Expresión y purificación de TSGP1 recombinante truncada (rtOmTSGP1)

60 _{Forest was a “A continuous stand of trees, at least 10 m tall and their crowns interlocking.” (Wass} 1995:8)

61 _{Forest was land cover with >25% tree cover at the Landsat pixel scale of 30 m X 30 m spatial}

resolution for trees >5 m in height. This definition facilitated global-scale assessments with Landsat and MODIS systems. (Hansen et al. 2010)

62 _{Forest loss was defined the same as above.}

63 _{Forest was defined as “areas under total forest cover.” (IUCN 2005)} 64 _{See Section 2.3.6 for background on local resistance to the IUCN project.}

81

This study used post-classification comparison (Singh 1989) also called delta classification (Coppin & Bauer 1996) to detect land change. This straightforward and inexpensive technique has successfully analyzed changes in forests, urban areas, and coastal zones (Hall et al. 1991; Jakubauskas 1989; Mas 1999; and Yuan 2005). Its advantage is that it can address variations and erroneous data in vegetation, soils, and atmospheric conditions since each image is classified independently. Its disadvantage is that the change map can represent the product of multiplying inaccuracies of each land classification such that the potential for inaccuracies will increase with the number of classifications (Coppin & Bauer 1996; Gordon 1980). For example, two remotely sensed images post-classified at 90% accuracy will generate a joint product of change detection that is 81% accurate. This study addressed this weakness by going to remotely-sensed points in the field during the main field work in 2014 and in follow up work in 2015, interviewing local people, and ground truthing land cover change.

4.3.1.1 Initial field verification and accuracy assessment

The investigator completed a course in ArcGIS at ESRI. As a Research Fellow at ILRI, she worked with geospatial analysts to calculate forest gain and loss using existing data. Land cover was initially assessed with satellite data of fine resolution (30 m) images of global forest loss and gain for 2000-2012 (Hansen et al. 2013). The data is freely available online at

http://earthenginepartners.appspot.com/science-2013-global-forest. Data were geo-referenced and maps were created to gain an initial understanding of loss and gain. Thirty GPS points were randomly selected for field verification of thematic accuracy, an important step in remote sensing analysis (Olson et al. 2004; Congalton & Green 2009). The research team visited those points (Figure 4.1) with key informants who shared local knowledge of land history and vegetation. Only one out of 15 (7%) forest gain points could be verified with confidence due to problems with interference in the forest gain image, the inability to reach points due to dense forest and wildlife, and questions about vegetation interpreted as forest.

Most (9 or 60%) of the 15 forest loss points were located where forest (entim) had never existed. Locals insisted that only tall swamp grasses (esere and oltiol) 65 had been there in 2000,

elephants were not visible in the grasses because they were so tall, the grasses were taller than a man plus his herding stick (engudi), and the grasses disappeared in the past 10 years. Only 5 out of the 15 (33%) loss points could be confirmed as forest loss from cultivation. Due to this low accuracy, this data could not be used to quantify forest loss because tall wetland vegetation (macrophytes) had been erroneously interpreted as forest in the reference data. This experience

65 _{Bulrush or cattail (esere) and papyrus (oltiol) are wetland plants that can reach up to 5-9 m}

(McClanahan & Young 1996) and are typical in Kenya wetlands (Mwita 2013). The shoulder height of a male African elephant can reach 3 m (Shrader et al. 2006). A man plus his herding stick (engudi) might reach 4 m (personal observation). Such metrics were helpful in assessing vegetation heights with informants.

82

Figure 4.1 Verification points visited in Loita Forest and Empurputia (bordered in white) for thematic accuracy assessment of satellite imagery in Hansen et al. (2013)

highlighted the limitations of global gaze GIS approaches that miss fine local scale land dynamics (Olson et al. 2004) and the very high-resolution data needed to distinguish small wetlands (Mwita et al. 2013).

4.3.1.2 Image acquisition

New Landsat image scenes were acquired for free from the USGS website at

http://earthexplorer.usgs.gov/. Images were reviewed for the years 1976, 1995 and 2014 to trace change from a baseline established at the earliest available year of a relatively cloud-free (< 10% cloud cover) image and to construct two comparable (19-year) time periods for analysis. Dry season anniversary months were selected because of the presence of persistent and perennial woody vegetation in those months and fewer problems with sun angle differences (Singh 1989). January and February dates were chosen because the area receives long rains in March-May,

83

short rains in October-November,66 _{and January and February fall within a relatively dry period} known by locals as a time of scarcity (Ole Saitabau 2014). Fortunately, a cloud-free image was available in January for 1995 when the area experienced an unusual amount of rain in February 1995. The image sources appear in Appendix 3.

