Monitoring Land Cover changes in the tropical high forests using multi-temporal remote sensing and spatial analysis techniques

Remote Sensing Applications: Society and Environment 16 (2019) 100264

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Monitoring Land Cover changes in the tropical high forests using multi-temporal remote sensing and spatial analysis techniques Etse Lossou a, *, Nat Owusu-Prempeh b, Godwin Agyemang c a

Stevdok Limited, P.O. Box CT. 2439, Cantonments, Greater Accra Region, Ghana Kwame Nkrumah University of Science and Technology, CANR, U. P. O., Kumasi, Ashanti Region, Ghana c Forestry Commission, Forest Services Division, P. O. Box 22, Mankranso District, Ashanti Region, Ghana b

A R T I C L E I N F O

A B S T R A C T

Keywords: Remote sensing Spatial analysis Forest reserves Accuracy assessment Change detection

The study applied multi-temporal optical remote sensing and spatial analytical techniques to assess the status of the eight (8) forest reserves in the tropical high forests of Ghana under the management of the Goaso Forest District. Landsat data of 1990 (Thematic mapper [TM]), 2013 (Enhanced Thematic Mapper [ETMþ]) and 2017 Operational Land Imager/Thermal Infrared Sensor (OLI_TIRS) were classified into closed canopy forest, open canopy forest, bare land/built-up, farmland, and degraded land using the Maximum Likelihood algorithm. Post classification change detection techniques were then used to analyse the changes that had occurred over the study period. It was observed that about 37.63% of the closed canopy forests have been converted to the other covers types. The findings showed that the forest estates in the District, which was one of the richest in the High forests in terms of species diversity and richness, had been severely degraded. This could have dire implications on biodiversity, carbon sequestration and livelihood of fringe communities. Concurrent restoration of degraded forest cover and integration of remote sensing and spatial analysis techniques into routine forestry management practices at both District and National levels would provide an effective mechanism to curb the high rate of forests depletion in Ghana.

1. Introduction Forests and woodlands are by far known to be important for sequestering atmospheric carbon dioxide (CO2). They play a vital role in storing atmospheric carbon (both natural and anthropogenic emissions) and thus help in the mitigation of climate change (Focus, 2018). The debate on climate change has intensified since the mid-to-late 1980s. It has dominated international headlines and prompted the international community and concerned organisations to intensify stakeholder con­ sultations with the aim of finding pragmatic and lasting adaptation and mitigation measures to the threats posed by climate change (Moser, 2010). The rapid depletion of forests around the world has been identified to be a contributing factor to the global climate change (Food and Agri­ culture Organization, 2012). Ghana is well endowed with diverse forest resources. It is known that the high forests covered an estimated 8.2 million hectares of Ghana’s landscape in the early 1900s but this has reduced to less than 2 million hectares currently (Council for Scientific and Industrial Research – Forest Research Institute of Ghana

[CSIR-FORIG], 2017; Teye, 2011). The depletion of forests in Ghana is a major concern because majority of the population rely on forestlands and forest goods and services for survival. However, increase in popu­ lation coupled with overexploitation of forests outside the working plan area of the Forest Service (off-reserves) has led to a heavy dependence on the reserved forests and their resources (Forestry Commission, 2010). Activities such as recurrent wild fires, illegal felling and mining activ­ ities and extension of admitted farms (farms that existed in the forest estates before their reservation by the Government) have decimated the forest reserves. Most reserves in Ghana face similar problems but at varying degrees. Unfortunately, the natural regeneration of the forest is at a slower pace than the destruction occurring in such reserves. For instance, in the Goaso Forest District (GFD) where the study was con­ ducted, a study in 2010 reported that the regeneration of the Subim forest at an average of 84 stems per ha was far below the average of 304 stems per ha for the Moist Semi-Deciduous North-West (MSNW) vege­ tation zone (Forestry Commission, 2010). Similarly, the regeneration of the Bia-Tano Forest Reserve estimated at 150 stems per ha was below the average of 295.3 stems per ha (Forestry Commission, 2016).

* Corresponding author. E-mail address: [email protected] (E. Lossou). https://doi.org/10.1016/j.rsase.2019.100264 Received 5 October 2018; Received in revised form 30 August 2019; Accepted 11 September 2019 Available online 14 September 2019 2352-9385/© 2019 Elsevier B.V. All rights reserved.

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It is reported that in Ghana, significant portions of the forest cover have been lost or degraded since 1926. The underlying causes of this spate of deforestation and forest degradation could be attributed to population and economic growth and weak governance structures (Oduro, 2012; Brown et al., 2016; Mensah et al., 2019). The high pop­ ulation and economic growth have led to high wood demand both locally and to satisfy timber export markets (Brown et al., 2016). However, aside studies carried out on selected isolated forest reserves, there is paucity of research to determine the extent of destruction and degradation of the high forests estates across Ghana and the GFD in particular (Mensah et al., 2019; Kusimi, 2015; Ankomah, 2012; Asare, 2000). For instance, Asare (2000) relied on remote sensing with Landsat data dating back to 1973 and 1991 to develop a methodology for monitoring changes in Ghanaian forest reserves whiles Kusimi (2015) focused on the Atewa Range Forest with satellite data ranging from 1986 to 2013. The sustainable management of forest resources is critical to addressing the impacts of climate change on the ecosystem but it hinges on up-to-date information about the state of the forest. Thus provision of vital information on current state of the high forests should be consid­ ered as sine qua non to designing better management practices geared towards the restoration of ecosystem equilibrium. The World Bank (World Bank, 2008), further stressed that current and accurate infor­ mation on forests could help raise the profile of the sector and increase awareness of forest resources’ potential. However, the difficulty in obtaining such information must not be ignored. The ESRI (Environ­ mental Systems Res, 1999), clearly identified today’s complex world as challenging to resource managers because they are constantly faced with

