EJERS, European Journal of Engineering Research and Science Vol. 4, No. 6, June 2019

Analysis of Forest Vegetal Characteristics of Forest Reserve from Optical Imageries and Unmanned Aerial Vehicle Data

Isaac A. Gbiri, Isaac A. Idoko, Michael O. Okegbola, and Latifat O. Oyelakin

 which earmarked at the beginning of the 20th century. Abstract—Forest vegetal characteristics monitoring has a References [11] and [19] estimated 285 hectares as the long tradition records with a success rate ranging from low to average annual rate of deforestation in between medium or high depends on the application at the hands. 1976 and1980, increasing into an estimated 400 hectares by Details information about the indication of association of the year 2000. Reference [9] reported Nigeria has lost 55.7% phenomena as forest indicators, such forest gap, estate and forest status, provides high spatial resolution images. The aim of its primary forest to logging, subsistence agriculture, of this study focuses on combining unmanned Aerial Vehicles collection of fuel wood and other agents between 2000 and (UAVs) and satellite multispectral imaging along side by side to 2005. details forest parameter during the seasons. UAVs image at The same patterns had been experienced in the tropics and 0.15m appeared more detailed of having features such as rock, sub-tropics Africa. For instance, the East African region lost road, bare ground, riparian trees among others than that of about 10% of its forest cover to deforestation between 1990 Landsat OLI image, though the features such as rock, road, bare ground, and riparian forest were also seen on the image and 2000, with Uganda recorded the highest rate [8]. In the but it was poorly seen due to the coarse spatial resolution of 30 humid tropical rainforest region of Cameroon, about m. The 3-Dimensional of UAVs, relief pattern and contour 200,000 hectares of forest reported to be degraded annually from Shuttle Radar Topography Mission was also compared due to high rate of exploitation. Such clearance has been and this study further demonstrated on the advantages of observed and documented from almost of four decades Unmanned Aerial Vehicle data over established remotely through land cover change detection based on Landsat-1-4 sensed data which includes flying blow the cloud, high spatial resolution, flexibility, inexpensive of data acquisition, time MSS, Landsat-5 Thematic Mapper (TM), Landsat-7 effective, using video footage to detect human activities such as Enhanced TM (ETM+) and Landsat -8 OLI data and has tree flora, burning and logging. resulted in extensive losses of forest and such discoveries in assessing deforestation has generated a lot of questions on Index Terms—Forest Vegetal Characteristics, Monitoring, the validity of data. Among the technical issues in question, Unmanned Aerial Vehicles (UAVs), Satellite Multispectral. the most challenging is that there was no consensus in the literature on the rate of deforestation in most of existing forest reserves globally and regionally often because of I. INTRODUCTION coarse resolution of the optical remote sensors. Early work on forest plantation in Nigeria commences at Recently, technologies such as GPS, miniaturized drones the beginning of the 20th century especially in the south- (UAVs) were initially developed for military use, but are west which practically involved on the economical increasingly being deployed in civilian applications important indigenous tree species [7], ever since Nigeria including mapping, monitoring and managing habitats and settlement after independent, over half of nation`s forests natural resources [14]. Although miniaturized drones are not and woodlands have been progressively cleared subsistence used widely in environmental applications yet, their use is agriculture [17]. Despite recognition of the factors likely to increase rapidly as their prices decrease and the associated with their clearance, deforestation rates technology becomes easier to use [5]. Although [12] cited accelerated in the uncontrollable manner [17]. Much of the [16], [5] in their reports that some initial attempts were clearance in south-west occurred in the more productive made to employ small drones in environmental research in forest ecosystems. Reference [2] revealed the remaining the 1990s and early 2000s, researchers have begun serious status of the tropical rainforest in Nigeria at only 10% of investigation on the use of drones over the last seven to tropical rainforest area as against 25% tropical rainforest eight years. The development of environmental remote sensing technologies and aerial drone has been closely Published on June 17, 2019. related to the study of forests [10]. Although, the bulk of I. A. Gbiri is with Geographic Information Systems (GIS) Department Federal School of Surveying, P. M. B. 1024, Oyo State, Nigeria (e-mail: academic research into the use of miniaturized drones has [email protected]). been greatly geared toward precision agriculture [21] and I. A. Idoko is with Survey and Geoinformatics Department Federal [18]. School of Surveying, P. M. B. 1024, Oyo State, Nigeria (e-mail: [email protected]). M. O. Okegbola is with Survey and Geoinformatics Department Federal School of Surveying, P. M. B. 1024, Oyo State, Nigeria (e-mail: II. GEOGRAPHIC LOCATION OF THE STUDY [email protected]). o o L.O. Oyelakin is with Survey and Geoinformatics Department Federal It lies between latitudes7 16`and 7 18` N of the Equator School of Surveying, P. M. B. 1024, Oyo State, Nigeria (e-mail: and longitudes 5o 9`and 5o11`E of the Greenwich Meridian. [email protected]).

