Rainfall and vegetation monitoring in the Savanna Zone of the Democratic Republic of Sudan using the NOAA Advanced Very High Resolution Radiometer

NOAA-6 and NOAA-7 Advanced Very High Resolution Radiometer (AVHRR) global-area coverage (4?km ground resolution) data were obtained at three-day intervals throughout each of the four-month periods covering the 1980, 1983 and 1984 growing seasons, between latitudes 10° and 22° North in the Democratic Republic of Sudan. Daily rainfall data for twelve meteorological stations spanning the Savanna Zone were analysed. Rainfall in Sudan during 1980 was below normal, but in 1983 and 1984 there were moderate and severe droughts. The satellite data were used to calculate normalized difference vegetation index (NDVI) values from the visible and near-infrared bands of the satellite data. These were processed into ten-day composite data sets using the AVHRR thermal-infrared channel as a cloud screen and a temporal compositing procedure that reduces cloud contamination and selects viewing angles closest to nadir.

The ten-day composite NDVI values and the integrals of NDVI for each growing season were found to be closely correlated with rainfall. The constants of regressions between NDVI and rainfall were lower in 1983 and 1984 than in 1980, which suggests there was reduced water-use efficiency by the rangeland vegetation in drought years. It was found that July and August NDVI values were closely related to the integrated NDVI values; hence early- and mid-season NDVI data could be used to predict annual primary production. Images showing the geographical distribution of values of NDVI prepared for the three years clearly illustrate the effects of the 1983 and 1984 droughts, compared with the higher rainfall of 1980. The precision of the relationship between rainfall and the vegetation indices for the meteorological stations encourages the view that NOAA AVHRR GAC composite NDVI values can be used to monitor effective rainfall in the Savanna Zone of the Democratic Republic of Sudan     

NDVI response to rainfall in different ecological zones in Jordan

The response of NDVI to rainfall was analyzed using NOAA/AVHRR satellite imagery acquired over a time period of ten growing seasons (1981 to 1992) and rainfall data from 16 weather stations in four ecological zones in Jordan. Results of linear regression analysis showed better response of NDVI to cumulative rainfall than to 10-day rainfall with best correlation in the Mediterranean zone. Significant relationships were found between seasonal rainfall and NDVI range (Δ_NDVI) with better correlations for logarithmic and power relationships than for linear relationship. A strong linear relationship occurred between the annual rainfall and end-of-season NDVI in the Mediterranean zone and weak or no correlation in other zones. The correlations were improved when the rainfall data were averaged, summed and correlated with the average NDVI. More agreement, however, was observed between the maximum NDVI image and rainfall than for the average NDVI image and rainfall. Results also showed that stratification of the data according to soil type and/or land cover would not necessarily improve the correlation. However, stratification of the data according to ecological zone demonstrated obvious differences between the NDVI-rainfall in the different zones.     

Monitoring East African vegetation using AVHRR data

NOAA Advanced Very High Resolution Radiometer satellite data are applied to regional vegetation monitoring in East Africa. Normalized Difference Vegetation Index (NDVI) data for a one-year period from May 1983 are used to examine the phenology of a range of vegetation types. The integrated NDVI data for the same period are compared with an ecoclimatic zone map of the region and show marked similarities. Particular emphasis is placed on quantifying the phenology of the Acacia Commiphora bushlands. Considerable variation was found in the phenology of the bushlands as determined by the satellite NDVI, and is explained through the high spatial variability in the distribution of rainfall and the resulting green-up of the vegetation. The relationship between rainfall and NDVI is further examined for selected meteorological stations existing within the bushland. A preliminary estimate is made of the length of growing season using an NDVI thresholding technique     

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.     

Sahelian rangeland production in relation to rainfall estimates from Meteosat

Abstract

Abstract. Satellite data are routinely used to monitor the growing season over the Sahelian zone of Africa. This study seeks to relate the vegetation indices and the rainfall estimates, both derived from meteorological satellites, to help monitor and predict the production of rangelands and marginal agricultural areas. Plant water use was calculated from a simple model which lakes into account the timing and distribution of rainfall: over a three-year period, the response of the Normalised Difference Vegetation Index (NDVI) to this quantity was consistent and was spatially quantified for two calibration years. A predictive model for end-of-season accumulated NDVI was developed and validated for a test year.     

