spatial modelling of dynamics of land use land cover due

African Journal of Mining, Entrepreneurship and Natural Resource Management (AJMENRM)

ISSN: 2706-6002 Volume 1, Issue 2 (April 2020), PP 70-77 www.ajmenrm.ttu.ac.ke/

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Spatial Modelling of Dynamics of Land Use Land Cover Due to

Mining Activities in Taita Taveta County

Francis Gitau1, Nicholas Muthama Mutua2 1Department of Mining and Mineral Processing Engineering, School of Mines and Engineering, Taita Taveta

University, P.O. Box 635-80300, Voi, Kenya

Email: [email protected] 2Department of Mathematics, Statistics & Physical Sciences, School of Science & Informatics, Taita Taveta

University, P.O. Box 635-80300, Voi, Kenya

Email: [email protected]

Abstract: Taita Taveta County, Kenya’s coastal mineral belt rich in a variety of minerals, faces environmental

challenges to resources management and conservation. Widespread small-scale mining activities pose a

significant threat to the environment, which has led to changes in Land Use and Land Cover (LULC), therefore

adversely affecting the environment. This study aims at spatial modeling of the dynamics of LULC, evaluating

the accuracy in different time epochs and detecting changes in the mined and mining areas at a temporal scale.

The modeling and analysis was done using spatial-temporal remote sensed data and digital image processing

techniques utilizing machine learning algorithms in GIS Software and R Studio. Interpretation of the processed

data led to the delineation of LULC categories and classes. It was observed that the mined/ mined and

developed areas increased by 19% and 12%, respectively, between 2011 and 2019. Also, the area with

vegetation land was decreased by 38%, and waste dumps increased significantly. Normalized differential

vegetation index (NDVI) was also done to correlate the state of the healthy vegetation. The overall accuracy of

classified images and kappa statistics was 83.393% and 0.7591, respectively. This study revealed the declining

nature of the vegetation and the significance of using remotely sensed data to model LULC. The modeling

showed that the key drivers for LULC changes resulting in environmental degradation in the study area are iron

ore mining and mineral exploration.

Keywords- Change Detection, Environment, GIS, Land use/ Land cover, Mining activities, and Remote Sensing

1. INTRODUCTION Land use and land cover act as a significant indicator of the changes experienced on the environment

globally; this is indicated by the influence of human activities on the physical environment. Land use and land

cover changes are dynamic, accelerating process and mostly widespread generally driven by human and

economic activities, which, as a result, bring changes that impact and affect the environment and ecosystem at

large. Land use and land cover commonly abbreviated as (LULC) modeling is a scientific field that is rapidly

growing [11]. This is because land-use change is one of the greatest and most important ways that human and

human activities influence and affect the environment.

Satellite images have been widely used to monitor the spatial extents of various changes occurring in

the environment in both mining and post-mining areas [6]. Satellite remote sensing data is one of the most

accurate and up to date maps. Especially with the dynamic nature of the environment, it is practically one of the

effective methods to follow up these changes actively ([20]; [21]). Unsupervised machine learning classification

techniques have been used widely for ecological and environmental modeling and mapping. They consist of

easy to implement ISO cluster classifier found in GIS packages ([22]; [23]) and statistical procedures that need

specialists’ knowledge and the right software to implement. They include decision tree classifier [12] and

support vector machine [15]. These methods can predict and model change but require significant training to set

up the models effectively.

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Spatial Modelling of dynamics of Land Use Land Cover Due to Mining Activities in Taita Taveta County


There exist some studies by [7] on LULC in Taita Taveta County in Kenya using Geographical

Information Systems to develop a change detection methodology and deduce the land use and land cover

changes due to human-nature interactions.

The increasing mining activities it Taita Taveta has led to degradation of the land cover and, in result,

has affected the land use progressively. The extraction of valuable minerals or any other geological materials

from the earth, vein, or an ore body for economic interest to the miner is defined as mining in accordance with

[24]. Mining and quarrying of various minerals and metals have been carried out since prehistoric times and has

been and still is very crucial to the development of a nation [3]. As outlined by the [25], mining is a critical

driver of economic activities with capabilities to the development of the areas with mineral resources. Small-

scale mining affects the environment adversely and often has safety and health risks to the workers and the

surrounding community. It can result in loss of biodiversity, soil erosion, soil, and groundwater contamination

resulting in the processing and extraction of various minerals [26].

