Woody vegetation and land cover changes in the Sahel of Mali (1967–2011)

Another very interesting publication using object based methods to detect single trees on very high resolution imagery is online.

Raphael Spiekermann, Martin Brandt, Cyrus Samimi, Woody vegetation and land cover changes inthe Sahel of Mali (1967–2011), International Journal of Applied Earth Observation and Geoinformation, Volume 34, February 2015, Pages 113-121.

It can be downloaded for free until late October using this link: http://authors.elsevier.com/a/1PeJA14ynR~DWs 

Highlights:

  • Woody cover, species and land cover change over 44 years are analyzed.
  • Object-based classifications are applied with high resolution images of 1967 and 2011.
  • Climate and especially human impact have caused extensive changes.
  • Changes are not always negative and a variety of spatial variations are shown.

Abstract:

In the past 50 years, the Sahel has experienced significant tree- and land cover changes accelerated by human expansion and prolonged droughts during the 1970s and 1980s. This study uses remote sensing techniques, supplemented by ground-truth data to compare pre-drought woody vegetation and land cover with the situation in 2011. High resolution panchromatic Corona imagery of 1967 and multi-spectral RapidEye imagery of 2011 form the basis of this regional scaled study, which is focused on the Dogon Plateau and the Seno Plain in the Sahel zone of Mali. Object-based feature extraction and classifications are used to analyze the datasets and map land cover and woody vegetation changes over 44 years. Interviews add information about changes in species compositions. Results show a significant increase of cultivated land, a reduction of dense natural vegetation as well as an increase of trees on farmer’s fields. Mean woody cover decreased in the plains (−4%) but is stable on the plateau (+1%) although stark spatial discrepancies exist. Species decline and encroachment of degraded land are observed. However, the direction of change is not always negative and a variety of spatial variations are shown. Although the impact of climate is obvious, we demonstrate that anthropogenic activities have been the main drivers of change.

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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

Detecting environmental change using time series, high resolution imagery and field work – a case study in the Sahel of Mali

Climatic changes and population pressure have caused major environmental change in the Sahel during the last fifty years. Many studies use coarse resolution NDVI time series such as GIMMS to detect environmental trends; however explanations for these trends remain largely unknown.

map

We suggest a five-step methodology for the validation of trends with a case study on the Dogon Plateau, Mali. The first step is to monitor long-term trends with coarse scale time series. Instead of GIMMS, we use a combination of LTDR (derived from AVHRR) and SPOT VGT NDVI data, covering the period from 1982-2010 with a temporal resolution of 10 days and a spatial resolution of 5 km.

Areas with significant trends are further analysed in a second step. Here we use a decomposed MODIS time series with a spatial resolution of 250 m to discover details of the blue spot i9n Figure 1. Due to the large scaled MODIS dataset, trends can be identified at a local scale / village level, see Figure 2.

Fig. 1: LTDR-SPOT showing spatial trends of NDVI. Spatial variations can be observed at a scale of 5.6 km (here the resolution is interpolated to 1 km). South of Fiko the large blue area stands out. This seems to be an area which does not show greening trends after the droughts in the beginning of the 80s.

Fig. 1: LTDR-SPOT showing spatial trends of NDVI. Spatial variations can be observed at a scale of 5.6 km (here the resolution is interpolated to 1 km). South of Fiko the large blue area stands out. This seems to be an area which does not show greening trends after the droughts in the beginning of the 80s.

Fig. 2: The map corresponds to the area of the rectangle in Fig. 1 and shows significant trends of MODIS time series since 2000. At a resolution of 250 m, the spatial patterns are far more diverse and variations within small areas can be detected. This demonstrates that the LTDR-SPOT trends merge many processes into single pixels. Thus further steps must be taken to explain local variations.

Fig. 2: The map corresponds to the area of the rectangle in Fig. 1 and shows significant trends of MODIS time series since 2000. At a resolution of 250 m, the spatial patterns are far more diverse and variations within small areas can be detected. This demonstrates that the LTDR-SPOT trends merge many processes into single pixels. Thus further steps must be taken to explain local variations.