Synergism between NOAA-AVHRR and Meteosat data for studying vegetation development in semi-arid West Africa

Abstract

Rainfall estimates, based on cold cloud duration estimated from Meteosat data, are compared with vegetation development depicted by data of the normalized difference vegetation index (NDVI) from the National Oceanic and Atmospheric Administration's (NOAA) advanced very high resolution radiometer (AVHRR) for part of the Sahel. Decadal data from the 1985 and 1986 growing seasons are examined to determine the synergism of the datasets for rangeland monitoring. There is a general correspondence between the two datasets with a marked lag between rainfall and NDVI of between 10 and 20 days. This time lag is particularly noticeable at the beginning of the rainy season and in the more northern areas where rainfall is the limiting factor for growth. Principal component analysis was used to examine deviations from the general relationship between rainfall and the NDVI. Areas of low NDVI values for a given input of rainfall were identified: at a regional scale, they give an indication of areas of low production potential and possible degradation of ecosystems. This study demonstrates in a preliminary way the synergism of such datasets derived from satellite--borne sensors with coarse spatial resolution, which may provide new information for the improved management of the Sahelian grasslands.     

遥感和地理信息系统在半干旱地区降雨-径流关系模拟中的应用

为解决黄土高原半干旱地区农业和生活缺水问题 ,许多地方实施了集水工程以收集雨水。为分析集水潜力 ,以黄土高原半干旱地区的 6个自然集水区为研究对象 ,利用遥感和地理信息系统获得研究区地形、植被、流域特征等参数。通过统计分析的办法得出该区域多年平均年径流量与降雨量、地形、植被等因素的关系模型。该模型将影响降雨 -径流关系的几个主要因素定量表示出来 ,可以快速、准确地计算一个集水区的径流量 ,克服采用径流等值线计算径流带来的误差     

Analysis of trends in the Sahelian ‘rain-use efficiency’ using GIMMS NDVI, RFE and GPCP rainfall data

Rain-use efficiency (RUE; the ratio of vegetation productivity to annual precipitation) has been suggested as a measure for assessing land degradation in arid/semi-arid areas. In the absence of anthropogenic influence, RUE has been reported to be constant over time, and any observed change may therefore be attributed to non-rainfall impacts. This study presents an analysis of the decadal time-scale changes in the relationship between a proxy for vegetation productivity (ΣNDVI) and annual rainfall in the Sahel-Sudanian zone of Africa. The aim is to test the quality of data input and the usefulness of both the RUE approach and an alternative method for separating the effects on vegetation productivity of rainfall change and human impact. The analyses are based on earth observation of both rainfall (GPCP (Global Precipitation Climatology Project), 1982-2007 and RFE (Rainfall Estimate) (1996-2007)) and ΣNDVI (AVHRR GIMMS NDVI dataset, 1982-2007). It is shown that the increase in ΣNDVI has been substantial in the Sahel-Sudanian zone over the 1982-2007 period, whereas for the period 1996-2007 the pattern of ΣNDVI trends is more complex. Also, trend analysis of annual rainfall from GPCP data (2.5° resolution) and RFE data (0.1° resolution) suggests that rainfall has increased over both periods. Further it is shown that RUE values are highly correlated to rainfall, undermining the use of earth observation (EO)-based RUE (using ΣNDVI) as a means of separating rainfall impacts from other factors. An alternative method identify temporal trends in residuals of ΣNDVI, after regressing it against annual rainfall, is tested, yet is shown to be useful only where a high correlation between ΣNDVI and annual rainfall exists. For the areas in the Sahel-Sudanian zone for which this condition is fulfilled, trend analyses suggest very limited anthropogenic land degradation in the Sahel-Sudanian zone.     

NDVI time series stochastic models for the forecast of vegetation dynamics over desertification hotspots