conflicting issues from planning, production, business and environ­ mental regulations. The use of Remote Sensing and Geographic Information Systems (GIS) in various fields of interests including natural resources moni­ toring and management, land cover change, urban land use, health and population studies, have been of immense benefit to resource managers. The availability of imagery dating back in time can help understand the dynamics among land cover types and help in designing better ways to interact with nature. This study therefore applied remote sensing and geospatial techniques to estimate the extent of forest cover loss in the high forests in the Goaso Forest District, Ghana and the land cover types replacing the forest cover. 2. Materials and methods 2.1. Study area The study area, GFD, cuts across four political Districts in the Brong Ahafo Region, Ghana. They are Asunafo North with its District capital at Goaso, which hosts the Forest Services Division (FSD) and lies approx­ imately 80 km away from Sunyani Municipal, the regional capital, Asunafo South, Asutifi North and Asutifi South. The GFD lies between latitudes 6� 200 N and 7� 150 N, and longitudes 2� 520 3000 W and 2� 130 3000 W and falls within the High Forest Zone (HFZ). The District manages eight (8) joint Forest Reserves (F/R) totalling about 853.89 sq. km, which were set up with the objective of protection, sustainable production and social services. The forest estates in the

Fig. 1. Map of the Ghana showing the Goaso Forest District, the study area. 2

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District form part of the 216 forest reserves established in the high forests during the forests reservation in the early 1920s (Tropenbos InternationalGhana, 2009) and were among the richest in terms of species diversity and richness. A Digital Elevation Model (see Fig. 1) shows that most parts of the District are between 136 m and 336 m above mean sea level. The highest point is found in Asunafo North District with an elevation of 644 m, located deep in Bonsambepo Forest. The District is well drained throughout the year by a number of river bodies and streams. Almost all the forests found in the District are named after a river. They are the Goa Shelterbelt F/R after the Goa river, the Aboniyere F/R after the Aboniyere river, the Ayum F/R after the Ayum river; the Bia Shelterbelt F/R after the Bia river; the Bonkoni and Subim F/Rs after the Bonkoni and the Subim rivers respectively. The remaining reserves are the Bia-Tano and Bonsambepo F/Rs. A significant proportion of the forests outside the reserves (off-re­ serves) have mostly been converted to farming lands (Kotey et al., 1998; Opare, 2013; Feurer, 2013). Cocoa is by far the single most grown cash crop in the District followed by food crops such as cassava and plantain. The District is within the wet semi-deciduous climatic zone and so experience substantial amount of rainfall. The major rains are received from April to July and minor rains occur between September and October. The mean temperature is about 25 � C (Ghana Statistical Ser­ vice, 2014).

the study proceeded through a series of processes using the ArcGIS (ver. 10.4) and Envi (ver. 5.3) software. In ArcGIS, the forest data was queried to obtain only the forests reserves in the GFD. Additionally, there was retrieval and subsequent merging of the four (4) Districts that formed the basis of the study (Fig. 1). The study processes are captured in Fig. 2. In Envi (ver. 5.3), the satellite data of 1990, 2013 and 2017 were calibrated for the purpose of noise removal (Fig. 4). This process was necessary to help obtain some relevant information about the data such as the sensor type used to capture the data, the sun elevation at the time of capture and the date of data acquisition to adjust to the atmospheric conditions at the time of capture. Prior to the calibration of the 2013 data, as shown in Fig. 3, scan lines were removed to enhance the image’s visual appearance using Land­ sat_gapfill (an extension/plugin in Envi). Scan lines are gaps that occurred due to the mechanical failure of Scan Line Corrector (SLC) of all Landsat 7 imagery collected after May 31, 2003 according to the USGS (https://landsat.usgs.gov/what-landsat-7-etm-slc-data). This notwithstanding, these products are still useful and maintain the same radiometric and geometric corrections as data collected prior to the SLC failure. Interoperability between Envi and ArcMap was very useful during the removal of scan lines. Envi 5.3 has the ability to calibrate multi­ spectral bands at a go, which saves time compared to the earlier versions where calibration was done separately on each band. For Envi to read bands available in a folder and group them, the files must be in TIFF format. However, when scan line removal function is triggered using Landsat_gapfill, the format of the resulting band is an Envi file with . hdr and. emp extensions. Thus the scan line removal was performed on each band available in 2013 data and the product for each band was exported as TIFF file in ArcMap environment. The products were stored in a different folder and given exactly the same name as in the originally downloaded imagery. Radiometric calibration was followed by sub-setting the individual data with the forest data obtained in ArcMap. To obtain the land cover types for each of the Landsat data, the study went through rigorous training of sites using signatures collected from the field for the super­ vised classification of 2017 data. Apart from the classified 2017 data, Google Earth was also used as guide for the classification of 2013 and 1990 data. The data were trained in ArcMap after stacking the calibrated bands in 7,4,2 (1990 & 2013 data) and 7,5,3 (2017 data) combinations (Quinn, 2001; Environmental Systems Res, 2013). These combinations were selected over 4,3,2 which is good for vegetation studies because using the latter in classifying the data was much of a difficulty as compared to the former where there was clear distinction between areas that were degraded and farmlands as well as between the different forest types. Thus, the Landsat data for the three different years were all classified by means of supervised classification. Supervised classification involves the training of sites by the analyst based on his/her knowledge of the area under study. The trained sites were then used to generate the image data classification by matching the pixels identified using the maximum likelihood classifier algorithm. This algorithm is the most commonly used method for classification and it employs a statistical decision rule that examines the probability function of a pixel for each of the classes and assigns the pixel to the class with the highest probability (Firdaus, 2014). Different from the supervised classification is the unsupervised classification where the software performs an automated classification either through K-means or IsoData such that the analyst does not have control over the classes generated (Rwanga and Ndambuki, 2017). In either case, the classification produces spectral groupings based on certain spectral similarities and does not require the analyst’s prior knowledge of the study area. The land cover types that were identified include closed canopy forest (same as closed forest), open canopy forest (same as open forest), bare land/built-up, farmlands, degraded areas and settlements as shown