There has been an ongoing renewed strategic focus on improving the contribution of the mining sector

to Kenya’s GDP progressively from 1% to 10% by 2030 [18]. The Mining Act of 2016 empowers communities,

small scale, and artisanal miners to be active partners in shaping decisions that directly affect their rights to

sharing in mineral benefits, capacity building, and access to a clean, healthy and safe operating environment

[19]. Taita Taveta, Kenya’s coastal mineral belt rich in various gemstones and industrial minerals such as iron

and manganese, faces environmental challenges. Widespread small-scale mining and artisanal mining activities

in Taita Taveta, if not well managed, threaten the sustainability of the natural resources.

Due to the multi-criteria decision challenge that is experienced in Taita Taveta, there is a need to shift

from conventional analytical thinking to system driven thinking and approach to mapping these land dynamics

that can’t be quantified directly. The Spatio-temporal approach can be used to aid actionable, transparent, and

location-specific decisions.

Geographical information systems (GIS) and Remote Sensing technology are essential tools employed

in this research to study the land use dynamics and patterns invariably associated with the mining of natural

resources by studying the changes in land use and land cover dynamics.

This spatiotemporal data is supported by ground truth data, which allows timely and consistent

estimates of notable changes in LULC over a wide area. The main objective of this paper is to clearly

understand the dynamics of land use and land cover change dynamics in time and space, in the backdrop of

Small-scale Mining in Taita Taveta County.

2. STUDY AREA AND METHODOLOGY 2.1. Study Area

This study was carried out in Taita Taveta County, which is South East of Kenya. It is situated

between latitudes 20° 46҆ N and 40° 10҆ N, and longitudes 37° 36҆ E and 39°14҆ E and is generally characterized

by arid and semi-arid climatic conditions and geologically lies within the Mozambique Belt where most

industrial minerals and gemstones are found [2]. The Tsavo National Park covers about 60% of the County.

Therefore, the need to protect the environment from where the mining activities occur.


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Fig. 1: Map of Study Area

The study focus was Kishushe area, where mining of iron ore is the major economic activity. These

mining activities have resulted in adverse environmental impacts [3].

Fig. 2: Aerial Image of the Study Area. (Source: Google Earth Engine)

2.2. Data Used

In the present study, one Landsat 7 TM satellite Image obtained in March 2011 and two Sentinel 2

images obtained in March 2015 and March 2019 were respectively downloaded from the USGS

(http://glovis.usgs.gov) in the dry season with no cloud cover. Spectral signatures of different land cover types

from bare areas are best distinguished in the dry season with zero cloud cover [6]. The images path and row

were 167 and 062 respectively, with a spatial resolution of 15 meters for the Landsat 7 image and 10-meter

spatial resolution for Sentinel 2 images.




http://glovis.usgs.gov/



Fig. 3: Methodology

2.3. Pre-Processing

The three satellite imagery datasets were image enhanced, after that Georectification, and the

atmospheric correction was done using QGIS 2.18 and R Statistics Software. For the detection of surface

changes, radiometric corrections were done in ArcGIS. Layers stack operation was used to combine different

bands. All bands of the image were used layer stacking though for the Landsat TM visible to shortwave infrared

(bands 1 to 5 and 7 with pixel size 30 m). A shapefile of the study area around Samrudha Resources mining area

formerly Wanjala Mining was overlaid on each image as the area of interest (AOI) and a subset obtained.

2.3.1. Classification Scheme

Based on the prior knowledge of the study area, a scheme for classification was developed in

accordance with FAO guidelines. Seven classes, namely: Soil Overburden, Stockpile, Savannah Grassland,

Rock Overburden, Bushland/ Shrubland, Bare Ground, and Mined/ Mining/ Developed Area as Described in

Table 1.

