New publications!

woody-plants-workshop-in-copenhagen19_01_2017_group-photo-2

Our fantastic team had some new publications within the past half year, they are all worth to have a look:

This one deals with the question if agricultural intensification in Sahel causes an increase or decrease in NDVI trends. Surprisingly, we find a negative NDVI trend coupled with an increase in cropped areas which means that fallowed fields have a substantially higher NDVI than cropped fields.

Open access! Here we use great data sources to document dynamics in woody vegetation in central Senegal. Field data from 2000 to 2015, fantastic aerial photos from 1994, repeat photography from 1994 and 2015, satellite imagery at 50 cm resolution from 2005-2015, and finally MODIS time series. We find a high spatial and temporal dynamic, encroachment, die off, etc. It’s a very a colourfully illustrated study which will make you feel like travelling to Senegal..

A great story as well: We document how conservation projects in Southern China are able to impact on vegetation trends and propose an index which allows to put the invested money (for conservation projects) in relation with vegetation trends to be able to determine the project effectiveness.

A very clever way to combine optical and passive microwave satellite data: We assume that optical satellite data senses the green part of the vegetation and the passive microwaves the green plus non-green parts. So we combine both to estimate the non green vegetation (i.e. the wood) and look at global trends from 2000 to 2012 which allows us to map gradual gains and losses in woody cover.

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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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Recent woody vegetation trends in Sahel

Our new paper looks at recent dynamics in woody vegetation in Sahel and finds some interesting patterns which are mainly controlled by human population density.

Martin Brandt, Pierre Hiernaux, Kjeld Rasmussen, Cheikh Mbow, Laurent Kergoat, Torbern Tagesson, Yahaya Ibrahim, Abdoulaye Wele, Compton J. Tucker, Rasmus Fensholt. Assessing woody vegetation trends in Sahelian drylands using MODIS based seasonal metrics. Remote Sensing of Environment, 2016, 183, 215-225.

  • Woody cover trends are estimated for Sahel based on MODIS dry season metrics.
  • Interannual fluctuations in foliage density are attenuated to monitor woody plant trends.
  • Increases (decreases) are seen in areas of low (high) human population.
  • Recent decreases only partially offset a general post-drought increase in Sahelian woody cover.

Woody plants play a major role for the resilience of drylands and in peoples’ livelihoods. However, due to their scattered distribution, quantifying and monitoring woody cover over space and time is challenging. We develop a phenology driven model and train/validate MODIS (MCD43A4, 500 m) derived metrics with 178 ground observations from Niger, Senegal and Mali to estimate woody cover trends from 2000 to 2014 over the entire Sahel at 500 m scale.

Over the 15 year period we observed an average increase of 1.7 (± 5.0) woody cover (%) with large spatial differences: No clear change can be observed in densely populated areas (0.2 ± 4.2), whereas a positive change is seen in sparsely populated areas (2.1 ± 5.2). Woody cover is generally stable in cropland areas (0.9 ± 4.6), reflecting the protective management of parkland trees by the farmers. Positive changes are observed in savannas (2.5 ± 5.4) and woodland areas (3.9 ± 7.3).

The major pattern of woody cover change reveals strong increases in the sparsely populated Sahel zones of eastern Senegal, western Mali and central Chad, but a decreasing trend is observed in the densely populated western parts of Senegal, northern Nigeria, Sudan and southwestern Niger. This decrease is often local and limited to woodlands, being an indication of ongoing expansion of cultivated areas and selective logging.

We show that an overall positive trend is found in areas of low anthropogenic pressure demonstrating the potential of these ecosystems to provide services such as carbon storage, if not over-utilized. Taken together, our results provide an unprecedented synthesis of woody cover dynamics in the Sahel, and point to land use and human population density as important drivers, however only partially and locally offsetting a general post-drought increase.

graph_abstract

Assessing Future Vegetation Trends and Restoration Prospects in the Karst Regions of Southwest China

Our latest article is not located in the Sahel, however, the method to assess the future persistence of vegetation trends is highly interesting in the context of ecosystem stability and resistance. The article is open access and freely available.

Tong, Xiaowei; Wang, Kelin; Brandt, Martin; Yue, Yuemin; Liao, Chujie; Fensholt, Rasmus. 2016. “Assessing Future Vegetation Trends and Restoration Prospects in the Karst Regions of Southwest China.” Remote Sens. 8, no. 5: 357.