2.2. Data The study used Landsat data of three different years. They were Landsat 5, Thematic Mapper (TM) sensor for 1990 data, Landsat 7, Enhanced Thematic Mapper Plus (ETMþ) for 2013 data and the latest version Landsat 8, the Operational Land Imager/Thermal Infrared Sensor (OLI_TIRS) for the 2017 data. All the three Landsat data, carefully selected with a cloud cover below 10% to enhance the accuracy of the results that were obtained, were obtained from USGS Earth Explorer website free of charge. Table 1 summarises the data used in the study. The authors undertook field data collection spanning over five months. The collected data were used to identify various land cover types in the study area. The study also used the Advanced Spaceborne Radiometer (ASTER) Global Digital Elevation Model (GDEM) – ASTER GDEM, a product of METI and NASA, obtained from USGS Earth Ex­ plorer to determine the elevation of the study area. The vector data of the forest reserves were obtained from Resource Management Support Centre (RMSC) of Ghana Forestry Commission, which was later edited by the authors to conform to the boundaries of the forests on the satellite imagery. Additionally, vector data of Ghana Districts and river bodies were obtained from Remote Sensing and Geographic Information Sys­ tems Lab (RSGIS) of the University of Ghana. Field survey was con­ ducted using Global Position System (GPS) device, alongside Maps. me and OSMTracker (both OpenStreetMap open source mobile applications) to get coordinates of various land covers in the study area. 3. Methods To determine the land cover types and the dynamics among them, Table 1 Dataset used in the study. Data

Acquisition Date

Source

Processing Level

Landsat TM 5 Landsat 7 ETMþ Landsat 8 OLI_TIRS ASTER

31 December 1990 22 December 2013 25 December 2017 17 October 2011

USGS Earth Explorer USGS Earth Explorer USGS Earth Explorer USGS Earth Explorer

TIER 1 TIER 1 TIER 1

3

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Fig. 2. Flow chart showing the processes undertaken during the study.

in Table 2. Since the study is centred on the forest reserves (on-reserves), the off-reserves areas in the study site were masked after classification. The study also overlooked the inclusion of water bodies in the area to avoid mismatch of the pixel of closed canopy forest and open canopy forest. Unlike the 2017 image data, classification of 1990 and 2013 imagery were done with the help of Google Earth images, available maps from the Forest Services Division (Goaso) and the colour interpretation of 7,4,2 band combination as provided by Quinn (2001). The training samples were collected from selected portions of the eight (8) forest reserves, off-reserve areas and fringe communities, which covered a total area of 2992.31 sq. km. The training samples used in the image classification are presented in Table 3. Additionally, post classification accuracy assessment was performed on the 1990, 2013 and 2017 classified imagery in ArcMap. The most commonly used post classification accuracy assessment method estab­ lishes the information value of the resulting data (Firdaus, 2014; Rwanga and Ndambuki, 2017). The overall accuracy and Kappa coeffi­ cient (K) were generated with the formula given as:

Κ¼

N

Pr

Pr ðx *x Þ i¼1 Xii Pr i¼1 iþ þi 2 N ðx *xþi Þ iþ i¼1

(1)

where r is the number of rows, xi is the number of observations in row i and column i, xiþ and xþi are the marginal totals of row and column, and N is the total number of observed pixels (Congalton, 1991; Firdaus, 2014; Rwanga and Ndambuki, 2017). The value greater than 0.8 to 1 means there is perfect agreement; the value between 0.40 and 0.80 means moderate classification and values from 0 to 0.40 means the agreement is no better than would be expected by chance (Jensen, 2004; Firdaus, 2014; Rwanga and Ndambuki, 2017). Aside the 1990 and 2013 classified images which were assessed with the help of three hundred (300) ground validation samples from his­ torical data on Google Earth, the classified image of 2017 was assessed by using three hundred (300) ground validation samples collected from field survey. The actual accuracy assessment was performed in ArcMap, through a series of processes, which included (i) the creation of refer­ ence points or ground control points, (ii) the conversion of the reference points from vector to raster data, (iii) the combination of the raster data and the classified imagery and (iv) the generation of the confusion matrix. Since Landsat image resolution is 30*30 (Quinn, 2001), each

4

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Fig. 3. Landsat 7 ETM þ images of GFD in 2013 shown in 7,4,2 combination with scan lines in (a) and no scan lines in (b).

4. Results and discussion

reference point was converted to a pixel with an area equivalent to 0.009 sq. km. Table 4 presents the details of the ground validation samples used for the accuracy assessment of the 2017 data and a summary of the error matrix. Thus applying equation (1), the resultant kappa coefficient would be as follows;

Κ¼

4.1. Land cover types in Goaso Forest District Analysis of the classified results as depicted in Fig. 5 revealed that in 1990, closed forest covered 552.41 sq. km of the total on-reserve forestland area. This made it the single most dominant land cover type

ð300�ð70 þ 55 þ 43 þ 60 þ 55Þ Þ ðð71�70Þ þ ð55*60Þ þ ð45*50Þ þ ð67*60Þ þ ð62*60ÞÞ � 3002 ðð71�70Þ þ ð55*60Þ þ ð45*50Þ þ ð67*60Þ þ ð62*60Þ

K ¼ 0:929

in the study area. Open forest covered 275.28 sq. km, while areas that have undergone degradation stood due at 16.15 sq. km. Bare land/Builtup and farmlands were far less important land cover types with 8.2 sq. km and 1.84 sq. km respectively. In 2013, closed forest remained the dominant class with an area of 439.33 sq. km. This was followed by open forest which covered 304.72 sq. km. Classified areas detected as degraded areas accounted for 66.38 sq. km of the total cover of onreserve forestlands whereas classes registered under bare land/builtup and farmlands covered 18.15 sq. km and 25.31 sq. km respectively. In 2017, open forest became the most dominant land cover in GFD. Its total cover extracted from the classified result was 422.18 sq. km. Closed forest claimed 344.57 sq. km followed by farmlands with an area of 34.24 sq. km. The remaining classes being bare land/built-up and degraded areas recorded 33.79 sq. km and 19.11 sq. km respectively. It was observed the forest reserves in GFD were relatively undisturbed in 1990 except for areas which were being exploited either through

This result is smaller than the overall accuracy (OA) which is generated by simply dividing the correctly classified cells over the total number of sampled cells. OA ¼ ð70 þ 55 þ 43 þ 60 þ 55Þ=300 OA ¼ 0:94333 Since 0.929 > 0.80 and closer to 1, the classification performed on 2017 was largely accurate and reliable. The 1990, 2013 images were associated with kappa coefficients of 0.878 and 0.937 respectively (Table 5). This also means the classifications performed on these images were accurate and reliable in spite of the fact that the reference points were selected arbitrarily.

