Effects of soil and water conservation on vegetation cover: a remote sensing based study in the Middle Suluh River Basin, northern Ethiopia
© The Author(s) 2017
Received: 25 May 2017
Accepted: 28 November 2017
Published: 21 December 2017
Soil and water conservation (SWC) has been implemented in the Tigray Region of Ethiopia since 1985. Besides this, the agricultural development strategy of the region which was derived from the national agricultural development led industrialization strategy formulated in 1993 was focused on natural resources rehabilitation and conservations. Accordingly, each year a 20-days free labor work on SWC activities were contributed by the rural communities. Other programmes such as productive safety net programmes, and sustainable land management project were deploying their resources aiming to reverse the degraded landscape in the region.
Multi-temporal remote sensing data of landsat imageries were used for estimating the normalized difference vegetation index, soil adjusted vegetation index (SAVI) and land surface temperature (LST) for the years 1985, 2000 and 2015. Long-term station based data on daily precipitation started from 1973 was aggregated to derive average annual precipitation (AAP) into three sections to correspond with the processed image data. The precipitation data then converted into raster format using the inverse distance weight interpolation method. The analysis was done using ENVI 5.3 software and results were mapped in ArcGIS 10.3 package. The correlation between AAP and SAVI; LST and SAVI was evaluated on village polygon based as well as pixel-by-pixel.
The results based on village polygons show that there is statistical significant inverse relationship between SAVI and LST in all the study periods. The correlations between AAP and SAVI pixel-by-pixel were r = − 0.14 in 2015 and r = 0.06, r = 0.25 for 2000 and 1985 respectively. In 1985, the total area with SAVI ≥ 0.2 was 23.57 km2. After 15 years (from 1985 to 2000), the total area with SAVI ≥ 0.2 increased to 64.94 km2. In 2015, the total area of SAVI with values ≥ 0.2 reached 67.11 km2, which is a 3.3% increment from year 2000.
Based on the field observation and the remote sensing analysis results, noticeable gain in vegetation cover improvement have been observed in the 30 years period. These improvements are attributable to the implementation of integrated SWC measures particularly in areas where exclosure areas were defined and protected by the local community. Therefore, this study concludes by providing a theoretical bases and an indicator data support for further research on vegetation restoration for the entire region.
Geographic information system (GIS) and remote sensing (RS) have become fundamental tools for characterizing watersheds and landscapes. Remote sensing is one of the most widely used technologies for discerning effective correlations of ecosystem properties via the reflectance of light in the spatial and spectral domain. Remote sensors, such as Landsat, SPOT, IKONOSs, MODIS and Quickbird, capture the reflectance from ground objects like vegetation which have their own unique spectral characteristics. The spectral signatures of photosynthetically and non-photosynthetically active vegetation show clear differences and are used to estimate the forage quantity and quality of grass prairie (Beeri et al. 2007) and vegetation density. Moderate to high resolution data are being extensively used at varying scales from local to regional landscapes for assessment of the ecosystem processes (Chawla et al. 2010).
In this investigation, the relationships between SAVI and LST; SAVI and long term AAP was assessed in the years 1985, 2000 and 2015 for the Middle Suluh River Basin in northern Ethiopia, thereby providing useful information about the effects of soil and water conservation on vegetation cover improvement.
The method of LST–NDVI space with standard meteorological data, as well as remote sensing data were combined by Moran et al. (1994), to estimate the water deficit index (WDI). However, the combination of SAVI and LST; NDVI and LST; SAVI and precipitation pixel-by-pixel bases can provide information about vegetation and moisture condition of the Earth surface. The major information used was the wavelengths of the thermal region, the visible/NIR region and station records of rainfall, which were assumed to be satisfactory for monitoring vegetation conditions.
