Agricultural Expansion and Intensification in the Foothills of Mount Kenya: A Landscape Perspective

Abstract

This study spatially assesses, quantifies, and visualizes the agricultural expansion and land use intensification in the northwestern foothills of Mount Kenya over the last 30 years: processes triggered by population growth, and, more recently, by large-scale commercial investments. We made use of Google Earth Engine to access the USGS Landsat data archive and to generate cloud-free seasonal composites. These enabled us to accurately differentiate between rainfed and irrigated cropland, which was important for assessing agricultural intensification. We developed three land cover and land use classifications using the random forest classifier, and assessed land cover and land use change by creating cross-tabulation matrices for the intervals from 1987 to 2002, 2002 to 2016, and 1987 to 2016 and calculating the net change. We then applied a landscape mosaic approach to each classification to identify landscape types categorized by land use intensity. We discuss the impacts of landscape changes on natural habitats, biodiversity, and water. Kappa accuracies for the three classifications lay between 78.3% and 82.1%. Our study confirms that rainfed and irrigated cropland expanded at the expense of natural habitats, including protected areas. Agricultural expansion took place mainly in the 1980s and 1990s, whereas agricultural intensification largely happened after 2000. Since then, not only large-scale producers, but also many smallholders have begun to practice irrigated farming. The spatial pattern of agricultural expansion and intensification in the study area is defined by water availability. Agricultural intensification and the expansion of horticulture agribusinesses increase pressure on water. Furthermore, the observed changes have heightened pressure on pasture and idle land due to the decrease in natural and agropastoral landscapes. Conflicts between pastoralists, smallholder farmers, large-scale ranches, and wildlife might further increase, particularly during the dry seasons and in years of extreme drought.

Full text

remote sensing
Article
Agricultural Expansion and Intensification in the
Foothills of Mount Kenya: A Landscape Perspective
Sandra Eckert 1, *ID , Boniface Kiteme 2, Evanson Njuguna 2and Julie Gwendolin Zaehringer 1
1Centre for Development and Environment, University of Bern, Hallerstrasse 10, 3012 Bern, Switzerland;
julie.zaehringer@cde.unibe.ch
2Centre for Training and Integrated Research in ASAL Development, 10400 Nanyuki, Kenya;
b.kiteme@africaonline.co.ke (B.K.); e.njuguna@cetrad.org (E.N.)
*Correspondence: sandra.eckert@cde.unibe.ch; Tel.: +41-31-631-5439
Received: 3 July 2017; Accepted: 28 July 2017; Published: 31 July 2017
Abstract:
This study spatially assesses, quantifies, and visualizes the agricultural expansion and land
use intensification in the northwestern foothills of Mount Kenya over the last 30 years: processes
triggered by population growth, and, more recently, by large-scale commercial investments. We made
use of Google Earth Engine to access the USGS Landsat data archive and to generate cloud-free
seasonal composites. These enabled us to accurately differentiate between rainfed and irrigated
cropland, which was important for assessing agricultural intensification. We developed three land
cover and land use classifications using the random forest classifier, and assessed land cover and land
use change by creating cross-tabulation matrices for the intervals from 1987 to 2002, 2002 to 2016,
and 1987 to 2016 and calculating the net change. We then applied a landscape mosaic approach to each
classification to identify landscape types categorized by land use intensity. We discuss the impacts
of landscape changes on natural habitats, biodiversity, and water. Kappa accuracies for the three
classifications lay between 78.3% and 82.1%. Our study confirms that rainfed and irrigated cropland
expanded at the expense of natural habitats, including protected areas. Agricultural expansion took
place mainly in the 1980s and 1990s, whereas agricultural intensification largely happened after
2000. Since then, not only large-scale producers, but also many smallholders have begun to practice
irrigated farming. The spatial pattern of agricultural expansion and intensification in the study
area is defined by water availability. Agricultural intensification and the expansion of horticulture
agribusinesses increase pressure on water. Furthermore, the observed changes have heightened
pressure on pasture and idle land due to the decrease in natural and agropastoral landscapes. Conflicts
between pastoralists, smallholder farmers, large-scale ranches, and wildlife might further increase,
particularly during the dry seasons and in years of extreme drought.
Keywords:
land cover and land use change; landscape change; agricultural intensification;
greenhouse cultivation; environmental impacts; remote sensing; Kenya
1. Introduction
Changes in land cover and land use worldwide have reached an unprecedented pace, magnitude,
and spatial extent [
1
–
5
]. The changes affect and contribute to local, regional, and global aspects of
earth system functioning [
1
–
3
,
6
]. In African landscapes, pastoralism, shifting cultivation, permanent or
semi-permanent agriculture, and agroforestry have altered the environment to a point that the present
landscape is the product of human-induced changes as much as natural variation in vegetation [
7
].
Over the last decades, anthropogenic impacts and competition over land have become issues of
major concern [
8
,
9
]. Many areas in Africa are experiencing substantial human population growth,
and, as a result, a shift away from extensive pastoral livestock-keeping to subsistence-oriented
Remote Sens. 2017,9, 784; doi:10.3390/rs9080784 www.mdpi.com/journal/remotesensing

Remote Sens. 2017,9, 784 2 of 20
small-scale farming and agroforestry [
10
]. In addition, the last 10–15 years have also seen agricultural
intensification. Triggered mainly by large-scale agricultural investments, it has become an additional
important driver of land cover and land use change [8,10,11].
In Kenya, horticulture has grown faster over the last decade than any other industry in the
agricultural sector [
12
,
13
]. In the Mount Kenya region, substantial agricultural expansion driven by
population growth, in combination with recurring cycles of drought, has since the late 1990s been
causing conflicts both between upstream and downstream water users and between pastoralists and
farmers [
12
,
14
]. The more recent expansion of horticultural agribusinesses in the same area is further
increasing pressure on limited natural resources, particularly water and land [15–17].
The livelihood systems of small-scale farmers in this region have been studied intensively for more
than 20 years [
18
–
23
]. However, to date, there is only little spatially explicit quantitative information on
agricultural expansion and land use intensification in the region, if any at all. It can only be estimated
to which land covers’ cost, and to what extent, land cover and land use changes related to agricultural
expansion and intensification have shaped the Mount Kenya region’s landscapes.
A growing body of literature focuses on the assessment of irrigated and rainfed cropland and
related changes using multi-temporal or multi-sensor data [
24
–
26
]. However, many studies focus on
the global or national scale, using multi-temporal MODIS satellite data that are too coarse to capture
small-scale irrigated plots or regional changes [
27
–
29
]. More recently, scholars have begun to explore
phenological profiles from Landsat time series, which has led to more accurate and more detailed
cropland identification and change assessments [
30
–
32
]. Some studies have successfully assessed
agricultural intensification in areas where it manifested itself in a change of land cover [
33
], a greater
number of cropping cycles [
34
], or an increase in center-pivot irrigation areas [
35
]. However, most of
these studies focus on pixel-level land cover and land use changes [
32
,
36
] rather than landscape-level
changes towards more intense land use systems.
Land change science offers a strong conceptual framework to analyze transitions in land use
systems dominated by smallholders [
37
]. It seeks to understand the dynamics of land cover and land
use as a coupled human-environmental system [
38
]. These dynamics lead to distinct spatial land cover
and land use patterns, creating mosaics of landscapes [
39
]. Landscape analysis is therefore considered
a suitable approach for monitoring these distinct land cover and land use patterns, and transitions
between them [
2
,
40
]. Geographical information sciences and remote sensing provide powerful tools to
undertake such research [37,41,42].
Against this background, the aim of this study is to spatially assess and quantify agricultural
expansion and land use intensification in the northwestern foothills of Mount Kenya over the last
30 years. Remote sensing-based land cover and land use classifications for three points in time
provided the basis for extracting land use intensification. We present a landscape mosaic approach
that enabled us, based on specific combinations of land cover and land use classes, to identify
different landscape types categorized by land use intensity. Further, we differentiated landscape
types according to the presence of woody biomass (e.g., trees, shrubs, and bushes). Focusing on
the landscape scale has enabled us to assess and visualize changes in landscape type—including
agricultural intensification—and to discuss their impacts on natural habitats, biodiversity, and water.
2. Materials and Methods
2.1. Study Area
The study area of 249,147 ha lies in the northwestern foothills of Mount Kenya, within the upper
Ewaso Ng’iro basin, and includes parts of Laikipia, Meru, and Nyeri counties. The upper Ewaso
Ng’iro basin encompasses steep ecological gradients, as it drops from 5199 m above sea level at the
summit of the mountain to an average altitude of 1500 m above sea level in the northwest. Climatic
conditions range from semi-humid (1000–1500 mm of rainfall annually) near Mount Kenya in the east