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Fig. 4. Landsat images of GFD in 1990, 2013 and 2017 respectively shown in 7,4,2 combination before (a, b and c) and after (d, e, and f) calibration.

especially at the outskirts of the reserves because of expansion of cocoa plantations – the most grown cash crop in the area and the continued exploitation of forest products. The result presented in Fig. 5 showed that forest reserves in the study areas were mostly covered with closed forest with steady reduction. From 1990 to 2013 and from 2013 to 2017 there was forest degradation with much of the closed forest losing ground primarily to open forest. The total reduction of closed forest from 1990 to 2017 was 207.84 sq. km. This means that closed forest was reducing at an average of 7.70 sq. km per year. Open forest was the second most dominant on-reserve class with less than half the total cover of closed forest in 1990, representing 32% that year. It increased by 10.69% in 2013 and further increased significantly by 38.55% in just four (4) years. Degraded areas, which included vegetation type of below 15% of canopy cover, were the third most dominant class in 1990. They rep­ resented 16.15% of the forestland cover. By 2013, they increased to 66.38 sq. km then decreased to 19.11 sq. km in 2017. Bare land/built-up experienced steady increase in the forest reserves since 1990. In 1990, it registered 8.20 sq. km and by 2017, it claimed 33.79 sq. km. There is evidence that farmlands were fast gaining ground in selected forest re­ serves in the study area. In 2017, farmlands registered a percentage increase of 1757.81%. It must be noted that except for admitted farms located in Subim F/R and in Aboniyere F/R (very insignificant) which were in existence before the conversion of the forests into forest re­ serves, there were no legal farmlands in the reserves. Available records at Forestry Commission revealed that there were 37 admitted farms in Subim F/R covering 1.57 sq. km (Forestry Commission, 2010). The classified result of 1990 identified 1.84 sq. km of farmlands in the reserve. It was observed that farmlands have since been on the rise, covering not only Subim F/R and Aboniyere F/R but also in reserves

Table 2 Land Cover types in Goaso Forest Reserves. Classes

Description

Closed canopy forest Open canopy forest Degraded areas

Include all lands with woody vegetation with canopy cover greater than 65% (Asante, 2014). Include all lands with woody vegetation with canopy cover greater than 15% but less than 65% All woody lands of canopy cover below 15%, areas within the forest reserve that are experiencing regeneration due to overexploitation of timber, or recent fire outbreak or areas that have undergone illegal felling and logging Include Forest Plantations, admitted farming and illegal farms Include settlements, unproductive topsoil, tarred and non-tarred roads, rocks, recently burnt surfaces, recently cleared agricultural lands, and quarry sites

Farmlands Bare Land/Builtup

Table 3 Summary of training samples. Classes Bare land/Built-up Closed forest Degraded areas Farmlands Open forest

Training Samples (in sq.km) 1990

2013

2017

1.08 5.69 0.14 0.40 0.74

2.2 14.47 1.30 0.72 6.44

0.31 0.15 0.06 0.15 0.15

lumbering or farming activities and clear paths where roads networks were laid to connect fringe communities. By 2013, 2017, as shown in Figs. 6 and 7, much of the forest reserves have undergone major changes 6

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Table 4 2017 error matrix table (in pixel). Classified

Bare Land/Built-up

Closed Canopy Forest

Degraded Areas

Farmlands

Open Canopy Forest

Classified Total

Bare land/Built-up Closed Canopy Forest Degraded Areas Farmlands Open Canopy Forest Reference Total

70 0 0 0 0 70

1 55 0 0 4 60

0 0 43 4 3 50

0 0 0 60 0 60

0 0 2 3 55 60

71 55 45 67 62 300

such as Bosambepo F/R, Bia Shelterbelt F/R and Goa Shelterbelt F/R. These farmlands include the existing admitted farms as well as localised forest plantations being government led-projects to maintain the forest. These projects include Modified Taungya System (MTS) – where farmers are given degraded reserve lands for food crop farming while planting and maintaining forest tree species (Forestry Commission, 2011), and Forest Investment Programmes (FIP). While the aforementioned could be the reasons accounting for the presence of farms or plantations in

Table 5 Summary of accuracy assessment of 1990, 2013 and 2017 classification. Accuracy Assessment

1990

2013

2017

Overall Accuracy Kappa

0.903 0.878

0.950 0.937

0.943 0.929

Fig. 5. Classified Landsat TM 1990 image of the Goaso Forest Reserves. 7

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Fig. 6. Classified Landsat 7 ETM þ image of the Goaso Forest Reserves.

some parts of the reserves, there is an undeniable fact that illegal farming in the area is on ascendency.

to closed forest. Overall, it expanded by 10.69% in 2013, a result of the accumulated gain of 3.95% from degraded areas, 15.96% from bare land/built-up, 40.67% from closed forest and 1.22% from farmlands. Degraded areas and farmlands, two of the least classes in terms of area cover, experienced astronomical percentage increase 311.02% and 1273.00% respectively over the period while bare land/built-up also increased by as much as 121.35%.

