Integrating meteorological data in biomass prediction models

Diouf, A.A.; Hiernaux, P.; Brandt, M.; Faye, G.; Djaby, B.; Diop, M.B.; Ndione, J.A.; Tychon, B. Do Agrometeorological Data Improve Optical Satellite-Based Estimations of the Herbaceous Yield in Sahelian Semi-Arid Ecosystems? Remote Sens. 2016, 8, 668.

Quantitative estimates of forage availability at the end of the growing season in rangelands are helpful for pastoral livestock managers and for local, national and regional stakeholders in natural resource management. For this reason, remote sensing data such as the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) have been widely used to assess Sahelian plant productivity for about 40 years.

This study combines traditional FAPAR-based assessments with agrometeorological variables computed by the geospatial water balance program, GeoWRSI, using rainfall and potential evapotranspiration satellite gridded data to estimate the annual herbaceous yield in the semi-arid areas of Senegal.

It showed that a machine-learning model combining FAPAR seasonal metrics with various agrometeorological data provided better estimations of the in situ annual herbaceous yield (R2 = 0.69; RMSE = 483 kg·DM/ha) than models based exclusively on FAPAR metrics (R2 = 0.63; RMSE = 550 kg·DM/ha) or agrometeorological variables (R2 = 0.55; RMSE = 585 kg·DM/ha). All the models provided reasonable outputs and showed a decrease in the mean annual yield with increasing latitude, together with an increase in relative inter-annual variation. In particular, the additional use of agrometeorological information mitigated the saturation effects that characterize the plant indices of areas with high plant productivity.

The date of the onset of the growing season derived from smoothed FAPAR seasonal dynamics showed no significant relationship (0.05 p-level) with the annual herbaceous yield across the whole studied area. The date of the onset of rainfall was significantly related to the herbaceous yield and its inclusion in fodder biomass models could constitute a significant improvement in forecasting risks of a mass herbaceous deficit at an early stage of the year.

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AGU 2015

We had three presentations at this years AGU fall meeting in San Francisco. Find the posters and presentations as PDFs here (the copyright is with the authors):

Special Issue: Remote Sensing of Land Degradation and Drivers of Change

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Together with my colleagues Rasmus Fensholt, Stephanie Horion and Torbern Tagesson we edit a special issue for the open access journal Remote Sensing and we want to encourage everyone working with remote sensing and land degradation to submit a well prepared manuscript. The submission deadline is 31 May 2016. Find below the introduction text from the journal website:

Human and climate induced degradation of arable lands has been of major concern for livelihoods and food security particularly in drylands during recent decades, supporting and affecting the wellbeing of more than one-third of the global population. Monitoring vegetation productivity is of great importance because crop and livestock production is the most essential economic activity, especially in arid and semi-arid regions of the world.

The United Nations Convention to Combat Desertification’s (UNCCD) definition of desertification, or dryland degradation states that: “Land degradation in arid, semi-arid and dry sub-humid areas resulting from various factors, including climatic variations and human activities” followed by “land degradation means reduction or loss, in arid, semi-arid and dry sub-humid areas, of the biological or economic productivity and complexity of rainfed cropland, irrigated cropland, or range, pasture, forest and woodlands”…  (UNCCD homepage, www.unccd.int).

This definition implies that change in vegetation productivity is a key indicator (but not the only one) of land degradation. Along with land mismanagement often caused by human pressure, climatic variability is a major determinant of land degradation. Given the harsh nature of the climate in drylands, it is of great policy relevance to understand potential damaging interactions between land degradation and climate change. Indeed, climate-induced changes in air temperature and soil moisture might inflict soil erosion, salinization, crusting, and loss of soil fertility or depletion of seed banks in dryland ecosystems.

Continuous long-term Earth Observation (EO) satellite data provides the only suitable means of temporally and spatially consistent data analysis across multiple scales, and EO based metrics of vegetation productivity and land degradation are of great interest for the assessment and monitoring of environmental changes in dryland regions.

This forthcoming special issue welcomes research papers focusing on: (i) monitoring ecosystem productivity (both vegetation and economic productivity) and ecosystem complexity (i.e., biodiversity); (ii) studying the impact of climate change and human pressure on land degradation processes; (iii) uncovering the driving mechanisms of observed changes in vegetation productivity. The primary region of interest will be drylands, but studies covering other parts of the globe are also welcomed.