Remote Sens. 2017,9, 784 3 of 20
to semi-arid (400–900 mm rainfall) and arid (about 350 mm rainfall) towards the west [
43
]. The farther
away from Mount Kenya, the dryer the conditions.
There are two distinct rainy seasons per year. The long rains last from mid-March to mid-June
(sowing and planting time). They are followed by a dry season from June to September or early
October (harvesting time). A second, much shorter rainy season occurs in November. The two
rainy seasons largely determine the cropping calendar in this semi-arid, water-scarce area, as most
small-scale farmers rely on the seasonal rains. The rains are unreliable and unpredictable in terms
of onset, duration, and termination [
44
]. This variability impacts greatly on all natural resources
and particularly on water, which continues to become scarcer. The major river systems in the area
show a significant decline in water even though there has been no significant change in the rainfall
regime [
18
]. The growing number of water abstractions for irrigation, livestock, and domestic purposes
has intensified competition for this scarce resource [45].
The study area has experienced substantial land use conversions since the beginning of the 20th
century. Traditionally, the area was inhabited by the Maasai. At that time, much of the study area was
covered with fire-modified acacia bushland and grassland [
19
], which the Maasai used as pasture.
Subsequently, several major land use and socio-economic transitions occurred [
14
]. The dominant
land use changed from pastoralism to extensive large-scale farming and ranching, which was reserved
for European settlers [
46
]. After Kenya’s independence, the land was distributed to immigrating
small-scale subsistence farmers, shifting the dominant land use from extensive ranching to small-scale
mixed farming [
14
], and leading to an increase in the population of Laikipia County from 60,000 in
1960 to over 400,000 in 2009 [
47
]. The past 15 to 20 years, finally, have seen the development of a highly
technologized, export-oriented horticulture sector practicing greenhouse and high-input vegetable and
flower production [
48
]. Today, the area’s very fertile soils are used both by these high-input, large-scale
commercial farms and by smallholders. The geospatial pattern of the different land use systems is
largely determined by water availability. Closer to Mount Kenya, a dense population of smallholders
practices small-scale farming; more recently, large-scale horticulture and greenhouse farms have been
established in the area. In the drier areas farther away from Mount Kenya, land use is dominated by
agropastoralism and pastoralism, interspersed with wildlife reserves and tourist lodges. The study
area is depicted in Figure 1.
Remote Sens. 2017, 9, 784 3 of 20
There are two distinct rainy seasons per year. The long rains last from mid-March to mid-June
(sowing and planting time). They are followed by a dry season from June to September or early
October (harvesting time). A second, much shorter rainy season occurs in November. The two rainy
seasons largely determine the cropping calendar in this semi-arid, water-scarce area, as most small-
scale farmers rely on the seasonal rains. The rains are unreliable and unpredictable in terms of onset,
duration, and termination [44]. This variability impacts greatly on all natural resources and
particularly on water, which continues to become scarcer. The major river systems in the area show
a significant decline in water even though there has been no significant change in the rainfall regime
[18]. The growing number of water abstractions for irrigation, livestock, and domestic purposes has
intensified competition for this scarce resource [45].
The study area has experienced substantial land use conversions since the beginning of the 20th
century. Traditionally, the area was inhabited by the Maasai. At that time, much of the study area
was covered with fire-modified acacia bushland and grassland [19], which the Maasai used as
pasture. Subsequently, several major land use and socio-economic transitions occurred [14]. The
dominant land use changed from pastoralism to extensive large-scale farming and ranching, which
was reserved for European settlers [46]. After Kenya’s independence, the land was distributed to
immigrating small-scale subsistence farmers, shifting the dominant land use from extensive ranching
to small-scale mixed farming [14], and leading to an increase in the population of Laikipia County
from 60,000 in 1960 to over 400,000 in 2009 [47]. The past 15 to 20 years, finally, have seen the
development of a highly technologized, export-oriented horticulture sector practicing greenhouse
and high-input vegetable and flower production [48]. Today, the area’s very fertile soils are used both
by these high-input, large-scale commercial farms and by smallholders. The geospatial pattern of the
different land use systems is largely determined by water availability. Closer to Mount Kenya, a
dense population of smallholders practices small-scale farming; more recently, large-scale
horticulture and greenhouse farms have been established in the area. In the drier areas farther away
from Mount Kenya, land use is dominated by agropastoralism and pastoralism, interspersed with
wildlife reserves and tourist lodges. The study area is depicted in Figure 1.
Figure 1. Overview of the study area located in the foothills of Mount Kenya (map projection of
detailed study area map: UTM 37S).
Figure 1.
Overview of the study area located in the foothills of Mount Kenya (map projection of
detailed study area map: UTM 37S).

Remote Sens. 2017,9, 784 4 of 20
2.2. Assessing Land Cover and Land Use Change since the Late 1980s
2.2.1. Satellite Data Preprocessing
For this study, we queried the USGS Landsat data archives for Landsat 5, Landsat 7, and Landsat
8 scenes using the Google Earth Engine (GEE) cloud computing environment. We worked with the
Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) surface reflectance products
of Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI provided by USGS. These products are already
geometrically coregistered, orthorectified, and atmospherically corrected. They are provided together
with a cloud mask and a quality assessment (QA) band.
We generated three different image collections representing the situation in 1987, 2002, and 2016.
In order to obtain cloud-free seasonal composites of surface reflectance we had to include several years
of imagery for each point in time. For 1987, we used imagery acquired between 1984 and 1987; for 2002,
we used data acquired between 1999 and 2002; and for 2016 we used data acquired between 2014
and 2016. Figure S1 in the Supplementary Materials provides an overview of the numbers of scenes
that were available for each month and year, indicating which ones were used for this study. For all
scenes acquired within these three time periods, we calculated the Normalized Difference Vegetation
Index (NDVI). Then, we used the LEDAPS QA band to remove clouded pixels, resulting in a stack
of cloud-free pixel values for each pixel location, which we then reduced to a monthly composite by
choosing the median pixel value for each pixel and for each month. The use of the median pixel value
ensures that outlier values (e.g., due to cloud shadows or clouds that were not previously removed
by the QA band) are excluded. This was done for each optical Landsat band as well as the NDVI.
Based on a visual check, we finally downloaded those monthly composites for the three time periods
that (1) contained no or few no-data areas (due to masked clouded pixels), and (2) did not contain
any cloud shadows. This resulted in three raster data stacks that contained three to four monthly
composites representing the study area in one of the two dry seasons and at least one of the two wet
seasons. Such seasonal composites representing key phenological time windows can be helpful for
separating certain land cover and land use classes in a reliable way [49,50].
2.2.2. Land Cover and Land Use Classification and Change Analysis
The field reference data required for training and validating the classifier were collected during a
field visit in February 2016. Additional reference data were digitized in Google Earth, which offered
high-resolution data captured in 2001, 2003, 2013, and 2016. For the 1984–1987 composite, reference data
were collected by inspecting the Landsat composites and delineating representative samples together
with fellow researchers who have been working in the study area and following its development since
the early 1980s, and have therefore acquired a great deal of local expertise [14,22,44,46,51].
We defined land cover and land use classes that reflect the natural vegetation cover in the
study area, as well as ones that reflect land covers and land uses that developed with increasing
human activity in the study area (Table 1). Further, we distinguished “rainfed cropland” from
“irrigated cropland”, and included a “greenhouses” land use class to identify and assess agricultural
intensification in the study area.
Table 1. Land cover and land use classes characterizing the landscapes in the study area.
Land Cover and Land Use Class Description
Bare land Bare soil including dirt roads, rock outcrops, and sand
Cropland
Rainfed cropland Plots of varying size covered with crops or ploughed
Irrigated cropland Plots showing a high amount of green vegetation cover during dry seasons
Savannah grassland Grassland interspersed with bushes, shrubs, and trees at a low to medium density,
and fallows with a vegetation cover
Bush- and shrubland Areas with a bush and shrub cover of medium to high density and an understory
that is bare or covered with grass or dry matter

Remote Sens. 2017,9, 784 5 of 20
Table 1. Cont.
Land Cover and Land Use Class Description
Forest Natural or plantation forests, including riparian forests, and very densely grown
bush- and shrubland with a high amount of green vegetation
Waterbodies Small and shallow natural waterbodies and larger artificial reservoirs for irrigation
Settlements Settlements, large buildings, tarmac
Greenhouses Glass or plastic greenhouses
The three data stacks representing typical dry- and wet-season conditions in 1987, 2002, and 2016
were split up into homogeneous subsets of predominantly natural habitats and predominantly cropland
areas in order to avoid misclassifications between bush- and shrubland and rainfed cropland, which
have very similar spectral characteristics.
All subsets were classified using random forest (RF), an ensemble method for supervised
classification, and regression trees (CART), first developed by Breiman in [
52
]. RF is a high-performance
machine learning algorithm based on an ensemble of decision trees. Even slight variations in training
data cause CARTs to differ significantly in their structure. This characteristic of CARTs can be combined
with bootstrap aggregation and random feature selection to create independent predictors [
53
]. It has
many benefits compared to traditional classifiers [
52
,
54
,
55
]. RF is relatively insensitive to the number
and multi-collinearity of input data, and makes no assumptions about distributions. Furthermore, it has
been shown to provide reliable and stable classification results, outperforming other classifiers [
56
–
59
].
In our classification, we used 1000 trees for the RF model, and the number of selected features was set
as the square root of all features. The Gini coefficient served as the impurity criterion. The accuracy
of the resulting land cover and land use classifications was assessed using 40% (1987), 51% (2002),
and 57% (2016) of the collected field reference data as independent validation points. We calculated
overall accuracy, class-wise user ’s and producer’s accuracies, as well as kappa and F1 accuracies [
60
].
The overall accuracies for 1987, 2002, and 2016 range between 83.8% and 86.7%. The kappa accuracies
lie between 78.3% and 82.1%, and the average F1 accuracies between 83.6% and 87.8%. Detailed class
accuracies are indicated in Table S1 in the Supplementary Materials.
We assessed land cover and land use change for each pixel of the study area by creating
cross-tabulation matrices for the intervals from 1987 to 2002, from 2002 to 2016, and from 1987 to 2016,
and by calculating net change. Furthermore, when analyzing class changes, we accounted for the
spatial extent of the various land cover and land use classes, as this makes it possible to differentiate
between random changes and systematic change processes [61,62].
2.2.3. A Landscape Mosaic Approach to Capture the Intensification of Agricultural Land Use
Land cover and land use change maps are of limited suitability for assessing land use intensification
processes and landscape changes. This is because certain landscape types, as well as certain
intensification processes and landscape changes, are hard to capture spatially by remote sensing.
Doing so requires analyzing changes in the combinations of land covers and land uses in a certain
context area. To address this problem, Messerli et al. [
63
] introduced an approach that interprets
a pixel’s land cover by taking into account human-environment interactions and the condition of
neighboring pixels. The approach was successfully applied to distinguish shifting cultivation systems
from permanent land use systems in Laos and Madagascar [
64
,
65
]. Arvor et al. [
40
] developed a similar
approach to map and analyze the soybean agricultural frontier in Mato Grosso, Brazil. In the present
study, we adapted the approach to the different land use systems found in Kenya in order to identify
land use intensification and landscape changes. The following paragraphs describe how we proceeded.
First, we defined the size of the context area to be considered as a square of 2
×
2 km. This is large
enough to contain the typical combinations of land cover and land use classes that define the different
land use systems of varying land use intensity (e.g., greenhouses with outdoor irrigated cropland
and irrigation water reservoirs). Once the size was defined, we used focal statistics to calculate class

