Insights on the implications of governance on land use/cover change in forest protected areas (FPAs): The case of six forest protected areas in
4.1: Introduction
4.2.2: Land use/cover change analysis
Landsat imagery is well suited to detecting changes in vegetation cover and condition because it contains several spectral bands in the vegetation-sensitive infrared region of the electromagnetic spectrum (Walsh, 1980; Trotter et al., 1997; Tomppo et al., 2002). For this study, a series of Landsat Thematic Mapper (TM, on-board Landsat 5 satellite), Enhanced Thematic Mapper (ETM+, on Landsat 8) imagery (path 171; row 73) (Table 4.1) covering the study forests were acquired from NASA portal (GloVis). The scarcity of imagery covering the forests we studied made it difficult to measure forest cover change over uniform 10 year periods. To try to make the periods over which change was measured almost similar in length, four dates of 1984, 1993, 2004 and 2013 were chosen as target years. Metadata files for imagery available in the NASA portal (GloVis) Landsat archive for path 171; row 73 were evaluated to identify suitable images for use in the analysis using the following Slayback and Sunderland (2013) criteria:
• whether it was produced in one of the target years (1984, 1993, 2004 and 2013);
• minimum cloud cover over the study forests, and
• April to June date to minimize changes in vegetation due to annual phenological cycles.
Given the available imagery and the stated selection criteria, four images covering inhabited Gwaai, Gwampa and Mbembesi forests were selected for cloud free days during 82
April, May, and June (Table 4.1). Another set of four images covering the uninhabited Fuller, Kazuma and Pandamasuie forests was selected using the same criteria. The satellite image for 1984 was selected as the base image for the study for this was four years after independence from colonial rule that coincided with the early launch of Landsat 5, which has the same band characteristics and spatial resolution with Landsat TM, ETM+ and Landsat 8. This allowed easier comparison of Landsat 7 and 8 images and Landsat 5 images from earlier dates. The imagery analysed was provided at 30-m spatial resolution, and contained six spectral bands covering the visible, near-infrared, and mid-infrared. Geographic Information System (GIS) and remote sensing software that were used to analyse the acquired imagery included ArcGIS version 10, Arcview 3.2, ENVI and Idrisi Andes. A hand held Global Positioning System (GPS) was used for ground truthing exercises to collect Ground Control Points (GCPs). All the data were downloaded into the workstation for analysis using the GIS software.
Table 4.1: Analysed Landsat imagery for the studied forest protected areas Study forests Satellite Path/Row Date of image acquisition
Gwaai Landsat 5 171/73 29 June 1984
Gwampa Landsat 5 171/73 03 April 1993
Mbembesi Landsat 5 171/73 04 June 2004
Landsat 8 171/73 12 May 2013
Fuler Landsat 5 173/73 26 May 1984
Kazuma Landsat 5 173/73 03 May 1993
Pandamasie Landsat 5 173/73 21 April 2004
Landsat 8 173/73 11 June 2013
Imagery data served as a tool to track changes in forest cover that short-term fieldwork could not establish. Spatial and temporal data from image analysis were used to determine the effectiveness of on-the-ground governance actions over the period under study between the two categories of study forests.
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4.2.2.1: Image preparation 4.2.2.2: Ground truthing
The main aim of the ground truthing exercise was to collect Ground Control Points (GCPs) which were used for geo-referencing and registration of satellite images. The GCPs were also used to determine training areas that were used during image classification. A hand held GPS receiver was used to collect coordinates on GCPs which were used to geo-reference all the images using the exact location of permanent features such as road junctions and bridges. The exact ground positions were automatically collected and reflected on the image. This method helped the researchers in identifying locations of interest such as open fallow areas, vegetated areas or bare ground. These locations were then overlaid on the satellite images so as to clearly identify and digitize the training sites. Systematic transect walks were conducted in a Y-shaped form through the study forests to observe environmental conditions which would assist in the process of supervised classification of the study area. The ground truthing exercise was carried out in June and July 2014. It also focused mainly on selecting training sites that were used to generate the classified images.
4.2.2.3: Image enhancement
Image enhancement techniques used were contrast stretching at 2.5% and false colour composites. These were applied to the images to facilitate the identification of features. The near infrared composite was combined with visible bands (band combination: NIR, Red and Green) to produce a false colour composite. Vegetation in the Near Infra-red (NIR) band was highly reflective due to chlorophyll pigment and false colour composites vividly showed vegetation in various shades of red. Built up areas were displayed in cyan blue in the false colour composites while soil colours varied from dark to light brown. Deep red hues indicated broadleaf health vegetation while lighter reds signified grasslands or sparse vegetation. Water appeared dark and black depending on depth and clarity, due to absorption of energy in the visible red and NIR bands. False colour composites helped in differentiating water, soil and vegetated areas.
4.2.2.4: Image normalization
Most sensors including Landsat record reflected electromagnetic radiation by earth features in the form of Digital Numbers (DN). These pose difficulties when comparing multi-temporal images because of differences in sun angle, sensor angle and flight height among other reasons. In this research, this problem was addressed by changing DN values to radiance and then radiance to reflectance, a process called image normalization. This combined surface and atmospheric reflectance of the Earth is computed with the following formulae (NASA, 2011, P.119):
Digital Numbers to Radiance:
rad = LMAX-LMIN
255*DN+LMIN Equation 4.1
Radiance to reflectance:
n*LA *d2
PP Equation 4.2
ESUNA * COS (0 s)
Where:
pp = Unitless planetary reflectance, L^=spectral radiance at sensor’s aperture,
ESUN= band dependant mean solar exo-atmospheric irradiance,
0 s = solar zenith angle in degrees, which is reciprocal of the sun elevation angle d= earth-sun distance, in astronomical units