Table 1: LULC Classes Categories Code LULC Categories Description

1 Soil Overburden This is the topsoil that was mucked from excavation and dumped on the surface during

mining.

2 Iron Ore Stockpile A storage location or a pile for bulk materials, forming part of the bulk material

handling process.

3 Savanna Grassland Sacred groves/planted woodlots/thick shrubs. This class has a few grass cover than the

close savannah.

4 Rock Overburden Rock removed to gain access to a mine: Dumped rocks ore

5 Bushland/ Shrubland These include areas that closely resemble a forest cover and reserved areas; gazetted

forest reserves/protected areas and natural growths. It has a tree Population density of

more than 150 trees per hectare.

6 Bare Ground Areas of Land within and around the forest that have no vegetation cover.

7 Mined/ Mining/ Areas with no vegetation where mining occurred, mining abandoned, or mining and

R

ArcGIS PRO

Classification

Landsat 8 and Sentinel-2

Images (2011, 2015 ,2019)

Data Pre -Processing: Atmospheric

Correction, Stacking, sub setting

and masking

Creation of Training Areas

R, ArcGIS 10.7

and QGIS

Random Forest

Accuracy Assessment

Change Detection

ArcGIS 10.7

2011-2015 2015-2019






Developed Area development are taking place.

An unsupervised classification using the ISO Cluster classifier was used to classify the images after

generation of spectral signatures of various classes. Ground truth data was used to evaluate the accuracy and do

a confusion matrix, which subsequently varied given the heterogeneous nature in the study area.

2.3.2. LULC Maps

Land use and land cover maps for the years 2011, 2015, and 2019 were produced, and areas of the

classes in Hectares were calculated in ArcGIS using Zonal Geometry tool to be used for statistical changes

analysis. The resulting area in hectares and the percentage changes for different years (2011, 2015, and 2019),

as shown in (figure 5) measured against each land use, and the land cover type was developed. Percentage

changes to determine the trend and dynamics of change calculated using the relation by [17] as the changes were

not linear to the timeline.

1

2

12

ln1

A

A

ttr (1)

Where, r is the rate of land use and land cover change, 1A and 2A are the Land use land cover class

cover at the time 1t and 2t , respectively [22], [14]. Any change notable in the study area was indicated as either

positive or negative.

2.3.3. Accuracy Assessment

Accuracy assessment validated the accuracy of the classification. A confusion matrix was applied to

evaluate the use and the producer accuracy to the most recent classified image for the year 2019. Stratified

random GPS coordinate points were used as ground truth data for the assessment. The overall accuracy values

and Kappa Statistics for the image is shown in (Figure 4).

The overall and general accuracy represents the percentage of correctly classified images [5] and was

achieved by division of the total number of correct observations by the number of the actual observations made.

In this, the overall accuracy and the resulting Kappa statistic was 83.393 % and 0.7591, respectively, as shown

in figure 4. Class Name Reference Totals Classified Totals Number Corrected Producers Accuracy (%) Users Accuracy (%) Kappa

Soil Overburden 25 22 19 78.9 87.35 0.695

Stockpile 61 60 49 80.33 82.26 0.7658

Savanna Grassland 57 53 39 67.8 87 0.764

Rocks Overburden 32 37 27 84.38 71.97 0.7641

Bushland/ Shrubland 59 50 45 80.33 82.04 0.7578

Bare Ground 106 112 98 92.45 87.5 0.789

Mined/ Mining/ Developed Area 105 111 96 91.25 85.63 0.778

Total 445 445 373

Overall Accuracy (%) 83.393

Overall kappa Statistics 0.7591 Fig. 4: Accuracy Assessment

LULC

2011 (%) 2015 (%) 2019 (%)