Land degradation was a serious problem in the Tigray Region with severe denudation of vegetation cover, depletion of soil fertility, and deterioration of surface and ground water potential (Berhanu et al. 2003). In order to reduce the extent of such problems, substantial rehabilitation work has been done through SWC practices. Some studies claim that SWC practices in the Tigray Region were started between 1975–1991 during the Tigray People’s Liberation Front (TPLF) movement with the effective mobilization of the rural communities (Carolyn and Kwadwo 2011). Another study by Esser et al. (2002) indicated that SWC was introduced with the assistance of donors, following the drought in Wello and Tigray in 1976. The agricultural development strategy of the region which was derived from the national Agricultural Development Led Industrialization (ADLI) strategy formulated in 1993 (Dercon and Zeitlin 2009) was focused on the rehabilitation, conservation and development of natural resources, and is known as a conservation-based agricultural development policy (Berhanu et al. 2003). As a policy emphasis, major strategies were designed in the region during the early 1990s for integrated soil and water conservation activities (Negusse et al. 2013). Environmental rehabilitation practices such as establishment and development of area exclosures and community woodlots; construction of check-dams; stone terraces; soil bunds; enforcement of rules and regulations for grazing areas; application of manure and compost were then implemented throughout the region (Berhanu et al. 2003; Carolyn and Kwadwo 2011).
Based on the socio-economic survey of 246 sample household heads (HH) in the study area, the average number of years a farmer practiced SWC was 23 years. The large proportion of the interviewed HHs (95.5%) reported that there was a declining in soil erosion and an improvement in vegetation cover over the past years. A study conducted by Nyssen et al. (2009) in the northern highlands of Tigray shows that it is possible to reverse environmental degradation through an active, farmer-centered SWC policy. Most of SWC focused studies conducted in northern Ethiopia looks at the effects to soil loss and run-off (Taye et al. 2013; Gebremichael et al. 2005; Selassie et al. 2015) and food security (Van der Veen and Tagel 2011). From this perspective, we can quantify the vegetation cover improvement attributed to from the effects of SWC by using GIS and RS application in a basin which is not previously studied.
Study area description
The study area is dominated by five soil types, namely Leptosol (37.6%), Luvisol (22.6%), Cambisol (22.8%), Regosol (14.7%) and Fluvisol (2.3%). The economy of the households was based on agricultural production, and is mainly dependent on rainfed agriculture. Some household’s practice small scale surface irrigation via micro dams and hand dug well water sources. According to FAO (2006) slope classification, 60% of the topography within the basin is flat to gently sloping and the remaining 40% from strongly sloping to very steep. Based on altitude, temperature and precipitation parameters, the agro-ecology of the area is described as warm temperate (Woina dega) zone (58%); and temperate (Dega) zone (42%). The mean annual rainfall from three stations around the study area for the period 2006–2015 was 536 mm with uneven distribution and the mean annual temperature is 18.7 °C.
Normalized difference vegetation index
Many vegetation indices have been developed to assess vegetation conditions. Among them, the normalized difference vegetation index (NDVI), which was proposed by Rouse et al. (1973), is a numerical indicator that uses the visible and near-infrared bands of the electromagnetic spectrum to analyze whether the target area contains live green vegetation or not. Healthy vegetation absorbs most of the visible light that falls on it, thereby reflecting a large portion of the NIR.
Landsat data used in the analysis and their specification
Spatial resolution (m)
26 February 1985
15 March 2000
Landsat 8 (OLI)
13 February 2015
The NDVI value falls between − 1 and + 1, where increasing positive values indicate increasing green vegetation and negative values indicate non-vegetated surface features such as water, barren land, ice, snow, or clouds (Sahebjalal and Dashtekian 2013). The NDVI of 1985 indicates that there was more vegetation cover in the northern part than in the middle and southern part of the study area. However, in 2000, the density of greenness radically decreased in the northern part while it improved towards the southern section of the study area. In 2015, the density of greenness showed a dramatic increase in the southern part where parts of Kilte Awulaelo district is located (Fig. 2). During field observation, it was learned that in this part of the valley free grazing was prohibited by community agreed by-laws.
Retrieval of LST from Landsat images
Conversion of DN values into radiance
Conversion of radiance to brightness
Conversion of radiance to reflectance
Estimating proportion of vegetation and emissivity
Weng (2009) noted that emissivity for ground objects from passive sensors like Landsat has been estimated using different techniques such as the (1) NDVI method; (2) classification-based estimation, and the (3) temperature-emissivity separation model. These techniques are applicable to separate temperature from emissivity, so that the effect of emissivity on estimated LST’s can be determined. Hence for this study, Eq. (6) shows that surface emissivity on pixel based remote sensing is derived using the NDVI method in conjunction with proportional vegetation (Pv) cover (Valor and Caselles 1996).