DOI: http://dx.doi.org/10.24018/ejers.2019.4.6.1340 57 EJERS, European Journal of Engineering Research and Science Vol. 4, No. 6, June 2019

Akure forest reserve is geographically located in rainforest Radar Topography Mission (SRTM) 30 m spatial zone of Local government area of , resolution(2017) and Landsat 2017 OLI. Nigeria. It was constituted as a reserve in 1936 and covered A. Data Processing 69.93 km2 but 2.463 km2 was selected for the study. The relief pattern is low lying, elevation ranges from 216 to This process involves restructuring the available data and 504(m) and gently undulating in southern part while the creating sequence order of proceeding or cartographic model northern part is hilly rock outcrops occurring at close required for data analyses. Basically, raster and vector intervals. The underlying rock is crystalline and gneiss. It is models are usually involved and they were employed. slightly neutral; pH of 6.7–7.3 and sandy-loam in nature. B. Primary data The dry season lasts from November to March while the wet X, Y locational coordinates of prominent settlements in season commences from April and ends in October with the the Akure Forest Reserve (AFR) were captured as points highest rainfall records between July and August [3] 0 0 with the Garmin eTrex20 GPS device. The dilution of Average daily temperature ranges between 21 C and 29 C precision (DoP), geometric dilution of precision (GDoP) and almost throughout the year [1]. The mean annual rainfall datum was set to zone 31 North Hemisphere 1984. After the varies from 2000m in southern area to –1500m in northern setting, the GPS was allowed to resolve and connect to at area with relative humidity of 80–85% annually experienced least minimum of four satellites before the data capture for in south-west [15]. Politically, it lies in Ondo State in the settlements. We gridded the Topographical map and Southwestern Nigeria and shares border with Osun State in coordinates were obtained from the edges of the map and the Northeast, being surrounded by five Local Government coordinates were pre-loaded into Quadcopter drone through Areas in Ondo State namely: Ile Oluji ,Oke-Igbo, , the designed path i.e. traverse from the origin to destination. Akure south, and . Aponmu and Owena 300 m altitude was chosen to fly due to trees obstructions Yoruba speaking communities owned the forest, though, it for the drone when it moves around, the drone speed was set also had minor settlements surrounded the forest included at 3m/s, and 16.1 mega pixels integrated camera was , Kajola/ Aponmu, Kajola, Ago Petesi, Akika Camp, onboard for the field of view (FOV) of 28.940 look angles. Owena Town, Ibutitan/Ilaro Camp, Elemo Igbara Oke Camp Images were captured in panchromatic mode of (RGB) with and Owena Water New Dam. shutter capture speed at 1/1000s. It covered 2.463 km2 /

246.284 ha / 608.897 acres Also UAVs imageries was processed through the drone2map software by the conversion of flight lines and points into points, clouds, Poisson surface reconstruction, Ortho generate Digital Surface Model, and orthomosaic of 2-dimension (2D) and 3-dimension (3D). Then after the stacking of the image, it was imported into ArcGIS environment through add data tool on the ArcGIS interface. C. Secondary Data The Digital Image Processing (DIP) Techniques is necessitated by having imageries in digital format. Landsat 8

