Determining Causes of Gully Erosion and Associated Rates of Change in South-east Nigeria, Using a Remote Sensing and GIS Methodology

In this work a study of gully erosion in southeast Nigeria is presented. The study of gully development on a regional scale is currently undermined by the inherent costs associated with consistent eld monitoring and the lack of historic measurements to perform time series analysis. The analysis of study area topography at 30m resolution reveals 85% of the surveyed gullies develop on concave slopes with high values of 6 plan curvatures and >50 inclines. Results also reveal high association with ferralsols soils. Statistical analysis to determine signicance of variables on the proportional yearly gully change in metre squared per square metre were conducted via principle component analysis. The analysis of this work was restricted to the time periods 2006/7, 2009/10, and 2014/15. The approach did not report any existence of one singular driver of erosion across the studied years and multiple sites conrming the complexity of gullies. The PCA showed that the level of variance explained in the yearly gully change variable was most similar in PC1 (representing the component with the highest eigenvalue) to Vegetation loss, Vegetation loss and slope in the respective years.The study offers a method of monitoring gully development from early stage to maturity and exemplies the complexity and variability of erosion drivers in the SE Nigeria region. It presents a veried approach to local and regional monitoring of gullies, enacted through use of low budget/computing cost remote sensing and classication technologies, and serves to embolden civilian and governmental efforts to manage the societal and environmental menace of gully erosion.


Introduction
The formation of gully erosion and sediments are a function of rainfall, soil properties, and topography, and can be induced by human interference including land management practices. The rainfall intensity is high in this area of Nigeria and often persists for long durations. Storms with over 25 mm/h intensity have been reported by  to be particularly erosive.  observed in the region that most gullies develop at slopes, cuestas, fractures and joints which are common features in the gullyerosion-prone areas of Southeast Nigeria and have been identi ed as signi cant factors in the formation of gullies and subsequent erosion. The study area has also seen increased erosion rates through the exacerbating effect of mineral extraction sites . The loss of soil degrades arable land and eventually renders it unproductive. As a result, there are signi cant per capita shortages of arable land. The effects of this erosive action are made more severe by recent and rapid population growth in the Southeast region of Nigeria. Loss of agricultural output is one of the greatest economic costs of gully erosion (Pimentel et al. 1995). Unquanti ed large portions of land have been degraded in recent years in towns such as Ekwulobia, Agulu-Nanka, Orlu, Iyioku, Njaba, Igboukwu, Okigwe, Abiriba, Mbaise, Uturu, Ideato, Amucha. In addition, infrastructure, and in particular roads, have been damaged, leading to numerous vehicle accidents and displacement of residential houses. Several studies have been conducted on the causes of gully erosion in Southeast Nigeria and ways to control them. Most of the studies primarily revolve around causes based on the immediate scenario rather than the long term causes as can be found in . They also deal more with combating gully erosion rather than its prevention and pay little attention to methods of managing this natural hazard.

Study Area
Gully Erosion occurs in numerous areas within the South-East states of Nigeria. Erosion problems arise mainly from natural causes, but their extent and severity are increasingly attributed to anthropogenic ignorance and unintentional action (Enabor and Sagua 1988). In spite of technological advancement including land-use planning (United Nations Development programm 2015), run-off catch pits , and drainage channels , gully erosion still remains a major problem in the region. The academic community has observed that gully erosion, is largely a result of natural factors including rainfall run-off (Njoku et al. 2014), and the geological  and geomorphological  context of the area. There is further agreement that these naturally occurring conditions are prime for gully erosion but are exacerbated by anthropogenic factors such as land-use change and degradation (Vander Veen, 2010). Each of these occurrences act as push factors in causing gully erosion ).
The study area is located in south-east Nigeria between 70 8'N 60 34' E and 40 49' N 80 15' E covering a land area of approximately 57,758.034Km2, as shown in Figures 1 and 2. It is characterised by coexisting types of land use and land cover, which are mainly affected by gully erosion.