4.2. Change detection statistics The classified results were further processed to determine the changes that have occurred among the identified classes. Three sets of change detection were performed: the first was between 1990 and 2013 data, the second was between 1990 and 2017 and the final comparison was between 2013 and 2017.

4.2.2. Change detection from 1990 to 2017 This period span over twenty-seven (27) years and estimates the current state of the forest reserves at the time of the research, as against the condition of the forest reserves in 1990. Change detection shown in Table 7 indicates that closed forest decreased by 37.62% less than its initial state in 1990 (51.15%) with 42.50% of its initial state unchanged; all other land cover types increased with farmland leading with an un­ precedented 1757.81%. As reported above, farmlands occupied only 1.84 sq. km in 1990 but by the end of 2017, it hit 34.24 sq. km. This exponential increase demonstrates clearly the pressure by fringes

4.2.1. Change detection from 1990 to 2013 Change detection statistics for the period, presented in Table 6 showed that closed forest reduced by 20.47%. It maintained 51.15% of its initial state, while 40.67% was converted into open forest. Similarly, 4.90% was degraded, whereas 1.76% was changed into unproductive land. Farmlands gained only 1.52% from closed forest over the period. Open forest retained only 28.36% of its cover in 1990, losing primarily 8

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Fig. 7. Classified Landsat 8 2018 image of the Goaso Forest Reserves. Table 6 Change detection statistics summary between 1990 and 2013. 1990 INITIAL STATE (%) 2013 FINAL STATE (%)

Bare Land/Built-up Closed Forest Degraded Areas Farmlands Open Forest Total Class Change Image Difference

Bare Land/Built-up

Closed Forest

Degraded Areas

Farmlands

Open Forest

19.22 31.08 24.46 9.28 15.96 100.00 80.78 121.35

1.76 51.15 4.90 1.52 40.67 100.00 48.85 20.47

6.56 24.16 39.32 26.01 3.95 100.00 60.68 311.02

7.32 6.10 35.45 49.90 1.22 100.00 50.10 1273.00

2.06 54.57 11.01 4.01 28.36 100.00 71.64 10.69

communities on the forest reserves in the GFD. A critical look at the summary table (Table 7) revealed that farmlands were mostly been established on degraded areas (39.91%)than any other land cover types.

4.2.3. Change detection between 2013 and 2017 This period represents the most current land cover change in the study area at the time of study. Closed forest has decreased by 21.57% in four years losing 42.22% to open forest and 4.85% to bare land/built-up, 9

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Table 7 Change detection statistics summary between 1990 and 2017. 1990 INITIAL STATE (%) 2017 FINAL STATE (%)

Bare Land/Built-up Closed Forest Degraded Areas Farmlands Open Forest Total Class Change Image Difference

Bare Land/Built-up

Closed Forest

Degraded Areas

Farmlands

Open Forest

32.46 19.17 1.64 19.18 27.55 100.00 67.54 312.00

3.45 47.39 1.44 2.74 45.00 100.00 52.61 37.62

7.34 7.99 8.70 39.91 36.06 100.00 91.30 18.30

5.18 0.78 19.92 37.01 37.11 100.00 62.99 1757.81

3.93 29.02 3.36 3.78 59.92 100.00 40.08 53.36

Table 8 Change detection statistics summary between 2013 and 2017. 2013 INITIAL STATE (%) 2017 FINAL STATE (%)

Bare Land/Built-up Closed Forest Degraded Areas Farmlands Open Forest Total Class Change Image Difference

Bare Land/Built-up

Closed Forest

Degraded Areas

Farmlands

Open Forest

19.41 10.26 3.99 10.11 56.24 100.00 80.59 86.13

2.92 52.94 0.65 1.28 42.22 100.00 47.06 21.57

5.89 3.02 11.45 22.01 57.55 100.00 88.55 71.22

3.53 0.05 21.35 40.64 34.44 100.00 59.36 35.31

4.15 34.47 0.83 0.61 58.94 100.00 41.06 38.55

degraded areas and farmlands combined (Table 8). Closed forest occu­ pied 35.47% of open forest, 10.26% of bare land/built-up and 3.02% of degraded areas. There was also 71.22% reduction in degraded portions in the study area. Degraded areas mostly lost to open forest (57.55%) and farmlands (22.09%) and gained from farmlands (21.35%) more than any land cover over the period. Farmlands experienced a percentage increase of 35.31% over the period demonstrating the steady increase in farming activities in onreserve zones in the study area. These activities result from suspected expansion of admitted farms beyond their defined boundaries and the proliferation of illegal farms, which was substantiated by visits to selected parts of the forest reserves by the authors. Added to these sources of expansion of farming activities in the reserves are the wellestablished and supervised forest plantation projects by Forestry Com­ mission in the bid to maintaining the forest reserves. There was a sharp increase in bare land/built-up with a totalling 86.13%. Whiles it retained only 19.41% of its 2013 coverage, it lost 56.24%, 10.26% and 10.11% to open forest, closed forest and farmlands respectively. An expansion of 38.55% was realised by open forest in four (4) years after assuming 56.24% of bare land/built-up, 57.55% of degraded areas, 42.22% of closed forest and 34.44% of farmlands.