ABSTRACT

Land degradation in semi-arid natural environments is usually associated with climate vulnerability and anthropic pressure, leading to devastating social, economic and environmental impacts. In this sense, remotely sensed vegetation parameters, such as the Normalized Difference Vegetation Index (NDVI), are widely used in the monitoring and forecasting of vegetation patterns in regions at risk of desertification. Therefore, the objective of this study was to model NDVI time series at six desertification hotspots in the Brazilian semi-arid region and to verify the applicability of such models in forecasting vegetation dynamics. We used NDVI data obtained from the MOD13A2 product of the Moderate Resolution Imaging Spectroradiometer sensor, comprising 16-day composites time series of mean NDVI and NDVI variance for each hotspot during the 2000–2018 period. We also used rainfall measured by weather stations as an explanatory variable in some of the tested models. Firstly, we compared Holt-Winters with Box-Jenkins and Box-Jenkins-Tiao (BJT) models. In all hotspots the Box-Jenkins and BJT models performed slightly better than Holt-Winters models. Overall, model performance did not improve with the inclusion of rainfall as an exogenous explanatory variable. Mean NDVI series were modelled with a correlation of up to 0.94 and a minimum mean absolute percentage error of 5.1%. NDVI variance models performed slightly worse, with a correlation of up to 0.82 and a minimum mean absolute percentage error of 22.0%. After the selection of the best models, we combined mean NDVI and NDVI variance models in order to forecast mean-variance plots that represent vegetation state dynamics. The combined models performed better in representing dry and degraded vegetation states if compared to robust and heterogeneous vegetation during wet periods. The forecasts for one seasonal period ahead were satisfactory, indicating that such models could be used as tools for the monitoring of short-term vegetation states.     

Monitoring regional drought using the Vegetation Condition Index

NDVI (Normalized Difference Vegetation Index) images generated from NOAA AVHRR GVI data were recently used to monitor large scale drought patterns and their climatic impact on vegetation. The purpose of this study is to use the Vegetation Condition Index (VCI) to further separate regional NDVI variation from geographical contributions in order to assess regional drought impacts. Weekly NDVI data for the period of July 1985 to June 1992 were used to produce NDVI and VCI images for the South American continent. NDVI data were smoothed with a median filtering technique for each year. Drought areas were delineated with certain threshold values of the NDVI and VCI. Drought patterns delineated by the NDVI and VCI agreed quite well with rainfall anomalies observed from rainfall maps of Brazil. NDVI values reflected the different geographical conditions quite well. Seasonal and interannual comparisons of drought areas delineated by the VCI provided a useful tool to analyse temporal and spatial evolution of regional drought as well as to estimate crop production qualitatively. It is suggested that VCI data besides NDVI may be used to construct a large scale crop yield prediction model.     

Estimating relations between AVHRR NDVI and rainfall in East Africa at 10-day and monthly time scales

Temporal relations between AVHRR NDVI and rainfall data over East Africa at 10-day and monthly time scales have been analysed using distributed lag models. On average, only 10 per cent of the variation in 10-day NDVI values could be explained by concurrent and preceding rainfall. Corresponding values for monthly data was 36 per cent. If it is assumed that rainfall data can be used as an indicator of vegetation development the study indicates that AVHRR NDVI may have limitations for temporal vegetation monitoring in these environments.     

NDVI time series for monitoring RUSLE cover management factor in a tropical watershed

Land cover, an important factor for monitoring changes in land use and erosion risk, has been widely monitored and evaluated by vegetation indices. However, a study that associates normalized difference vegetation index (NDVI) time series to climate parameters to determine soil cover has yet to be conducted in the Atlantic Rainforest of Brazil, where anthropogenic activities have been carried out for centuries. The objective of this paper is to evaluate soil cover in a Brazilian Atlantic rainforest watershed using NDVI time series from Thematic Mapper (TM) Landsat 5 imagery from 1986 to 2009, and to introduce a new method for calculating the cover management factor (C-factor) of the Revised Universal Soil Loss Equation (RUSLE) model. Twenty-two TM Landsat 5 images were corrected for atmospheric effects using the 6S model, georeferenced using control points collected in the field and imported to a GIS database. Contour lines and elevation points were extracted from a 1:50,000-scale topographic map and used to construct a digital elevation model that defined watershed boundaries. NDVI and RUSLE C-factor values derived from this model were calculated within watershed limits with 1 km buffers. Rainfall data from a local weather station were used to verify NDVI and C-factor patterns in response to seasonal rainfall variations. Our proposed method produced realistic values for RUSLE C-factor using rescaled NDVIs, which highly correlated with other methods, and were applicable to tropical areas exhibiting high rainfall intensity. C-factor values were used to classify soil cover into different classes, which varied throughout the time-series period, and indicated that values attributed to each land cover cannot be fixed. Depending on seasonal rainfall distribution, low precipitation rates in the rainy season significantly affect the C-factor in the following year. In conclusion, NDVI time series obtained from satellite images, such as from Landsat 5, are useful for estimating the cover management factor and monitoring watershed erosion. These estimates may replace table values developed for specific land covers, thereby avoiding the cumbersome field measurements of these factors. The method proposed is recommended for estimating the RUSLE C-factor in tropical areas with high rainfall intensity.     