Methodology
This study adopts remote sensing and GIS methodologies in processing the satellite data. This involvesLandcover classi cation, study area DEM analysis, gully area analysis and Analysis of forest degradation and deforestation of the study area.
In the case of this study, the remote sensing data used in this research were acquired from Landsat images from December 1986December , 1987December , 1988December , 1989December , 1990December , 1991December , 1992December , 1993December , 2000December , 2001December , 2004December , 2005December , 2006December , 2007December , 2008December , 2009December , 2010December , 2011December , 2012December and 2013December , 2014December and 2015. Attempts to compile a complete annual data set were impeded by unavailability of Landsat images in the study area from 1994 -1999. During this time period the data was not available, not because of cloud cover, but because of data acquisition issues within this period. The study area is found in the tropical region where the presence of cloud cover is extremely common throughout the year . Images were then chosen from the month of December during the Dry season when the sky is mostly cloud free. The use of Landsat is warranted for several reasons. It is observed that no other current or planned remote sensing system, public or private, lls the role of Landsat in global, regional environmental change research, or in civil and commercial applications (National Air Space Agency, 1999). The Landsat archive contains data spanning over 40 years (Lee and Liu, 2001) and continues to be collected through Landsat 8 launched in February 2013.

SRTM (DEM) for Topographical Outlook of the Study Area
In order to obtain the structure and contribution of topography to the development of gullies in the study area. The DEM were downloaded and cropped to the area of interest. The elevation values range from low = -11m to high = 516m. This elevation data from each of the 14 studied gullies which includes hill shade, slope gradient, slope aspect, slope curvature, contour of the area, cross pro le and gully stream order, represent an independent variable for use in further statistical analysis to determine its in uence on gully formation and erosion rates. These DEM rasters used in this analysis were processed and calculated with spatial analyst tools of GIS software (ArcGIS, Erdas Imaging etc).

Google Earth Images for Gully Analysis
Google Earth images were used in order to aid analysis of gullies hidden from view in the Landsat images due to vegetation cover , weak spectral signatures, or because of the low spatial resolution of the Landsat image compared to the speci c images sourced from Google Earth . Google Earth images were downloaded, and gully edges were digitized using the polygon tool from the Google Earth for digitization and quanti cation of the gully areas, starting from the rst available year, 2006, to 2015 to act as a supporting dataset to the Landsat archive. Some of the gullies that are found in Landsat images are as well found in Google Earth images and they were digitized and measured to compare with Landsat measurement.

Land-cover Classi cation of the Study Area
Land cover classi cations were deduced from Landsat and ALOS raster data by ISO Cluster Analysis, a form of unsupervised classi cation for pixel oriented and supervised for OBIA oriented. These unsupervised and supervised classi cations were assisted using the 40 gully points and 60 other landuse points picked during eld work. Five classes were chosen to represent the land based on the Landcover types of the study area. The classes identi ed were 1. Water, 2. Vegetation, 3. Agriculture, 4. Urban-Land and 5 Gully/Open-Land. Accuracy Assessment was done with Google Earth to extract 100 KLM points from the classi ed data which gave between 80% to 93% accuracy. This was checked with the 100 Random points extracted from the classi ed data; at the location of each random point, a land-cover of that part using Google-Earth was used to compare it with the land-cover of the classi ed raster. Google-Earth was used because it has better resolution than Landsat image and the features can be better observed (Virginia, 2011).
The missing Landsat data from 1994 -1999 were obtained by calculating the linear interpolation by connecting two adjacent known values of 1994 and 1999. The Linear Interpolation method used here is shown in equation (2) to estimate the value of a function between two known values. If the two known values are (x 1 , y 1 ) and (x 2 , y 2 ), then the y value for some point is: Presentation of Result

