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Key Factors Driving Deforestation in North-Kivu Province, Eastern DR-Congo Using GIS and Remote Sensing

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Deforestation has become one of major problems in tropical forest regions. Understanding causes of the forest cover loss is an important step to reduce deforestation. This study analyses the relationship between the Forest cover loss and its explaining key factors in North Kivu province, eastern Democratic Republic of Congo (DRC). Geospatial methods were used in this study to estimate the loss of the forest cover change in North Kivu using Landsat 7 ETM+ and Landsat 8 OLI/TIRS images for the period from 2001 to 2015. The regression analysis was performed using the Ordinary Least Square Regression (OLS) to analyze the spatial stationary factors of deforestation and the Geographically Weighted Regression (GWR) in ArcGIS 10.3 software for the non-stationary factors. The findings reveal an annual rate of deforestation of 1.7% meaning that an average of 70,000 ha of forest area is lost each year. The GWR model was found as the best predictor that explains the Forest cover loss at 93% with the Agriculture Expansion (AE), Slope (SL), Distance from road (DR) and Population Density (PD) as key factors to explain the Forest cover loss. Measures of reducing deforestation in Nord-Kivu should be based on these four key factors for more effectiveness.
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American Journal of Geographic Information System 2019, 8(1): 11-25
DOI: 10.5923/j.ajgis.20190801.02
Key Factors Driving Deforestation in North-Kivu
Province, Eastern DR-Congo Using GIS and Remote
Sensing
Musumba Teso Philippe1,*, Kavira Malengera2, Katcho Karume3
1Faculty of Environment Sciences, Université Evangélique en Afrique (UEA), Bukavu, DR, Congo
2Department of Public Health and Family Medecine, Université Evangélique en Afrique (UEA), Bukavu, DR, Congo
3Goma Volcano Observatory, Department of Geochemistry and Environment, Goma, DR, Congo
Abstract Deforestation has become one of major problems in tropical forest regions. Understanding causes of the forest
cover loss is an important step to reduce deforestation. This study analyses the relationship between the Forest cover loss and
its explaining key factors in North Kivu province, eastern Democratic Republic of Congo (DRC). Geospatial methods were
used in this study to estimate the loss of the forest cover change in North Kivu using Landsat 7 ETM+ and Landsat 8
OLI/TIRS images for the period from 2001 to 2015. The regression analysis was performed using the Ordinary Least Square
Regression (OLS) to analyze the spatial stationary factors of deforestation and the Geographically Weighted Regression
(GWR) in ArcGIS 10.3 software for the non-stationary factors. The findings reveal an annual rate of deforestation of 1.7%
meaning that an average of 70,000 ha of forest area is lost each year. The GWR model was found as the best predictor that
explains the Forest cover loss at 93% with the Agriculture Expansion (AE), Slope (SL), Distance from road (DR) and
Population Density (PD) as key factors to explain the Forest cover loss. Measures of reducing deforestation in Nord-Kivu
should be based on these four key factors for more effectiveness.
Keywords Modelling, Forest Cover loss, Geographic Information System (GIS), Remote Sensing, Deforestation,
North-Kivu
1. Introduction
The tropical forest is very important for the life of human
being on the earth. It provides ecological services at the
global scale [1] and plays an important role in the global
carbon cycle [2, 3]. On a local scale, forests regulate water
cycles and provide vegetative cover that protects the soil
from erosion [4]. In the last several decades, the disturbance
and loss of tropical forest have been observed in many
developing countries. Forest cover has been converted to
cropland, pasture and other man-made cover types in
response to the humans’ demand of food, energy and other
economic interests [5, 6]. This phenomenon has induced
biodiversity loss, erosion and floods [7].
The North Kivu province in the Eastern part of DRC is
not an exception of the deforestation phenomenon taking
place in the tropical region. The DRC Ministry in charge of
* Corresponding author:
musumbateso@yahoo.fr (Musumba Teso Philippe)
Published online at http://journal.sapub.org/ajgis
Copyright © 2019 The Author(s). Published by Scientific & Academic Publishing
This work is licensed under the Creative Commons Attribution International
License (CC BY). http://creativecommons.org/licenses/by/4.0/
Environment and Nature Conservation and Tourism reported
the hotspot of loss of forest cover in some regions around
popular cities namely Kinshasa, Lubumbashi, Kananga,
Kisangani and Kindu, as well as in the Albertin Rift (Figure
1): Province Orientale, South-Kivu and North-Kivu [8].
Except Kinshasa, the province of North Kivu had the highest
annual population growth rate in DRC [9] and this number
increased by more than 15% between 2010 and 2015 [10].
Hence, population increases pressure on the available forest
in the region for their livelihood.
Currently, an integrated approach of Remote Sensing and
GIS has become an important tool in the forest assessment
and monitoring [11-13]. Remote Sensing supports creating
multi-spectral images and layers which are analyzed and
help to produce thematic maps [14]. Since the statistical tools
are integrated in GIS softwares, Remote sensing and GIS
have been used in modeling key drivers of deforestation
[15-18] and assess drivers between forest loss and factors
associated to it such as roads [19, 20] and urban expansion
[21].
This study has been therefore undertaken to estimate the
forest cover loss in North Kivu province by processing
Landsat images acquired in 2001 and 2015. In addition,
underlying forces of deforestation have been determined
12 Musumba Teso Philippe et al.: Key Factors Driving Deforestation in North-Kivu
Province, Eastern DR-Congo Using GIS and Remote Sensing
using geospatial analysis tools in ArcGIS 10.3 environment.
The main contribution of this study is to analyze the
potential key factors driving the deforestation throughout the
North-Kivu province. It includes some environmental
parameters such as slope and Euclidean distance from road.
Figure 1. Albertin Rift Region
Figure 2. Location map of the Nord-Kivu province
American Journal of Geographic Information System 2019, 8(1): 11-25 13
Table 1. Band specifications and scalar information of used Landsat images
2. Study Area
2.1. Location of the Study Area
The study area the North-Kivu province located in the
Eastern part of the DRC. North-Kivu is one of the provinces
of DRC that extends on both sides of the Equator at latitude
0° 58’ - - 3’ and Eastern longitude 27° 14’ - 29° 58’
(Figure 2).
2.2. Relief and Climate of North-Kivu
The North-Kivu topography varies between 800 and
2,500 meters of altitude. However, mountains reach up to
2,500 such as mount Ruwenzori (5,119 m), volcanoes
Nyamulagira volcano (3,056 m) and Nyiragongo volcano
(3,470 m) [22]. The physiology of North Kivu is the result
of the cracking in the Albertin Rift valley that created low
and up lands from Lake Albert in North-East DRC to Lake
Malawi [23].
The diversity of climate in North Kivu is a result of the
heterogeneity of the topography. Below 1,000 meters
the average temperature is 23°C, around 1,500 meters
the temperature is near 19°C and around 2,000 m the
temperature is near 15°C. The average annual rainfall varies
between 1,000 mm and 2,000 mm. The lowest monthly
precipitation is recorded between January and February and
July and August. The North Kivu climate is characterized by
four seasons: two wet seasons and two dry seasons. The first
wet season appears between mid-August and mid-January
and the second in mid-February to mid-July. However, the
two dry seasons appear in a very short time. The first is
observed between mid-January and mid-February and the
second between mid-July and mid-August [22].