reserve, from 0.02 sq. km from 1990 classified result to 0.99 sq. km in 2017. A clear expansion of farmlands is shown in Fig. 8B where there is virtually no demarcation between farmland and forest. This expansion could be a contributing factor to the decline in forest resources in the reserve. 4.3.2. Ayum forest reserve This reserve is bordered by Subim to the west, Bonsambepo to the south and up north by the intersection of Bia-Tano and Bonkoni Forest reserves. It geographically lies between latitudes 6� 390 3000 N and 6� 520 3000 N and longitudes 2� 440 3000 W and 2� 380 W and covers 117.35 sq. km. The forest reserve is drained notably by Ayum River. It falls within Asunafo North District. Results for Ayum F/R are not different from the general trend of degradation associated with the reserves in the District. From 68.53 sq. km in 1990, closed forest reduced to 27.45 sq. km in 2017 to the benefit of open forest (Fig. 7). 4.3.3. Bia Shelterbelt forest reserve Covering 30.87 sq. km of land, Bia Shelterbelt F/R is the second smallest reserve in GFD. It is bordered to the north-west by River Bia from which it derives its name and to the east by Bia-Tano F/R. It falls within latitudes 7� 00 2000 N and 7� 50 4000 N and longitudes 2� 380 2500 W and 2� 450 W. The reserve is almost entirely under the jurisdiction of Asutifi North District. The reserve has experienced high levels of degradation since 1990. In the year 2013, 10.58 sq. km of land that was considered as degraded areas. In recent times, forest enrichment programmes were launched to reduce the rate of degradation in the reserve. As a result, open forest has which decreased to 0.95 sq. km by 2013 increased from to 10.78 sq. km by 2017 (Fig. 7), thus becoming the dominant land cover. Farmlands, mostly forest plantations and covering 9.56 sq. km were occurring under the auspices of Forestry Commission. The possi­ bility that illegal farms may exist in parts of the forest cannot be overlooked.

4.3. Status of the eight forest reserves in Goaso Forest District Having presented the general overview of the forest reserves in GFD, the land cover of each reserve was looked at in alphabetical order. 4.3.1. Aboniyere forest reserve Located between latitudes 6� 390 3000 N and 6� 320 3000 N and longitudes 2� 380 W and 2� 270 W in the Asunafo South District, Aboniyere F/R covers 51.10 sq. km. It is drained by two main rivers namely Mintumi River and Aboniyere River. At the North-western end of the reserve, lies Bosam­ bepo F/R. Classified results from 1990 to 2017 showed that the reserve has undergone rapid depletion of rich forest areas. Closed forest lost considerably from 36.77 sq. km to about 0.39 sq. km in 2017 (Figs. 5 and 7). In contrast to closed forest, open forest increased from 14.01 sq. km in 1990 to 45.23 sq. km in 2017. Over the period, farmlands, which were mostly illegal, except for a confined area, located between compartment 17 and 19 (Goaso Forest Services Division, 1962) have sprung up in the

4.3.4. Bia-Tano forest reserve With a total landmass of 189 sq. km, Bia-Tano is the second largest reserve in GFD. The upper part of the forest falls within Asutifi North District whiles its lower part falls within Asunafo North District. It ex­ tends from latitudes 6� 520 5000 N to 7� 50 2000 N and longitudes 2� 300 5000 W 10

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Fig. 8. On-going activities encountered during field validation survey in selected areas in the study site. A: A compartment in Bosambepo F/R recovering from fire outbreak. B: Vast field of plantain farms established inside a compartment in Aboniyere F/R. C: Drone image of the Bia-Tano F/R taken in March 2018. This reserve is arguably the less endangered reserve in GFD. D: Drone image of the Goa Shelterbelt F/R showing degraded areas. E: A road carved by illegal chainsaw operators in the Bia-Tano Forest Reserve. F: A sawmill in Akrodie, destroyed by forestry official to curb the spate of illegal logging activities. G: Officials of Goaso FSD and Military Unit supervising the destruction of sawmills in the GFD. H: Photo showing illegal chainsaw activities in the Bosambepo F/R. Ironically, the area is a hilly sanctuary where logging is prohibited.

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to 2� 420 5000 W. The reserve is well drained and forms part of the water­ shed for the Bia and Tano rivers from which it derives its name (Asare, 2000). Bia-Tano F/R remains one of the well-performing reserves in the District. In 1990, closed forest stood at 133.98 sq. km. By 2013, an additional 33.89 sq. km was gained from the other classes. In 2017, the closed forest decreased and was then covering 149.15 sq. km. There has been significant increase in bare land/built-up in the reserve (8.77 sq. km) and a decrease in open forest (28.42 sq. km). It has been observed from field inspection in parts of the reserve the emergence of illegally carved routes by illegal chainsaw operators. In 2013, there was signifi­ cant degradation of areas in the reserve (15.16 sq. km) and this was attributed to wildfires that affected part of the forest reserve.

4.3.8. Subim forest reserve Subim F/R is the largest reserve in GFD with 230.13 sq. km. It is well drained by Bia River and Subim River with its tributaries. It falls entirely in Asunafo North District and is separated from Western Region to the west by the Bia River. Lying between latitudes 6� 410 25 N and 6� 540 N, and longitudes 2� 400 5500 W and 2� 520 W, the only forest reserve in the District it shares boundary with is Ayum Forest Reserve. Subim F/R has shown a moderated rate of degradation from 1990 to 2017. Closed forest remained the main dominant land cover since 1990. Classified results in 2013 showed an increase in degraded areas but reduced significantly in 2017. Before the forest was classified as a reserve, there were already well-established farms. These farms were demarcated and admitted in the reserve boundary (Goaso Forest Services Division, 1962; Goaso Forest Services Division, 1988; Goaso Forest Services Division, 1998). In 1990, only 0.49 sq. km were categorised as farmlands (against the official records of 1.57 sq. km (Forestry Commission, 2010)). In 2013, that figure escalated to 7.85 sq. km and further to 15.55 sq. km in 2017. The increase in farmlands in the reserves could be attributed to the expansion of already admitted farms beyond their legally accepted boundaries and the establishment of new illegal farms. Fig. 8 (A-H) catalogues a number of on-going activities (including illegal activities) within the reserves during the field validation survey for data collection.

4.3.5. Bonkoni forest reserve This reserve is located south of Bia-Tano F/R and share boundary with Ayum F/R to the west. Located within Asunafo North District, it is geographically located between latitudes 6� 480 4000 N to 6� 540 1000 N, and longitudes 2� 340 W to 2� 410 2000 W. The reserve is drained by Bonkoni River. There is a sign of primary vegetation loss in Bonkoni over the years. Notwithstanding, the rate of degradation is one of the lowest in the District. Closed forest has since 1990 remained the most dominant land cover with a total area of 40.58 sq. km in 2017 (Fig. 7). Open forest has also increased over the period. It is observed that farming activities in the reserve are quasi non-existent.