Remote Sens. 2017,9, 784 6 of 20
percentages per context area. Then, we developed a matrix of landscape types based on five categories
of agricultural land use intensity and three categories of woody biomass cover, as well as a decision
rule set for each category.
As a guideline for categorization, we used the conceptual model of frontier dynamics proposed
by DeFries et al. [
2
]. The five agricultural land use intensity categories were defined based on (1) the
share of natural land cover classes in the context area, and (2) the relation between rainfed cropland,
irrigated cropland, and greenhouses. This resulted in the following intensity categories:
•
Natural landscape: Natural vegetation cover classes (bare land, savannah grassland, bush- and
shrubland, and forest) cover more than 80% of the context area (I1)
•
Agropastoralism: Savannah grassland covers a greater share of the context area than rainfed
cropland, but cropland covers at least 5% of the context area (I2)
•
Rainfed farming: Rainfed cropland and irrigated cropland together cover more than 20% of the
context area, but the share of rainfed cropland is larger than that of irrigated cropland (I3)
•
Irrigated farming: Rainfed cropland and irrigated cropland together cover more than 20% of the
context area, but the share of irrigated cropland is larger than that of rainfed cropland (I4)
•
Large-scale commercial farming: Greenhouses and waterbodies together cover more than 3% of
the context area (I5)
The four woody biomass categories were defined based on the amount and type of woody
biomass present in the context area. This categorization makes it possible to capture change in forest
and woodland areas, which is closely linked to biodiversity [
66
] as well as to agroforestry land use
systems, in which tree crops play an important role. Agroforestry is considered to be an important
sustainable farming system in Africa [67].
•High forest cover: Forest covers at least 20% of the context area (FO)
•
Mostly bush- and shrubland: Bush- and shrubland and savannah grassland together cover a
larger share of the context area than forest (BS)
•Little woody biomass: Bare land covers at least 20% of the context area (BA)
•
No woody biomass: All areas that do not fall in one of the above categories, e.g., monoculture
cropland (NO)
The resulting matrix of landscape types is presented in Figure 2.
Remote Sens. 2017, 9, 784 6 of 20
on five categories of agricultural land use intensity and three categories of woody biomass cover, as
well as a decision rule set for each category.
As a guideline for categorization, we used the conceptual model of frontier dynamics proposed
by DeFries et al. [2]. The five agricultural land use intensity categories were defined based on (1) the
share of natural land cover classes in the context area, and (2) the relation between rainfed cropland,
irrigated cropland, and greenhouses. This resulted in the following intensity categories:
 Natural landscape: Natural vegetation cover classes (bare land, savannah grassland, bush- and
shrubland, and forest) cover more than 80% of the context area (I1)
 Agropastoralism: Savannah grassland covers a greater share of the context area than rainfed
cropland, but cropland covers at least 5% of the context area (I2)
 Rainfed farming: Rainfed cropland and irrigated cropland together cover more than 20% of the
context area, but the share of rainfed cropland is larger than that of irrigated cropland (I3)
 Irrigated farming: Rainfed cropland and irrigated cropland together cover more than 20% of the
context area, but the share of irrigated cropland is larger than that of rainfed cropland (I4)
 Large-scale commercial farming: Greenhouses and waterbodies together cover more than 3% of
the context area (I5)
The four woody biomass categories were defined based on the amount and type of woody
biomass present in the context area. This categorization makes it possible to capture change in forest
and woodland areas, which is closely linked to biodiversity [66] as well as to agroforestry land use
systems, in which tree crops play an important role. Agroforestry is considered to be an important
sustainable farming system in Africa [67].
 High forest cover: Forest covers at least 20% of the context area (FO)
 Mostly bush- and shrubland: Bush- and shrubland and savannah grassland together cover a
larger share of the context area than forest (BS)
 Little woody biomass: Bare land covers at least 20% of the context area (BA)
 No woody biomass: All areas that do not fall in one of the above categories, e.g., monoculture
cropland (NO)
The resulting matrix of landscape types is presented in Figure 2.
Figure 2. Landscape types categorized by land use intensity and woody biomass cover (adapted from
Zaehringer et al. [65]).
The five binary maps representing the five intensity categories are largely complementary, but
some overlaps nonetheless exist. For this reason, we combined them according to their intensity
levels, with the map showing the most intensive category at the top and the one showing the least
Figure 2.
Landscape types categorized by land use intensity and woody biomass cover (adapted from
Zaehringer et al. [65]).
The five binary maps representing the five intensity categories are largely complementary,
but some overlaps nonetheless exist. For this reason, we combined them according to their intensity
levels, with the map showing the most intensive category at the top and the one showing the least

Remote Sens. 2017,9, 784 7 of 20
intensive one at the bottom. The four binary maps representing woody biomass were processed
analogously, with the map showing the category with the most woody biomass at the top and the one
showing the category with the least woody biomass at the bottom. The resulting two categorical maps
were then combined to produce a landscape mosaic map. This procedure was repeated for each of the
three points in time.
The resulting three landscape mosaic maps depict aggregated land use information at the
landscape level for the three points in time studied. This makes it possible to detect changes that affect
entire land use systems, as well as to assess intensification processes [
65
]. In order to analyze landscape
change and quantify the intensification of agricultural land use, we cross-tabulated the landscape
mosaic statistics for the three points in time.
The landscape mosaic maps were evaluated based on visual inspection and local expertise.
A quantitative assessment at the landscape level is not feasible based on the available field reference
data. Furthermore, single pixel classification errors in the land cover maps have little influence on the
landscape-level mosaic maps.
3. Results
3.1. Land Cover and Land Use Changes in the Study Area
Table 2shows the net changes in area that occurred in each land cover and land use class over
the last 30 years, split up into two time intervals. A look at the changes over the entire 30 years
shows strikingly that massive changes between 1987 and 2016 occurred mainly in four classes: while
savannah grassland and bush- and shrubland decreased by 46,105 ha and 11,837 ha, respectively,
cropland (rainfed and irrigated) increased by 47,752 ha. This change is also clearly visible in Figure 3.
A look at the two partial intervals shows that the transition to cropland first occurred on the
northwestern slopes and foothills closer to Mount Kenya, followed by areas farther away from the
mountain and areas near rivers. Smaller amounts of savannah grassland were converted to forest,
bush- and shrubland, and irrigated cropland. While the increase in rainfed cropland is much smaller
during the second interval (0.85%), irrigated cropland continues to increase at a similar rate as during
the first interval (3.87%). The conversion to irrigated cropland mainly happened near Mount Kenya,
near greenhouses or irrigation water reservoirs, or along rivers, where riparian forests and wetlands
were converted to irrigated or rainfed cropland.
A number of forest plantations were established during the first interval, increasing the forest
area by 8509 ha. During the second interval, the total forest area shrank again (by 605 ha) due to the
harvesting of trees from plantations and the conversion of riparian and other small forest patches to
irrigated and rainfed cropland. Class changes between savannah grassland, bush- and shrubland,
and forest were also observed. These can partly be attributed to natural succession and tree harvesting,
but in part they are also caused by variability in green vegetation cover between the three points in
time investigated due to rainfall variability.
Table 2.
Net area changes for each land cover and land use class within the study area in hectares (ha)
and percent (%) of the total area analyzed between 1987 and 2002, between 2002 and 2016, and across
the entire time interval from 1987 to 2016.
Net Changes 1987–2002 2002–2016 1987–2016
ha % ha % ha %
Rainfed cropland 28,740 11.6 2079 0.8 29,438 11.9
Bare land 228 0.1 1026 0.4 3351 1.4
Waterbodies 24 0.0 73 0.0 97 0.0
Irrigated cropland 8882 3.6 9515 3.9 18,315 7.4
Savannah grassland −41,023 −16.6 −6030 −2.5 −46,105 −
18.7
Forest 8509 3.4 −605 −0.2 7816 3.2
Settlements 306 0.1 31 0.0 322 0.1
Greenhouses 21 0.0 604 0.2 624 0.3
Bush- and shrubland −5689 −2.3 −6693 −2.7 −11,837 −4.8