Regional Topographical Analysis
Rendering Digital Elevation Models (DEM) to detect changes and calculate gully dimensions of focused gully sites (to observe how slope, nature of slope, aspect and gully stream order in uence gully development)". The adoption of Digital Elevation Model for this analysis is a new method that can easily reveal the nature of the landscape. Land surface topography has been reported to signi cantly affect the processes of runoff and erosion . The presentation of the regional topographic analysis in this study looks into the natural causes of gully development in the study area although known to be caused by both the contributions of topographic and anthropogenic disturbances (Arash et al. 2011;. In this section, the analysis of Elevation, slope, curvature of slope, gradient of slope, slope Aspect, stream order generation, contour generation and cross pro le of gully sites are presented with a subsection provided to cover the in uence of each variable.

Elevation
The digital elevation map of the study area is produced from the SRTM data at 30m  resolution. The elevation map of the study area presented has a minimum elevation value of -11 metres and a maximum of 516 metres ASL. When gully points were overlaid, as can be seen in Appendix I, it reveals that gullies are found on areas that are higher in elevation compared with the surrounding areas. All the 14 surveyed gullies are located on areas with elevation points above 10m.

Slope
The slope gradient is one of the most important factors affecting gully erosion ). Ofomata 2001 also emphasizes the importance of slope by showing that the studied gullies are located at the base of slopes or hills.  observed that in the simplest terms, land located on steep inclines is more vulnerable to water erosion than at land. The highest elevations in the region are detected at elevations of 516m. In terms of degrees Appendix II shows that areas with 0 -10 0 are mostly found in low lying areas which are mainly found on top of plateaux, ood plains, at areas and areas liable to ooding. Areas with 10 0 -20 0 -30 0 and above accommodate most gullies revealing that these areas are where gullies are most commonly developing. The analysis of slope and overlay of gully points have revealed that gullies mainly develop in areas with 10 0 and above. Of all the 14 surveyed gullies, 8 gullies (57%) are found at 10 0 -20 0 , and 6 gullies (43%) at 20 0 and above. Appendix IIprovides a graphic showing slope and elevation.

Slope Aspect
Further analysis of topography was conducted via analysis of gully locations in respect to the slope aspect. Beullens et al. 2014, Marque and Mora 1992, maintained that slope orientation affects gully development which depends on the side that is receiving rainfall more which determines the amount of runoff. The aspect map of the study area, Appendix II, was classi ed into ten classes, de ned as: at, N, NE, E, SE, S, SW, W, NW. On this basis, the aspect classes of southeast Nigeria highlight a fairly homogeneous distribution. Slopes facing from North to North-west slightly predominate when compared with South, South-east and south-west while the value of -1 is used to identify at surfaces such as ood plains, uvial terraces, river courses and hill plains. None of the gullies were located on areas with value of -1 which represent a at area.

Slope Plan Curvature
This section also looks at slope plan curvature as part of topography that in uences gully development in the study area and answers part of objective 3 above. The slope geometry of hill sides whether convex or concave often contribute signi cantly to soil loss and gully development. , in working on gully erosion and environmental change in Leuven, Belgium, recorded that uplands act as a link through which run-off transports sediments down the hill, contributing to the development of gullies. Zapp and Nearing agreed that Slope shape has a signi cant impact on rill patterns and gully developemt.
The curvature is very important in understanding how run-off ows in the study area, which in uences gully erosion and deposition. The low values of -5 (x10 6 ) of plan curvatures de ne convexity; while high values of 6 (x10 6 ) plan curvatures characterize concavity of slope curvature. Values of plan curvatures around zero indicate that the surface is at.

Gradient of the Slope
The slope gradient is one of the most important factors affecting gully erosion. Under the same rainfall runoff, gully erosion could be drastically different on different slope gradient .  maintained that as surface water continues to ow, it starts to remove the cementing materials of the soil through the ssures, which develop into gullies depending on the nature and gradient of the slope. In the analysis of geographical gradient of the slope of the 14 test gully sites, the data reveals that they have different gradient values. The Iyioku, Okigwe, Igboukwu, Njaba, Orlu, Amucha, Ngwo1, Ngwo2, Oguta, Umuahia, Isinweke, Nekede, Urualla, and Naw a gully sites are shown in Table 10. The contours at the head of the 14 gully sites were higher than those at the lower end of the gully sites. The average gradient of the 14 studied gullies is 1 in 28.6m. This is represented on the gradient chart Figure 3 showing individual gullies.