Fig. 1: Map of the study area OLI-TIRS (2017) were sourced through Path 190/Row 055 (Source: Author, 2019 Map of Study area) and downloaded from GLCF/USGS in digital format into the computer via earth explorer window and then it was imported into ERDAS Imagine 9.2 version through classic III. MATERIALS AND METHODS viewer. Landsat 8, 2017 OLI-TIRS alone was sourced for Methodology employs various techniques and approaches the sensor that acquired image on the 23rd of March 2017 to integrate this study. Such techniques and approaches downloaded because it was exactly the times that drone focus on data acquisition, data processing and data image was acquired. The images noise was filtered through presentation. It commences with database design, radiometric enhancement. The ground truthing, visual image conceptual, logical and schema. [4] describes conceptual interpretation and digital image processing were combined design as a process by which real world entities and their to Layer stacking, Sub-Map Creation of Raster Data, and relationships are modeled to achieve maximum output while ground truth were verified and Shuttle Radar Topography utilizing minimum amount of data. The views of reality in Mission was also employed by using the Filter tool on the this study were Roads, rivers, settlements and forest. Vector ArcGIS 10.3 version to filter away the redundant data and and raster data model were employed to represent the spatial then the creation of Triangulated Irregular Network (TIN) entities as points, lines and polygons [13] these were and contour of the study area. translated to logical and physical design which represented D. Physical Design and Database Creation Phases data model designed to reflect the recording of the data to be computerized using relational database management system This phase is known as the implementation stage. It (RDBMS) and digital image processing involved. Two involves the representation of the data structure in the major sources of data were used in this study; namely, format of the implementation software and two primary data: x,y prominent settlements, imageries from implementation software involved. The spatial database for Unmanned Aerial Vehicles and secondary data: Shuttle the study area was created in ArcGIS 10.3 while digital

DOI: http://dx.doi.org/10.24018/ejers.2019.4.6.1340 58 EJERS, European Journal of Engineering Research and Science Vol. 4, No. 6, June 2019 image processing is done in ERDAS Imagine 9.2 version. images encoded in various shades and textures. The interpretation of digital images is basically possible in two TABLE I: SAMPLE OF SETTLEMENTS AND THEIR ATTRIBUTES ways, usually referred to as visual interpretation and FID Shape Settlement Point X Point Y computer interpretation. But visual interpretation is 0 Point Kajola/Aponmu 728375 802044 employed in this study to further the analyses 1 Point Kajola 729137 802777 2 Point Ago Petesi 729812 803195 B. Comparism of UAVs Orthomosaic Image and Landsat 3 Point Akika Camp 730513 803703 of the Selected Parts of Akure Forest Reserve 4 Point Owena Town Dam 722786 796298 The Comparism of optical remotely sensed image and 5 Point Ibutitan/Ilaro Camp 722263 814288 orthomosaic image acquired from the drone is presented in Owena Water New 6 Point 719474 811928 the (Figure 2) and the comparison was based on the image Dam characteristics which included: spatial, radiometric, spectral Elemo Igbara Oke 7 Point 725748 815423 Camp and temporal resolution. Both the two images were acquired 8 Point Ipogun Town 729313 808836 of the same area, of the same month but with different 9 Point Obada 1 722318 805689 techniques in acquiring them. They were arranged side by 10 Point Obada 722001 805398 side for the visual interpretation because there was strongly difference in their spatial resolution. The area covered was TABLE II: SAMPLE OF ROADS AND THEIR ATTRIBUTES 246.284 hectares of the study and Landsat 2017 was also FID Shape Road_Name Road_length (km) masked by the exact the same boundary and it was covered Owena water new dam - 0 Polyline 8 the same area of 246.284 hectares. UAVs image appeared of Elemo 1 Polyline Dam-crin office rd 2 having detailed information such as rock, road, bare ground, 2 Polyline Idance Sec. Rd 1 riparian trees among others were manifested on the image 3 Polyline Idance Sec. Rd 3 while in Landsat 2017 image the features such as rock, road, 4 Polyline Aponmu-Kajola Rd 10 bare ground, and riparian forest were also seen on the image 5 Polyline Akure-Ondo Township Rd 1 but it was poorly seen. They were arranged in the pixels order and these features were seen clearer in the drone TABLE III: SAMPLE OF RIVERS AND THEIR ATTRIBUTES image than the Landsat image due to the coarse spatial FID Shape Road_Name Road - length (km) resolution. This evidence has shown as an added advantage 0 Polyline Owena River 25320 over Landsat OLI, 2017 image because drone image was 1 Polyline Aponmu River 25119 captured of 0.15m spatial resolution due to the fact it can 2 Polyline Iyonmiyo River 4465 drive at any altitude for capturing while Landsat image was 3 Polyline Iyonmiyo River 918 30-meter spatial resolution. The relevant entities such as 4 Polyline Iyonmiyo River 523 bare ground, riparian forest among others were not seen 5 Polyline Osse River 3062 clearly as it was showed on the UAVs image due to poor 6 Polyline Iyonmiyo River 968 spatial resolution but what found on the Landsat image was 7 Polyline Iyonmiyo River 1173 patches of the forest. 8 Polyline Iyonmiyo River 1529