遥感和地理信息系统在半干旱地区降雨-径流关系模拟中的应用

为解决黄土高原半干旱地区农业和生活缺水问题,许多地方实施了集水工程以收集雨水。为分析集水潜力,以黄土高原半干旱地区的6个自然集水区为研究对象,利用遥感和地理信息系统获得研究区地形、植被、流域特征等参数。通过统计分析的办法得出该区域多年平均年径流量与降雨量、地形、植被等因素的关系模型。该模型将影响降雨-径流关系的几个主要因素定量表示出来,可以快速、准确地计算一个集水区的径流量,克服采用径流等值线计算径流带来的误差。     

Characterizing post-fire recovery of fynbos vegetation in the Western Cape Region of South Africa using MODIS data

Post-fire recovery trajectories of five fynbos vegetation stands in the Western Cape Region of South Africa were characterized using moderate-resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) 250 m data. Indices of NDVI recovery relative to pre-fire values or values from unburnt control plots indicated full recovery within 7 years and particularly rapid recovery in the first two post-fire years. Intra-stand variability of pixel NDVIs generally increased after fires and also exhibited a rapid recovery to pre-fire conditions. While stand age was the dominant determinant of NDVI recovery, drought interrupted the recovery pathways and this effect was amplified on drier, equator-facing slopes. Post-fire recovery characteristics of fynbos NDVI were found to be similar to those documented for chaparral vegetation in California despite contrasting rainfall and soil nutrient conditions in the two regions.     

Monitoring forest conditions in a protected Mediterranean coastal area by the analysis of multiyear NDVI data

The operational utilization of remote sensing techniques for monitoring terrestrial ecosystems is often constrained by problems of under-sampling in space and time, particularly in heterogeneous and unstable Mediterranean environments. The current work deals with the use of the NOAA-AVHRR and Landsat-TM/ETM+ images to produce long-term NDVI data series characterising coniferous and broadleaved forests in a protected coastal area in Tuscany (Central Italy). Two methods to extract NDVI values of relatively small vegetated areas from NOAA-AVHRR data were first evaluated by comparison to estimates from higher resolution Landsat-TM/ETM+images. The optimal method was then applied to multitemporal AVHRR data series to derive 10-day NDVI profiles of coniferous and broadleaved forests over a 15-year period (1986-2000). Trend analyses performed on these data series showed that notable NDVI decreases occurred during the study period, particularly for the coniferous forest in summer and early fall. Further analysis carried out on local meteorological measurements led to identify the likely causes of these negative trends in contemporaneous winter rainfall decreases which were significantly correlated with the found NDVI variations.     

Temporal and spatial vegetation cover changes in Israeli transition zone: AVHRR-based assessment of rainfall impact

The ability of NOAA/AVHRR data to monitor vegetation response to rainfall in three different vegetation zones was assessed along a north-south transect in Israel. The NDVI database was developed from atmospherically- and radiometrically-corrected NDVI composites from observations spanning three years. Three vegetation zones, Mediterranean region, transition zone, and semiarid region, were geographically separated by means of NDVI values. Based on three years of AVHRR observations during a relatively dry year and two years with near average rainfall, the phenological characteristics for all three vegetation zones were very similar and stable. The results showed that only a few AVHRR observations are necessary to monitor the seasonal and spatial variability of vegetation cover in different climatic zones located in Israel. The NDVI of the Israeli transition zone was found to be very sensitive to rainfall. The difference between maximum and minimum NDVI values in rainy season in the transition zone was at least two times higher than that in the Mediterranean and the semiarid regions. This phenomenon can be used as an indicator of any environmental changes in this region.     