3. Materials and Methods
3.1. Variables
The area covered by forest in 2001 and converted into
non-forest cover area in 2015 was the dependent variable
used to analyze the key factors explaining the Forest Cover
Loss (FL) in the province of North Kivu, DRC. According to
the United Nations Food Agriculture Organization (FAO),
the forest is considered as a land spanning 0.5 hectare with
trees higher than 5 meters and a canopy cover more than 10
percent [24]. The Forest loss includes not only the loss of the
natural forest area but also the afforested and reforested area.
The potential explaining factors we analyzed are described in
the Table 1. Direct factors, in case of developing countries,
are the replacement of the forest area to a non-forest land due
to human activities (e.g. conversion of forest area into
PATH RAW DATE SUN ELEVATION
WAVELENGTH
(micrometers)
BAND-ID
RADIANCE
_ADD
RADIANCE
_MULT
173 59 25/11/2001 51.7537 0.533 - 0.590 LE71730592001009SGS00_B2.TIF -7.1988 0.7990
0.636 - 0.673 LE71730592001009SGS00_B3.TIF -5.6217 0.6220
0.851 - 0.879 LE71730592001009SGS00_B4.TIF -6.0693 0.9690
173 60 11/12/2001 54.2762 0.533 - 0.590 LE07_L1TP_173060_20011211_20170202_01_T1_B2.TIF -7.1988 0.0013
0.636 - 0.673 LE07_L1TP_173060_20011211_20170202_01_T1_B3.TIF -5.6217 0.0012
0.851 - 0.879 LE07_L1TP_173060_20011211_20170202_01_T1_B4.TIF -6.0693 0.0028
173 61 11/12/2001 55.0708 0.533 - 0.590 LE71730612001345SGS00_B2.TIF 0.7990
0.636 - 0.673 LE71730612001345SGS00_B3.TIF 0.6220
0.851 - 0.879 LE71730612001345SGS00_B4.TIF 0.9690
174 60 1/2/2001 53.5324 0.533 - 0.590 LE71740602001032SGS00_B2.TIF -7.1988 0.7990
0.636 - 0.673 LE71740602001032SGS00_B3.TIF -5.6217 0.6220
0.851 - 0.879 LE71740602001032SGS00_B4.TIF -6.0693 0.9690
174 61 1/2/2001 54.0432 0.533 - 0.590 LE71740612001032SGS00_B2.TIF -7.1988 0.7990
0.636 - 0.673 LE71740612001032SGS00_B3.TIF -5.6217 0.6220
0.851 - 0.879 LE71740612001032SGS00_B4.TIF -6.0693 0.9690
173 59 9/2/2015 58.2607 0.52 - 0.60 LC81730592015056LGN00_B3.TIF -60.4647 0.0121
0.63 -0.69 LC81730592015056LGN00_B4.TIF -50.9872 0.0102
0.77 - 0.90 LC81730592015056LGN00_B5.TIF -31.2016 0.0062
173 60 8/1/2015 56.4634 0.52 - 0.60 LC81730602015040LGN00_B3.TIF -60.8655 0.0122
0.63 -0.69 LC81730602015040LGN00_B4.TIF -51.3252 0.0103
0.77 - 0.90 LC81730602015040LGN00_B5.TIF -31.4085 0.0063
173 61 9/1/2015 56.9160 0.52 - 0.60 LC81730612015040LGN00_B3.TIF -60.8655 0.0000
0.63 -0.69 LC81730612015040LGN00_B4.TIF -51.3252 0.0000
0.77 - 0.90 LC81730612015040LGN00_B4.TIF -31.4085 0.0000
174 60 16/2/2016 55.7911 0.52 - 0.60 LC08_L1TP_174060_20160203_20170330_01_T1_B3.TIF -60.9850 0.0122
0.63 -0.69 LC08_L1TP_174060_20160203_20170330_01_T1_B4.TIF -51.4259 0.0103
0.77 - 0.90 LC08_L1TP_174060_20160203_20170330_01_T1_B5.TIF -31.4701 0.0063
174 61 16/02/2015 56.0589 0.52 - 0.60 LC81740612015031LGN00_B3.TIF -61.0376 0.0122
0.63 -0.69 LC81740612015031LGN00_B4.TIF -51.4703 0.0103
0.77 - 0.90 LC81740612015031LGN00_B5.TIF -31.4973 0.0063
Landsat 7 ATM+ (2001)
Landsat 8 OLI (2015)
14 Musumba Teso Philippe et al.: Key Factors Driving Deforestation in North-Kivu
Province, Eastern DR-Congo Using GIS and Remote Sensing
agriculture) or to natural hazards like volcano eruption.
However, the indirect drivers relate to complex interactions
of social, economic, technologic, cultural and political
processes that affect direct drivers to cause deforestation
[25, 26].
3.2. Data Used
This study was carried out using LANDSAT 7 ETM+ for
2001 and LANDSAT 8 OLI/TIRS for 2015 temporal
images downloaded from http://earthexplorer.usgs.gov
(Figure 3).
Figure 3. Raw and path of Landsat images covering the Nord-Kivu
province
The topography data was extracted from the Digital
Elevation Model (MNT/ ASTER GLOBAL DEM)
downloaded from the same website. All the Landsat images
and the DEM used have 30 meters of spatial resolution. All
landsat scenes with path = 174 and raw = 60 acquired in 2015
were not clear (full of cloud). Thus, the one acquired on 3
February was considered for analysis.
In addition to the above raster data used for the study,
vector data of protected areas, localities, roads, rivers
downloaded from the official website of the Common
Geographic Reference Database: www.rgc.cd were used.
This website was created by United Nations Agencies and
Non-Governmental Organizations to address the mapping
issue in the DRC and is managed by the DRC National
Institute of Statistics. GIS layers not found in this website
such as Health areas, armed groups area of influence, mining
zones and Internal Displacement of population (IDP) were
provided by the Information Geographic Center (CIG) and
the United Nations for Stabilization of Congo (MONUSCO).
The attribute data used for some specific layers, such as
population per health area and road category were provided
by the Health Province Division (DPS) and the Office of
Roads (OR) which are the DRC public services dealing with
health and roads respectively.
3.3. Landsat Processing
The forest cover, the agriculture, the urban and the lava
cover areas were derived from LANDSAT 8 OLI/TIRS
and LANDSAT 7 ETM+, using the supervised multispectral
classification. All used satellite images of 30 m of spatial
resolution were captured during the wet season for
comparison (Figure 4). The main scalar information and
band specifications of used images are found in Table 1.
Figure 4. Landsat Processing Methodology
The landsat images were processed using ArcGIS 10.3
software. The composite images of Near Infrared, Red and
Green Bands were created to facilitate the extraction of
the needed information such as Forest cover and the
Agriculture areas. The SPOT images with a very high
spatial resolution (2.5 meters) acquired in 2013 were also
used as support to the interpretation keys of LANDSAT 8
OLI/TIRS as suggested by Potapov P. et al. [27]. The 2000
AFRICOVER land cover maps were also used to interpret
the LANDSAT 7 ETM+.
The Atmospheric corrections were applied to remove
errors and increase accuracy using the Equation 1 from
Raster Calculator (Spatial Analyst) of ArcGIS 10.3 software.