Soil Overburden 1,530,800 20.13 1,027,400 13.51 184,400 2.42

Stockpile - 0.00 502,600 6.61 704,600 9.26

Savanna Grassland 1,614,000 21.22 357,200 4.70 459,400 6.04

Rock Overburden - 0.00 1,269,100 16.69 1,191,900 15.67

Bushland/ Shrubland 1,401,000 18.42 2,108,500 27.73 1,862,300 24.49

Bare Ground 2,418,000 31.79 969,500 12.75 996,000 13.10

Mined/ Mining/ Developed Area 641,200 8.43 1,370,700 18.02 2,206,400 29.01

Total 7,605,000 100 7,605,000 100 7,605,000 100

Areas in Square Meters

Fig. 5: Areas of LULC Classes

2.3.4. Change Detection

For change detection, an image-differencing technique in ArcGIS was used. This is from the fact that

digital numbers in resulting difference images are considered to exist a normal distribution, where the pixels






with the least amount of change are observed around the mean [13]. This involves the subtraction of different

pixels between two data sets to identify the areas where changes have occurred [14]. The difference was

obtained statistically also for the land use and land cover maps between the years 2011-2015 and 2015-2019. A

transition matrix that indicated the respective changes from one class to another was calculated.

2.3.5. Normalized Difference Vegetation Index (NDVI)

The normalized difference vegetation index is an indicator that is used widely to detect various changes

in land cover by measuring the greenness [8]. NDVI calculations use the near-infrared (NIR) and Visible Red

bands to calculate the ratio of the difference in reflectance to the resulting sum of the two bands as expressed in

the formula below

The NDVI values range from -1to +1, making scaling and interpretation easy. Statistical analysis using

mean, maximum, standard deviation for different years in accordance with [6] for different years is determined

basing on in-depth analysis of spectral signatures and NDVI images.

3. RESULTS AND DISCUSSION The land use and land cover categories delineated in the study area were Soil Overburden, Stockpile,

Savannah Grassland, Rock Overburden, Bushland/ Shrubland, Bare Ground, and Mined/ Mining/ Developed

Area.

The details for the LULC as obtained from the Year 2011 (Figure 6), the year 2015 (Figure 7) and year

2019 (figure 8) clearly showed the LULC statistics in (meters squared and percentages ) that have taken place

during the period between 2011 to 2019; this is clearly shown in (Figure 10).

Fig. 6: LULC Map for the Year 2011






Fig. 9: LULC Maps for Years 2011, 2015 and 2019

Soil Overburden -10% -43%

Stockpile 70% 8%

Savanna Grassland -38% 6%

Rock Overburden 68% -2%

Bushland/ Shrubland 10% -3%

Bare Ground -23% 1%

Mined/ Mining/ Developed Area 19% 12%

LULC Change 2011-2015 Change 2015-2019

Fig. 10: LULC Changes in Percentage

The results of the land use and land cover dynamics analysis are graphically presented in (Figure 11,

12, 13), respectively, for the years 2011, 2015, and 2019.

Fig. 11: LULC Graph for the Year 2011








The activities of mining that heightened in different years imply the conversion of different classes

such as grasslands and shrublands. This is because mining of iron ore in the area is the significant driver for

environmental degradation that also led to a significant decrease in the years between 2011 and 2015.

As outlined by Agyeman [1], studies done in a mining region in Ghana reveal that mining activities

result in significant loss of vegetation. Comparing with other studies such as Gibb et al. [10] and Zwane, N. et

al. [16] reveals that there has been provable evidence that activities of mining have a considerable effect on

vegetation loss and thereby affecting the resultant land cover.

Figure 9 shows all changes from 2011–2015 and 2015–2019. Figure 14 also shows the statistics of

change in LULC classes based on either increase or subsequent decrease. From (figure 14) 45% and 27% of

LULC classes increased, and 71% and 48% of LULC classes decreased respectively for 2011 to 2015 and 2015

to 2019.

LULC Difference Year 2011-2015 Year 2015-2019

Increased (%) 45 27

Decreased (%) 71 48

Fig. 14: LULC Differencing Statistics

NDVI values for the years 2011 and 2019 were derived, as showed in (figure 15). This was needful in

correlating the NDVI results with the LULC model maps that were generated in the study area. The values as

from the years 2011 to 2019 changed from -0.267139 to −0.339806 and from 0.39168 to 0.001126. This

signified a reduction in the health of green vegetation in the area. Healthy vegetation, as put across by [9],

usually ranges from the positive values close to 1. There are similar studies in Ghana where the NDVI values

showed evidence of the reduction of vegetation and remarkable land degradation [1].