Remote Sens. 2017,9, 784 8 of 20
Remote Sens. 2017, 9, 784 8 of 20
Figure 3. Land cover and land use classification maps of the study area around 1987, 2002, and 2016
(map projection: UTM 37S).
Settlements grew considerably during the first interval, while waterbodies and greenhouses
expanded mostly during the second interval. Between 1987 and 2016, greenhouses increased by 624
ha and waterbodies by 97 ha. The appearance of greenhouses surrounded by small water reservoirs
can be observed particularly along the main road connecting Nairobi with the towns of Naro Moru,
Nanyuki, and Timau. Figure 4A shows this impressive development east of Timau, while Figure 4B
shows the conversion of savannah grassland, wetlands, as well as riparian and other small forests to
rainfed and irrigated cropland northwest of Naro Moru, along the Naro Moru and Burguret rivers.
Areas cultivated by large-scale investors are mostly used for highly technologized commercial
floriculture or horticulture. Floriculture farms are typically characterized by greenhouses and water
storage reservoirs, while horticulture farms are generally surrounded by irrigated cropland.
Areas classified as greenhouses in 2016 were formerly either used as rainfed cropland (297 ha)
or converted from savannah grassland (239 ha), bush- and shrubland (37 ha), and forest (25 ha).
Detailed land cover and land use class change matrices for the two time intervals are provided in
Tables S2 and S3 in the Supplementary Materials.
Figure 3.
Land cover and land use classification maps of the study area around 1987, 2002, and 2016
(map projection: UTM 37S).
Settlements grew considerably during the first interval, while waterbodies and greenhouses
expanded mostly during the second interval. Between 1987 and 2016, greenhouses increased by 624 ha
and waterbodies by 97 ha. The appearance of greenhouses surrounded by small water reservoirs
can be observed particularly along the main road connecting Nairobi with the towns of Naro Moru,
Nanyuki, and Timau. Figure 4A shows this impressive development east of Timau, while Figure 4B
shows the conversion of savannah grassland, wetlands, as well as riparian and other small forests to
rainfed and irrigated cropland northwest of Naro Moru, along the Naro Moru and Burguret rivers.
Areas cultivated by large-scale investors are mostly used for highly technologized commercial
floriculture or horticulture. Floriculture farms are typically characterized by greenhouses and water
storage reservoirs, while horticulture farms are generally surrounded by irrigated cropland.
Areas classified as greenhouses in 2016 were formerly either used as rainfed cropland (297 ha) or
converted from savannah grassland (239 ha), bush- and shrubland (37 ha), and forest (25 ha). Detailed
land cover and land use class change matrices for the two time intervals are provided in Tables S2
and S3 in the Supplementary Materials.

Remote Sens. 2017,9, 784 9 of 20
Remote Sens. 2017, 9, 784 9 of 20
Figure 4. Two subsets of the land cover and land use classifications for 1987, 2002, and 2016 showing
(A) the development of greenhouses and irrigation water reservoirs east of Timau, and (B) the
conversion of savannah grassland, forests mostly along rivers, and wetlands northwest of Naro Moru,
along the Naro Moru and Burguret rivers to rainfed and irrigated cropland (map projection: UTM
37S).
3.2. Landscape Changes in the Study Area
The availability of aggregated land use information at the landscape level makes it possible to
detect changes that affect entire land use systems, as well as to assess and visualize processes of
cropland expansion and use intensification (Figure 5).
In 1987, the study area was dominated by natural vegetation and agropastoral land use systems,
which together covered around 75% of the study area. In 2016, this was no longer the case, with these
landscape types now covering only 45% of study area (Figures 6 and 7). This reduction happened at
the expense of landscapes characterized by natural vegetation (forest, bush- and shrubland,
grassland; −22%). Such areas were converted, mostly during the first interval, in the vicinity of Mount
Kenya, where rainfall in those years was comparably reliable; during the second interval, the change
affected natural landscapes in the lower, more arid plains farther away from Mount Kenya but near
rivers and wetlands. Most of these landscapes were converted to smallholder farmland (23%)
dominated by rainfed, low-input agriculture (BS-I3, BA-I3). One exception is an area northeast of
Timau, where large-scale cereal cultivation in monoculture (NO-I3) was practiced already before
1987. Here, there was little change over the past 30 years, except in some plots that were given to
smallholder farmers practicing multicropping and agroforestry, resulting in a conversion from NO-
Figure 4.
Two subsets of the land cover and land use classifications for 1987, 2002, and 2016 showing
(
A
) the development of greenhouses and irrigation water reservoirs east of Timau, and (
B
) the
conversion of savannah grassland, forests mostly along rivers, and wetlands northwest of Naro
Moru, along the Naro Moru and Burguret rivers to rainfed and irrigated cropland (map projection:
UTM 37S).
3.2. Landscape Changes in the Study Area
The availability of aggregated land use information at the landscape level makes it possible to
detect changes that affect entire land use systems, as well as to assess and visualize processes of
cropland expansion and use intensification (Figure 5).
In 1987, the study area was dominated by natural vegetation and agropastoral land use systems,
which together covered around 75% of the study area. In 2016, this was no longer the case, with these
landscape types now covering only 45% of study area (Figures 6and 7). This reduction happened at
the expense of landscapes characterized by natural vegetation (forest, bush- and shrubland, grassland;
−
22%). Such areas were converted, mostly during the first interval, in the vicinity of Mount Kenya,
where rainfall in those years was comparably reliable; during the second interval, the change affected
natural landscapes in the lower, more arid plains farther away from Mount Kenya but near rivers
and wetlands. Most of these landscapes were converted to smallholder farmland (23%) dominated
by rainfed, low-input agriculture (BS-I3, BA-I3). One exception is an area northeast of Timau, where
large-scale cereal cultivation in monoculture (NO-I3) was practiced already before 1987. Here, there
was little change over the past 30 years, except in some plots that were given to smallholder farmers

Remote Sens. 2017,9, 784 10 of 20
practicing multicropping and agroforestry, resulting in a conversion from NO-I3 to FO-I3 or FO-I4.
Throughout the study area, an increase in tree cover—mostly due to the adoption of agroforestry
systems—occurred particularly in areas with sufficient water availability, that is, in the semi-humid
areas near Mount Kenya or along perennial rivers. Since 2002, agroforestry has expanded along rivers,
where riparian forests and wetlands were converted mainly to irrigated cropland. Agroforestry is also
increasingly found at higher elevations near the boundaries of the Mount Kenya National Park and
National Forest, where water is more readily available.
Remote Sens. 2017, 9, 784 10 of 20
I3 to FO-I3 or FO-I4. Throughout the study area, an increase in tree cover—mostly due to the adoption
of agroforestry systems—occurred particularly in areas with sufficient water availability, that is, in
the semi-humid areas near Mount Kenya or along perennial rivers. Since 2002, agroforestry has
expanded along rivers, where riparian forests and wetlands were converted mainly to irrigated
cropland. Agroforestry is also increasingly found at higher elevations near the boundaries of the
Mount Kenya National Park and National Forest, where water is more readily available.
Figure 5. Landscape mosaic maps for 1987, 2002, and 2016, overlaid with waterways (map projection:
UTM 37S). The map for 2016 additionally shows different types of protected or managed areas and
the road network. Land use intensity increases with the color gradient changing from dark green
towards dark pink. The darker and more intense the green and olive colors, the more woody biomass
there is. The more intense the orange, red, and pink colors, the less woody biomass there is and the
more intensively the land is used.
Figure 5.
Landscape mosaic maps for 1987, 2002, and 2016, overlaid with waterways (map projection:
UTM 37S). The map for 2016 additionally shows different types of protected or managed areas and the
road network. Land use intensity increases with the color gradient changing from dark green towards
dark pink. The darker and more intense the green and olive colors, the more woody biomass there
is. The more intense the orange, red, and pink colors, the less woody biomass there is and the more
intensively the land is used.

Remote Sens. 2017,9, 784 11 of 20
Remote Sens. 2017, 9, 784 11 of 20
Figure 6. (A) Shares of landscape types in the total study area in percent for 1987, 2002, and 2016, and
(B) gains and losses in each landscape type expressed as percentages of the total study area for the
three intervals from 1987 to 2002, from 2002 to 2016, and from 1987 to 2016. FO = high forest cover, BS
= mostly bush- and shrubland, BA = little woody biomass, NO = no woody biomass.
Figure 6.
(
A
) Shares of landscape types in the total study area in percent for 1987, 2002, and 2016,
and (
B
) gains and losses in each landscape type expressed as percentages of the total study area for the
three intervals from 1987 to 2002, from 2002 to 2016, and from 1987 to 2016. FO = high forest cover,
BS = mostly bush- and shrubland, BA = little woody biomass, NO = no woody biomass.