First, the Digital Numbers (DN) were converted to Top of
Atmospheric (ToA) using the Equation 2 where DN2ToAr is
Digital Numbers to Top of Atmospheric, B_Mult_B is Band
specific of multiplicative bands, DN_V is Digital Numbers
value and Ref-Add is Reflectance Additive. And after, the
sun angle was corrected using Equation 2 where CoSun is
American Journal of Geographic Information System 2019, 8(1): 11-25 15
correction for sun elevation, ToAr is Top of atmospheric
reflectance and SinSunE is Sinus of sun elevation. The
values used are found in the Metadata files of sets of
images downloaded from the USGS website
(https://earthexplorer.usgs.gov/). All band specifications and
most important scalar information are found in Table 1.
Bands of the scene with path-raw equal to 173-61 were not
corrected owing to the lack of additive reflectance value in
the metadata file.
"DN2ToAr = "((B_Mult_B)" "* (DN_V) + Ref_Add))" (1)
CoSin = ToAr
SinSunE (2)
The annual rate of deforestation was calculated using
Equation 3 as recommended by Puyravaud, J. [28]. In this
equation A1 and A2 represent the forest cover areas for the
years t1 and t2. The deforestation rate can also be estimated
in square kilometers of deforested area every year by the
Equation 4 [28].
q=2
11/(12) 1 (3)
R=12
12 (4)
Figure 5. Extraction and modeling schema of dependent and explaining variables
16 Musumba Teso Philippe et al.: Key Factors Driving Deforestation in North-Kivu
Province, Eastern DR-Congo Using GIS and Remote Sensing
Table 2. Dependent and independent variables
Variables
Descriptive
Units
Source
1. Dependent variable
FL
Forest Cover Loss, converted to non-forest area
Ha
Classification Images Landsat
(http://earthexplorer.usgs.gov)
2. Independent variables
2.1. Direct factors
UE
Urban expansion
Ha
Images Landsat
AE
Land converted to agriculture
Ha
Images Landsat
VL
Variation of volcanic lava area
Ha
Images Landsat
2.2. Indirect factors
2.2.1. Environmental factors
NR
Euclidean Distance from National roads
Km
RGC www.rgc.cd
NPR
Euclidean Distance from National + provincial roads
Km
RGC www.rgc.cd
DR
Euclidean Distance from roads
Km
RGC www.rgc.cd
PR
Euclidean Distance from Main road
Km
Landsat Images & Office of Roads/DRC
ML
Euclidean Distance from Major localities
Km
RGC & Atlas Admin./DRC
LC
Euclidean Distance from Localities
Km
RGC www.rgc.cd
AL
Altitude
Meter
DEM
SL
Slope
%
DEM
DV
Eclidean distance from rivers
Km
RGC
PA
Protected area
Sq-km
RGC (www.rgc.cd)
MZ
Mining zones
%
UGADEC
MS
Euclidean Distance from Mining sites location
Km
UGADEC
DH
Eclidean distance from hospitals
Km
CIG
2.2.2. Demographic factors
PD
Population density
People per Sq-km
IPS
2.2.3 Social trigger events
AG
Area influenced by Armed groups
ha
MONUSCO
ID
Internally Displaced Persons
Km
MONUSCO
3.4. Cartographic Modelling of the Variables
The cartographic modelling of the variables from the
table 2 as summarized in the Figure 5 was processed. This
operation facilitated the measurement of these variables
before their integration in one fishnet layer for statistic tests.
A shape file of fishnet with rectangular grids of 3 km x 3km
for the study area was first created. This has resulted to a
total of 6812 patterns. The Raster Calculator and other tools
of ArcGIS 10.3 were used in the process to produce thematic
maps for the dependent and explaining variables. Figure 5
presents the workflow followed to create thematic maps of
all variables used for this study.
3.5. Statistical Analysis
3.5.1. Ordinary Least Square and Geographically Weighted
Regression
Three tools were used to assess the relationship between
the forest cover loss and the candidate variables. The
Explanatory Regression tool was used for the model
selection by generating a list of models with AICc
coefficients which indicated the best model that was tested
by OLS tool to detect the driving factors explaining the
deforestation in the study area. The OLS regression and
GWR were used to identify the key factors of deforestation
and analyze their spatial heterogeneity. Subsequently, the
GWR tool was also used to assess a limited key factor that
can be used to predict the forest cover loss in space and time
throughout the study area [29, 30].
3.5.2. Calculation of Measures for Goodness-Of-Fit
Measures of goodness-of-fit for this study aims at
quantifying how well used models (Exploratory regression,
OLS and GWR) fits the data and identifying the best model.
A total of six main statistic tests were calculated using
ArcGIS 10.3 software and interpreted. These measures
include Adjusted R-Squared, Variable Inflation Factor (VIF),
Jarque-Bera (JB) statistic, Moran’s Index (Moran’s I) Spatial
autocorrelation, KOENKER Breusch-Pagan (BP) statistic
and AIKAKE’s Information Criterion (AIC). These statistics
tests helped to specify a model which meets some key
requirement, i.e. the model cannot miss key explanatory
American Journal of Geographic Information System 2019, 8(1): 11-25 17
variables; residuals must be normally distributed and free
from spatial autocorrelation [31] to determine the passing
model among two or more.
The coefficient of determination R2 value, which
determines how well independent variables are explaining
the dependent variable, is a measure that helps to judge the
performance of the model. However, a good choice for OLS
and GWR models is rather based on a high Adjusted R2
which is a calibration of R2 value. The adjusted R2 value
generally increases when more independent variables are
added to the model [32]. The Adjusted R2 is calculated using
the Equation 5 referring to the general Equation 6 for
R-Squared with RSS = residual sum-of-squares, TSS = total
sum-of-squares, y = response values, ŷ = fitted values,
=
the mean of measure values, n = sample and p = number of
parameters [33].
Radj
2= 1 n1
np(1 R2) (5)
R=1 RSS
TSS = 1 (y+y
)2
(y+y
)2 (6)
The explanatory variables in a model are also expected to
be free from multicollinearity. The VIF is a measure used for
that and which helped in deciding which redundant variable
could be removed from the model without jeopardizing it.
This measure was assessed using Equation 7 where the VIF
for explanatory variable j is just the reciprocal of the inverse
of R2 from the regression. The higher the VIF value is,
the higher the collinearity is [32] which may indicate
redundancy among explanatory variables [37].
VIFj=1
1 Rj
2 (7)
Normality assumption being important in regression
analysis, the JB statistic helped to test whether residuals (the
observed/known dependent values minus the predicted
values) follow a normal distribution. Because the model is
biased if the residuals are not normally distributed (34, 35).
The JB test was used to examine whether the OLS model
results were trustworthy and could be used for predictions.
The Equation 8 is used for calculation of the JB test, where n
is the number of observations; S the sample skewness, C is
sample kurtosis, and k sample estimate of the kurtosis (the
number of regressors when examining residuals to an
equation). The value of S and C are also defined by the
mathematical Equations IX and X, where 3 and 4 are
the estimated of the third and fourth moments respectively, x
is the sample mean, and 2 is the variance. Parameters used
to estimate 3 and 4 are found in the same equations.