Local Soil
Some environmental Scientists have attributed soil as the main in uence on gully development, ). Arash et al. (2011), attribute gully erosion to physical factors, but suggest that its severity is greatly in uenced by the structure and texture of the prevalent soil. Ofomata 2008; Onwumerobi 2002; Igwe 2012 recommended soil as a strong factor in gully erosion development of southeast Nigeria.
Taking a look on the analysis from Table 1above, it reveals that even though the soils appear to have similar characteristics, the gradient of the gullies tends to be lower in areas where there are Ferralic-Arenosol soils (mean gradient = 1 in 37.7); rather than Gleysols and Fluvisols (mean gradient = 1 in 24). Ferralic-Arenosols soils; Gleysols and Fluvisols have deeper weathering and also unconsolidated sandy sediments . Gleysols and Fluvisols have loose sandy sediments and have similar characteristics with Red Ferralsols and Hydromorphic soils but their weathered soil is not as deep as Ferralic-Arenosols and Feralsols and Nitosols soils . All these physical factors are highly in uenced by anthropogenic factors . In the location of the 14 gullies 8 gullies are located on (Ferralic and Arenosols), 3 gullies are located on (Feralsols and Nitosols) and 3 gullies are located on (Gleysols and Fluvisols). Which is represented by 57%, 21% and 21% of the number of gullies respectively.

Gully Strahler Stream Order
The studied gullies are found at the segment of the drainage which has mostly the hierarchy of tributary number 1, 2, 3 and 4. Showing that 1 and 2 contribute to 3 while 1, 2 and 3 contribute to 4 which produce high runoff. The hierarchy of 1, 2, 3 and 4 show that the gully sites are located at slope areas with high runoff. The cells that have 5, and above are surface water. The 14 gullies are located thus; hierarchy 1 (6 gullies), hierachy 2 (5 gullies), hierachy 3 ( 1 gully) and 4 ( 3 gullies). The 14 gullies are represented by 1, 2, 3 and 4 hierachy as 43%, 36%, 7% and 14% respectively Appendix V.