Remote Sens. 2017,9, 784 12 of 20
Remote Sens. 2017, 9, 784 12 of 20
Figure 7. Sankey plot showing changes from one landscape type to another between 1987, 2002, and
2016. The colors and labels follow the scheme shown in Figure 2.
Mixed agropastoral landscape types remained fairly stable in size, but shifted farther away from
Mount Kenya towards more arid areas. Irrigated farming was still rare in 1987 (<1% of study area),
but became more prominent and appeared on the landscape mosaic map in 2002 (2.5%); in 2016,
finally, it covered 4% of the study area. As mentioned above, it is mainly practiced by horticulture
farms along the main road from Nanyuki to Timau, as well as by small-scale farmers, who mostly
practice it in conjunction with agroforestry and have reliable access to irrigation water (FO-I4 and BS-
I4). Another feature that first appears in the 2002 landscape mosaic map is the horticulture and
floriculture hotspot area (FO-I4 and FO-I5) between Nanyuki and Timau. At that time, there were no
greenhouses in the study area, and flowers were grown in open fields. However, greenhouses are
needed for the production of high-value, high-quality flowers [68], which the floriculture sector
started investing in around 2002 [69,70]. In 2016, landscapes characterized by such high-intensity
agriculture already covered 3% of the study area. Most of them are located along rivers and along or
only a short distance away from the main road.
3.3. Changes in Tree Cover and Woody Biomass
Landscapes with a high amount of woody biomass (FO and BS) experienced an increase between
1987 and 2002, followed by a slight decrease until 2016 (see Figure 8). The increase is mainly due to
an expansion of riparian forests, and, even more so, of bush- and shrubland west to northwest of
Naro Moru. By 2016, the area covered by these landscapes shrank again; the establishment of several
tree plantations east of Nanyuki was unable to compensate for the loss of natural, forest-dominated
landscapes that occurred, for example, north of Timau as well as south and southwest of Naro Moru,
where large tracts of natural landscape were converted to rainfed farmland (clearly visible in the 2016
landscape mosaic map). Landscapes with little woody biomass have increased slightly since 1987,
but at a low level. Landscapes without woody biomass show an overall increase since 1987, but have
been on the decline since 2002.
Figure 7.
Sankey plot showing changes from one landscape type to another between 1987, 2002,
and 2016. The colors and labels follow the scheme shown in Figure 2.
Mixed agropastoral landscape types remained fairly stable in size, but shifted farther away from
Mount Kenya towards more arid areas. Irrigated farming was still rare in 1987 (<1% of study area),
but became more prominent and appeared on the landscape mosaic map in 2002 (2.5%); in 2016, finally,
it covered 4% of the study area. As mentioned above, it is mainly practiced by horticulture farms along
the main road from Nanyuki to Timau, as well as by small-scale farmers, who mostly practice it in
conjunction with agroforestry and have reliable access to irrigation water (FO-I4 and BS-I4). Another
feature that first appears in the 2002 landscape mosaic map is the horticulture and floriculture hotspot
area (FO-I4 and FO-I5) between Nanyuki and Timau. At that time, there were no greenhouses in
the study area, and flowers were grown in open fields. However, greenhouses are needed for the
production of high-value, high-quality flowers [
68
], which the floriculture sector started investing
in around 2002 [
69
,
70
]. In 2016, landscapes characterized by such high-intensity agriculture already
covered 3% of the study area. Most of them are located along rivers and along or only a short distance
away from the main road.
3.3. Changes in Tree Cover and Woody Biomass
Landscapes with a high amount of woody biomass (FO and BS) experienced an increase between
1987 and 2002, followed by a slight decrease until 2016 (see Figure 8). The increase is mainly due to
an expansion of riparian forests, and, even more so, of bush- and shrubland west to northwest of
Naro Moru. By 2016, the area covered by these landscapes shrank again; the establishment of several
tree plantations east of Nanyuki was unable to compensate for the loss of natural, forest-dominated
landscapes that occurred, for example, north of Timau as well as south and southwest of Naro Moru,
where large tracts of natural landscape were converted to rainfed farmland (clearly visible in the 2016
landscape mosaic map). Landscapes with little woody biomass have increased slightly since 1987,
but at a low level. Landscapes without woody biomass show an overall increase since 1987, but have
been on the decline since 2002.

Remote Sens. 2017,9, 784 13 of 20
Remote Sens. 2017, 9, 784 13 of 20
Figure 8. Area (ha) covered by each of the four woody biomass categories in 1987, 2002, and 2016. FO
= high forest cover, BS = mostly bush- and shrubland, BA = little woody biomass, NO = no woody
biomass.
3.4. Landscape Changes in Protected Areas
Most larger natural landscapes that have remained stable in the study area are protected areas
where land uses other than conservation are restricted. The most prominent protected area in the
study area is Mount Kenya National Park and National Forest, which has experienced only minor
changes along its boundaries. These changes are either from one natural landscape category to
another or from landscapes dominated by forest or bush- and shrubland to landscapes dominated by
rainfed and irrigated agroforestry. In contrast, the small Lusoi Forest Reserve in the south of the study
area experienced substantial change. In 1987, it was surrounded by a vast area of natural bush- and
shrubland. By 2016, most of this landscape disappeared, and about half of the reserve was converted
into a landscape dominated by rainfed farming, albeit still surrounded by a large area of bush- and
shrubland. In the west, partially outside the study area, lies the Ol Pejeta Conservancy, which today
encompasses about 360 km2. A cattle ranch in colonial times, it became a wildlife reserve in the 1980s
and gradually increased in size to its current extent. Although its natural landscape has largely
remained stable, a notable change occurred in the southern part of the reserve, which was converted
to large-scale monoculture wheat fields after 1987. This change is clearly visible in the landscape
mosaic maps for 2002 and 2016. The 2016 map further shows that land use intensified along the rivers
forming the borders of the Ol Pejeta Conservancy. Besides these protected areas, the study area also
contains two private ranches: Solio Ranch in the south, dedicated to conserving rhinoceros, and
Borana Ranch and Conservancy in the north. On Borana Ranch, the natural landscape still covers a
large area, whereas in the southeastern part of Solio Ranch it was recently converted to rainfed
smallholder farmland, which is clearly visible on the landscape mosaic map for 2016.
4. Discussion
Important prerequisites for this study were the successful and accurate mapping of the most
important natural vegetation covers, the differentiation between rainfed and irrigated cropland, and
the identification of greenhouses and irrigation water reservoirs. All three could only be achieved by
Figure 8.
Area (ha) covered by each of the four woody biomass categories in 1987, 2002, and 2016.
FO = high forest cover, BS = mostly bush- and shrubland, BA = little woody biomass, NO = no
woody biomass.
3.4. Landscape Changes in Protected Areas
Most larger natural landscapes that have remained stable in the study area are protected areas
where land uses other than conservation are restricted. The most prominent protected area in the
study area is Mount Kenya National Park and National Forest, which has experienced only minor
changes along its boundaries. These changes are either from one natural landscape category to another
or from landscapes dominated by forest or bush- and shrubland to landscapes dominated by rainfed
and irrigated agroforestry. In contrast, the small Lusoi Forest Reserve in the south of the study area
experienced substantial change. In 1987, it was surrounded by a vast area of natural bush- and
shrubland. By 2016, most of this landscape disappeared, and about half of the reserve was converted
into a landscape dominated by rainfed farming, albeit still surrounded by a large area of bush- and
shrubland. In the west, partially outside the study area, lies the Ol Pejeta Conservancy, which today
encompasses about 360 km
2
. A cattle ranch in colonial times, it became a wildlife reserve in the
1980s and gradually increased in size to its current extent. Although its natural landscape has largely
remained stable, a notable change occurred in the southern part of the reserve, which was converted
to large-scale monoculture wheat fields after 1987. This change is clearly visible in the landscape
mosaic maps for 2002 and 2016. The 2016 map further shows that land use intensified along the rivers
forming the borders of the Ol Pejeta Conservancy. Besides these protected areas, the study area also
contains two private ranches: Solio Ranch in the south, dedicated to conserving rhinoceros, and Borana
Ranch and Conservancy in the north. On Borana Ranch, the natural landscape still covers a large
area, whereas in the southeastern part of Solio Ranch it was recently converted to rainfed smallholder
farmland, which is clearly visible on the landscape mosaic map for 2016.
4. Discussion
Important prerequisites for this study were the successful and accurate mapping of the most
important natural vegetation covers, the differentiation between rainfed and irrigated cropland, and the

Remote Sens. 2017,9, 784 14 of 20
identification of greenhouses and irrigation water reservoirs. All three could only be achieved by
using multi-seasonal satellite data composites, making subsets of predominantly natural habitats and
predominantly cropland areas, and verifying the mapping results through careful visual comparison
with Google Earth imagery. We would like to note that settlement areas might be under-represented in
our land cover and land use maps, as most individual buildings and small settlements in the study area
are too small to be captured clearly in the Landsat data. Furthermore, variations in the shares of natural
vegetation covers and bare soil, as well as of rainfed cropland and irrigation cropland, between the
three points in time investigated might partially be due to interannual variations in seasonal rainfall.
Thus, the observed increase in irrigated cropland might be slightly overestimated. The same applies
to the increase in rainfed cropland at the cost of savannah grassland, a small portion of which may
have been caused by misclassifications between the two classes due to the very heterogeneous and
small-scale pattern of cropland and grazing plots. We estimate these errors to be within one percent of
net change.
We would further like to note that the accuracies of the three pixel-based land cover and land use
change analyses are only as good as the multiplied accuracies of each individual land cover and land
use classification [
71
]. However, single pixel classification errors in these classification maps have little
influence on the landscape-level mosaic maps.
Depending on how agricultural intensification manifests itself, a pixel-level assessment may
make sense. This is the case, for example, when natural vegetation cover changes to cropland [
72
],
when plot size increases [
73
], when irrigated cropland increases [
35
], or when the number of cropping
cycles increases [
34
]. However, this study shows that when agricultural intensification manifests itself
in land use system changes, an assessment at the landscape level may be better suited to capture
these processes. Based on the idea that certain landscape types can only be captured by analyzing
the combination of land covers and land uses in a certain context area, we defined landscape type
categories and developed a decision rule set for each category that enabled us to differentiate landscape
types by land use intensity and the amount of woody biomass present. We defined the categories
based on the landscape compositions observed in the field. The category definitions might need to
be adjusted if the approach is transferred to a different agroecological zone composed of different
landscapes and land use systems.
The results presented in this paper show impressively how much, where, and at the expense of
which landscapes cropland has expanded and land use has intensified since 1987. The transformation
from extensive agropastoral use to rainfed smallholder farming mainly happened between 1987 and
2002, but it still continues wherever land, and, more importantly, water is available. Between 2002 and
2016, we can observe a shift away from purely rainfed to increasingly irrigated smallholder farming,
as well as the development of high-input commercial vegetable and flower farming, which is practiced
in open fields and increasingly also in greenhouses. Commercial growers require year-round irrigation
to supply export markets regardless of the season. Indeed, commercial water use is highest precisely
when the least water is available: one reason being that European demand for vegetables peaks during
the study area’s driest period. The same is true of flowers, with Valentine’s Day falling in February,
the driest month in the study area. Increasing commercial use strains water resources during times of
scarcity and sets the stage for conflicts among different water users [
48
,
51
]. To avoid such conflicts
while further increasing production, most horticulture farms in the study area have begun to reduce
their dependence on local river water. Over the last decade, they have invested in the construction
of irrigation water reservoirs and boreholes. This explains the increase in surface water by 97 ha
between 1987 and 2016. Furthermore, most commercial farms harvest rainwater from the greenhouse
roofs and apply drip irrigation. Nonetheless, it remains unknown whether the intensification of
water use will have long-term impacts on the groundwater table, be it in the highlands or in the
Merti aquifer downstream. Furthermore, intensive small- and large-scale farming is impairing the
water’s quality, as it affects the physico-chemical properties of surface waters [
16
,
74
–
76
]. Muriithi
and Yu [
16
] took water samples in the study area and examined several physico-chemical parameters.