JB =nk
6 S2+ 1
4(  3)2 (8)
=3
3=
1
n(xix
)3
n
i=1
(1
n(xix
)2
n
i=1 )3/2 (9)
C = 4
4=
1
n(xix
)4
n
i=1
(1
n(xix
)2
n
i=1 )2 (10)
Normality assumption being also important in regression
models, the BP test helped to determine whether the
explanatory variables had a consistent relationship to
dependent variable, both in geographic and data space [34].
Considered as an asymptotical way distributed as x2 with k
degrees of freedom, the BP test is defined by the Equation 12
where the elements of f are defined by fi = (ei / s)2 - 1, Z is a
(n x k) matrix containing the variables thought to influence
the heteroskedasticity, e is the (n x 1) vector of OLS residuals
and S2 the maximum likelihood variance (36).
Apart from JB and BP tests which deals with residuals in
regression analysis, the Moran’s I statistic was also used
to measure the degree to which residuals are spatially
autocorrelated in different patterns (fishnet grids) of the
study area i.e. their associated data value tended to be
clustered together or dispersed in space. It is defined by the
Equation 11 where
is the mean of the X variable, Wij are
elements of the weight matrix and S0 the sum of the elements
of the weight matrix [37].
I = n wij (yiy)(yjy)
n
j=1
n
i=1
  wij (y iy )2
n
i1=
n
j=1
n
i=1 (11)
To detect the best model among two or more, a statistic
measure is helpful. For this study, AICc measures were
calculated and used for comparing models with the same
dependent variable and choosing the best model for forest
cover loss prediction. The AICc value is a number that gives
measures of information distance between any model which
has fitted and the unknown true model [38]. The high AICc
value indicates the best model and its value, as suggested by
Chalton and Fotheringham [38] is calculated by Equation 13
where n is the number of observations in the dataset,
is the
estimate of standard deviation of the residuals, and (S) is the
trace of the hat matrix.
B P = 1
2fZZZ1Zf (12)
AICc= 2n logen loge2n ( n+tr(S )
nttr(S)) (13)
3.5.3. Criteria Used for Model Selection
Based on the calculation in section 3.5.2, five criteria were
considered, as suggested by Jichuan Sheng et al. [3] to assess
the best models:
1) The Adjusted R-squared should be equal to or greater
than 0.5 (Adjusted R2 _ 0.5), which denotes that the
model's goodness of fit should not be less than 0.5;
2) The p-value for regression coefficients should be no
more than 0.05 (p-value _ 0.05), which suggests that
the variable is statistically significant to the model;
3) The variance inflation factor value of regression
variables should be no more than 7.5 (VIF _ 7.5),
which ensures that there is not multicollinearity and
redundant independent variables in the regression
model;
4) The p-value for Jarque-Bera statistic should be greater
than 0.1, which can ensure that the residuals of
regression model are normally distributed. If the
18 Musumba Teso Philippe et al.: Key Factors Driving Deforestation in North-Kivu
Province, Eastern DR-Congo Using GIS and Remote Sensing
p-value for the Jarque-Bera statistic (test) is
statistically significant, the regression model is biased
and the model predictions cannot be fully trusted.
When the model is biased, a key explanatory variable
may be missed;
5) The p-value for spatial autocorrelation should be more
than 0.1, which ensures that there is no spatial
autocorrelation in the regression model based on the
Moran's I value. A significant Moran's I value
indicates that there is spatial autocorrelation in the
model; a positive Moran's I value suggests a clustering
trend, and a negative value suggests the existence of a
discrete trend.
4. Results
4.1. Land Cover Change and Thematic Maps of
Variables
After the supervised classification of the Landsat images
of the two periods (2001 and 2015), the change detection
method was applied to estimate the forest cover loss, the
agriculture area expansion, the urban area expansion and
volcanic lava area expansion as indicated on the land cover
maps (Figure 6). The overall classification accuracy was
87% following the Ground Control Points collected from
Google Earth Professional application.
During the 2001 to 2015 period, the annual rate of
deforestation was estimated to 1.7% using Equation 3.
Moreover, we estimated to 700 square kilometers, the forest
cover disappearing every year in North Kivu province
(Equation 4). In some areas of the southern and northern
parts of Lake Edward within the Virunga National Park,
urban area has decreased in 2015 in reference to 2001
(Figure 6). This phenomenon may be explained by large
evacuation operations of human population from the park
initiated by the National Institute of Nature Conservation
(ICCN) during 2003 and 2013 period. These operations were
supported by ICCN partners and followed by some good
governance measures of protected areas such as demarcation
of the Virunga National Park on some parts of its border (39,
40).
Figure 7 presents 21 cartographic maps modelled from the
result of the Figure 5. Each map was produced using the data
relating to each variable. The Dependent variable (FL) and
the independent variables AE, UE and VL were extracted
from data used to produce the Figure 6 while 16 other
variables were extracted from data received from other
sources as described in section 3.2.
4.2. Explanatory Regression Model Result
We used the exploratory regression of ArcGIS 10.3
software and came up with 17 regression models shown in
the Table 3. Based on the criteria listed in the section 3.5.3,
the 15th model with the lowest AICc of 106,053 was the best
fit. All these 17 models have the Adjusted R square value
greater than 0.5 and a p-value for regression coefficients less
than 0.05 that shows the fitness to the criteria in section 3.4.3.
For all these 17 models, Jarque-Bera, Koenker (BP) and
spatial autocorrelation of residuals returned a p-value of
0.000. This result was not included in Table 3 for
conciseness.
The 15th model of the Explanatory Regression proposed
12 explaining variables among a total of 20 candidates
(Table 4). Consequently, the global OLS model in Table 4
tested only these 12 independent variables.
Figure 6. Land cover maps of Nord-Kivu for 2001 and 2015
American Journal of Geographic Information System 2019, 8(1): 11-25 19
Figure 7. Thematic maps of variables
4.3. OLS Output Result
The global OLS linear model output of the 12 explaining
variables is shown in the Table 4. After the test, the
coefficients of Euclidean distance from Mining Sites (MS)
and from National Road (NR) did not indicate the expected
signs. This means that these unexpected coefficient signs
indicate problems in the OLS model [41]. Consequently,
OLS was launched for the second time without these
variables and the results of the output are presented in the
Table 5 including only 10 independent variables.
Relating to the model in the Table 5, the forest loss (FL) in
the North Kivu province during the period starting from
2001 to 2015 was explained by the following parameters:
Agriculture Expansion (AE), Volcanic lava Expansion (VL),
Urban Expansion (UE), Population Density (PD), Euclidean
distance from Roads (DR), Protected Area (PA), presence of
Armed groups in the area (AG), Slope (SL) and Euclidean
20 Musumba Teso Philippe et al.: Key Factors Driving Deforestation in North-Kivu
Province, Eastern DR-Congo Using GIS and Remote Sensing
distance from hospitals (DH). The obtained global linear
regression equation is: FL = 0.9386(AE) + 0.9017(VL) +
0.1172(PD) 2.2173(PA) - 0.0128(DR) 0.6084(AG)
9.804922(SL) 0.1290(DH) 386.1089.
The OLS results in the Table 5 indicates that all the above
independent variables were statistically significant at 0.05
level. The Adjusted R square value (Adj R2 = 0,8946)
indicated that the deforestation could be explained by the
variables AE, VL, UE, PD, DR, PA, AG, SL and DH at 89%.