Database management systems involved data security, data integrity measures and database maintenance [4]. Data security involves the measures adopted while designing the database using necessary backup or fitness model for the data from being lost. In ensuring data integrity, inconsistency between two features must be done away with in checking the correctness of the records in database and finally database maintenance checks on the quality and the fitness of the database. Fig. 2: Subset images from UAVs and Landsat of part of Akure forest reserve

IV. RESULTS C. Comparism of 3-Dimensional of UAVs, Relief Pattern A. Approach of Complementary Use of the Optical and Contour from Shuttle Radar Topography Mission Remote Sensing Imageries and UAVs Technology The analyses below presented 3-Dimension orthomosaic Interpretation of satellite imagery was a method of of 0.15m from UAVs. This showed the capability of obtaining information about objects and the landscape. This identifying the forest estate, tree stand, rocky, bare ground at has been extensively used by [20] in context of studying the aerial view while the Landsat ancillary data cannot portray geographical reality which based on the detection, the aerial view. This result has contributed tremendously as identification and spatial localization of individual objects an added advantage over the Landsat data due to the coarse and terrain shapes captured in a satellite image records. resolution and the relevance of Landsat data was only traced Interpreting the image represented the deciphering of its back to the it historical time of existence. In Figure 3(a) multifaceted content from the point of view of the purpose it below the contour map display values change across the serves. The information that we are looking for in the surface where there is little change in value, the lines are