5. Discussion

4.3.6. Bonsambepo Forest reserve Bosambepo F/R is found in Asunafo South District. It extends from latitudes 6� 340 5000 N to 6� 430 2000 N, and longitudes 2� 340 3000 W to 2� 450 5500 W and shares boundary with Aboniyere to the east and with Krokosua F/R which belongs to Western Region territories, to the South. Bosambepo F/R is the only reserve in the District with well-defined hilly area. The highest point is 644 m above sea level. There is a welldemarcated hilly sanctuary in the area where timber production is prohibited. Classified images (Figs. 5–7) clearly indicate that there has been drastic transformation of the forest in the past twenty-seven years. The fast depletion nature of the forest is a public knowledge. Illegal logging and illegal farming activities coupled with occurrence of wild­ fires have disrupted the natural regeneration of tree species in the reserve. The closed forests reduced from 81.80 sq. km in 1990 to an alarming 4.06 sq. km in 2017 whilst the open forests increased to 108.90 sq. km from 48.66 sq. km in 1990, thus becoming the dominant land cover. It was observed that 11.91 sq. km of land was classified as farmlands in 2013. These farmlands were mostly located around Abuom and down south to the boundary of Krokosua Hill F/R. Most parts of those areas unfortunately fall within the protected areas set aside by Forestry Commission. This confirms the assertion by Kusimi (2015) that despite the high level of protection given to national parks and other protected areas, many are not functioning well as originally envisioned owing to ecological pressures such as fires, floods, climate regimes, and expansion of human activities on lands surrounding protected areas.

The research was able to determine the various land cover types that exist in the on-reserves of the GFD. The closed forest was the single most dominant land cover with an area 552.41 sq. km in 1990. It however reduced to 344.57 sq. km in 2017. Open forest, which was the second largest land cover in terms of land size, had a size less than half that of closed forest in 1990 but in 2017 it became the most dominant land cover with a total size of 422.18 sq. km. The reduction in closed forest over the period confirms the report by Boon et al.‘s (Boon et al., 2009) who posited that closed forest in Ghana has reduced to less than 25% of its original value and now existed in fragmented patches. Also, the Ministry of Lands and Natural Resources (MLNR) (Ministry of Lands and Natural Resources, 2014) reported that forest cover has almost halved since 2000: only 4.6 million hectares remain today with 1.6 million hectares as forest reserves and that Ghana’s deforestation rate is about 2% per year, representing a loss of 65,000 ha of closed forest per year. Recent assessments indicate that rates may have been accelerating in the Brong-Ahafo and Western Regions. Based on the evidence of reduction of closed forest in the area, this paper is of the view that forest reserves in the study areas would all soon be converted into open forests, which in turn may quickly be degraded if measures are not put in place to halt the overexploitation of the forest reserves. From all indication, the loss of closed forest in GFD on-reserves is predominantly human-induced. Aside few timber logging contractors who go through the laydown procedure to obtain logging permits before starting operation in the forest reserves, the number of illegal chainsaw operators who operate in the forest reserves cannot be established hence making it difficult for officials of the Forest Services Division to monitor their activities. According to Marfo et al. (2009), chainsaw milling in Ghana constitutes the main item on the illegal logging agenda. The practice has been banned about a decade ago but the practice is wide­ spread despite measures put in place by government to enforce the ban. Enforcement has been ineffective leading to proliferation of the practice at levels that threatens sustainability of Ghana’s forest resources. Illegal chainsaw lumbering is an important component of livelihoods especially in local indigenous forest dependent communities and the conflict associated with it is high. Degraded areas, bare land/built-up and farmlands were the less occurring land cover types in the District but statistics from the classi­ fication and change detection showed that they were fast expanding. For example, the highest percentage increase recorded during the period was attributed to farmlands with 1757.81% between 1990 and 2017.

4.3.7. Goa Shelterbelt forest reserve Located in Asutifi North District, this forest is the smallest forest reserve in GFD with a landmass of 25.39 sq. km. It is geographically located within latitudes 6� 560 N and 7� 10 4000 N, and longitudes 2� 250 5000 W and 2� 310 3000 W. The reserve is drained by Goa River and smaller streams. Since 1990, the forest reserve has been experiencing rapid degradation. It was observed that in 1990, throughout GFD, Goa Shelterbelt F/R was the only reserve with the total cover of closed forest almost as dominant as open forest. In 2013, the open forest was mainly converted into degraded area. However, in 2017, both closed forest and degraded areas have lost considerably to open forest. The recovery from degraded areas, may be attributed to the constant monitoring along the reserve boundary by forest guards and the implementation of forest enrichment programmes by Forestry Commission in affected compart­ ments of the reserve. 12