LST is very important not only for soil development and erosion studies, but also to estimate amounts of vegetative cover and land cover changes (Li et al. 2013). This is because the natural phenomena on the Earth’s surface have no homogeneous characteristics in terms of land surface emissivity. It is true that surface emissivity is highly dependent on the type of vegetation cover, roughness of the topography and soil and mineral composition of the Earth surface.
Using this approach, the land surface emissivity of the three Landsat images (1985, 2000 and 2015) were calculated for further computation of land surface temperature (LST) of the study area. The LST results are all in degree celsius. For Landsat 5 (year 1985) and Landsat 7 ETM+ (year 2000), band 6 was used from the Thermal Infrared Sensor. For Landsat 8 OLI (year 2015), bands 10 and 11 from the thermal infrared sensor (TIRS) were also used. The land surface temperature (LST) is the radiative skin temperature of the ground which depends on albedo, vegetation cover and soil moisture of the land surface (Suresh et al. 2016).
Land surface temperature (LST)
Soil adjusted vegetation index
The AAP was computed for long-term seven gauge stations distributed within and outside the study area from 1973 to 2015. Using the spatial analyst tool in ArcGIS, inverse distance weight (IDW) interpolation technique was employed to generate a surface of mean precipitation on pixel basis. These data were then used for further regression analysis to examine relationships with the SAVI results.
Results and discussion
Interpretation of AAP distribution and SAVI
From Fig. 3 (bottom) it can be revealed that there was high density of SAVI coverage in 1985 mainly in the northern part of the study area. This area was characterized with more of flat topography covered with grasses and gradually converted into agricultural lands. In the middle part where there is more exposed granitic rocks at the ground shows less density of SAVI while it slightly increases in the southern part. After 15 years in 2000, the SAVI distribution shows low density throughout, while it shows a relatively increase in the southern part of the study area. In 2015, the highest density of SAVI was distributed in the southern part of the study area (Fig. 3, bottom) where high vegetation restoration was observed during conducting the fieldwork assessment. During the discussion made with the village administrators and agricultural development agents, they have agreed that the improvement is in the effect of community based SWC activities. One of the villages located in the study area called Abraha Atsibaha was evidenced as winner of the 2012 UNDP Equator Prize at Rio de Janeiro in recognition of outstanding success to restoration of degraded landscape through SWC practices (Kahsai 2015).
Similarly in Fig. 5e, a woman is harvesting grass from the area exclosure for cattle feed. In Abraha Atsibaha and May Kuha villages where the highest mean SAVI values are observed, free grazing was seriously restricted. Farmers have set their own communal resource use bylaw locally called “Sirit”, and practically implemented it in the area. Therefore, zero grazing was used as the most beneficial land rehabilitation mechanism and farmers are allowed to harvest grasses without limit from all area exclosures, hillside terraces and other protected areas. Park et al. (2013) addressed that area exclosure is one of SWC practice considered a well-known management tool to restore vegetation cover and in turn increase soil organic matter.
In general, the average annual increment rate observed over a period of 30 years (1985–2015), using SAVI image, was 6.2%. The vigorous SWC activities performed throughout country are also evidenced by EBI (2014) for the rehabilitation and restoration of degraded areas. This resulted in increased vegetation cover and enhancement of biodiversity.
Interpretation of LST and SAVI
Relationship of NDVI with LST and vegetation abundance
The relationship between NDVI and LST was investigated for each period (1985, 2000, and 2015) through regression analysis. The regression was performed from the mean values extracted in the zonal statistics in ArcGIS. Within the study river basin, there are 28 tabias situated some fully and some partially within the boundary.
Linear regression and correlation coefficients for the relationship between LST and SAVI in 1985, 2000, and 2015
Y = 0.615 − 0.013x
Y = 0.557 − 0.010x
Y = 0.375 − 0.005x
Y = 0.275 − 0.004x
Y = 0.282 − 0.003x
Y = 0.275 − 0.004x
Similarly, a regression of NDVI in 2000 as dependent and LST in 2000 as an independent variables was carried out. The linear regression established shows that LST is statistically inversely significantly predicted NDVI, F(1,26) = 18.21, p < 0.0005, (= 0.000) and LST accounted for 64% of the explained variability in NDVI. The regression equation was expressed as NDVI_2000 = 0.557 − 0.01 LST_2000 (Table 2).