DOI: http://dx.doi.org/10.24018/ejers.2019.4.6.1340 59 EJERS, European Journal of Engineering Research and Science Vol. 4, No. 6, June 2019 space further apart, where values rise or fall, the lines are REFERENCES closer together. Flat and steep distance between contour and [1] O. Adejoba, M. Kleine and T. Taboada “Reducing deforestation and ridges, hill and valleys (converging or diverging polylines; forest degradation and enhancing environmental services from Forests in Figure 3(b) indicates the terrain relief pattern and (REDDES), with support from the International Tropical Timber Organization (ITTO), IUFROSPDC and FORNESSA, Akure, Ondo, graduate the surface with values and colour and shows area Nigeria,2014.https://www.iufro.org/download/file/.../ that is steepness, low and high while In Figure 3(c) the FORNESSA_Factsheet_Nigeria_final_pdf digital surfaces model (DSM) aimed of supplying the [2] A. E. Akachukwu, “Strategies for sustained environmental conservation through Resource development” In: Proceedings of the elevation information and the display information was 1997annual conference of the forestry association of Nigeria, colour ramps where light black shows rise or fall area while September 22nd–26th 1997, Ibadan, Nigeria, pp. 258–270. dark black shows low area and this familiar with the work [3] Akinseye, “Climate variability and effects of weather elements on cocoa and cashew crops in Nigeria” M.Tech Thesis, Depart. conducted by [6], and it also uses various colours for distinct Agribusiness and Management, Michael Okpara Univer. Agriculture, description where colour blue and red indicates lower area, Umudike, Abia State, Nigeria, 2010. green colour indicates hilly, yellow colour indicates high [4] K. W. Y. Albert & G. Brent hall “Spatial Database System. Design, implementation and project management” The Geojournal library, while pale red indicates also the high. springer, Dordrecht, Netherland, Vol.87, 2007, pp 30-33. [5] C. Anderson, Here Come the Drones; Wired Magazine: London, UK, 2012, pp. 102–111. [6] H. Arefi, P. d’Angelo, H. Mayer and P. Reinartz (2009) Automatic generation of digital terrain models from CARTOSAT-1 stereo images. Proceedings of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2009.www.isprs.org/proceedings/XXXVIII-1-4-7_W5/paper/Arefi- 169.pdf [7] FAO, “Tropical forest resources assessment” Forest Resources of Tropical Africa, part II: Country Briefs: Nigeria. UN 32/6.1301-78- 04, Technical Report No. 2. FAO, Rome, 1981, pp. 359–385. [8] FAO, “Sustainable forest management and the ecosystem approach two concepts” one goal, working paper for 25, 2003,Rome Italy. [9] FAO, “Global forest resources assessment 2006. Progress Towards

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Gbiri, I. A and Idoko, I. A, Okegbola, M. O and surveying Oyo in years 2000 and 2009 respectively. He also holds a PGD Oyelakin L. Surveying and Geoinformatics from Nnamdi Azikiwe University Awka in Gbiri, Isaac Adelakun is hailed from Oka-Akoko 2017 and currently runs an M.Sc degree with specialization in GIS and in Ondo State, Nigeria. He obtained his B.Sc (Hons) Remote Sensing in the same Institution. Current position: Lecturer at Zoology Degree from Lagos State University Federal School of surveying Oyo. (LASU), Ojo, Apapa, Laogs in 2008. Okegbola, Michael Oketunde had HND and PD in Surveying and Thereafter, he attended Federal School of Geoinformatics from Federal School of Surveying, Oyo, Oyo State, in the Surveying (FSS), Oyo, Oyotownship (2011 to 2012) years 2006 and 2008 respectively. He also holds a PGD and M.Sc in in pursuing Post Graduate Diploma (PGD) in the Geoinformatics and Surveying from the University of Nigeria (UNN) field of Geographic Information Systems (GIS). He Nsukka in 2015 and 2019 respectively with specialization in GIS and later proceeded to Obafemi Awolowo University (OAU) Ile-Ife, Osun Remote Sensing in the same Institution. He is currently a Lecturer at State, Nigeria in (2015 to 2018) where he obtained (M.Sc. in Remote Federal School of surveying, Oyo, Oyo State,Nigeria. sensing and GIS) in Geography Department in 2018. Oyelakin Latifat Olaide received a National Diploma, Higher National In August 2013, he joined the workforce of Federal School of Surveying Diploma and Professional Diploma in Surveying and Geoinformatics from (FSS) and where he is currently a Senior Instructor and a student of Ph.D. Federal School of Surveying, Oyo in 2006, 2009 and 2011 respectively. At present, He teaches GIS and Remote Sensing and all related courses. She also holds Post Graduate Diploma in Surveying and Geoinformamtics He is an Active member (NES) Nigeria environmental Society and from The Federal University of Technology, Akure in year 2018 and (ACG) Associate Certified Geographer and he is happily married and currently runs M.Tech in the same school with specialization on Remote blessed with children and others decide later. Sensing. Idoko, Isaac Arome received HND and PD Surveying and Geoinformatics from Auchi Polytechnic, Edo State, and Federal School of

DOI: http://dx.doi.org/10.24018/ejers.2019.4.6.1340 61