Predicting agricultural drought in eastern Rajasthan of India using NDVI and standardized precipitation index

In the present study, prediction of agricultural drought has been addressed through prediction of agricultural yield using a model based on NDVI-SPI. It has been observed that the meteorological drought index SPI with different timescale is correlated with NDVI at different lag. Also NDVI of current fortnight is correlated with NDVI of previous lags. Based on the correlation coefficients, the Multiple Regression Model was developed to predict NDVI. The NDVI of current fortnight was found highly correlated with SPI of previous fortnight in semi-arid and transitional zones. The correlation between NDVI and crop yield was observed highest in first fortnight of August. The RMSE of predicted yield in drought year was found to be about 17.07 kg/ha which was about 6.02 per cent of average yield. In normal year, it was 24 kg/Ha denoting about 2.1 per cent of average yield.     

Hyperspectral remote sensing analysis of short rotation woody crops grown with controlled nutrient and irrigation treatments

Hyperspectral remote sensing research was conducted to document the biophysical and biochemical characteristics of controlled forest plots subjected to various nutrient and irrigation treatments. The experimental plots were located on the Savannah River Site near Aiken, SC. AISA hyperspectral imagery were analysed using three approaches, including: (1) normalized difference vegetation index based simple linear regression (NSLR), (2) partial least squares regression (PLSR) and (3) machine-learning regression trees (MLRT) to predict the biophysical and biochemical characteristics of the crops (leaf area index, stem biomass and five leaf nutrients concentrations). The calibration and cross-validation results were compared between the three techniques. The PLSR approach generally resulted in good predictive performance. The MLRT approach appeared to be a useful method to predict characteristics in a complex environment (i.e. many tree species and numerous fertilization and/or irrigation treatments) due to its powerful adaptability.     

内蒙古煤炭能源基地植被时空变化

利用Modis数据集250m分辨率植被指数数据分析了鄂尔多斯内蒙古能源基地植被的时空变化特征及其影响因子。研究表明：内蒙古能源基地的植被空间分布从东南至西北递减,其中东北部的准格尔旗、伊金霍洛旗及东胜区植被生长最好。内蒙古能源基地全区总体上植被指数NDVI值不高,植被指数值较低的地区占全区总面积的60％。时间演变上从2000～2008年整体上呈现变好的趋势。植被变好的区域可以占全区总面积的83．7％,植被变差的区域仅占1.39％,主要集中在东北部的准格尔旗、伊金霍洛旗、东胜区,以及局域地势低洼的沟谷地区及湖泊的周边区域。对其影响因子地形、地下水及降水的分析表明,大气降水是影响植被变化的最主要的因素。通过对鄂尔多斯盆地植被演变趋势及影响因子分析,能为能源基地的生态环境保护提供依据,促进人类,以及资源与环境的和谐发展。     

东北三省城市扩展及植被覆盖状况

基于三期遥感影像数据( MSS、ETM、CBERS) 对城市建成区的面积进行了调查,研究30 多年来东北三省城市的扩展情况,并且结合NDVI 数据,对研究区范围内的植被覆盖情况进行了研究。结果表明: 1976-2000 年、2000-2007 年两个阶段,城市建成区的面积持续增加,年均扩展率由 5. 10%增加至6. 32%,扩展强度比较显著的区域主要分布在哈大交通经济带上; 研究区范围内东部的植被覆盖情况明显好于西部,NDVI 均值随与城市距离的增加而提高。     

基于ASTER数据的城市热环境遥感监测研究

以ASTER数据为数据源,采用同一颗卫星上的MODIS数据得到大气透过率;利用可见光和近红外波段对下垫面类型进行分类和利用JPL(Jet Propulsion Lab)提供的光谱库计算地表比辐射率,进而采用劈窗算法进行地表温度(Land Surface Temperature,LST)的反演。在此基础上,利用反演的LST、分类结果和归一化差值植被指数(NDVI),对沧州地区的城市热环境进行了定量分析,研究结果可为进一步深入探讨城市热岛的发生发展规律以及城市热环境的模拟调控、优化布局提供一定的科学依据。     

MODIS NDVI和AVHRR NDVI 对草原植被变化监测差异

以草地作为研究载体,对比分析草原植被AVHRR NDVI和MODIS NDVI两种NDVI序列的年内、年际变化特征,讨论两种NDVI序列对降水量、平均气温和水汽压3种气候因子的响应差异,为合理选择NDVI序列对植被进行监测研究提供参考。结果表明：(1)两种NDVI序列所反映的草原植被年内变化趋势相似,但MODIS NDVI对各类草原的区分度优于AVHRR NDVI;(2)两种NDVI序列所反映的2000年—2003年草原植被年际变化差异明显。较之于MODIS NDVI,AVHRR NDVI变化趋势分类图表现出更强的植被改善趋势,植被改善面积在AVHRR NDVI变化趋势分类图中占94.25%,在MODIS NDVI中为83.33%;两种NDVI变化趋势分类图反映的植被变化趋势吻合度为52.88%。(3)两种NDVI序列与水汽压、降水量相关性差异显著。MODIS NDVI与各站点平均气温的相关系数均大于GIMMS NDVI;而MODIS NDVI与水汽压的相关系数83%(10个站点)小于GIMMS NDVI,与降水量的相关系数67%(8个站点)小于GIMMS NDVI。     