Remote Sens. 2017,9, 784 15 of 20
They found high pollutant concentrations in areas with intensive small-scale farming and large-scale
commercial horticulture.
Our results regarding land use system changes further show that natural habitats in the study
area have decreased in the last 30 years. Some of the protected areas—which are important habitats for
Kenya’s wildlife—are continuously shrinking. The southern part of the Ol Pejeta Conservancy was
converted into large-scale monoculture wheat fields. In the case of Solio Ranch, the Government of
Kenya purchased the southeastern part of the rhinoceros reserve in 2007 to resettle landless small-scale
farmers, leading to a transformation of the natural habitat into rainfed farmland [
77
]. The farmers
had previously been evicted from their original homes in the Mount Kenya and Aberdare forests
due to concerns about adverse environmental impacts of the area’s overpopulation, and since then
had had to live as squatters in the surrounding towns. Bond [
15
] found that, besides water, the local
population perceives land and pasture as the main natural resources related to conflicts in Laikipia
County. The potential for conflict between pastoralists, smallholder farmers, large-scale ranches,
and wildlife is particularly prevalent during the dry seasons, when migrating pastoralists move into
the area in search of pasture, water, and idle land to feed their livestock [15].
The massive reduction in natural vegetation and the intensification of agricultural land use
also have consequences for biodiversity and cause land degradation. The replacement of bush- and
shrubland, grassland, and forests with crops reduces plant species diversity, although mixed cropping
and agroforestry conserve native plant species better than single-crop farming [
10
]. In formerly less
forested areas, such as the area northeast of Timau that used to consist mainly of large monoculture
wheat farms, mixed cropping and agroforestry has led to an increase in trees, which may also have led
to an increase in biodiversity by attracting new species of birds [
10
]. However, wherever farming is
intensified and habitat diversity is reduced, biodiversity also declines. Land degradation is affected
in a similar way: soil erosion increases with decreasing vegetation cover, and the productivity of
agricultural land declines unless manure or other fertilizers are added. The main drivers of land
degradation besides soil erosion are the depletion of organic matter (soil organic carbon), degradation
of the soil structure, and a decline in the availability of major nutrients (N, P, K) and micro elements in
the soil. Furthermore, toxicity may increase due to acidification and salinization, especially in land
used for irrigated farming [10].
In sum, agricultural intensification and the expansion of horticulture agribusinesses further
increase pressure on the study area’s limited natural resources, potentially aggravating the condition
of the environment and the situation of disadvantaged land users. These developments certainly offer
new economic opportunities, but they also come with constraints for sustainable
development [17,78]
.
Therefore, it is important to ensure that agricultural production in Africa is intensified in a
sustainable manner.
5. Conclusions
This study provides spatially explicit information about the expansion of farming and its more
recent intensification in the northwestern foothills of Mount Kenya. We analyzed how these changes
affect the landscapes in the study area. We made use of Google Earth Engine to access the USGS
Landsat data archive and to generate cloud-free seasonal composites. These seasonal composites then
enabled us to differentiate between rainfed and irrigated cropland with considerably high accuracy
(F1 class values ranging between 73.5% and 84.7% for the two classes). By applying a landscape
mosaic approach to the land cover and land use classifications, we were able to derive landscape types
categorized by land use intensity and the amount of woody biomass present. The results and their
analysis led us to the following conclusions:
•
Rainfed and irrigated cropland expanded by 47,752 ha, mainly at the expense of savannah
grassland, bush- and shrubland, and forest, which showed overall losses of 46,105 ha, 11,837 ha,
and 605 ha, respectively. This amounts to a 30% decrease in natural habitats in the study area
over the last 30 years. The conversion to rainfed cropland mainly happened between 1987 and

Remote Sens. 2017,9, 784 16 of 20
2002, although it continued on after that at a much lower level. The intensity of agricultural
land use began to increase between 2002 and 2016, as further humid forest, bush- and shrubland,
and grassland areas along rivers, as well as rainfed cropland areas were converted into irrigated
cropland. Not only large-scale producers, but also many smallholders have begun to practice
irrigated farming. In addition, the area has seen a rapid development of high-input commercial
greenhouse horticulture farming (since 2002, greenhouses increased by 604 ha and irrigation
water reservoirs by 73 ha).
•
Natural wildlife habitats continue to shrink. Agricultural expansion and intensification affects
not only non-protected areas, but also private ranches and wildlife reserves as well as small
forest reserves in the study area. However, Mount Kenya National Park and National Forest
remained fairly stable. The overall forested area has decreased only slightly thanks to a number
of afforestation projects near the boundary of Mount Kenya National Park and National Forest.
•
The massive reduction in natural habitats and the intensification of agriculture have diverse
impacts on biodiversity. While the observed reduction in natural habitats has reduced biodiversity
at the regional level, the observed increase in agroforestry farming has increased it locally.
The changes also affect land degradation. Potential future consequences of agricultural
intensification include soil erosion and—unless fertilizer is applied—a decline in the soil’s organic
matter, degradation of the soil structure, and a reduction in major nutrients.
•
Water availability defines the spatial pattern of agricultural expansion and intensification in the
study area. Water has always been a scarce resource in the region. Agricultural intensification
and the expansion of horticulture agribusinesses further increase pressure on this limited natural
resource. Furthermore, the observed changes have also heightened pressure on pasture and
idle land due to the decrease in natural and agropastoral landscapes. As a result, conflicts
between pastoralists, smallholder farmers, large-scale ranches, and wildlife might further increase,
particularly during the dry seasons and in years of extreme drought.
Spatially explicit information on agricultural expansion and intensification is highly relevant to
understand patterns of land use change and their impacts on the environment and human well-being.
Particularly in developing countries, which often lack such spatial information and where land use
is undergoing substantial change, such up-to-date spatial information can support policymakers,
land use planners, and land users in achieving sustainable agricultural intensification.
Supplementary Materials:
The following are available online at www.mdpi.com/2072-4292/9/8/784/s1,
Figure S1: Number of Landsat surface reflectance products sorted by year, month, and season that were available
in Google Earth Engine. Collections highlighted in green were chosen for the analysis. Table S1: Overall and
class-wise accuracies for 1987 (A), 2002 (B), and 2016 (C). Table S2: Matrix of land cover and land use class changes
between 1987 and 2002. Table S3: Matrix of land cover and land use class changes between 2002 and 2016.
Acknowledgments:
The research for this publication was conducted as part of the BELMONT Forum and
FACCE–JPI project “African Food, Agriculture, Land and Natural Resource Dynamics, in the context of global
agro-food-energy system changes (AFGROLAND)” (Grant Number: 40FA40_160405). The project is funded by
the Swiss National Science Foundation, the French National Research Agency, and the South African National
Research Foundation.
Author Contributions:
Sandra Eckert and Julie Gwendolin Zaehringer conceived and designed the experiments;
Sandra Eckert processed and analyzed the data and wrote the paper; Boniface Kiteme and Evanson Njuguna
contributed to the collection of field reference data and to the writing of the paper; and Julie Gwendolin Zaehringer
contributed to the writing of the paper as well.
Conflicts of Interest: The authors declare no conflict of interest.
References
1.
Lambin, E.F.; Turner, B.L.; Geist, H.J.; Agbola, S.B.; Angelsen, A.; Bruce, J.W.; Coomes, O.T.; Dirzo, R.;
Fischer, G.; Folke, C.; et al. The causes of land-use and land-cover change: Moving beyond the myths.
Glob. Environ. Chang. 2001,11, 261–269. [CrossRef]