Satellite images with the least cloud cover were downloaded. Cloud removal using masking techniques was not possible with Landsat 2 because ArcMap was not available for the 1976 image. The extent of clouds and shadows in the 1976 image were classified by visual inspection and then added to the appropriate land classification total. This accommodation was considered reasonable because the 1976 totals were only used for comparing relative land cover percentages, not for calculating percent change. Images were orthorectified in a Universal Transverse

Mercator projection with a WGS84 datum, resampled by cubic convolution, and were in GeoTIFF data format.

Images were clipped to the extent of the entire forest (with four boundary points of 1.571⁰S, 35.804⁰W; 1.571⁰S, 36.011⁰W; 2.048⁰S, 36.012⁰W; and 2.048⁰S, 35.804⁰W) and the study area (1.80529⁰S, 35.9048⁰W; 1.81576⁰S, 35.92515⁰W; 1.8706⁰S, 35.86946⁰W; and 1.88001⁰S,

35.91893⁰W). The band combination was changed to 4-3-2 which is commonly used for vegetation analysis (USGS 2015). Band 4 is near infrared, Band 3 is red, and Band 2 is green. 4.3.1.3 Training data, field verification, and change detection

Data processing and classification were accomplished with methods described by Lillesand et al. (2014). The simple classification scheme adopted was dense canopy forest (≥ 40% canopy with closed stands of canopy trees ≥ 5 m tall),67 _{light forest/bushland (< 40% canopy with open stands} of mostly bushes), grassland (mostly grasses interspersed with woody plants), hydrophytic vegetation (mostly wetland plants), and bare land.68 Rigorous and time-consuming validation of training samples for each image were based on ground-truthed data and visual evaluation of color tones, texture, pattern, and associations with topographic features. Dense forest appeared deep red, light forest/bushland was less red and interspersed with mottled patterns of individual trees, grassland was grayish pink, hydrophytic vegetation was consistently light pink with no

66 _{Rainfall in Loita in February 1976, February 1995, and January 2014 was 45 mm, 130 mm, and 27 mm,} respectively (KMD 2004, 2014). High rainfall in 1995 affected reflectance in the forest canopy so considerable time was devoted to differentiating forest and hydrophytic classifications in that image. 67 _{Most countries use minimum crown cover of 30% to define forest. The forest classifications in this} study were differentiated between dense (≥40% canopy) and light forest by ground-truthing the difference in the field using a spherical crown densiometer according to Lemmon (1956).

68 _{Characteristic trees and shrubs found in dense forest included olpiripiri, enkashe, olarioi, olgilai,}

olkonyil, osokonoi, oltarakua, oloirien, and oleparmunyo.

Characteristic trees and shrubs found in light forest/bush land included osentu (oleleshua) entulelei,

oleparmunyo, olkilorita, oltarara, olmusakua, osinoni, oloiyapiyap, olamuriaki, olmisigiyioi, oloponi, olosesiai, and olgirigiri. According to Talbot (1960), osentu (oleleshua) has been described as the

characteristic bush in succession from red oats (orperesi wouas) grassland to leleshwa (osentu/oleleshua) bushland to cedar (oltarakua) forest in this ecosystem (Talbot 1960).

Characteristic grasses were orperesi wouas and olmagutian.

84

patterns or gradations of color, and bare land appeared grey. To generate high-quality training data, polygons representing the five classifications were created in ArcGIS.

On return trips to the field, the team took GPS points at sites representing the five land

classifications. This reference data was helpful in confirming and further training map data. Due to practical limitations, there was not enough data to generate a valid error matrix for thematic map accuracy assessment (Congalton & Green 2008). The total extent of each of the five classifications was calculated in square meters and then compared between years to detect change. Due to dramatic differences in image quality between 1976 (Landsat 2) and the other years (Landsat 5 and 8) only relative percentages of land cover were compared between 1976, 1995 and 2014. Change detection was calculated in percentages for 1995-2014 only.