The Impact of Agricultural Practices in China on Land-Atmosphere Interactions

Human-induced land use changes and the resulting alterations in vegetation features are major but poorly recognized drivers of regional climatic patterns.In order to investigate the impacts of anthropogenically-induced seasonal vegetation cover changes on regional climate in China,harmonic analysis is applied to 1982-2000 National Oceanic and Atmospheric Administration(NOAA) Advanced Very High Resolution Radiometer(AVVHRR)-derived normalized difference vegetation index(NDVI) time series(ten day interval data).For two climatic divisions of South China,it is shown that the first harmonic term is in phase with air temperature,while the second and third harmonics are in phase with agricultural cultivation.The Penman-Monteith Equation and the Complementary Relationship Areal Evapotranspiration(CRAE) model suggest that monthly mean evapotranspiration is out of phase with temperature and precipitation in regions with signiffcant second or third harmonics.Finally,seasonal vegetation cover changes associated with agricultural cultivation are identiffed:for cropped areas,the temperature and precipitation time series have a single maximum value,while the monthly evapotranspiration time series has a bimodal distribution.It is hypothesized that multi-cropping causes the land surface albedo to sharply increase during harvesting,thereby altering the energy distribution ratio and contributing to observed seasonal vegetation cover changes.     

Can a 25-year trend in Soudano-Sahelian vegetation dynamics be interpreted in terms of land use change? A remote sensing approach

This study is based on the premise that, in the Sahel/Sudanian belt of Africa, the main determinants of interannual variation in vegetation dynamics are rainfall and land cover type. We analyzed the spatio-temporal sensitivity of the NOAA-AVHRR 8 km-resolution vegetation index (NDVI) to (i) annual rainfall variability (0.5° × 0.5° resolution) acquired over a 25-year period (1982-2006); and (ii) land use changes in the different eco-climatic regions of the Bani catchment in Mali (130 000 km²). During the period 1982-2006, there was no clear trend in rainfall over the catchment, whereas there was a strong positive trend in the NDVI, both when the NDVI values were corrected using annual rainfall variability and when they were not. We divided the catchment into three eco-climatic regions based on the relationship between the annual NDVI and rainfall. In each region, we analyzed the observed greening in relation to changes in land use after correcting for the effect of annual rainfall on the NDVI. Results show that there is a mixed level of agreement between the land cover changes at the grid cell scale and the spatial pattern of the NDVI trend. Increased cropping does not explain the increase in the annual NDVI, except in the Sahelian part of the catchment. We hypothesize that the natural vegetation dynamics related to the non-linear rainfall patterns during the 25-year study period were responsible for these results.     

Environmental zonation across the Australian arid region based on long-term vegetation dynamics

Zonation of landscapes is generally based on broad scale biophysical data, field surveys, imagery and expert knowledge. Such zonation represents a static view of the environment and does not reflect dynamics and function. Arid environments are however often highly dynamic, and spatial and temporal patterns may be expressed over long periods of time. These dynamics need to be understood for management. Our aim is to understand the dynamics and functional response of vegetation in the Australian arid zone, and use this to inform and potentially improve the currently employed stratification. Principal component analysis of 25 years of satellite imagery identified underlying factors influencing patterns of arid vegetation growth, and regions of similar long-term response. Dominant factors of variation were identified as the spatial distribution of total vegetation growth, seasonality of growth, magnitude of seasonal variability in growth, and regularity of variation in growth. Additional variation resulted from episodic vegetation growth of limited spatial extent and duration. Classes expressing these functional components were compared with the existing biogeographical regions, revealing agreement in some instances, and in other cases adding information previously not available. The study demonstrates a new approach to Australian landscape zonation that has potential for much wider application.     

Desertification in the Sahel: Towards better accounting for ecosystem dynamics in the interpretation of remote sensing images

To date, the interpretation of remote sensing images has not revealed wide-spread degradation of the vegetation in the Sahel. However, the interpretation of spectral information depends on a range of assumptions regarding the dynamics of the Sahelian vegetation as a function of rainfall variability and human management. Recent papers have presented diverging views on the vegetation dynamics of the Sahel and how these can be analysed with remote sensing images. We present a further analysis of the vegetation dynamics of semi-arid rangelands, in particular the Sahel, and the subsequent implications for the interpretation of remote sensing images. Specifically, the ecological processes driving the response of the Sahelian vegetation to rainfall variation are re-examined, and a regression analysis of NPP versus rainfall data is carried out. It is shown that the relation between the interannual variation in NPP and rainfall in the Sahel is non-linear and that this relation differs between sites with different average annual rainfall. It has been common practise in remote sensing studies for the Sahel to aggregate data from various Sahelian sites in order to obtain an average relation between rainfall, NPP and Rain Use Efficiency, and to assume these relations to be linear. This paper shows that this approach may lead to a bias in the interpretation of remote sensing images and that further work is required to clarify if wide-spread ecosystem degradation has occurred in the Sahel.