Jonathan Seaquist

A remote sensing-based primary production model for grassland biomes

That data from polar orbiting satellites have detected a widespread increase in photosynthetic activity over the last 20 years in the grasslands of the Sahel is justifies investigating its role in the tropical carbon cycle. But this task is undermined because ground data that are generally used to support the use of primary production models elsewhere are lacking. In this paper, we profile a Light Use Efficiency (LUE) model of primary production parameterised with satellite information, and test it for the West African Sahel; solar radiation is absorbed by plants to provide energy for photosynthesis, while moisture shortfalls control the efficiency of light usage. In particular, we show how an economical use of existing, yet meagre data sets can be used to circumvent nominal, yet untenable approaches for achieving this for the region. Specifically, we use a cloudiness layer provided with the NOAA/NASA 8 km Pathfinder Land data archive (PAL) data set to derive solar radiation (and other energy balance terms) required to implement the model (monthly time-step). Of particular note, we index growth efficiency via transpiration by subsuming rangeland-yield formulations into our model. This is important for partially vegetated landscapes where the fate of rainfall is controlled by relative vegetation cover. We accomplish this by using PAL-derived Normalised Difference Vegetation Index (NDVI) to partition the landscape into fractional vegetation cover. A bare soil evaporation model that feeds into bucket model is then applied, thereafter deriving actual transpiration (quasi-daily time-step). We forgo a formal validation of the model due to problems of spatial scale and data limitations. Instead, we generate maps showing model robustness via Monte Carlo simulation. The precision of our Gross Primary Production (GPP) estimates is acceptable, but falls off rapidly for the northern fringes of the Sahel. We also map the locations where errors in the driving variables are mostly responsible for the bulk of uncertainty in predicted GPP, in this case the water stress factor and the NDVI. Comparisons with an independent model of primary production, CENTURY, are relatively poor, yet favourable comparisons are made with previous primary production estimates found for the region in the literature. A spatially exhaustive evaluation of our GPP map is carried out by regressing randomly sampled observations against integrated NDVI, a method traditionally used to quantify absolute amounts of primary production. Our model can be used to quantify stocks and flows of carbon in grasslands over the recent historical period.

Improving the estimation of noise from NOAA AVHRR NDVI for Africa using geostatistics

Abstract The accuracy of NOAA AVHRR NDVI data can be poor because of interference from several sources, including cloud cover. A parameter of the variogram model can be used to estimate the contribution of noise from the total variation in an image. However, remotely sensed information over large areas incorporates non-stationary (regional) trend and directional effects, resulting in violation of the assumptions for noise estimation. These assumptions were investigated at five sites across Africa for a range of ecological environments over several seasons. An unsupervised spectral classification of multi-temporal NDVI data partially resolved the problem of non-stationarity. Quadratic polynomials removed the remaining regional trend and directional effects. Isotropic variograms were used to estimate the noise contributing variation to the image. Standardized estimates of noise ranged from a minimum of 18.5% in west Zambia to 68.2% in northern Congo. Cloud cover and atmospheric particulates (e.g. dust) expl...

The supply and demand of net primary production in the Sahel

Net primary production (NPP) is the principal source of energy for ecosystems and, by extension, human populations that depend on them. The relationship between the supply and demand of NPP is important for the assessment of socio-ecological vulnerability. We present an analysis of the supply and demand of NPP in the Sahel using NPP estimates from the MODIS sensor and agri-environmental data from FAOSTAT. This synergistic approach allows for a spatially explicit estimation of human impact on ecosystems. We estimated the annual amount of NPP required to derive food, fuel and feed between 2000 and 2010 for 22 countries in sub-Saharan Africa. When comparing annual estimates of supply and demand of NPP, we found that demand increased from 0.44 PgC to 1.13 PgC, representing 19% and 41%, respectively, of available supply due to a 31% increase in the human population between 2000 and 2010. The demand for NPP has been increasing at an annual rate of 2.2% but NPP supply was near-constant with an inter-annual variability of approximately 1.7%. Overall, there were statistically significant (p < 0.05) increases in the NPP of cropland (+6.0%), woodland (+6.1%) and grassland/savanna (+9.4%), and a decrease in the NPP of forests (−0.7%). On the demand side, the largest increase was for food (20.4%) followed by feed (16.7%) and fuel (5.5%). The supply-demand balance of NPP is a potentially important tool from the standpoint of sustainable development, and as an indicator of stresses on the environment stemming from increased consumption of biomass.