 

To alleviate the severe rocky desertification and improve the ecological conditions in Southwest China, the national and local Chinese governments have implemented a series of Ecological Restoration Projects since the late 1990s. In this context, remote sensing can be a valuable tool for conservation management by monitoring vegetation dynamics, projecting the persistence of vegetation trends and identifying areas of interest for upcoming restoration measures.

In this study, we use MODIS satellite time series (2001–2013) and the Hurst exponent to classify the study area (Guizhou and Guangxi Provinces) according to the persistence of future vegetation trends (positive, anti-persistent positive, negative, anti-persistent negative, stable or uncertain). The persistence of trends is interrelated with terrain conditions (elevation and slope angle) and results in an index providing information on the restoration prospects and associated uncertainty of different terrain classes found in the study area.

The results show that 69% of the observed trends are persistent beyond 2013, with 57% being stable, 10% positive, 5% anti-persistent positive, 3% negative, 1% anti-persistent negative and 24% uncertain. Most negative development is found in areas of high anthropogenic influence (low elevation and slope), as compared to areas of rough terrain. We further show that the uncertainty increases with the elevation and slope angle, and areas characterized by both high elevation and slope angle need special attention to prevent degradation. Whereas areas with a low elevation and slope angle appear to be less susceptible and relevant for restoration efforts (also having a high uncertainty), we identify large areas of medium elevation and slope where positive future trends are likely to happen if adequate measures are utilized.

The proposed framework of this analysis has been proven to work well for assessing restoration prospects in the study area, and due to the generic design, the method is expected to be applicable for other areas of complex landscapes in the world to explore future trends of vegetation.

graphical_abstract

The Hurst exponent, a measure of the persistence of a time series, can be easily calculated on a raster time series in R:

library(raster)
library(rgdal)
library(pracma)

# set working directory with the raster files, here in TIF format, 
# and load the files in a rasterbrick

setwd("/media/2016_Xiaowei/")
gsn = brick(list.files(pattern='*.tif'))
# test on the average of the study area

g=cellStats(gsn, stat='mean')
gsn.ts = ts(g, start=c(2001,1), end=c(2013,1), frequency=1)
plot(gsn.ts)

h=hurstexp(gsn.ts)$Hs
h

# run the function on the rasterbrick gsn

fun=function(x) { 
  v=as.vector(x)
  if (is.na(v[1])){ NA } else
  gsn.ts = ts(v, start=c(2001,1), end=c(2013,1), frequency=1)
  x=hurstexp(gsn.ts, display=F)$Hs 
}
h <- calc(gsn, fun)
plot(h)

Remote sensing of vegetation in drylands: Evaluating vegetation optical depth (VOD) using NDVI and in situ data over Sahel

Tian, F.; Brandt, M.; Liu, Y. Y.; Verger, A.; Tagesson, T.; Diouf, A. A.; Rasmussen, K.; Mbow, C.; Wang, Y.; Fensholt, R. Remote sensing of vegetation dynamics in drylands: Evaluating vegetation optical depth (VOD) using AVHRR NDVI and in situ green biomass data over West African Sahel. Remote Sensing of Environment 2016, 177, 265–276.

 

  •  A Long-term VOD dataset is evaluated against NDVI and in situ biomass observations.
  • Both VOD and NDVI reflect the spatio-temporal patterns of biomass in West Sahel.
  • VOD captures variations of woody plant foliage biomass better than NDVI.
  • VOD and NDVI seasonal metrics differ for optimal long-term monitoring of biomass.

Monitoring long-term biomass dynamics in drylands is of great importance for many environmental applications including land degradation and global carbon cycle modeling. Biomass has extensively been estimated based on the normalized difference vegetation index (NDVI) as a measure of the vegetation greenness. The vegetation optical depth (VOD) derived from satellite passive microwave observations is mainly sensitive to the water content in total aboveground vegetation layer. VOD therefore provides a complementary data source to NDVI for monitoring biomass dynamics in drylands, yet further evaluations based on ground measurements are needed for an improved understanding of the potential advantages.

In this study, we assess the capability of a long-term VOD dataset (1992–2011) to capture the temporal and spatial variability of in situ measured green biomass (herbaceous mass and woody plant foliage mass) in the semi-arid Senegalese Sahel.