Remote Sens. 2017,9, 784 17 of 20
2.
DeFries, R.S.; Foley, J.A.; Asner, G.P. Land-use choices: Balancing human needs and ecosystem function.
Front. Ecol. Environ. 2004,2, 249–257. [CrossRef]
3.
Lambin, E.F.; Meyfroidt, P. Global land use change, economic globalization, and the looming land scarcity.
Proc. Natl. Acad. Sci. USA 2011,108, 3465–3472. [CrossRef] [PubMed]
4.
Mustard, J.F.; Defries, R.S.; Fisher, T.; Moran, E. Land-use and land-cover change pathways and impacts.
In Land Change Science; Springer: Dordrecht, The Netherlands, 2012; pp. 411–429.
5.
Hansen, M.C.; Potapov, P.V.; Moore, R.; Hancher, M.; Turubanova, S.A.; Tyukavina, A.; Thau, D.;
Stehman, S.V.; Goetz, S.J.; Loveland, T.R.; et al. High-Resolution Global Maps of 21st-Century Forest
Cover Change. Science 2013,342, 850–853. [CrossRef] [PubMed]
6.
Foley, J.A.; DeFries, R.; Asner, G.P.; Barford, C.; Bonan, G.; Carpenter, S.R.; Chapin, F.S.; Coe, M.T.; Daily, G.C.;
Gibbs, H.K.; et al. Global Consequences of Land Use. Science 2005,309, 570–574. [CrossRef] [PubMed]
7.
Bongers, F.; Tennigkeit, T. Degraded Forests in Eastern Africa: Management and Restoration; Routledge: London,
UK, 2010.
8.
Brink, A.B.; Bodart, C.; Brodsky, L.; Defourney, P.; Ernst, C.; Donney, F.; Lupi, A.; Tuckova, K. Anthropogenic
pressure in East Africa—Monitoring 20 years of land cover changes by means of medium resolution satellite
data. Int. J. Appl. Earth Obs. Geoinform. 2014,28, 60–69. [CrossRef]
9.
Niamir-Fuller, M.; Kerven, C.; Reid, R.; Milner-Gulland, E. Co-existence of wildlife and pastoralism on
extensive rangelands: Competition or compatibility? Pastor. Res. Policy Pract. 2012,2, 1–14. [CrossRef]
10.
Maitima, J.M.; Mugatha, S.M.; Reid, R.S.; Gachimbi, L.N.; Majule, A.; Lyaruu, H.; Pomery, D.; Mathai, S.;
Mugisha, S. The linkages between land use change, land degradation and biodiversity across East Africa.
Afr. J. Environ. Sci. Technol. 2009,3, 310–325.
11.
Cotula, L.; International Institute for Environment and Development; Food and Agriculture Organization of
the United Nations; International Fund for Agricultural Development. Land Grab or Development Opportunity?
Agricultural Investment and International Land Deals in Africa; IIED: London, UK; FAO: Rome, Italy; IFAD:
Rome, Italy, 2009.
12.
Notter, B.; MacMillan, L.; Viviroli, D.; Weingartner, R.; Liniger, H.-P. Impacts of environmental change on
water resources in the Mt. Kenya region. J. Hydrol. 2007,343, 266–278. [CrossRef]
13.
Ministry of Agriculture, Livestock and Fisheries, Government of Kenya, National Horticultural Policy.
Available online: http://extwprlegs1.fao.org/docs/pdf/ken147935.pdf (accessed on 3 July 2017).
14.
Wiesmann, U. Sustainable Regional Development in Rural Africa: Conceptual Framework and Case Studies from
Kenya; Geographica Bernensia: Bern, Switzerland, 1998.
15.
Bond, J. Conflict, Development and Security at the Agro–Pastoral–Wildlife Nexus: A Case of Laikipia County,
Kenya. J. Dev. Stud. 2014,50, 991–1008. [CrossRef]
16.
Muriithi, F.K.; Yu, D. Understanding the Impact of Intensive Horticulture Land-Use Practices on Surface
Water Quality in Central Kenya. Environments 2015,2, 521–545. [CrossRef]
17.
Ulrich, A. Export-Oriented Horticultural Production in Laikipia, Kenya: Assessing the Implications for Rural
Livelihoods. Sustainability 2014,6, 336–347. [CrossRef]
18.
Ulrich, A.; Ifejika Speranza, C.; Roden, P.; Kiteme, B.; Wiesmann, U.; Nüsser, M. Small-scale farming in
semi-arid areas: Livelihood dynamics between 1997 and 2010 in Laikipia, Kenya. J. Rural Stud.
2012
,28,
241–251. [CrossRef]
19.
Roden, P.; Bergmann, C.; Ulrich, A.; Nüsser, M. Tracing divergent livelihood pathways in the drylands:
A perspective on two spatially proximate locations in Laikipia County, Kenya. J. Arid Environ.
2016
,124,
239–248. [CrossRef]
20.
McCord, P.F.; Cox, M.; Schmitt-Harsh, M.; Evans, T. Crop diversification as a smallholder livelihood strategy
within semi-arid agricultural systems near Mount Kenya. Land Use Policy 2015,42, 738–750. [CrossRef]
21.
Ogalleh, S.; Vogl, C.; Eitzinger, J.; Hauser, M. Local Perceptions and Responses to Climate Change and
Variability: The Case of Laikipia District, Kenya. Sustainability 2012,4, 3302–3325. [CrossRef]
22.
Wiesmann, U. Socioeconomic Viewpoints on Highland-Lowland Systems: A Case Study on the Northwest
Side of Mount Kenya. Mt. Res. Dev. 1992,12, 375–381. [CrossRef]
23.
Kohler, T.; Wiesmann, U.M. Kleinbäuerliche Besiedlung und die Ausprägung von Haushaltsstrategien am
Mount Kenya. In Welt der Alpe—Gebirge der Welt; Jeanneret, F., Wastl-Walter, D., Wiesmann, U., Schwyn, M.,
Eds.; Haupt Verlag: Bern, Switzerland, 2003; pp. 185–196.

Remote Sens. 2017,9, 784 18 of 20
24.
Atzberger, C. Advances in Remote Sensing of Agriculture: Context Description, Existing Operational
Monitoring Systems and Major Information Needs. Remote Sens. 2013,5, 949–981. [CrossRef]
25.
Ozdogan, M.; Yang, Y.; Allez, G.; Cervantes, C. Remote sensing of irrigated agriculture: Opportunities and
challenges. Remote Sens. 2010,2, 2274–2304. [CrossRef]
26.
Thenkabail, P.S.; Wu, Z. An automated cropland classification algorithm (ACCA) for Tajikistan by combining
Landsat, MODIS, and secondary data. Remote Sens. 2012,4, 2890–2918. [CrossRef]
27.
Estel, S.; Kuemmerle, T.; Levers, C.; Baumann, M.; Hostert, P. Mapping cropland-use intensity across Europe
using MODIS NDVI time series. Environ. Res. Lett. 2016,11. [CrossRef]
28.
Gumma, M.K.; Thenkabail, P.S.; Teluguntla, P.; Rao, M.N.; Mohammed, I.A.; Whitbread, A.M. Mapping
rice-fallow cropland areas for short-season grain legumes intensification in South Asia using MODIS 250 m
time-series data. Int. J. Digit. Earth 2016,9, 981–1003. [CrossRef]
29.
Teluguntla, P.; Thenkabail, P.S.; Xiong, J.; Gumma, M.K.; Congalton, R.G.; Oliphant, A.; Poehnelt, J.; Yadav, K.;
Rao, M.; Massey, R. Spectral matching techniques (SMTs) and automated cropland classification algorithms
(ACCAs) for mapping croplands of Australia using MODIS 250-m time-series (2000–2015) data. Int. J.
Digit. Earth 2016,10, 1–34. [CrossRef]
30.
Müller, H.; Rufin, P.; Griffiths, P.; Barros Siqueira, A.J.; Hostert, P. Mining dense Landsat time series for
separating cropland and pasture in a heterogeneous Brazilian savanna landscape. Remote Sens. Environ.
2015,156, 490–499. [CrossRef]
31.
Jin, N.; Tao, B.; Ren, W.; Feng, M.; Sun, R.; He, L.; Zhuang, W.; Yu, Q. Mapping Irrigated and Rainfed Wheat
Areas Using Multi-Temporal Satellite Data. Remote Sens. 2016,8, 207. [CrossRef]
32.
Knauer, K.; Gessner, U.; Fensholt, R.; Forkuor, G.; Kuenzer, C. Monitoring agricultural expansion in Burkina
Faso over 14 years with 30 m resolution time series: the role of population growth and implications for the
environment. Remote Sens. 2017,9, 132. [CrossRef]
33.
Morton, D.C.; DeFries, R.S.; Shimabukuro, Y.E.; Anderson, L.O.; Arai, E.; del Bon Espirito-Santo, F.;
Freitas, R.; Morisette, J. Cropland expansion changes deforestation dynamics in the southern Brazilian
Amazon. Proc. Natl. Acad. Sci. USA 2006,103, 14637–14641. [CrossRef] [PubMed]
34.
Kontgis, C.; Schneider, A.; Ozdogan, M. Mapping rice paddy extent and intensification in the Vietnamese
Mekong River Delta with dense time stacks of Landsat data. Remote Sens. Environ.
2015
,169, 255–269.
[CrossRef]
35.
Maron, M.; Fitzsimons, J.A. Agricultural intensification and loss of matrix habitat over 23 years in the West
Wimmera, south-eastern Australia. Biol. Conserv. 2007,135, 587–593. [CrossRef]
36.
Edlinger, J.; Conrad, C.; Lamers, J.; Khasankhanova, G.; Koellner, T. Reconstructing the spatio-temporal
development of irrigation systems in Uzbekistan using Landsat time series. Remote Sens.
2012
,4, 3972–3994.
[CrossRef]
37.
Turner, B.L.; Lambin, E.F.; Reenberg, A. The emergence of land change science for global environmental
change and sustainability. Proc. Natl. Acad. Sci. USA 2007,104, 20666–20671. [CrossRef] [PubMed]
38.
Reenberg, A. Land systems research in Denmark: Background and perspectives. Geogr. Tidsskr. Dan. J. Geogr.
2006,106, 1–6. [CrossRef]
39.
Dubreuil, V.; Laques, A.-É.; Nédélec, V.; Arvor, D.; Gurgel, H. Paysages et fronts pionniers amazoniens sous
le regard des satellites: L’exemple du Mato Grosso. L’Espace Géographique 2008,37, 57–74. [CrossRef]
40.
Arvor, D.; Dubreuil, V.; Simões, M.; Bégué, A. Mapping and spatial analysis of the soybean agricultural
frontier in Mato Grosso, Brazil, using remote sensing data. GeoJournal 2013,78, 833–850. [CrossRef]
41.
Gutman, G.; Janetos, A.C.; Justice, C.O.; Moran, E.F.; Mustard, J.F.; Rindfuss, R.R.; Skole, D.; Turner, B.L., II;
Cochrane, M.A. Land Change Science: Observing, Monitoring and Understanding Trajectories of Change on the
Earth’s Surface; Springer: Dordrecht, The Netherlands, 2004.
42.
Kuemmerle, T.; Erb, K.; Meyfroidt, P.; Müller, D.; Verburg, P.H.; Estel, S.; Haberl, H.; Hostert, P.; Jepsen, M.R.;
Kastner, T.; et al. Challenges and opportunities in mapping land use intensity globally. Curr. Opin. Environ.
Sustain. 2013,5, 484–493. [CrossRef] [PubMed]
43.
Berger, P. Rainfall and Agroclimatology of the Laikipia Plateau, Kenya; Geographica Bernensia: Berne, Switzerland,
1989.
44.
Liniger, H.; Gikonyo, J.; Kiteme, B.; Wiesmann, U. Assessing and Managing Scarce Tropical Mountain Water
Resources: The Case of Mount Kenya and the Semiarid Upper Ewaso Ng’iro Basin. Mt. Res. Dev.
2005
,25,
163–173. [CrossRef]