Also, all the above variables had a Robust Probability of less
than 0.01. Moreover, both the Joint F-Statistic and the Joint
Wald Statistic had a P-value less than 0.01 that indicated that
there is a significant linear relationship between the
dependent variable and the independent variables.
Therefore, the Jarque Bera test was not statistically
significant meaning that the model is unbiased.
Also, the Koenker (BP) Statistic had a statistically
significant p-Value that indicate that the regression model
was not stationary. This means that the regression model is
not stationary and the variables have a consistent relationship
in the geographic space. Consequently, the Geographically
Weighted Regression (GWR) model was considered more
appropriate to examine the association between the
dependent variable and the explaining variables.
Table 3. Summary of the Explanatory Regression model
Model
AdjR2
AICC
VIF
+AE* ** +VL*** +UE*** +PD*** -PA*** -SLOPE*** -DH***
0.90
106216
1.24
+AE*** +VL*** +UE*** -PA*** -SLOPE*** +LC*** -DH***
0.90
106238
1.25
+AE*** +VL*** +UE*** -PA*** -DR*** -SLOPE*** +LC*** -DH***
0.90
106160
1.93
+AE*** +VL*** +UE*** +PD*** -PA*** -SLOPE*** +LC*** -DH***
0.90
106172
1.73
+AE*** +VL*** +UE*** +PD*** -PA*** -SLOPE*** +ML*** -DH***
0.90
106181
3.53
+AE*** +VL*** +UE*** +PD*** -PA*** -DR*** -SLOPE*** +LC*** -DH***
0.90
106107
1.93
+AE*** +VL*** +UE*** -PA*** +NR*** -DR*** -SLOPE*** +LC*** -DH***
0.90
106138
2.85
+AE*** +VL*** +UE*** +PD*** -PA*** +NR*** -SLOPE*** +LC*** -DH***
0.90
106145
2.85
+AE*** +VL*** +UE*** +PD*** -PA*** +NR*** -DR*** -SLOPE*** +LC***
0.90
106080
2.86
+AE*** +VL*** +UE*** +PD*** -PA*** -DR*** -SLOPE*** +ML*** +LC***-DH***
0.90
106095
4
+AE*** +VL*** +UE*** +PD*** -PA*** -DR*** -AG*** -SLOPE*** +LC*** -DH***
0.90
106097
1.95
+AE*** +VL*** +UE*** +PD*** -PA*** +NR*** -DR*** -AG*** -SLOPE*** +LC*** -DH***
0.90
106066
2.88
+AE*** +VL*** +UE*** -MZ*** +PD*** -PA*** +NR*** -DR*** -SLOPE*** +LC*** -DH***
0.90
106069
2.86
+AE*** +VL*** +UE*** +PD*** -PA*** +NR*** -DR*** -SLOPE*** +ML*** +LC*** -DH***
0.90
106069
4.61
+AE*** +VL*** +UE*** -MZ*** +PD*** -PA*** +NR*** -DR*** -AG***
0.9
106053
2.89
+AE*** +VL*** +UE*** -MZ*** +PD*** -PA*** +NR*** -DR*** -SLOPE*** +ML*** +LC*** -DH***
0.90
106059
4.61
+AE*** +VL*** +UE*** +PD*** -PA*** +NR*** -DR*** -AG*** -SLOPE*** +ML*** +LC*** -DH***
0.90
106060
4.62
Adj R2, AICc, J-B, K(BP), VIF and SA indicate respectively, Adjusted R-squared, corrected Akaike Information Criterion, p-value for
Jarque-Bera statistic, p-value for Koenker statistic, variance inflation factor value and p-value for spatial autocorrelation.
* Significant at 0.10 level, ** Significant at 0.05 level and *** Significant at 0.01 level.
Table 4. Summary of Explanatory Regression output
Variable
Coefficient(a)
StdError
t-Statistic
Probability(b)
Robust_SE
Robust_t
Robust_Pr(b)
VIF (c)
Intercept
-530.8
45.252
-11.7299
0.000000*
52.3520
-10.1390
0.000000*
--------
AE
0.9347
0.0046
204.9950
0.000000*
0.0054
173.9800
0.000000*
1.29
VL
0.8912
0.0308
28.9814
0.000000*
0.0225
39.6840
0.000000*
1.032
UE
0.0423
0.0015
28.7226
0.000000*
0.0023
18.5820
0.000000*
1.251
MZ
-0.6870
0.1731
-3.9676
0.000082*
0.1596
-4.3047
0.000021*
1.123
PD
0.0994
0.0133
7.4815
0.000000*
0.0302
3.2930
0.001010*
1.05
PA
-2.6547
0.2105
-12.6097
0.000000*
0.2450
-10.8370
0.000000*
1.323
NR
0.00196
0.0003
6.0232
0.000000*
0.0002
8.9880
0.000000*
2.208
DR
-0.0140
0.0017
-8.2869
0.000000*
0.0014
-9.8609
0.000000*
1.302
AG
-1.0425
0.2453
-4.2491
0.000026*
0.2005
-5.1982
0.000000*
1.088
SL
-9.4610
0.6619
-14.2933
0.000000*
0.7708
-12.2750
0.000000*
1.101
LC
0.0133
0.0015
8.8606
0.000000*
0.0010
12.4590
0.000000*
1.96
DH
-0.1290
0.0081
-15.8680
0.000000*
0.0068
-18.8930
0.000000*
2.889
American Journal of Geographic Information System 2019, 8(1): 11-25 21
Table 5. Summary of the OLS output
Variable
Coefficient
(a)
t-Statistic
Probability
(b)
Robust_
SE
Robust_t
Robust_
Pr(b)
VIF
(c)
Intercept
-386.1089
-8.7774
0.0000*
50.2431
-7.6848
0.0000*
--------
AE
0.9386
210.5456
0.0000*
0.0052
181.4320
0.0000*
1.1837
VL
0.9017
28.8043
0.0000*
0.0214
42.2084
0.0000*
1.0266
UE
0.0332
23.8466
0.0000*
0.0021
15.9935
0.0000*
1.0723
LC
0.1172
8.7716
0.0000*
0.0326
3.5899
0.0004*
1.0179
PD
0.1172
8.7716
0.0000*
0.0326
3.5899
0.0004*
1.0179
PA
-2.2173
-10.7305
0.0000*
0.2402
-9.2303
0.0000*
1.2226
DR
-0.0128
-8.1330
0.0000*
0.0014
-9.0960
0.0000*
1.0861
AG
-0.6084
-2.4869
0.0129*
0.1893
-3.2133
0.0013*
1.0377
SL
-9.8049
-14.5694
0.0000*
0.7933
-12.3592
0.0000*
1.0915
DH
-0.1290
-15.8680
0.0000*
0.0068
-18.8932
0.0000*
2.8885
Adjusted RSquare
0.8983
AICc
106086
Joint F-Statistic
475.3116
p-Value
0.0000*
Joint Wald Statistic
85576.8254
p-Value
0.0000*
Koenker (BP) stat.
373.4926
p-Value
0.0000*
Jarque-Bera Stat.
46272.9902
p-Value
0.0000*
* An asterisk next to a number indicates a statistically significant p-value (p < 0.05).