Regional Land Cover Classi cation
This section used remote sensing data (Landsat and ALOS PALSAR) to determine change in land-cover through Pixel based and Object Based Image Analysis (OBIA) classi cation over a maximum 30-year period (1986 -2015) in SE Nigeria". The classi cation methods for this purpose will be compared and contrasted". Many researchers have attributed landcover removal as the main source of gully development. In South East Nigeria, Igwe (2005); Onyekwere (2001);  have separately agreed that gullies mostly develop on soil on which vegetal growth has been disturbed due to infrastructural developments, for example roads and housing developments. Land cover classi cation is one of the modern methods of ascertaining the level of landcover removal by human interference.
Pixel and Object Based Image Analysis (OBIA) land cover classi cation is conducted for the study region. Data is provided for each individual year within the de ned study period. The regional study area measures approximately 57,758.034km 2 . According to the two different classi cation methodologies, the results reveal that the vegetated land surface, at the beginning of the study in the year 1986, comprises 90% and 83% of the study area for Pixel and Object Based classi cation methods respectively. These values highlight the original dense canopy coverage of the region. By 2015, over a period of almost 30 years, this classi ed vegetated proportion of the total land surface has reduced to 35% according to Pixel based approaches and 41% for OBIA classi cation. According to both independent methodologies this highlights a signi cant loss in vegetated land surface. Losses of vegetated area are estimated at 55% and 41% of the total studied area, for Pixel and OBIA classi cation respectively, between 1986-2015.
With respect to the regional land cover classi cation presented in Table 2 and 3 for each of the available study years, the signi cant loss in vegetation is predominantly attributable to increases in Urban-land and Agriculture. As well as appearing to contribute to a loss in vegetation these increases in urban and agricultural areas appear to have in uenced the existence and development of Gully/Open-land formation in the study area. While a 55% reduction in vegetated land has been detected over the study period, other land use classes exhibit increases. The increases exhibited for the other classi cations are 38% (Urban), 13% (Gully) 0.4% (Water), and 3.6% (Agriculture) according to pixel-based classi cation, see Table 2. For the 41% reduction in vegetated land evident using OBIA classi cation over the study period, these classes account for increases of 31% (Urban), 10% (Gully), 0% (Water), 0% (Agriculture)  Land-cover Classi cation using ALOS PALSAR L-Band.
In comparing the results of Landsat and ALOS PALSAR images of 2008 and 2009, they present similar trends with the exception of water class which increased when compared with Landsat images, Table 4. Also, showing that SAR differentiated water from other classes better than Landsat, may be because of the resolution which is higher than Landsat but more likely through the spectacular scattering resulting in zero backscatter to the SAR sensor.  Overall Accuracy = 89/100 = 89%  This approach was applied in this study to demonstrate the reactions of the variables and their contributions to gully formation and development of the study area. The main purpose of principal component analysis in this study is to obtain a minimal number of independent linear combinations.
(PCA) was identi ed as an appropriate statistical tool to determine the in uence of gully factors on gully development, relationships of gully factors and the effects of these gully factors.
The results of the Principal Component Analysis will help to know the weight and relationship of the gully variables. PCA has been used in this manner in studies such as  and . In a similar way this will be conducted here. The strategy to testing outlined in this section is as follows: PCA ( In each case, 7 gully variables are examined across the 14 gully sites. With the yearly gully change in metre squared per square metre the focus. The aim of this PCA is to identify the variables that are closely associated with the rate of change of the gullies using the rst two components in this analysis. The expectation is that gully variables like vegetation loss, soil, and gully stream order have a strong impact on the initiation of gully development and yearly gully change in metre squared per square metre in the study area. Again, some researchers such as (  gullies. This is evident by looking at the Score plots for the three time periods when excluding these gullies. Also, from the biplots all the gullies, they continually appear to be dominated by all the variables. There is no need removing the less signi cant ones to rerun the PCA model again since all the variables entered in all the years appeared strongly in either PC1 or PC2 or both. The reason for all these will be discussed more in the discussion section of the study.  (2001); ; that used different methodology of interview and site measurement to observe that vegetation loss was a big factor in gully area development in southeast Nigeria. For each year, the gully area variable is correlated with high vegetation loss associated more with smaller gullies which ts the model of vegetation loss acting as an initial driver rather than the key variable driving advanced behaviour.

Topographical In uences on Gully Development
Topography has a strong in uence on gully development. Several studies have identi ed topography as the main link to gully development. , Marquisee (2010), Boardman (2006), Bochet (2004) and  observed that topographical in uence was the prime reason for gully development in different locations. Some of the topographical factors include the contribution to runoff as the amount and intensity of rainfall combines with these. In southeast Nigeria, rainfall data is high because it is in uenced by tropical monsoon climate which generates over 1000mm of monthly rainfall every year during rainy season (March -November). The slope of land, properties of soil, and the nature and extent of ground cover are all deemed essential contributors to gully formation as reported in Sharhrivar and Christopher (2012), , and . In southeast Nigeria, many works such as , found that there is a positive relationship between relief and gully erosion leading to more pronounced and aggressive gully erosion in areas with valley topography than areas with at land. This is expected due to the physics of the scenario. Ofomata pointed out that in areas like Agulu-Nanka, Njaba, Nekede-Owerri, Iyioku, Okigwe, A kpo, Oha a, and Umuahia, the gullies can be traced to the natural slope of the topography but the occurrence of gullies must be in uenced by more than just this, otherwise gullies would form on all steep topography. The result of this study tends to agree with Ofomata on the importance of slope by showing that the studied gullies are located at the base of slopes or hills. For example, the slope degree of Iyioku, Okigwe, Umuahia and Nekede are 15 0 , 11 0 , 10 0 and 10 0 respectively. with the gullies evidently developing at the base of the slope because it is the area where runoff converges to form the gully head before it develops. PCA and Cluster analysis conducted here shows there is high variance and clustering between the actual magnitude of slope and the proportional yearly gully change in metre squared per square metre, indicating that the gully speci c metrics are largely independent of the slope. This study therefore indicates the importance of slope but only to the extent that it exists for a gully to form. This is further supported by the existence of the slope and other variables clustering well with yearly gully change in metre squared per square metrefor each year considered.