Remote Sens. 2017,9, 784 19 of 20
45.
Aeschbacher, J.; Liniger, H.; Weingartner, R. River water shortage in a highland-lowland system: A case
study of the impacts of water abstraction in the Mount Kenya region. Mt. Res. Dev.
2005
,25, 155–162.
[CrossRef]
46.
Kohler, T. Land Use in Transition: Aspects and Problems of Small Scale Farming in a New Environment: The Example
of Laikipia District, Kenya; Geographica Bernensia: Bern, Switzerland, 1987.
47.
KNBS—Kenya National Bureau of Statistics. Available online: https://www.knbs.or.ke/ (accessed on
6 June 2017).
48.
Lanari, N.; Liniger, H.; Kiteme, B.P. Commercial Horticulture in Kenya: Adapting to Water Scarcity; University of
Bern: Bern, Switzerland, 2016.
49.
Griffiths, P.; Müller, D.; Kuemmerle, T.; Hostert, P. Agricultural land change in the Carpathian ecoregion
after the breakdown of socialism and expansion of the European Union. Environ. Res. Lett.
2013
,8, 1–12.
[CrossRef]
50.
Griffiths, P.; Kuemmerle, T.; Baumann, M.; Radeloff, V.C.; Abrudan, I.V.; Lieskovsky, J.; Munteanu, C.;
Ostapowicz, K.; Hostert, P. Forest disturbances, forest recovery, and changes in forest types across the
Carpathian ecoregion from 1985 to 2010 based on Landsat image composites. Remote Sens. Environ.
2014
,
151, 72–88. [CrossRef]
51.
Kiteme, B.P.; Liniger, H.; Notter, B.; Wiesmann, U.; Kohler, T. Dimensions of Global Change in African
Mountains: The Example of Mount Kenya. In International Human Dimensions Programme on Global
Environmental Change: Mountain Regions: Laboratories for Adaptation; Rechkemmer, A., Ed.; IHDP: Bonn,
Germany, 2008; Volume 2, pp. 18–23.
52. Breiman, L. Random Forests. Mach. Learn. 2001,45, 5–32. [CrossRef]
53.
Jakimow, B.; Oldenburg, C.; Rabe, A.; Waske, B.; van der Linden, S.; Hostert, P. Manual for Application:
ImageRF (1.1). Available online: http://dev.geo.hu-berlin.de/enmap-box/documentation/applications/
imageRF/help/imageRF_Manual.pdf (accessed on 31 July 2017).
54.
Friedman, J.; Hastie, T.; Tibshirani, R. The Elements of Statistical Learning; Springer: New York, NY, USA, 2001.
55.
Gislason, P.O.; Benediktsson, J.A.; Sveinsson, J.R. Random forests for land cover classification. Pattern
Recognit. Lett. 2006,27, 294–300. [CrossRef]
56.
Belgiu, M.; Drăgu¸t, L. Random forest in remote sensing: A review of applications and future directions.
ISPRS J. Photogramm. Remote Sens. 2016,114, 24–31. [CrossRef]
57.
Karlson, M.; Ostwald, M.; Reese, H.; Sanou, J.; Tankoano, B.; Mattsson, E. Mapping tree canopy cover and
aboveground biomass in Sudano-Sahelian woodlands using Landsat 8 and random forest. Remote Sens.
2015
,
7, 10017–10041. [CrossRef]
58.
Li, X.; Chen, W.; Cheng, X.; Wang, L. A comparison of machine learning algorithms for mapping of complex
surface-mined and agricultural landscapes using ZiYuan-3 stereo satellite imagery. Remote Sens.
2016
,8, 514.
[CrossRef]
59.
Rodriguez-Galiano, V.F.; Ghimire, B.; Rogan, J.; Chica-Olmo, M.; Rigol-Sanchez, J.P. An assessment of the
effectiveness of a random forest classifier for land-cover classification. ISPRS J. Photogramm. Remote Sens.
2012,67, 93–104. [CrossRef]
60.
Congalton, R.G.; Green, K. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices; CRC Press:
Boca Raton, LA, USA, 2008.
61.
Pontius, R.G.; Huffaker, D.; Denman, K. Useful techniques of validation for spatially explicit land-change
models. Ecol. Model. 2004,179, 445–461. [CrossRef]
62.
Aldwaik, S.Z.; Pontius, R.G. Intensity analysis to unify measurements of size and stationarity of land changes
by interval, category, and transition. Landsc. Urban Plan. 2012,106, 103–114. [CrossRef]
63.
Messerli, P.; Heinimann, A.; Epprecht, M. Finding homogeneity in heterogeneity—A new approach to
quantifying landscape mosaics developed for the Lao PDR. Hum. Ecol.
2009
,37, 291–304. [CrossRef]
[PubMed]
64.
Hett, C.; Castella, J.-C.; Heinimann, A.; Messerli, P.; Pfund, J.-L. A landscape mosaics approach for
characterizing swidden systems from a REDD+ perspective. Appl. Geogr. 2012,32, 608–618. [CrossRef]
65.
Zaehringer, J.G.; Hett, C.; Ramamonjisoa, B.; Messerli, P. Beyond deforestation monitoring in conservation
hotspots: Analysing landscape mosaic dynamics in north-eastern Madagascar. Appl. Geogr.
2016
,68, 9–19.
[CrossRef]

Remote Sens. 2017,9, 784 20 of 20
66.
Cumming, D.H.; Fenton, M.B.; Rautenbach, I.L.; Taylor, R.D.; Cumming, G.S.; Cumming, M.S.; Dunlop, J.M.;
Ford, A.G.; Hovorka, M.D.; Johnston, D.S. Elephants, woodlands and biodiversity in southern Africa. S. Afr.
J. Sci. 1997,93, 231–236.
67.
Mbow, C.; Van Noordwijk, M.; Luedeling, E.; Neufeldt, H.; Minang, P.A.; Kowero, G. Agroforestry solutions
to address food security and climate change challenges in Africa. Curr. Opin. Environ. Sustain.
2014
,6, 61–67.
[CrossRef]
68.
Gebreeyesus, M.; Sonobe, T. Global Value Chains and Market Formation Process in Emerging Export Activity:
Evidence from Ethiopian Flower Industry. J. Dev. Stud. 2012,48, 335–348. [CrossRef]
69.
Xia, Y.; Deng, X.; Zhou, P.; Shima, K.; da Silva, J.A.T. The World Floriculture Industry: Dynamics of
production and markets. In Floriculture, Ornamental and Plant Biotechnology: Advances and Topical Issues.
Available online: http://www.globalsciencebooks.info/Books/images/FOPBVolume5Outline.pdf (accessed
on 31 July 2017).
70.
Whitaker, M.; Kolavalli, S. Floriculture in Kenya. In Technology, Adaptation, and Exports—How Some Developing
Countries Got it Right; Chandra, V., Ed.; The World Bank: Washington, DC, USA, 2006; pp. 335–367.
71.
Verburg, P.H.; Neumann, K.; Nol, L. Challenges in using land use and land cover data for global change
studies. Glob. Chang. Biol. 2011,17, 974–989. [CrossRef]
72.
Galford, G.L.; Mustard, J.F.; Melillo, J.; Gendrin, A.; Cerri, C.C.; Cerri, C.E. Wavelet analysis of MODIS time
series to detect expansion and intensification of row-crop agriculture in Brazil. Remote Sens. Environ.
2008
,
112, 576–587. [CrossRef]
73.
White, E.V.; Roy, D.P. A contemporary decennial examination of changing agricultural field sizes using
Landsat time series data. Geo Geogr. Environ. 2015,2, 33–54. [CrossRef] [PubMed]
74.
Kibichii, S.; Shivoga, W.A.; Muchiri, M.; Miller, S.N. Macroinvertebrate assemblages along a land-use
gradient in the upper River Njoro watershed of Lake Nakuru drainage basin, Kenya. Lakes Reserv. Res.
Manag. 2007,12, 107–117. [CrossRef]
75.
Otiang’a-Owiti, G.E.; Oswe, I.A. Human impact on lake ecosystems: The case of Lake Naivasha, Kenya.
Afr. J. Aquat. Sci. 2007,32, 79–88. [CrossRef]
76.
Nyakundi, W.O.; Magoma, G.; Ochora, J.; Nyende, A.B. Survey of pesticide use and application patterns
among farmers: A case study from selected horticultural farms in Rift Valley and Central provinces, Kenya.
In Proceedings of the 2010 JKUAT Scientific Technological and Industrialization Conference, Nairobi, Kenya,
17–19 November 2010; pp. 618–630.
77.
Meersohn Meinecke, S.M.; Pandey, A.; Thant, A.M.; Madalinska, A.; Sørensen, C.; Wilson, M.
Crop Production and Livelihood Strategies in a Resettlement Village in Kenya. Available online:
http://www.tropentag.de/2012/abstracts/full/371.pdf (accessed on 31 July 2017).
78.
Zaehringer, J.G.; Wambugu, G.; Kiteme, B.P.; Eckert, S. Large-scale agricultural investments on the western
slopes of Mount Kenya: Perceived impacts on small-scale farmers’ land use and the environment. Ecol. Soc.
2017, submitted.
©
2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).