Results show that the magnitude and peak time of VOD are sensitive to the woody plant foliage whereas NDVI seasonality is primarily governed by the green herbaceous vegetation stratum in the study area. Moreover, VOD is found to be more robust against typical NDVI drawbacks of saturation effect and dependence on plant structure (herbaceous and woody compositions) across the study area when used as a proxy for vegetation productivity. Finally, both VOD and NDVI well reflect the spatial and inter-annual dynamics of the in situ green biomass data; however, the seasonal metrics showing the highest degree of explained variance differ between the two data sources. While the observations in October (period of in situ data collection) perform best for VOD (r2 = 0.88), the small growing season integral (sensitive to recurrent vegetation) have the highest correlations for NDVI (r2 = 0.90).

Overall, in spite of the coarse resolution of 25 km, the study shows that VOD is an efficient proxy for estimating biomass of the entire vegetation stratum in the semi-arid Sahel and likely also in other dryland areas.

1-s2.0-S0034425716300852-fx1

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):

Woody plant cover estimation in drylands from Earth Observation based seasonal metrics

Brandt, M., Hiernaux, P., Tagesson, T., Verger, A., Rasmussen, K., Diouf, A.A., Mbow, C., Mougin, E., Fensholt, R., 2016. Woody plant cover estimation in drylands from Earth Observation based seasonal metrics. Remote Sensing of Environment 172, 28–38.

Download a free copy here (until 28 December 2015)
.

Trees, shrubs and bushes are an important element of savanna ecosystems and for livelihoods in dryland areas dependent on fuel–wood supply. During the past decades, several studies have seriously questioned prevailing narratives of a widespread and Sahel-wide decrease in woody cover, commending the relevance of large scale woody cover monitoring systems.

trees

Most studies estimating tree canopy cover with remote sensing rely on high resolution imagery which allow direct mapping at a scale recognizing trees of a certain size as objects. However, imageries with a spatial resolution of 1–5 m are cumbersome to process, expensive, susceptible to clouds, and do only provide a static situation for a limited spatial area. Moreover, considering trees as objects, smaller isolated woody plant are missed. Moreover, the reliability of global tree cover products in semi-arid regions with open tree cover is contested.

We suggest an approach driven by vegetation phenology including in situ measured woody cover data across the Sahel and seasonal metrics from time series of MODIS and SPOT-VGT. The method is an indirect estimation of the canopy cover of all woody phanerophytes including trees, shrubs and bushes, and is based on the significant difference in phenophases of woody plants as compared to that of the herbaceous plants. In the Sahel, annual herbaceous plants are only green during the rainy season from June to October and senescence occurs after flowering in September towards the last rain events of the season. The leafing of most trees and shrubs is longer, with several evergreen species, and many woody species green-up ahead of the rains during the last month of the dry season, while annual herbaceous are dependent on the first rains to germinate.

pheno

Figure from Brandt et al., 2016: Seasonal distribution of woody leaf mass depending on the phenological type, modeled within the STEP primary production simulation model (Mougin, Lo Seen, Rambal, Gaston, & Hiernaux, 1995). The months of the wet season during which herbaceous grow are highlighted in a shaded box. Illustrations of typical herbaceous growing curves can be found in Mougin et al. (2014).

We tested 10 metrics representing the annual FAPAR dynamics for their ability to reproduce in situ woody cover at 43 sites (163 observations between 1993 and 2013). Both multi-year field data and satellite metrics are averaged to produce a steady map. Multiple regression models using the integral of FAPAR from the onset of the dry season to the onset of the rainy season, the start date of the growing season and the rate of decrease of the FAPAR curve achieve a cross validated r2 /RMSE (in % woody cover) of 0.73/3.0 (MODIS) and 0.70/3.2 (VGT). The extrapolation to Sahel scale shows an almost nine times higher woody cover than in the global tree cover product MOD44B which only captures trees of a certain minimum size. The derived woody cover map of the Sahel is made publicly available and represents an improvement of existing products and a contribution for future studies of drylands quantifying carbon stocks, climate change assessment, as well as parametrization of vegetation dynamic models.

science

Download the woody cover map for Sahel here

Find the full article here

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

remotesensing-logo

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.

kuh

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.

cse2 cse1

cseb

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.

pheno

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.

biom

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):

1

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.

cover1

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

tab

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