For 1985, a linear regression was run to predict NDVI in Landsat images from LST of similar period. The independent variable (LST) has statistically significantly predicted NDVI, F(1,26) = 8.6, p < 0.05, R 2 = 0.249. The LST accounted for 22.9% of the explained variability in NDVI. The regression equation has been derived as NDVI_1985 = 0.375 − 0. 005 LST_1985 (Fig. 9c).
As Evans (1996) suggested for the value of r, the Pearson correlation coefficient in 2000 shows a strong correlation between NDVI and LST (Table 2). In the year 1985 and 2015, the correlation coefficient was classified as a moderate correlation between NDVI and LST. Similar results were attributed for the relationship between NDVI and LST by Yue et al. (2007) using Landsat 7 ETM+ data in Shanghai, and by Sahana et al. (2016) using Landsat 5 TM and Landsat 8 OLI in the Sundarban Biosphere Reserve, India; Karnieli et al. (2010).
Relationship of SAVI with LST using village polygons
Accordingly, a regression of SAVI in 1985 as a dependent and LST in 1985 as an independent variable was carried out to see the relationship. The linear regression established shows that LST is statistically inversely significant predicted SAVI, F(1,26) = 10.3, p < 0.005, (= 0.004) and LST accounted for 28.3% of the explained variability in SAVI. The regression equation was derived as SAVI_1985 = 0.275 − 0.004 LST_1985.
Likewise, a simple linear regression was conducted to exhibit the relationship between SAVI and LST for every village polygons in the year 2000. The results are shown in Table 2 where Y is the mean SAVI associated with the village polygons and X is LST associated with the village polygons constructed under zonal statistics in ArcGIS software. The result indicates that at 95% confidence interval, LST is statistically significant predicted SAVI, F(1,26) = 9.06, p < 0.05, (= 0.006) and LST accounted for 25.8% of the explained variability in SAVI. The regression equation was presented as SAVI_2000 = 0.282 − 0.003 LST_2000.
The relationship between SAVI and LST for the period of 2015 was also computed. The significant regression was checked through a t-test α = 0.05. The result revealed that there was not statistically significant predicted SAVI, F(1,26) = 2.67, p > 0.05, (= 0.114) and LST accounted for only 5.83% of the explained variability in SAVI which is relatively lower than in the year 1985 and 2000. The fitted line plot for the linear model was SAVI_2015 = 0.279 − 0.004 LST_2015. Like the NDVI and LST relationship tested previously, SAVI also exhibited an inverse relationship with LST. This was similar with the result found by Badreldin and Goossens (2015) who studied for monitoring mitigation strategies effects on desertification change in an arid environment. This means that areas with high vegetation density are represented with a low surface temperature and vice versa.
With the Pearson’s correlation classes suggested by Evans (1996) there was negatively weak relationship between SAVI and LST in 2015 and where as in the year 1985 and 2000, a negatively moderate relationship was observed (Fig. 10a–c; Table 2).
Relationships between SAVI and LST; SAVI and AAP pixel by pixel
Linear regression and correlation coefficients for the relationship between SAVI and LST; SAVI and AAP in 1985, 2000, and 2015 pixel-by-pixel
Y = 0.331 − 0.0057x
Y = 0.352 − 0.0052x
Y = 0.193 − 0.0011x
Y = 0.287 − 0.0002x
Y = 0.138 − 0.00004x
Y = 0.051 − 0.0002x
However, the significant increase of vegetation density in the study area could be due to other factors: (1) effect of appropriate SWC practices implemented to rehabilitate the degraded landscape; (2) vegetation water use efficiency; (3) the impact of zero grazing for protection of area exclosure. In a Reuters news report written by Whiting (2017) as cited from Chris Reij, desertification expert at the World Resources Institute, addresses that the Tigray Region of Ethiopia is now greener than it has ever been during the last 145 years and the improvement of the vegetation cover is not due to an increase in rainfall, but due to human investment in restoring degraded land to productivity. For this reason, Ethiopia’s Tigray Region won gold in a U.N.-backed award in 2017 for the world’s best policies to combat desertification and improve fertility of dry lands (Whiting 2017). Davenport and Nicholson (1993) observed the notable inconsistencies in the vegetation index and rainfall associations that argued the relationships between precipitation and NDVI are not direct and causal. Contrarily, Kassie et al. (2008) argued that physical-based SWC measures did not have a positive impact but reduced yield and biomass in the high-rainfall areas of the Ethiopian highlands compared with non-conserved plots.