Keywords

  • EO-based methods for monitoring land degradation;
  • Vegetation/climate/anthropogenic productivity indicators;
  • Human versus climate-induced land degradation;
  • Land-use land-cover change in monitoring land degradation;
  • Multi-temporal/time-series analysis/multiple datasets;
  • Local to global scales;
  • Drivers attribution;
  • Case studies on land degradation and climate change;
  • Field evidence of degradation linked with EO data.

Fodder Biomass Monitoring in Sahelian Rangelands Using Phenological Metrics from FAPAR Time Series

From: Diouf, A.A., Brandt, M., Verger, A., Jarroudi, M.E., Djaby, B., Fensholt, R., Ndione, J.A., Tychon, B., 2015. Fodder Biomass Monitoring in Sahelian Rangelands Using Phenological Metrics from FAPAR Time Series. Remote Sensing 7, 9122–9148. doi:10.3390/rs70709122

Livestock farming constitutes the most widespread human activity and the dominant land use in rangeland ecosystems. At a global scale, it contributes 40% of the agricultural gross domestic product, and provides income for more than 1.3 billion people and nourishment for at least 800 million food-insecure people. In particular for the West African Sahel, livestock constitutes the first renewable resource and is mainly characterized by an extensive use of pastures in rangelands.

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Since 1987 the Centre de Suivi Ecologique (CSE) operationally estimates the total annual biomass in Senegal in order to monitor the fodder availability of the national pastoral rangelands. Field data is collected along 1 km transects at 24 sites at the end of the wet season. Here, herbaceous and woody leaf biomass is measured and summed to the total available fodder biomass. This is done each year since 1987.

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Using a linear regression with satellite images, the field data is projected to whole Senegal and gives stakeholders an estimation on the quantity and distribution of fodder biomass. Between 1987 and 1999, this method was implemented using the seasonal integrated NDVI (i.e., seasonal weighted average) from the Advanced Very High Resolution Radiometer (AVHRR) of the National Oceanic and Atmospheric Administration (NOAA) satellites acquired in Local Area Coverage (LAC) format at the CSE receiving station in Dakar. Since 2000, the 1-km SPOT-VEGETATION NDVI have been used.

In this context, we developed a new operational system for monitoring total fodder biomass, including both herbaceous and woody leaf biomass. The proposed method is based on multiple linear regression models using phenological variables derived from the seasonal dynamics of the FAPAR SPOT-VEGETATION time series and ground measurements of total biomass production collected in different Sahelian ecosystems in Senegal over 15 years.

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A model with three variables – large seasonal integral (LINTG), length of growing season and end of season decreasing rate – performed best (MAE = 605 kg DM/ha; R² = 0.68) across Sahelian ecosystems in Senegal (data for the period 1999-2013). A model with annual maximum (PEAK) and start date of season showed similar performances (MAE = 625 kg DM/ha; R² = 0.64), allowing a timely estimation of forage availability. The subdivision of the study area using metrics related to ecosystem properties increased overall accuracy (MAE = 489.21 kg DM/ha; R² = 0.77). LINTG was the main explanatory variable for woody rangelands, whereas for areas dominated by herbaceous vegetation it was the PEAK metric. The proposed approach outperformed the established single-predictor model (MAE = 818 kg DM/ha and R² = 0.51) and should improve the operational monitoring of forage resources in Sahelian rangelands.

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In the future, such early warning models should enable stakeholders to take decisions as early as September (current year as biomass shortage) with regard to livestock by triggering protocols designed for livestock management (e.g., Opération de Sauvegarde du Bétail ) in Senegal.

see the full document here: MDPI

Text and Figures: A.A. Diouf; Fotos: M. Brandt

What Four Decades of Earth Observation Tell us about Land Degradation in the Sahel

From: Mbow, C.; Brandt, M.; Ouedraogo, I.; de Leeuw, J.; Marshall, M. What Four Decades of Earth Observation Tell Us about Land Degradation in the Sahel? Remote Sens. 2015, 7, 4048-4067.

Land degradation mechanisms are related to two main categories, one related to climate change and one associated with local human impact, mostly land use change such as expansion of cultivation, agricultural intensification, overgrazing and overuse of woody vegetation. Land degradation characteristics, triggers and human influence are manifold and interrelated. Some of the indicators can be monitored using Earth Observation techniques (underlined in red):

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During the last four decades, the Sahel was affected by below-normal precipitation with two severe drought periods in 1972–73 and in 1983–84. Because of this negative climate trend, many studies prioritized the Sahel “crisis” in terms of productivity loss and land degradation. These negative perceptions have been opposed with recent findings of improved greenness mostly in relation to recent improvement in rainfall.