Using very high resolution imagery (e.g. SPOT, Quickbird) areas of interest can be compared with pre-drought Corona-imagery from 1967. This offers a detailed overview of the environmental change at tree-level. A comparison of high resolution imagery with the Corona images show major land use changes over the past fifty years. What used to be dense bush cover has partially been converted to farmer managed agro-forestry and a significant proportion is now degraded land. Furthermore, an increase of tree cover on the fields can be detected. These different trends can also be observed in figures 3 and 4.

Fig. 3:  Left: Quickbird 2007 (Google Earth) Right: Corona 1967

Fig. 3: Left: Quickbird 2007 (Google Earth) Right: Corona 1967

Fig 4:  Left: Quickbird 2007 (Google Earth) Right: Corona 1967

Fig 4: Left: Quickbird 2007 (Google Earth) Right: Corona 1967

Yet many explanations for the changes identified remain unclear.

On-site field work provides information on the land use systems, vegetation composition and the current environmental condition. An initial field trip validated the suspected soil erosion and ongoing loss of trees and shrubs outside the fields used for farming purposes. On the fields surrounding the village many useful trees of all ages were identified. Still many explanations for change can only be speculated and hypothesized.

Fig. 5: There is a huge difference of farming areas (second row) and grazing areas (first row) which are adjacent (images taken in Nov. 2011).

Fig. 5: There is a huge difference of farming areas (second row) and grazing areas (first row) which are adjacent (images taken in Nov. 2011).

Fig. 6: On the one hand trees are protected on farmer's fields, on the other hand the „brousse / forêt“ is exploited for firewood (images taken in Nov. 2011).

Fig. 6: On the one hand trees are protected on farmer’s fields, on the other hand the „brousse / forêt“ is exploited for firewood (images taken in Nov. 2011).

Still many explanations for change can only be speculated and hypothesized. For this reason, interviews with the local population are vital for providing missing details.

Interviews with local people showed that good farmer-management using traditional methods, without outside-influence of projects, led to an increase of tree cover on the fields and healthy environmental conditions.

The land outside of the current farming area is highly degraded, which locals explain by the following points:

  • the extreme droughts in the 1970s and 1980s,
  • lack of rain in the past 30 years,
  • lack of protection by farmers,
  • legal and illegal felling by inhabitants of provincial towns in the region,
  • increased livestock numbers put pressure on the soil and vegetation.

Due to the declining vegetation cover and supported by the unfavourable morphology, the susceptibility to soil erosion increases. Many useful trees and shrubs have become rare or disappeared in these areas (e.g. Butyrospermum parkii, Crataeva adansonii, Combretum micranthum, Piliostigma reticulatum, Pterocarpus lucens, Sclerocarya birrea, etc).

Fig. 6: On the one hand trees are protected on farmer's fields, on the other hand the „brousse / forêt“ is exploited for firewood (images taken in Nov. 2011).

Fig. 6: On the one hand trees are protected on farmer’s fields, on the other hand the „brousse / forêt“ is exploited for firewood (images taken in Nov. 2011).

Fig. 7: Interviewing a farmer from Djamnati (image taken in Nov. 2011).

Fig. 7: Interviewing a farmer from Djamnati (image taken in Nov. 2011).

This example demonstrates the importance of land use and how an integrative and qualitative approach as well as input of local inhabitants expands knowledge and understanding of environmental change in the Sahel. Greening and degradation have many reasons which need to be varified by field work. Our example demonstrates, that climatic factors are important drivers of environmental changes. But land use concepts lead to oppositional results in vegetation development and therefore heterogenous landscape patterns.

see the poster:

egu_poster_small

see: Brandt, M., Samimi, C., Romankiewicz, C. & R. Spiekermann (2012): Detecting environmental change using time series, high resolution imagery and field work – a case study in the Sahel of Mali. Geophysical Research Abstracts, Vol. 14, EGU2012-10583, EGU General Assembly 2012.