Broad‐scale increase in NPP quantified for the African Sahel, 1982–1999

In association with a recently discovered greening trend in the Sahel, several interesting new perspectives have appeared in the literature regarding its climate and ecology. In this Letter, satellite data from 1982 to 1999 and a light use efficiency model are used to map net primary production (NPP) increases throughout the Sahel (total area of 1.13×1013 m2). A patchy, east‐west band of increasing NPP is identified, with several hotspots showing large increases. The total rate of NPP change for the Sahel is estimated to be 51.0 Mt C year−1 over the 18‐year period, yielding an absolute net gain of 918.0 Mt C. This increase is associated with a decrease in the inter‐annual variability of NPP for the 1990s compared to the 1980s. These results lay the groundwork for untangling the effects of direct, localised human impact and climate forcing on land cover by conducting model intercomparison experiments, contextualizing the role the Sahel may play in the tropical carbon cycle, and for reducing the uncertainty regarding Sahelian carbon sequestration.

Rapid estimation of photosynthetically active radiation over the West African Sahel using the Pathfinder Land Data Set

Photosynthetically Active Radiation (PAR) is important for assessing both the impact of changing land cover on climate, and for modelling productivity on a regional scale, as well as its potential in areas that are vulnerable to food shortfalls. A relatively simple method that generates spatially comprehensive and representative values of PAR at time scales of 1 O-days .(dekads) or longer is described, tested and implemented over a portion of West Africa. With simple equations to describe the geographical and temporal variation of global radiation receipt at the top of the atmosphere, daily cloud flags from the NOA~NASA AVHRR Pathfinder Land Data Set (PAL) are used in conjunction with an empirical formula developed by Angstrdm and constants tailored to West African conditions to estimate surface receipt of global radiation there. Ground observations of PAR from the HAPEX Sahel experiment (at 13’66’ N and 2”53’ E from 1992) are used to parameterise the relative sunshine duration variable in the angstrom relation so as to m~nimise errors between observed and modelled PAR. Results indicate that PAR may be estimated to within 20 percent of observed values for 28 out of 36 IOday summation periods over a year. End-of-year accumulated PAR is estimated to within 1.96 percent. Normalised root mean square errors (NRMSEs) and normalised mean absolute errors (NMAEs) of 15.69 percent and 12.46 percent, respectively, were obtained for IO-day sums, with values of 10.96 percent and 8.74 percent, respectively, for monthly sums. The spatial variability of end-of-year PAR for 1992 is in accordance with what was expected. Though more accurate methods exist for achieving this, the technique is merited for its ease of application, using an accessible data set, over areas where solar irradiation measurements are lacking.

Green and blue water demand from large-scale land acquisitions in Africa

Significance Freshwater appropriation can have vast impacts, depending on management and scale of water use. Since 2000, foreign investors have contracted an area the size of the United Kingdom in Africa, leading to increased pressure on water resources. Here we couple site-specific water demand for the crops planted there to the efficiency of different irrigation systems, while relating these estimates to local water availability. This approach enables us to identify “hotspot” areas of freshwater use where crops demand more water from irrigation than can be supplied by soil moisture, where the potential water demands from large-scale land acquisitions pose a risk for increased competition over water resources. Of these land acquisitions, 18% would be hotspots even with the most efficient irrigation system implemented. In the last decade, more than 22 million ha of land have been contracted to large-scale land acquisitions in Africa, leading to increased pressures, competition, and conflicts over freshwater resources. Currently, 3% of contracted land is in production, for which we model site-specific water demands to indicate where freshwater appropriation might pose high socioenvironmental challenges. We use the dynamic global vegetation model Lund–Potsdam–Jena managed Land to simulate green (precipitation stored in soils and consumed by plants through evapotranspiration) and blue (extracted from rivers, lakes, aquifers, and dams) water demand and crop yields for seven irrigation scenarios, and compare these data with two baseline scenarios of staple crops representing previous water demand. We find that most land acquisitions are planted with crops that demand large volumes of water (>9,000 m3⋅ha−1) like sugarcane, jatropha, and eucalyptus, and that staple crops have lower water requirements (<7,000 m3⋅ha−1). Blue water demand varies with irrigation system, crop choice, and climate. Even if the most efficient irrigation systems were implemented, 18% of the land acquisitions, totaling 91,000 ha, would still require more than 50% of water from blue water sources. These hotspots indicate areas at risk for transgressing regional constraints for freshwater use as a result of overconsumption of blue water, where socioenvironmental systems might face increased conflicts and tensions over water resources.