Multi-month memory effects on early summer vegetative activity in semi-arid South Africa and their spatial heterogeneity

In semi-arid African regions (annual rainfall between 200 and 600 mm), variability of vegetative activity is mainly due to the rainfall of the current rainy season. In most of South Africa, the rainy season occurs from October to March. On average, vegetative activity lags rainfall by 1 to 2 months. The interannual variability in early summer (December to September) normalized difference vegetation index (NDVI) depends primarily on precipitation at the beginning (October to November) of the rainy season. However, once this primary control is removed, the residual interannual variability in NDVI highlights a double memory effect: a 1-year effect, referred to as Mem₁, and a 7- to 10-month effect, referred to as Mem₂. This article aims at better describing the influence of soil and vegetation characteristics on these two memory effects. The data sets used in this study are as follows: (1) a 19-year NDVI time series from National Oceanic and Atmospheric Administration (NOAA) satellites, (2) rainfall records from a network of 1160 rain-gauge stations compiled by the Water Research Commission (WRC), (3) vegetation types from Global Land Cover (GLC) 2000 and (4) soil characteristics from the soil and terrain database for Southern Africa (SOTERSAF). Results indicate that among 20–30% of NDVI variance that is not explained by the concurrent rainfall, one-third is explained by the two memory effects. Mem₁ is found to have maximum effect in the northwest of our study domain, near the Botswana boundary, in the South Kalahari. Associated conditions are open grasslands growing on Arenosols. Mem₁ is less important in the southeast, particularly in open grassland with shrubs growing on Cambisols. Thus, Mem₁ mainly depends on soil texture. Mem₂ is more widespread and its influence is the greatest in the centre, the south and the east of our domain. It is related to rainfall from January to April, which controls, beyond the intervening dry season, the interannual variations of NDVI (December to September) at the beginning of the next rainy season. Through these new findings, this article emphasizes again the high potential of remote-sensing techniques to monitor and understand the dynamics of semi-arid environments.     

Real-time vegetation monitoring with NOAA-AVHRR in Southern Africa for wildlife management and food security assessment

The monitoring of vegetation in Southern Africa with satellite data has become increasingly important over the past decade because it is linked to variation in agricultural production and climate change with implications for wildlife management and tourism. This study shows how maps of vegetation status were produced in near real time from NOAA images acquired from the local receiving stations in Etosha National Park, Namibia and in Zambia. Map products based on the NDVI were put into historical context and stratified to remove effects of the main vegetation types in order to assess vegetation status. The historical data were extracted from the FAO ARTEMIS NDVI archive and processed to obtain a statistical distribution of the NDVI for each 10-day period of the year and vegetation type by applying techniques commonly used in hydrology for the prediction of extreme events. The quintile probability ranges were used to define five classes of a Vegetation Productivity Indicator (VPI). LAC NDVI images obtained in real-time from the receiving station were processed to derive a VPI map for each 10-day period. In Etosha National Park and in Zambia, the VPI was strongly related to the rainfall and the VPI maps provided improved information on the spatial variations. The weighted average VPI for the main agricultural region of Zambia was significantly correlated with maize production.     

Study of the potential of alternative crops by integration of multisource data using a neuro‐fuzzy technique

This work proposes a neuro‐fuzzy method for suggesting alternative crop production over a region using integrated data obtained from land‐survey maps as well as satellite imagery. The methodology proposed here uses an artificial neural network (multilayer perceptron, MLP) to predict alternative crop production. For each pixel, the MLP takes vector input comprising elevation, rainfall and goodness values of different existing crops. The first two components of the aforementioned input, that is, elevation and rainfall, are determined from contour information of land‐survey maps. The other components, such as goodness values of different existing crops, are based on the productivity estimates of soil determined by fuzzyfication and expert opinion (on soil) along with production quality by the Normalized Difference Vegetation Index (NDVI) obtained from satellite imagery. The methodology attempts to ensure that the suggested crop will also be a high productivity crop for that region.     

Monitoring desertification and land degradation over sub-Saharan Africa

A desertification monitoring system is developed that uses four indicators derived using continental-scale remotely sensed data: vegetation cover, rain use efficiency (RUE), surface run-off and soil erosion. These indicators were calculated on a dekadal time step for 1996. Vegetation cover was estimated using the Normalized Difference Vegetation Index (NDVI). The estimation of RUE also employed NDVI and, in addition, rainfall derived from Meteosat cold cloud duration data. Surface run-off was modelled using the Soil Conservation Service (SCS) model parametrized using the rainfall estimates, vegetation cover, land cover, and digital soil maps. Soil erosion, one of the most indicative parameters of the desertification process, was estimated using a model parametrized by overland flow, vegetation cover, the digital soil maps and a digital elevation model (DEM). The four indicators were then combined to highlight the areas with the greatest degradation susceptibility. The system has potential for near-real time monitoring and application of the methodology to the remote sensing data archives would allow both spatial and temporal trends in degradation to be determined.