(a) Coefficient: Represents the strength and type of relationship between each explanatory variable and the dependent
variable.
Table 6. Summary of GWR output
Variables
AICc
R2
Adjusted
R2
Neighbors
AE ; PD ; DR ; SL
103054.83
0.9393
0.9363
305
4.4. GWR Output Result
The result of our analysis with OLS in Table 5 confirmed
that the relationship between forest cover loss and its
explaining variables varies over space. So, the output of the
GWR analysis examining the heterogeneous association
between the variables is shown in the Table 6.
Unlike the OLS, the GWR regression indicated four key
factors explaining the forest cover loss in the North Kivu
province: the Agriculture Expansion (AE), the Euclidean
Distance from road (DR), the slope (SL) and the Population
Density (PD). Other parameters such as VL, UE, PA, AG
and DH were excluded by GWR to avoid redundancy and
multicollinearity of explaining variables.
The comparative results reveal that the GWR
outperformed the OLS. The GWR model has the smallest
AICc (103054.83) than OLS (106086.85), and the Adjusted
R-square significantly increased from 0.898 (with OLS) to
0.936 (with GWR). This indicates that 93% of the variation
of the forest cover loss can be explained by only four key
parameters: AE, DR, SL and PD.
5. Discussion
Among the twenty potential explaining variables tested
in Table 4, ten passed with OLS regression model (Table 5)
and only four passed with the GWR model (Table 6).
According to the OLS regression results, there was a positive
association between Forest cover loss (FL) and Agriculture
expansion (AE), Volcanic lava expansion (VL), urban area
expansion (UE) and the Euclidean distance from localities
(LC). Our results corroborate those of Ghislain R. et al. [21]
who found that Agriculture and urban expansion were the
key parameters impacting forest cover loss in Panama
corridor. Some other studies also revealed a positive
association between the forest and the distance from roads
and the density of population such as Vincent Bax et al. [20]
in Amazonian Peruvian. The actor’s based survey conducted
by the Ministry of Environment, Nature Conservation and
Tourism (MECNT) of DRC also cited the agriculture among
the key driving factors of deforestation in North Kivu [42].
The same OLS regression model results indicated a
negative association with the Presence of Protected Area
(PA), the Euclidean distance from roads (DR), the presence
of armed groups (AG), the Slope (SL) and the Euclidean
distance from hospitals (DH). The findings of Christopher P.
B. et al. [19] and Van B. et al. [42] also revealed a positive
association between the deforestation and the presence of
protected area in Amazonian forest and DRC respectively.
Conflict may decrease or increase deforestation depending
on the relationship between conflict and other causes of land
use change [43]. In the North Kivu province, the population
does not have access to agriculture lands in the regions
22 Musumba Teso Philippe et al.: Key Factors Driving Deforestation in North-Kivu
Province, Eastern DR-Congo Using GIS and Remote Sensing
influenced by local or foreign armed groups. Consequently,
the forest cover in the areas influenced by armed groups had
less pressure from population than secured area.
However, the Koenker (BP) statistic indicated that the
OLS was not a good predictor of the forest cover loss in the
North-Kivu province. This was because the independent
variables vary over space. Hence, the GWR was considered
as the model, which can predict better the deforestation
parameters in North Kivu province as this consider the
variation of the explaining factors over the geographic
space.
In general, there was a positive correlation between the
forest cover loss and the Agriculture expansion (AE) and
the Population density (PD), and a negative correlation with
the Distance from roads (DR) and the Slope (SL). This
situation is due to the increase of population of North Kivu
province last decades. As twenty six percent of North Kivu
population rely on agriculture [44], so they need accessing
to agriculture lands not far from roads and in flat areas.
Figure 8. Spatial distribution of Key factors explaining deforestation
American Journal of Geographic Information System 2019, 8(1): 11-25 23
Furthermore, the Figure 8 (AE) indicates that the
coefficients of the Geographically Weighted Regression
model for the Agriculture expansion had different values
over the North Kivu province. The deeper color (blue)
suggests the strong associations between the Forest cover
loss and the agriculture expansion, while the lighter color
(yellow) shows the week correlations. The Figure 8 (DR,
PD, SL) shows the same reality for the parameters Distance
from roads, Population Density and Slope.
Considering the above reality, the correlation between
forest loss and agriculture expansion was strong in the
western part of Lubero territory and in North of Beni
territory (Figure 8.AE). The survey on deforestation causes
initiated in 2012 by the DRC national Ministry of
Environment and Tourism revealed agriculture as key
driver of forest cover loss in Beni and Lubero territories.
According to the survey report, forests have been
disappearing in these territories due to shifting agriculture,
perennial crop plantations and slash-and-burn agriculture
[41]. In the same way, according to Figure 8 (DR), the
relationship between the Forest cover loss and the distance
from road was stronger in North-west of Beni territory,
center of Lubero and northern part of Rutshuru than other
parts of the province.
6. Conclusions
This study assessed the forest cover loss in the North Kivu
province, Eastern part of DRC and analyzed its key
explaining factors. The study was carried-out for the 2001 to
2015 period. A set of twenty potential independent variables
were modelled and analyzed via a geospatial approach to
identify the key explaining factors from them.
Using ArcGIS 10.3 software tools, results revealed an
annual deforestation rate of 1.7% in the North Kivu province
that equal to 700 hectares of forest cover loss every year.
Both OLS and GWR regression models were tested and the
GWR was estimated as the best predictive model for the
Forest cover loss in the study area. The Koenker (BP)
Statistic of OLS had a statistically significant p-Value
(0.000*) indicating that the regression model was not
stationary. Hence, there was a variation of the explaining
variables in the geographic space, thus the OLS was not a
good model to explain the Forest cover loss. The GWR
model has the smallest AICc (103054.83) than OLS
(106086.8456) and, the highest Adjusted R-square (0.9364)
than the OLS (0.8984).
Using the GWR model, we identified four key factors that
explained the forest cover loss in the study area. We found a
positive correlation between the forest cover loss and
Agriculture expansion (AE) and the Population density (PD),
and a negative correlation between the Forest cover loss and
the Euclidean Distance from roads and the Slope (SL).
In the last decades, the population of North Kivu province
has increased while most of them rely on the agriculture
for their livelihood. Accordingly, more forest cover was
converted to agriculture area, especially in the regions near
the roads as well as in less steep areas.
Based on our findings, we recommend the promotion of
the sedentary farming in North Kivu and the prohibition of
the stubble-burning and shifting agriculture. Moreover, the
steep areas should be taken as priority during the
afforestation and reforestation activities.
AKNOWLEDGEMENTS
Autors are thankful to the Evangelical University of
Africa (UEA-Bukavu), for access to the virtual library.
Special thanks to all who accepted to read this paper and
provided valuable suggestions.
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... These forest ecosystems provide a valuable refuge for herpetofauna and other living creatures. However, these forests are considerably reduced by human activities [8]. This reduction in vegetation is detrimental to the survival of amphibians and reptiles. ...
... Approximately 20% of amphibians in Africa are found in this rift [18] Newer species descriptions in the genus Hyperolius [20][21] have added knowledge to the known amphibian diversity on the Albertine Rift. (6), Duberria sp (7), Bitis arientans (8), Lygodactylus sp (9) We have found that the batraco-herpetological fauna of the Rift is rich and diverse (Tables 1 and 2). As for the species recorded, 7 specimens of Afrixalus fulvovittatus were captured mainly on the Poaceae (Herbaceae) located in the swamps and most of them were vocal, especially males. ...