Fig. 15: NDVI for Years 2011 and 2019

These analyses of the dynamics of the land use and land cover changes that have taken place under

various land classes categories from the year 2011 to 2019 due to rapid expansion and increase in mining

activities at the study area are as given in the table below. However, there exists a unique case in the year 2019,

where there was a possible regeneration of vegetation in the mined areas.

Table 2: LULC Changes Inferences LULC Change (%) 2011-

2015

Inferences Change (%)

2015-2019

Inferences

Rock Overburden 68 Increased mining activities led to the

excavation of more materials that were

dumped near the mine sites as identified by the modeling.

-2 There was a decrease in Rock being deposited on

the surface as there were no mining activities

taking place due to community disputes late in 2016.

Stockpile 70 As mining activities heightened at

Kishushe mines, there were more stockpiles for Iron Ore mined.

8 An increase of the stockpiles was observed, this

might be attributed to cleaning up of the mine sites, more materials from the old mines were

added to the old stockpiles.

Mined/ Mining/

Developed Areas

19 There were more exploration and opening up of new mines. Many exploration

activities were witnessed.

12 Probably due to the takeover of formerly Wanjala Mining Co. by Samrudha Resources,

developments in the site started, which was

evident though not much.

Bushland and

Scrublands

10 The following of the ore reef as it dipped downward didn’t affect the growth of the

scrubland and bushlands; instead, there

was a handful increase.

-3 During this period, more exploration in more areas was taking place hence resulting in

clearance of the vegetation. The need for

development by the new owner also led to a decrease of the same.

Soil Overburden -10 Mining of the iron ore was at its peak, and

therefore not much topsoil was being shoveled from the active mines instead

more rock overburden was evident.

-43 The decrease in the soil overburden was a result

of developments that were done at this particular time.

Bare Ground -23 The bare ground at the study area was deposited with materials such as rock and

soil overburden and the iron stockpile

thereby causing a decrease

1 Clearance of the exploration sites and probably development led to the creation of significant

bare lands in the study area.

Savannah Grasslands -38 Grasslands were significantly reduced as a result of an increase in mining activities

and the creation of more mines

6 As a result of the halting mine operations in the study area, there was a possible regeneration of

grasslands. This was as a result of no activities

for more than five years.

4. CONCLUSION The study has revealed that considerable land use and land cover changes have taken place in and

around Kishushe Area in Taita Taveta County from 2011 to 2019. Before the start of iron ore mining, the region

was covered with tropical dense scrublands and grasslands. Iron mining operation on a large scale has

significantly changed the environment scenario. The mining shows an increase in the years 2011-2015, which is

as a result of a rapid increase in iron production. Vegetation is also decreasing as evident in vegetative methods






mainly NDVI but the overburden dumps and stockpiles going up, as evident from the LULC modeling as clearly

demonstrated by the potential of GIS and Remote sensing techniques. In addition to mining activities, the

infrastructure development and human activities in the surrounding have also adversely affected the land use

and land cover, air, and water quality of the study area. It may be concluded that the spatial modeling of the land

use/land cover change dynamics in Kishushe Area has taken place due to the rapid expansion and increase of

mining and influx of industrial activity during the period 2011 to 2018. This has resulted in drastic changes in

the land cover dynamics of the fragile ecosystem. The outcomes of this research are raising a lot of concerns

about progressive decreasing vegetation in mining areas. It would help in future projections as well as enable the

relevant authorities to come up with policies for potential environmental changes and protection in the area.

ACKNOWLEDGMENTS We acknowledge CEMEREM and Taita Taveta University for inviting us to the 2nd Biennial Conference in

September 2019 to present our research findings. Many thanks to Dr. Mika Siljander of the University of

Helsinki for his valuable insights and assistance.

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