Nature of Gully Development on Slope
One of the objectives of this study is to generate Digital Elevation Models (DEM) to detect changes and calculate gully dimensions (including slope) of focused gully sites. The South East Nigeria study area is characterized by gentle to steep slopes with extreme slopes also found in certain areas where 20 0 inclines are exhibited. Slope areas less than 20 0 are seen in gentle slope and at areas including river courses, ood plains and hilltop areas. Areas classi ed to have slope of 10 0 and greater, are expected to favour erosion activities based on . The theory suggests correctly that the greater kinetic energy is gained at the plane with the highest slope angles, but the data presented here suggests that this is not as important a driver as theorised. The revelation from this study shows that most of the gullies develop at the base of the slope for example Iyioku and Njaba gullies with slope areas of 15 0 and 9 0 respectively have their upper part of the slope ahead of them with areas ranging from 35 0 to 40 0 at 30 metre resolution. The energy increases down the slope as they converge from lower stream order to higher stream orders at the slope base while carrying eroded materials from deep incisions made at those points. Slope has an effect on run-off and drainage therefore having a profound in uence on the moisture regime of the soil. Studies such as ; Teme (2001) and  have observed that slope generates the runoff that causes gully erosion. These studies were of the opinion that valley topography is also an underlying factor in gully generation, with steeper and longer slopes providing the higher erosion risk. This theory is not debated here but it is strongly proposed that other factors need to be in place before such erosion can occur; the prime driver proposed here being the loss of vegetation. In South-East Nigeria, Ofomata (2001) 95% of the gully erosion sites examined in south-east Nigeria, as part of this study, develop down the hill side areas, determined initially from eld visits and through the overlaying of gully points on calculated slope maps. Gully sites such as Iyioku, Okigwe, Njaba, Umuahia, Ngwo1 and Ngwo2 show that they have developed on slope areas greater than 5 0 at 30m resolution. The slope analysis has shown that gullies in the study area, amongst other gully factors, anchor their development on the nature of the slope, revealing that when every other contributing factor is in place such as vegetation loss, gully stream order and unconsolidated soil, the slope provides the ideal conditions to trigger gully development. Without this characteristic the level of erosion required to form gullies is unlikely to occur. Although not groundbreaking information the multivariate analysis allows the importance of the magnitude of slope to be put in context.

