Is there a link between the number of land cover changes and land degradation?

It is assumed that especially areas with intense cultivation meaning e.g. crop production throughout the year are facing higher degradation risk and vegetation decrease over time than areas where also intercropping takes place or the soil is not monotonous exploited. Even if not solely focused on croplands Map III.12 shows the overlay of the number of land cover changes - as seen in Map III.11 - with NDVI decrease and increase between 2001 and 2011 referring to NDVI trends. Three classes among the trends are built. Besides a “tolerance class” meaning NDVI trends between -0.005 and +0.005 the dataset was classified into “decreasing” (NDVI Trend <-0.005) and “increasing” (NDVI trend >0.005) vegetation trends. The overlay highlights the southern part of Kenya, especially the counties Narok and Kajiado where a stable land cover and decreasing trends overlap. Within this overlap are also Kitui and Isiolo – both counties that were also highlighted in the OLS-regression output as underpredicting –, parts of Marsabit and some small areas along the coastline. Also again the northwestern area, mainly Turkana Region but also West Pokot and Baringo are expressing increasing trends and seem to be linked to a more stable land cover.

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Map III.12: Number of Land Cover Changes overlaid with vegetation decrease and increase over time (2001-2011).

Calculating the number of pixels with vegetation decrease and increase Figure III.6 presents the percentage of positive and negative pixel that can be found in the number of land cover class changes. The analysis focuses only on the actual number of land cover changes in general which means that intercropping – e.g. maize as the main crop with short cultivation of e.g. beans between two maize cropping periods – were not taken into account as this would go beyond the scope of this study.

Obviously the more often the land cover classes changed the less vegetation decrease could be observed also validated by a correlation coefficient of -0.92 between the number of land cover changes and the NDVI trends. With a correlation of -0.90 also increasing trends are reported to be lower the more often land cover changes. By comparing the same land cover changes with vegetation trends corrected for rainfall we could observe that this distinction only had minor effects. Even the correlation coefficients for NDVI trends corrected for rainfall and the number of land cover changes were exactly the same:

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Figure III.6: Percentage of vegetation pixel with increasing and decreasing trends within a certain number of land cover changes (tolerance means “no trend” from -0.005 to +0.005 NDVI value trend)

As stable land cover over a long period of time shows higher vegetation degradation than multiple changes in land cover it is questioned which land cover classes create the highest LD rates. Interestingly, the relation of increasing and decreasing trends seems to go in line with a correlation coefficient of 0.9. The more land cover changes take place the more “stable” and thereby sustainable land management seems to be.

By extracting the land cover classes that have been stable from 2001 to 2011 and overlaying those with decreasing and increasing trends of NDVI within the same time period land cover classes could be identified that overlap with the areas with highest losses in terms of vegetation cover (Map III.13).

Map III.13 shows the overlap of stable land cover classes in areas with vegetation decrease (left) and stable land cover classes in areas with vegetation increase (right) referring to the time period 2001-2011. Again Southern Kenya and the counties Kajiado and Narok, are highlighted with regard to vegetation decrease as well as northern and central parts of Kenya such as Marsabit and Isiolo. Vegetation decrease in croplands is highest in western Kenya affecting the counties Trans Nzoia, Busia, Siaya, Kakamega, Kisumu, Vihiga, Kisumu and Migori.

With regard to Figure III.7 we can observe that mainly grasslands are affected by decreasing vegetation trends with 84.44% of the all stable land cover classes with decreasing trends.

Vegetation increase in shrublands can be observed in the northern regions such as Turkana in northwest or Mandera and Wajir in northeastern Kenya bordering Ethiopia.

0 5 10 15 20 25 30 35

0 1 2 3 4 5 6 7 8 9

Percentage of Area affected

Overlap of number of LC Changes and NDVI trend for 2001-2011 in percentage of area

Decreasing VegetationTrend Increasing Vegetation Trend

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Map III.13: Stable Land Cover Classes in areas with vegetation decrease (left) and vegetation increase (right) from 2001-2011.

Figure III.7: Vegetation decrease and increase in stable land cover classes between 2001 and 2011.

Cropland Forest Shrubland Grassland Bareland

Sparse veg

Decrease 4.636 1.296 9.621 84.443 0.005

Increase 3.919 1.493 45.715 48.532 0.341

0 10 20 30 40 50 60 70 80 90

Percentag of Pixels "affected" among all stable land cover pixel

NDVI decreasing and increasing trends in stable Land Cover Classes (2001-2011)

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It would be of further interest what kind of changes took place in those areas where we had high numbers of land cover changes. As it goes beyond the scope of this study to analyze every single land cover change in detail we stick to the statement that shifting land cover which includes also crop rotation and inter-cropping in a certain amount is an advantage for sustainable land management. As the land cover classes used here refer to broader land cover type classes without distinguishing within the single classes especially for e.g. cropland we are not able to look at detailed changes for e.g. crop types. Nevertheless the classified land cover data by MODIS helped to get insights in land cover dynamics at least within greater land cover classes and analyze how vegetation dynamics occur within these classes.