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

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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.

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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

50 years of woody vegetation and land-cover change in the Sahel of Mali

The Sahel region has often been acclaimed as one of the “hot spots” of environmental change. Degradation of environmental conditions was accelerated by droughts and an overall decrease in precipitation. The resulting loss of woody vegetation cover was often considered as irreversible desertification. Recent findings, based on small-scaled analyses of satellite images, show an increase of vegetation greenness since the mid-1980s. However, it often remains unclear if this is a return to pre-drought conditions or a transformation of land cover. This study uses remote sensing techniques, supplemented by ground truth data to compare pre-drought woody vegetation and land cover with the current situation on the Dogon Plateau and the Seno Plains in Mali in a 3600 km² study area. High resolution panchromatic Corona imagery (1.8 m) of December 1967 and multispectral RapidEye imagery (6.5 m) of December 2011 form the basis of this regional scaled study. The feature extraction and classification operations included in ERDAS Imagine Objective are used in an object-oriented approach in combination with spectral properties to analyse the datasets and map millions of individual trees and large shrubs for 1967 and 2011.

Fig. 1: IMAGINE Objective: Example of results in a sparsely vegetated area (Corona 1967).

Fig. 1: IMAGINE Objective: Example of results in a sparsely vegetated area (Corona 1967).

Fig. 2: Comparison of GPS-tagged photography (1-2) with very high resolution images (1a-2a) and RapidEye images (2a-2b). Source: Photos 1-2: R. Spiekermann 2011; 1a-2a: Microsoft Corporation and its data suppliers 2010; 1b-2b: RapidEye 2011.

Fig. 2: Comparison of GPS-tagged photography (1-2) with very high resolution images (1a-2a) and RapidEye images (2a-2b). Source: Photos 1-2: R. Spiekermann 2011; 1a-2a: Microsoft Corporation and its data suppliers 2010; 1b-2b: RapidEye 2011.

Land cover maps are created for 1967 and 2011 at a resolution of 20 m. An unsupervised classification method is used for the Corona images and a supervised classification for the RapidEye images. The two main classes selected are „sparse woody vegetation“ and „dense woody vegetation“. The densely vegetated areas are mostly areas of dense woody vegetation, which have not been deforested for cultivation, or also areas which have been laid fallow for extended periods of time and are now covered by shrubbery and grass. Groups of large trees within cropland areas are also included in this class. Sparsely vegetated areas are usually used for agricultural purposes and include cultivated, fallow and grazing areas.

Fig. 3: Diambara, Seno Plains as a typical example for land cover change. The darker shades of grey on the Corona image to the east and south of Diambara represent typical bush fallow areas (see also Fig. 2), which have been classified as “Densely Vegetated”. These areas no longer exist as such in 2011. However, due to an increase of woody vegetation on the sparsely vegetated fields surrounding Diambara, many of the cultivated areas are classified as “Densely Vegetated” areas. Almost a total reverse of land cover has thus occurred in the space of half a century.

Fig. 3: Diambara, Seno Plains as a typical example for land cover change. The darker shades of grey on the Corona image to the east and south of Diambara represent typical bush fallow areas (see also Fig. 2), which have been classified as “Densely Vegetated”. These areas no longer exist as such in 2011. However, due to an increase of woody vegetation on the sparsely vegetated fields surrounding Diambara, many of the cultivated areas are classified as “Densely Vegetated” areas. Almost a total reverse of land cover has thus occurred in the space of half a century.

All individuals of trees inside a 1 ha pixel are converted to a point and counted to quantify and map the tree density in 1967 and 2011. Polygons larger 225 m² are divided by this figure to approximate the actual number of trees and shrubs represented by the single feature. Figures 4 and 5 show a case study area. According to the prevailing land cover change from dense to sparse vegetation, an overall decrease of tree density can be observed. This results in a loss of natural bushland and a spreading of degraded areas on the plateau. Agricultural land in the immediate surroundings of villages see an increase of tree density, mainly on the primary fields which are fertilized and protected.

Fig. 4: The woody vegetation density in the degraded area to the southwest of Diamnati (Dogon Plateau) has drastically decreased, whereas an increase of up to 5-15 features per hectare is seen on most cultivated areas to the northeast and southeast of Diamnati village.

Fig. 4: The woody vegetation density in the degraded area to the southwest of Diamnati (Dogon Plateau) has drastically decreased, whereas an increase of up to 5-15 features per hectare is seen on most cultivated areas to the northeast and southeast of Diamnati village.

Fig. 5: Change to woody vegetation density in Diamnati (Dogon Plateau) 1967 – 2011 at a pixel resolution of 1 ha.

Fig. 5: Change to woody vegetation density in Diamnati (Dogon Plateau) 1967 – 2011 at a pixel resolution of 1 ha.

Our results show, that neither the desertification paradigm nor the greening paradigm can be generalized in the Sahel. Rather spatial variations of changes exist; the explanations for these are equally manifold. Figure 6 demonstrates, that both greening and degradation are present in the whole study area over a period of 50 years. The main causative factor for change in tree cover and density proves to be anthropogenic. Human induced land-cover change corresponds well to tree cover change in that an increase is observed on historic primary fields and a decrease mapped in areas where the dense bushland areas of 1967 have been converted to secondary cropping fields. Furthermore, many areas of the plateau are now degraded, which is often indirectly, if not, directly related to the intense droughts of the 1970s and 80s. On the other hand, the awareness and knowledge of the advantages gained when protecting the environment, i.e. ensuring the sustainable use of trees on farmland, has increased among local inhabitants. This has led to a strong increase of woody vegetation, particularly in the immediate surroundings of settlements. The number of features extracted in the Corona images is roughly four times greater than the number extracted from the RapidEye images. The reverse is true concerning the average area of the features, mainly due to the different pixel size. Thus, there is an obvious dilemma in comparing these maps quantitatively. However, although the quantitative change may not be entirely correct, the trend certainly is.

Photos (taken in Nov. and Dec. 2011) and RapidEye (Dec. 2011): a: erosion and gully systems near Gama; b: formerly dense bush, this area near Diambara was cleared and is a fallow today; c: these fields on the surroundings of Diambara show a dense and healthy woody vegetation today; d: formerly densely vegetated with tiger bush, these areas near Diamnati are degraded land today; e: only few areas of dense bush fallow are left nowadays.

Photos (taken in Nov. and Dec. 2011) and RapidEye (Dec. 2011):
a: erosion and gully systems near Gama; b: formerly dense bush, this area near Diambara was cleared and is a fallow today; c: these fields on the surroundings of Diambara show a dense and healthy woody vegetation today; d: formerly densely vegetated with tiger bush, these areas near Diamnati are degraded land today; e: only few areas of dense bush fallow are left nowadays.

Land cover change over 50 years on the Dogon Plateau and the Seno Plan

Fig. 6: Land cover change over 50 years on the Dogon Plateau and the Seno Plan

EGU poster: EGU_2013_spiekermann_small

Spiekermann, R., Brandt, M. & C. Samimi (2013): Using high resolution imagery to detect woody vegetation and land-cover change over 50 years in the Sahel of Mali. Geophysical Research Abstracts, Vol. 15, EGU2013-11937, EGU General Assembly 2013.

master thesis: Spiekermann 2013