Studies revealed that SWC has been implemented in the Tigray Region, of northern Ethiopia since 1985. The implementation was more effective from the early 90s due to the more emphasis given by the government towards land rehabilitation. The implementation of SWC in the region as a strategy was to reduce run-off, improve soil fertility and finally reverse the degraded landscape for the betterment of the rural livelihood. This study evaluated changes in vegetation cover following the implementation of SWC measures. Satellite images were used to generate SAVI and LST, whereas long-term AAP records were also used to account for the effects of precipitation. The implementation of different forms of SWC activities, such as area exclosure, stone terraces, soil bunds, contour ditches, moisture retention reservoirs and check dams are an optimal solution to reverse the vegetation degraded landscape of arid and semi-arid regions in Ethiopia. The supplemental survey made in the study area asserts that 95% of the respondents observed a vegetation cover improvement in their locality over the last 25 years. This was due to the proper implementation of SWC, particularly the practice of area exclosure in protecting from human and livestock interference for better restoration. When degraded landscape protected with different SWC practices, run-off will reduce, infiltration capacity will increase, which retain soil moisture and finally improve vegetation density. In order to achieve such results, the involvement of local communities at all processes in the conservation program is essential. On this matter, Bewket (2007) argued that the success of any SWC intervention depends on the extent to which the introduced conservation technologies are accepted and adopted by the farmers.
The pixel-by-pixel correlation between SAVI and long-term AAP explained better estimates as compared to the village polygon results. Even though the AAP distribution shows a declining trend over the 30 years of study period, the vegetation cover shows an increasing trend. This was proved statistical inversely significant correlation between SAVI and AAP (r = − 0.135) in the year 2015. This clearly indicates that the significant increase in vegetation cover was not the result of precipitation rather other factors like the integrated SWC practices applied in the area contributes significantly. When appropriate SWC techniques were applied, runoff can be reduced and instead the infiltration rate and water holding capacity of the soil can be improved. To assert such a result, similar studies shall be done in other SWC practiced areas and their results will be compared for a better conclusion.
It is recommended that the implementation, protection and follow-up of SWC activities require the direct involvement of rural communities at all stages for the better and sustainable restoration of vegetation cover. The study has shown that SAVI and LST derived from Landsat images in different periods, and AAP of long-term station measurements are useful data when analyzing the relationship between precipitation and vegetation cover and detecting vegetation cover improvement.
SH has made substantial contribution in conception design, acquisition of data, interpretation of results and leading the overall activities of the research; WB and JL have been involved in guiding the principal author and critically commenting the manuscript. Both have given approval of the current version to be published. All authors read and approved the final manuscript.
The authors would like to thank TRECCAfrica II for providing scholarship to the corresponding author to study Ph.D. programme at the Institute of Resources Assessment, University of Dar es Salaam, Tanzania. The USGS website that allowed the authors to download the Landsat images freely from their archives should also be acknowledged. The authors would also like to thank the Metrological agency of Mekelle branch for providing us climatic data; and local community of the study area for their cooperation during the fieldwork. Moreover, many thanks to Dr. Haile Muluken and Mr. Ramzy Bejjani for proof reading and editing the manuscript. Finally, special thanks go to the anonymous reviewers which helped to improve this manuscript.
The authors declare that they have no competing interests.
Availability of data and materials
Authors declare that the data and materials presented in this manuscript can be made publically available by Springer Open as per the editorial policy.
Consent for publication
Ethics approval and consent to participate
The first author is also grateful to Mekelle University for granting research fund under Registration Number CRPO/CSSL/PhD/003/08; and to Association of African Universities (AAU) for awarding small grants for thesis writing.
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