Detecting recent disturbance on Montane blanket bogs in the Wicklow Mountains, Ireland using the MODIS enhanced vegetation index

Irish peat soils are extensive, covering approximately 14–20% of the national land area. They contain between 53% and 62% of the national soil organic carbon stock. Montane blanket bog covers approximately 25% or 242 650 ha of the total peatland area in Ireland and is the dominant peatland type covering the upland area of Wicklow. Blanket bogs are very sensitive systems and have experienced much disturbance in Ireland due to overgrazing, burning, drainage, forestry and turf cutting. It has been estimated that disturbance of blanket bog, on a national area basis, ranges from 74% to 82% and in Wicklow is 57%. Disturbance can be detrimental to stocks of soil organic carbon in peatlands. Monitoring disturbance in peatlands, which tend to cover large, remote areas, is difficult and expensive using conventional surveying methods. Satellite remote sensing offers a way to gather data for these areas. In this paper a method of determining the probability of disturbance is presented. This method uses the Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) in combination with univariate image differencing along with thresholding and binary logistic regression. A probability map was produced depicting the geospatial patterns and pressures on the peatland soil organic carbon stock in Wicklow. Peat soils in higher and steeper areas were more disturbed and the primary disturbance in between 2000 and 2005 was fire. Lower, flatter areas did not experience as much disturbance probably because they are wetter. The consumers and producers accuracy for the map was 76% and 42%, respectively.

Dynamic response of NDVI to soil moisture variations during different hydrological regimes in the Sahel region

ABSTRACT Over the last few decades, the African Sahel has become the focus of many studies regarding vegetation dynamics and their relationships with climate and people. This is because rainfall limits the production of biomass in the region, a resource on which people are directly dependent for their livelihoods. In this study, we utilized a remote-sensing approach to answering the following two questions: (1) how does the dynamic relationship between soil moisture and plant growth vary across hydrological regimes, and (2) are vegetation-type-dependent responses to soil moisture availability detectable from satellite imagery? In order to answer these questions, we studied the relationship between monthly modelled soil moisture as an indicator for water availability and the remotely sensed normalized difference vegetation index (NDVI) as a proxy for vegetation growth between a “recovery rainfall period” (1982 to 1997) and a “stable rainfall period” (1998 to 2013), at different time lags across the Sahel region. Using windowed cross-correlation, we find a strong significant positive relationship between NDVI and soil moisture at a concurrent time and at NDVI lagging behind soil moisture by 1 month for grassland, cropland, and deciduous shrubland vegetation – the dominant vegetation classes in the Sahel. South of the Sahel (the Sudanian and Guinean areas), we find longer optimal lags (soil moisture lagged by 1–3 months) in association with mixed forest and deciduous shrubland. We find no major significant change in optimal lag between the recovery and stable periods in the Sahelian region; however, in the Sudanian and Guinean areas, we observe a trend towards shorter time lags. This change in optimal lag suggests a vegetation change, which may be a response to a climatic shift or land-use change. This approach of identifying spatiotemporal trends in optimal lag correlations between modelled soil moisture and NDVI could prove to be a useful tool for mapping vegetation change and ecosystem behaviour, in turn helping inform climate change mitigation approaches and agricultural planning.

Exploring and improving NOAA AVHRR NDVI image quality for African drylands

The accuracy of NOAA AVHRR NDVI maximum value composites can be poor due to interference from several sources, including cloud cover. The objectives of this paper are; 1. to accurately quantify noise in this imagery over Africa using geostatistics, and 2. to test four compositing techniques that may be able to reduce this noise. The nugget of the variogram model is used to compute standardized noise for five sites across Africa over 4 seasons. After removing trend and anisotropy in the NDVI sub-scenes, standardized noise estimates range from 18.5% in West Zambia to 68.2% in northern Congo. Four automated compositing methods are also tested over the West African Sahel for 13-day periods in order to improve image quality: the MVC, Maximum Value Temperature (MVT), a two-criteria algorithm that compares the two highest NDVI values for a period thereafter retaining the value with the smallest scan angle (MVCMISC), and a temperature-based algorithm similar to MVCMISC (MVTMISC). Results show that the MVT performs best for minimising cloud contamination, while the MVC is better for removing extreme scan angles. For the dual criteria algorithms, the MVTMISC performs best. The MVCMISC is better able to reduce scan angle bias for all land cover classes during the dry season, with the MVTMISC giving superior performance over the vegetative season. This work has implications for interpreting NDVI data in the context of famine early warning and developing biophysical descriptors of the African land surface at broad scales.