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Aims: Amphibians and reptiles of the Albertine Rift in the Congolese part of the country are poorly documented. The objective of this research was to perform a preliminary inventory of the diversity of amphibians and reptiles in the region. Study Design: This study was designed following scientific expeditions related to studies on chimpanzees. Litterature search revealed that amphibians and reptiles are poorly documented in these habitats. Place and Duration of Study: Amphibians and reptiles were collected in 12 days between April and May 2017 in Dzu (N01.94753°; E030.88848°), Dzoo (N01.92742°; E030.89179°), Nzerku 3 (N01.94119°; E030.90612°) and Nzonzo (N01.90352°; E30.91030°). Methodology: To collect amphibians and reptiles, we used the most minimally invasive method. This method consists of capturing 1 specimen for a known species and a maximum of 5 specimens for those for an unknown species. The surplus specimens were released into their environment. During the night between 7 pm and 9 pm, amphibians were captured by hand using a flashlight. Snakes had been captured using the snake stick. All captured specimens were scanned with a camera and then identified using amphibian and snake species identification keys. Necropsies (tongue and muscle tissue) stored in Eppendorf tubes containing alcohol (90-75%). Specimens had been fixed with formaldehyde (10%), before being preserved in alcohol (75%) in the long term. Tissues were shipped for molecular analysis to the University of Texas (United States). Results: In the four study sites, 149 amphibian specimens were collected, consisting of 19 species, 9 genera and 8 families. According to the reptiles, 27 specimens divided into 21 species grouped into 19 genera and 11 families were recorded. Conclusion: The batraco-herpetological fauna in the Albertine Rift in Ituri province in the Democratic Republic of the Congo is rich and diversified, hence this deserves the attention of other researchers.
... In 2000, the study area (ca. 88 500 km 2 ) had an estimated forest coverage of 73.1 %. Between 2001 and 2018, 4.5 % of this forest was cleared, mainly for the purpose of agriculture (Hansen et al., 2013;Tyukavina et al., 2018;Musumba Teso et al., 2019). Deforestation is therefore an indirect result of the fast-growing population, which increased from 89 inhabitants per km 2 in 1975 to 241 inhabitants per km 2 in 2015 (Hansen et al., 2013;JRC and CIESIN, 2015). ...
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Deforestation is associated with a decrease in slope stability through the alteration of hydrological and geotechnical conditions. As such, deforestation increases landslide activity over short, decadal timescales. However, over longer timescales (0.1–10 Myr) the location and timing of landsliding is controlled by the interaction between uplift and fluvial incision. Yet, the interaction between (human-induced) deforestation and landscape evolution has hitherto not been explicitly considered. We address this issue in the North Tanganyika–Kivu rift region (East African Rift). In recent decades, the regional population has grown exponentially, and the associated expansion of cultivated and urban land has resulted in widespread deforestation. In the past 11 Myr, active continental rifting and tectonic processes have forged two parallel mountainous rift shoulders that are continuously rejuvenated (i.e., actively incised) through knickpoint retreat, enforcing topographic steepening. In order to link deforestation and rejuvenation to landslide erosion, we compiled an inventory of nearly 8000 recent shallow landslides in © Google Earth imagery from 2000–2019. To accurately calculate landslide erosion rates, we developed a new methodology to remediate inventory biases linked to the spatial and temporal inconsistency of this satellite imagery. Moreover, to account for the impact of rock strength on both landslide occurrence and knickpoint retreat, we limit our analysis to rock types with threshold angles of 24–28∘. Rejuvenated landscapes were defined as the areas draining towards Lake Kivu or Lake Tanganyika and downstream of retreating knickpoints. We find that shallow landslide erosion rates in these rejuvenated landscapes are roughly 40 % higher than in the surrounding relict landscapes. In contrast, we find that slope exerts a stronger control on landslide erosion in relict landscapes. These two results are reconciled by the observation that landslide erosion generally increases with slope gradient and that the relief is on average steeper in rejuvenated landscapes. The weaker effect of slope steepness on landslide erosion rates in the rejuvenated landscapes could be the result of three factors: the absence of earthquake-induced landslide events in our landslide inventory, a thinner regolith mantle, and a drier climate. More frequent extreme rainfall events in the relict landscapes, and the presence of a thicker regolith, may explain a stronger landslide response to deforestation compared to rejuvenated landscapes. Overall, deforestation initiates a landslide peak that lasts approximately 15 years and increases landslide erosion by a factor 2 to 8. Eventually, landslide erosion in deforested land falls back to a level similar to that observed under forest conditions, most likely due to the depletion of the most unstable regolith. Landslides are not only more abundant in rejuvenated landscapes but are also smaller in size, which may again be a consequence of a thinner regolith mantle and/or seismic activity that fractures the bedrock and reduces the minimal critical area for slope failure. With this paper, we highlight the importance of considering the geomorphological context when studying the impact of recent land use changes on landslide activity.
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Tropical mountainous regions are often identified as landslide hotspots with particularly vulnerable populations. Anthropogenic factors are assumed to play a role in the occurrence of landslides in these populated regions, yet the relative importance of these human-induced factors remains poorly documented. In this work, we aim to explore the impact of forest cover dynamics, roads and mining activities on the occurrence of landslides in the Rift flank west of Lake Kivu in the DR Congo. To do so, we compile an inventory of 2730 landslides using © Google Earth imagery, high resolution topographic data, historical aerial photographs from the 1950’s and extensive field surveys. We identify old and recent (post 1950’s) landslides, making a distinction between deep-seated and shallow landslides, road landslides and mining landslides. We find that susceptibility patterns and area distributions are different between old and recent deep-seated landslides, which shows that natural factors contributing to their occurrence were either different or changed over time. Observed shallow landslides are recent processes that all occurred in the past two decades. The analysis of their susceptibility indicates that forest dynamics and the presence of roads play a key role in their regional distribution pattern. Under similar topographic conditions, shallow landslides are more frequent, but of smaller size, in areas where deforestation has occurred since the 1950’s as compared to shallow landslides in forest areas, i.e. in natural environments. We attribute this size reduction to the decrease of regolith cohesion due to forest loss, which allows for a smaller minimum critical area for landsliding. In areas that were already deforested in 1950’s, shallow landslides are less frequent, larger, and occur on less steep slopes. This suggests a combined role between regolith availability and soil management practices that influence erosion and water infiltration. Mining activities increase the odds of landsliding. Mining and road landslides are larger than shallow landslides but smaller than the recent deep-seated instabilities. The susceptibility models calibrated for shallow and deep-seated landslides do not predict them well, highlighting that they are controlled by environmental factors that are not present under natural conditions. Our analysis demonstrates the role of human activities on the occurrence of landslides in the Lake Kivu region. Overall, it highlights the need to consider this context when studying hillslope instability characteristics and distribution patterns in regions under anthropogenic pressure. Our work also highlights the importance of considering the timing of landslides over a multi-decadal period of observation.
