected narrowband indices.     

Vegetation dynamics and their response to groundwater and climate variables in Qaidam Basin,China

This article discusses an evaluation of Moderate Resolution Imaging Spectroradiometer (MODIS) time-series data for monitoring vegetation variation in Qaidam Basin, Northwest China. In this study, 16 day composite 250 m normalized difference vegetation index (NDVI) products (MOD13Q1) acquired from 2000 to 2011 were processed to determine vegetation cover fraction (VCF) for detecting the annual dynamics of different types of vegetation cover in the basin and the products were validated by comparing field measurement in spatial distribution. The results show that the annual NDVI value increased from 0.126 to 0.172 on average between 2000 and 2011. The basin interior is dominated by desert and 74% of the area is covered by low-density shrubs and bare soil. Both areas of bare soil and low-density vegetation present a decreased rate, whereas medium-, medium-high-, and high-density vegetation show increase trends in the vegetation cover. Generally, the vegetation fluctuation depends on various attributes such as climate change, elevation, water table depth, and total dissolved solids (TDS) in arid areas. We found strong statistical correlation between NDVI time series and climatic factors such as air temperature and precipitation. There is also an agreement between the spatial distribution of NDVI value and elevation, because elevation has important impacts on the distribution of vegetation pattern, which are different in coverage. The vegetation dependent on water table depth is more complicated: shrubs of Phragmites australis, Artemisia desertorum, and Tamarix ramossissima Ledeb. are sensitive to water table depth and the maximum NDVI occurred at a water table depth shallower than 2 m. However, high-height shrub such as Nitraria Schoberi L. reflects less dependence on water table depth. Normally, vegetation can develop well at TDS between 0 and 3 g l^?1 whereas Tamarix ramossissima Ledeb. can still survive when the TDS is larger than 8 g l^?1.