Curvature as an Aspect of Slope in Gully Development
Slope curvatures were determined in conjunction with slope magnitudes. The curvatures of the study area ranged from -5 plan, de ning convexity; to high values of 6 plan characterizing concavity. The relationship between gullies and plan curvatures in south-east Nigeria shows that gully erosion processes commonly occur on concave slopes. Studies by Gobin revealed that 60% of the gullies in southeast Nigeria occur on concave slopes (Gobin et al. 1998) as can be found in Iyioku, Okigwe, Isinweke, Njaba, Ngwo1, and Umuahia, gully sites Figure 52. In terrain analysis, hill and moderate relief can produce curvatures that vary from -0.5 to 0.5; while for mountain, steep, rugged extreme relief, the values can vary between -4 and 4 (Environmental Systems Research Institute 2011). The nature of the landscape can in part be determined through analysis of these curvatures with negative values typically representing gullies and river courses while positive values are more representative of uneroded landscapes (Igwe 1999). This analysis shows that the nature of the topography is in part responsible for gully development in the study area. In the study area for this work the values range between -5 and 6 which signi es a hilly relief Figure 52, therefore, the surface of the area that is receiving high runoff from hilly areas could be the reason for gully development. The high value of 6 shows that the surface is upwardly concave at more cells (a cell is the area covered on the ground and represented by a single pixel) which contributes to generate accelerated ow and theoretically in uence gully development. This helps to explain the development of gullies in the area in combination with other identi ed factors. This nding is supported by ; Bewke (2003) and , where it is observed that the slope geometry of concave hill sides can often contribute signi cantly to soil loss and gully development. Mat et al. (2009) suggested that Gullies in Okigwe Local Government Area of Imo-State, southeast Nigeria, developed through association with particular slope shapes. That such shapes can be determined accurately from Digital Elevation Models of the area with concave formations in general resulting in more severe gullying. This was observed and concurred in this study. Among other gullies, Okigwe gully developed on concave slope to encourage severe gullying and result in high gully area rate of change. In all the 14 surveyed gullies in the study area, 11 are located on concave while 3 are located on convex slopes evidencing the high proportion.
In uence of Aspect (slope direction) on Gully Development The Aspect map produced for the South East Nigeria region is dominated by slopes facing N (337.5 -360) to NW (292.5 -337.5) Appendix III and evident via the histogram. The gullies are not necessarily conforming to this dominance. The South slopes are intermittently the drier and wetter landscape due to the greater exposure to the sun and being inundated by high rainfall due to the Tropical Maritime Air mass moving up from the southern part of Nigeria (Oladipupo 2003). The southern facing slopes are subject to both extremes in the study area. A consequence of this extreme alteration of soild condition is a loss of soil Godwin 2013). Nine of the studied gullies in the study area are developing towards the south facing slopes while the remaining 5 are developing on North facing slopes. This is expected due to the north slopes being exposed to the more drying winds of the Harmattan. Values of aspect map around -1 indicate at surfaces Appendix III. These at areas are areas where oods, areas liable to ooding and where rivers are located. Slopes experience faster geomorphic evolution because of high rainfall received from Tropical Maritime Air Mass as reported in .  and  report that the aspect of a slope can indirectly in uence gully erosion processes, controlling the exposition to several climate conditions (duration of sunlight exposure, precipitation intensity and moisture retention). Although the studied gullies are located in numerous aspect locations, they remain dominated by those on south facing slopes where the climatic extremes are more severe.