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Deforestation increases landslide activity over short, contemporary timescales. However, over longer timescales, the location and timing of landsliding is controlled by the interaction between uplift and fluvial incision. Yet, the interaction between (human-induced) deforestation and landscape evolution has hitherto not been explicitly considered. We address this issue in the North Tanganyika-Kivu Rift region (East African Rift). In recent decades, the regional population has grown exponentially and the associated expansion of cultivated and urban land has resulted in widespread deforestation. On a much longer time scale, tectonic uplift has forged two parallel mountainous Rift shoulders that are continuously rejuvenated through knickpoint retreat, enforcing topographic steepening. In order to link deforestation and rejuvenation to landslide erosion, we compiled an inventory of nearly 8,000 recent shallow landslides in © Google Earth imagery from 2000-2019. To accurately calculate landslide erosion rates, we developed a new methodology to remediate inventory biases linked to the spatial and temporal inconsistency of this satellite imagery. We find that erosion rates in rejuvenated landscapes are roughly 40 % higher than in the surrounding relict landscapes, upstream of retreating knickpoints and outside of the Rift shoulders. This difference is due to the generally steeper relief in rejuvenated landscapes which more than compensates for the fact that rejuvenated slopes, when compared to similarly angled slopes in relict zones, often display a somewhat lower landslide erosion rate. These lower rates in the rejuvenated landscapes could be the result of a drier climate, the omission of earthquake-induced landslide events in our landslide inventory, and potentially a smaller regolith stock. More frequent extreme rainfall events in the relict zones, and possibly the presence of a thicker regolith, cause a stronger landslide response to deforestation compared to rejuvenated landscapes. Overall, deforestation initiates a landslide peak that lasts approximately 15 years and increases landslide erosion by a factor 2 to 8. Eventually, landslide erosion in deforested land falls back to a level similar to that observed under forest conditions, most likely due to the depletion of the most unstable regolith. Landslides are not only more abundant in rejuvenated landscapes but are also smaller in size, which may be a consequence of the seismic activity that fractures the bedrock and reduces the minimal critical area for slope failure. With this paper, we highlight the importance of considering the geomorphological context when studying the impact of recent land use changes on landslide activity.
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Le problème des feu de forêt est son impact sur le développement socio-économique et environnemental en Algérie est devenu une question inquiétante. La forêt étant un patrimoine qui joue un rôle très important dans cet axe, est soumis actuellement à ce phénomène causé par différents facteurs. L’étude et le suivi des changements subis au niveau des massifs forestiers nécessitent l’utilisation des techniques nouvelles pour gérer l’espace. L’outil « télédétection » reste une voie incontournable. Ainsi, une étude technique a été menée sur la forêt de Lardjem dans la wilaya de Tissemsilt en utilisant la télédétection et les SIG. Les résultats obtenus (cartes issues par classifications, indices de végétation) montrent des mutations régressives graves au cours des années 1990, ce qui augmente la portion des sols nue pour atteindre 70,70% en l’année 2000 au lieu de 32,04% en 1987. L’image de l’année 2012 et celle de 2014 montrent une légère amélioration traduite par une évolution progressive (régénération). Les vides ont diminués pour atteindre 50,8%, alors que le végétal prend sa place.
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Concerns over rising fuel consumption have prompted research into the influences of built environments on travel behavior. On the basis of data from origin-destination(OD) travel survey data of Mashhad (74287 trip data in 2011) and using Geographically Weighted Regression, socio-demographic characteristics, are shown to be strongly and positively associated with the fuel consumption per capita (car ownership elasticity=0.347878); we also found a positive association between distance to center and designs that are not pedestrian friendly with fuel consumption (average block size=0.147489, distance to center =0.334953) Although the study demonstrates a moderately strong negative elasticity between population density and the fuel consumption(population density = -0.259335). It suggests that the largest energy consumption reductions would come from creating compact communities which have land-use diversity and more walkable areas with pedestrian cycling infrastructure around all of the stations along transit lines.In order to enhance a sustainable urban plan, the socio-economic driving factors should be considered as one of the main element of energy consumption as well.
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This study explores the main direct and underlying causes of deforestation in Brazil's Legal Amazon region by considering spatial differences. The computation of localized parameters is based on geographically weighted regression (GWR). The novelty of this paper lies in its incorporation of economic, rather than Euclidean, distances into the GWR. Economic distances are measured by travel time, sourced from Google Inc. A global approach revealed several important factors that affect deforestation, including: rural population, GDP (suggesting a U-shaped environmental Kuznets curve), forest stock, cattle ranching, timber value, and road networks (both official and unofficial). Local analysis uncovered patterns not seen under global models, especially in the state of Pará. Most notably, crop cultivation was found to accelerate deforestation in southeastern Pará and northeastern Mato Grosso, while in some regions (especially in the northeastern corner of Pará), the area covered by crop plantations was negatively associated with deforestation. For Pará, rural credit constraints, larger territories designated as sustainable use areas and indigenous lands, and higher levels of precipitation inhibit deforestation. Further, rural population has a very heterogeneous impact on deforestation across Legal Amazon: it is not a significant factor of deforestation in northern Pará and Amapá, but it has a relatively strong effect in the western parts of Mato Grosso and Rondônia. Also, official and illegal roads create significantly more pressure on forests in remote regions compared to developed areas. Finally, the use of economic distances, as opposed to Euclidean distances, leads to notably different GWR results.
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Background: In Senegal, considerable efforts have been made to reduce malaria morbidity and mortality during the last decade. This resulted in a marked decrease of malaria cases. With the decline of malaria cases, transmission has become sparse in most Senegalese health districts. This study investigated malaria hotspots in Keur Soce sites by using geographically-weighted regression. Because of the occurrence of hotspots, spatial modelling of malaria cases could have a considerable effect in disease surveillance. Methods: This study explored and analysed the spatial relationships between malaria occurrence and socio-economic and environmental factors in small communities in Keur Soce, Senegal, using 6 months passive surveillance. Geographically-weighted regression was used to explore the spatial variability of relationships between malaria incidence or persistence and the selected socio-economic, and human predictors. A model comparison of between ordinary least square and geographically-weighted regression was also explored. Vector dataset (spatial) of the study area by village levels and statistical data (non-spatial) on malaria confirmed cases, socio-economic status (bed net use), population data (size of the household) and environmental factors (temperature, rain fall) were used in this exploratory analysis. ArcMap 10.2 and Stata 11 were used to perform malaria hotspots analysis. Results: From Jun to December, a total of 408 confirmed malaria cases were notified. The explanatory variables-household size, housing materials, sleeping rooms, sheep and distance to breeding site returned significant t values of -0.25, 2.3, 4.39, 1.25 and 2.36, respectively. The OLS global model revealed that it explained about 70 % (adjusted R(2) = 0.70) of the variation in malaria occurrence with AIC = 756.23. The geographically-weighted regression of malaria hotspots resulted in coefficient intercept ranging from 1.89 to 6.22 with a median of 3.5. Large positive values are distributed mainly in the southeast of the district where hotspots are more accurate while low values are mainly found in the centre and in the north. Conclusion: Geographically-weighted regression and OLS showed important risks factors of malaria hotspots in Keur Soce. The outputs of such models can be a useful tool to understand occurrence of malaria hotspots in Senegal. An understanding of geographical variation and determination of the core areas of the disease may provide an explanation regarding possible proximal and distal contributors to malaria elimination in Senegal.