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The assessment of land degradation and quantifying its effects on land productivity have been both a scientific and political challenge. After four decades of Earth Observation applications, little agreement has been gained on the magnitude and direction of land degradation in the Sahel. The number of Earth Observation datasets and methods, biophysical and social drivers and the complexity of interactions make it difficult to apply aggregated Earth Observation indices for these non-linear processes. Hence, while many studies stress that the Sahel is greening, others indicate no trend or browning. The different generations of satellite sensors, the granularity of studies, the study period, the applied indices and the assumptions and/or computational methods impact these trends.

4

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While there is a clearly positive trend in biomass production at Sahel scale, a loss in biodiversity and locally encroaching barren land are observed at the same time. Multi-scale Earth Observation analyses show that neither the desertification nor the greening paradigms can be generalized, as both attempt to simplify a very complex reality. Heterogeneity is an issue of scale, and very coarse-scaled vegetation trend analyses reveal a greening Sahel. However, locally-scaled studies are not uniform, observing greening and degradation at the same time.

We suggest several improvements: (1) harmonize time-series data, (2) promote knowledge networks, (3) improve data-access, (4) fill data gaps, (5) agree on scales and assumptions, (6) set up a denser network of long-term fields-surveys and (7) consider local perceptions and social dynamics, as local people’s perception of land degradation/improvements often disagree with Earth Observation analyses.

Thus, to allow multiple perspectives and avoid erroneous interpretations caused by data quality/scale issues/generalizations, we recommend combining multiple data sources at multiple scales. Furthermore, we underline the relevance of field data and experience, and results achieved by remote sensing techniques should not be interpreted without contextual knowledge.

Download the full article here: Paper at MDPI

see also:

Knauer, K., Gessner, U., Dech, S., Kuenzer, C., 2014. Remote sensing of vegetation dynamics in West Africa. International Journal of Remote Sensing 35, 6357–6396. doi:10.1080/01431161.2014.954062

Be cautious using GIMMS3g v0 for trend analysis!

GIMMS3g NDVI is widely used to assess vegetation trends from local to global scale. And even though it is the best long term dataset available right now (July 2015), people should be aware that a serious error affects the dataset in semi arid areas, like the Sahel, which has severe impacts on trend analysis. The dry season values around 2004 suddenly drop, which is not happening in reality. It is definitely related to the sensor change from NOAA16 to NOAA17, and also the last change to NOAA18 has an impact on the time series. The good thing is that the wet season values should be usable. The developers are aware about this problem and a new version is on it’s way. To illustrate the issue, find attached the GIMMS3g NDVI (v0) averaged over the Sahel belt, note the drop at the sensor change (first blue line) and the recovery at the next sensor change:

gimms

To further illustrate how serious this problem affects trend analysis, the same area is shown in the VOD dataset, which is found to be reliable:vod

a rough overlay highlights the different directions of annual trends:

gimms_vod

The problem can be solved by excluding the dry season and using the small integral, calculated in TIMESAT:

sintgim_vo

see further literature:

  • Tian, F., Fensholt, R., Verbesselt, J., Grogan, K., Horion, S., Wang, Y., 2015. Evaluating temporal consistency of long-term global NDVI datasets for trend analysis. Remote Sensing of Environment 163, 326–340. doi:10.1016/j.rse.2015.03.031
  • Horion, S., Fensholt, R., Tagesson, T., Ehammer, A., 2014. Using earth observation-based dry season NDVI trends for assessment of changes in tree cover in the Sahel. International Journal of Remote Sensing 35, 2493–2515. doi:10.1080/01431161.2014.883104
  • Pinzon, J.E., Tucker, C.J., 2014. A Non-Stationary 1981–2012 AVHRR NDVI3g Time Series. Remote Sensing 6, 6929–6960. doi:10.3390/rs6086929
  • Liu, Y.Y., van Dijk, A.I.J.M., McCabe, M.F., Evans, J.P., de Jeu, R.A.M., 2013. Global vegetation biomass change (1988–2008) and attribution to environmental and human drivers. Global Ecology and Biogeography 22, 692–705. doi:10.1111/geb.12024
  • Jonsson, P., Eklundh, L., 2004. TIMESAT–a program for analyzing time-series of satellite sensor data* 1. Computers & Geosciences 30, 833–845.