Conclusion
Determining Causes of Gully Erosion and Associated Rates of Change in South-East Nigeria, using a Remote Sensing and GIS Methodology, was conceived out of the numerous gully developments, inaccessibility to some and the helplessness of the communities in nding solutions to the rampant problem in southeast Nigeria. The eld work was carried out in southeast Nigeria and the rest of the developmental research was carried out at the University of Brighton. The research work analysed the topography, the land-cover satellite images for a period of 30 years as well as detailed interpretation of 14 gully sites from the study area. This long period of data collection and analysis provided enough information on what has been happening in the past and the anthropogenic activities that are responsible for gully development. The 14 gullies studied in detail, were traced and tracked from 1986 -2015 for the older gullies and for 2006 -2015 for the younger (30Yrs and 10Yrs period respectively). This was primarily to observe their relationship with landcover and topography of the study area.
It was observed from the analysis that as the vegetation of the study area continues to reduce, open lands and gullies continue to develop while new gullies are expected to form. The open land development that was tied to vegetation loss could be responsible for the gully development as can be found from the location of gully points overlaid on classi ed satellite images. In general, the tracking and tracing of the 14 gully sites showed that their occurrence and development positively correlated with vegetation removal for the 30 years' period. Exceptions were found in some year(s) when gullies were being tackled by communities, ministries and agencies to reduce or stop development. The topographical analysis of the study revealed that gullies develop mainly on slope with angle of 5 0 and above, continuous removal of vegetation, soil (deeply weathered, unconsolidated sandy sediments and friable soils) and on high stream order of 1 -4 stream order. Even though the topography of the area is a moderate one, it acts to help the gullies to develop in unconsolidated friable soils which are deeply weathered. The unconsolidated and deeply weathered soil enables gully incision and widening.
Three types of multivariate statistical analysis were utilised to assess and explore the gully factors extracted from the landcover and topographical analysis of the study area with limited success at determining generic driving factors to explain gully behaviour in the region. Cluster analysis, PCA and Multiple Regression were used both on data derived from proprietary and open source software with very similar results. These tests were applied to the data as a means to predict the gully factors similarity, characteristics and relationship to incipient gully generation, development and yearly proportional area gully change. The novel application of this methodology to this setting allows a low-cost GIS and Remote Sensing methodology that can be used for monitoring and quantifying gully erosion and development over time. The geospatial location of the study is appropriate as a low-cost methodology is required to help such areas. The research has determined the change in land-cover classi cation over a maximum 30-year period and quanti cation of gully extent, rates of change and rate of yearly gully change in metre squared per square metre of gully sites over identi ed life spans in a very successful manner which will allow site speci c rather than generic trends to be identi ed. This study has detected changes in gully dimensions in association with Digital Elevation Models (DEM) and Mapped dynamics of deforestation and forest degradation in southeast Nigeria forests using radar satellite data and has successfully identi ed links between gully erosion rate and vegetation removal on the local and regional scale. This work has been conducted with the aims and the objectives earlier stated clear in mind with a key outcome being the success of the open source approach producing similar results to the more illustrious and proprietary counterparts. The signi cance of this being that this work can be replicated in low GDP countries with similar environmental problems.
It can be concluded that there is no single variable responsible across the region for gully formation and generation in the study area. All the identi ed gully variables combine to cause gully development and consistent with the literature they are shown to be driven by different variables in different locations.
What is not disputed in this study is the importance of each of the variables examined on gully formation and subsequent growth with each tested variable identi ed from robust literature analysis and shown statistically to contribute to gully formation with the exception of elevation. By examining and establishing a list of driver variables required for gully formation, this thesis can be used to alert those concerned with gully erosion of the risk factors and drivers of this destructive phenomenon. Most importantly it has provided an accessible route to achieve this. Following the identi ed causes of gullies in the study area which has shown the ability of using remote sensing and GIS to monitor gully development, mitigation measures can now be put in place to prevent further gully development and be able to control already developed ones on a local and regional level and through civilian or governmental pathways.

Recommendations
As a result of the ndings of this study, 3 key recommendations are offered to help in future to mitigate gully formation, generation and development in southeast Nigeria and potentially in any region having similar environmental problems.
1. Retention and in ltration of surface water should be provided in areas where runoff is high to avoid high runoff which erodes the soil from upland. Therefore, since slope, gradient and elevation is natural and cannot be changed, the retention and in ltration of runoff will be very important.
2. Proper land-management practices must be employed to prevent forest res and illegal wood logging, and to avoid openlands development which can evidently lead to gully development. If the vegetal covers are allowed, it may lead to soil stabilisation, rainfall runoff retention and also control the already developed gullies but may not curb their progress entirely.
3. Control of urbanlands (road construction, building structures and mining) which can reduce the effect on soil and vegetation removal to avoid gully development. Since urban development is tied on the use and removal of physical environment and mining which helps to create openlands, it can be reduced and controlled, which will reduce the level of gully formation and development.
(scene ID, path and row 188055, 188056, 189055, and 189056)  Chart using the gradient data of the 14 gullies by category