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The MLNR (Ministry of Lands and Natural Resources, 2014), adduced that farms and settlements allowed in the forest reserves are expanding beyond their original permitted area when the reserves were consti­ tuted. The expansion is mostly fuelled by the extensive production of cocoa in Ghana which is the most widely practiced and ubiquitous land use in GFD. What this means on the ground is that in order to maintain or increase yields (and income) farmers establish new farm, at the expense of forests, instead of investing in improved management of existing farms or replanting/rehabilitation of old farms (Ministry of Lands and Natural Resources, 2014). Degradation of the forest is a pervasive challenge affecting all forest reserves in the study area. The rate or pace of degradation however differs from one reserve to the other. Closed forest and open forest were summed up and the difference between 1990 and 2017 results were divided by 27 years. This revealed that the average rate of degradation of Bosambepo was the highest in GFD with an average of 0.65 sq. km loss a year while Bonkoni F/R was the lowest with 0.09 sq. km loss of forestland a year. If care is not taken, Bosambepo could suffer the fate of Pamu Berekum reserve, which according to World Bank (World Bank, 2006) was 189 sq. km in 1932 but reduced to 91 sq. km in 1990 and further reduced by 90 sq. km over the next 10 years such that by 2000 only 1 sq. km of forest area remained. The study’s concentration was not on the factors causing the degradation of the forest reserves in GFD. However, the role played by man’s interaction with the forest reserves toward the gradual degrada­ tion of the forest reserves cannot be neglected. Except for Subim F/R and Aboniyere F/R, which are known to have admitted farms during their designation as reserves, no on-reserves in GFD have admitted farms. There is strong evidence from the classified images especially in 2013 and 2017 and from field survey that farmlands had been established in the other forest reserves too. The authors deliberately avoided sampling farmlands in other reserves other than Subim F/R during training of sites. Notwithstanding that fact, Bia Shelterbelt F/R, Goa Shelterbelt F/ R, Bia-Tano F/R, Bosambepo F/R, Aboniyere F/R, Ayum F/R and Bon­ koni F/R (to some extent) registered a high presence of farms. Aside the government led-projects to establish Forest Plantations that are being executed in selected forest reserves in GFD, one of which is Goa Shel­ terbelt F/R where 3 sq. km have been afforested from 2016 to 2017 (Forest Services Division, 2018), there exists illegal plantations which were found to be expanding uncontrollably. Converting forestlands into farmlands could possibly have the potential of triggering timber and fuelwood scarcity in the near future. There are already predictions to the effect that if care is not taken, in the next fifty (50) years, most humid tropical forestlands could be transformed into unproductive lands and the deterioration of the Savannah into deserts will be accelerated (Kio, 1992; Ezebilo, 2004; Armah, 2014). Aside official demarcated access routes to the reserves, there exist illegal roads that are purposely created by illegal chainsaw operators for conveying logs without being detected by the monitoring agents. Rautner et al. (2013) observed that the establishment of roads in forest areas tends to have limited impacts on forest cover, but the opening of access to previously inaccessible areas can then facilitate legal and illegal logging, and the conversion of forest to farm land. This identified phenomenon in GFD (Fig. 8G) and evidenced from the classification maps (Figs. 6 and 7) may lead to the disequilibrium of the natural habitat in the District. In addition, annual wildfires have also altered the composition and structure of most forest lands (Mensah et al., 2019). Rautner et al. (2013) explained that harvesting of wood involves either indiscriminate or selective felling of trees in a forest or plantation and the resulting forest degradation can leave the area more susceptible to fires and exploitation by other extractive industries. Many fire outbreaks incidents triggered either by drought or by forest fringe communities have occurred in forest reserves across the GFD and Ghana; the latest in 2011 consumed hundreds of hectares from Bosambepo F/R to Ayum (no official records). The fact remains that bush fire features prominently as one of the causes of forest degradation as reported in several studies

Food and Agriculture Organization (2012); Forestry Commission, 2010; Forestry Commission, 2016; Kusimi, 2015; Ministry of Lands and Nat­ ural Resources, 2014; Armah, 2014; Rautner et al., 2013). 6. Conclusions The study effectively employed Remote Sensing and GIS to establish, quantify and explain the changes in land cover types that are found in Goaso Forest District. It was observed that the Forest District has lost about 37.63% of its closed canopy forest, while open canopy forest increased by 53.37%. Farmlands (Forest plantation, admitted farms and illegal farms), degraded areas and bare land/built-up have increased to more than thousand-fold put together. This showed that the depletion of the forest in the study area was occurring at an alarming rate. The Bosambepo F/R recorded forest loss of 0.65 per annum, the Subim F/R recorded 0.52 sq. km per annum, the Bia Shelterbelt F/R recorded 0.30 sq. km per annum while the other forest reserves recorded below 0.30 sq. km loss per annum with Bonkoni F/R being the least, recording 0.09 sq. km forest lost a year. The authors acknowledge the effort being deployed by Forestry Commission, (the agency mandated to maintain and protect forest re­ serves in the country) to curbing the spate of illegalities occurring in the GFD with the aim of fulfilling the vision of the Commission which is to “leave future generations and their communities, with richer, better and more valuable forest and wildlife endowments than we inherited”. Some of the actions taken in recent times, to the knowledge of the authors, include the destruction of illegal sawmills that have sprung around the forest reserves in GFD (Fig. 8F), the seizure of chainsaws, the engagement with military to beef up the patrolling efforts of forest guards, the engage­ ment with informants and the prosecution of arrested illegal operators by the judiciary. However, more must be done as regards replanting of degraded forests and routine monitoring to halt the pace of degradation in the District and the first right step is to assess the current state of the forest reserves not only in the District but also throughout the country. The study recommends that concurrent restoration of degraded for­ ests and integration of Remote Sensing and GIS into routine forestry management practices in Ghana would be very helpful especially at the decision-making levels. There is readily available satellite data to carry out studies that will contribute towards the protection of the high forests and by so doing secure a healthy living for current forest dependent communities around forest reserves in Ghana. Admittedly, the cost involved in efficient management of forests is high. This notwith­ standing, the authors advocate the use of free and open source platforms such as OpenStreetMap and involvement of dedicated volunteer map­ pers in participatory mapping with stakeholders of the forestry sector. This could foster the constant update of forest data, its availability and reliability, added to the ease of acquiring data by concerned agencies and departments for a more sustainable use of forest resources. Acknowledgements The authors give special appreciations to the Goaso Forest Services Division Management team, especially, Mr. Godfred Quashigah (District Manager) for his contributions towards the data collection by way of giving the authors unrestricted access to the reserves as well as relevant information about the forest reserves, and to Mr. George Danyo (Senior Cartographer) for his guidance and immense contribution to the study. We would want to thank the RSGIS unit for providing us with the licensed version of ArcMap 10.4.3 that was used during the research. Our special thanks also go to Mr. Sylvester Afram Boadi and to all anonymous editors who have painstakingly reviewed this paper.

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