The construction of Baku-Tbilisi-Ceyhan (BTC) Oil and South Caucasus Gas (SCP) pipelines was completed in 2005. The Azerbaijan section of BTC Oil and SCP Gas pipelines is 442 km long and 44 m wide corridor named as the Right-of-Way. BTC and SCP pipelines are aligned parallel to each other within the same 44m corridor. The construction process of the pipelines significantly disturbed vegetation and soil cover along Right-of-Way of pipelines. The revegetation and erosion control measures were conducted after the completion of construction to restore the disturbed footprints of construction activities. The general goals of the present studies, dedicated to the environmental monitoring of revegetation and planning of erosion control measures were: to evaluate the status of the revegetation in 2007 since the completion of the construction activities in 2005, to determine the climate and ground factors controlling the vegetation regrowth and to predict erosion-prone areas along Right-of-Way of pipelines. Regression and root mean square error analysis between the Normalized Difference Vegetation Index (NDVI) of IKONOS images acquired in 2007 and in-situ estimations of vegetation cover percentage revealed R2 equal to 0.80 and RMSE equal to 6% which were optimal for the normalization of NDVI to vegetation cover. The total area of restored vegetation cover between 2005 and 2007 was 8.9 million sq. m. An area of 10.7 million sq. m. of ground vegetation needed restoration in order to comply with the environmental acceptance criteria. Based on the Global Spatial Regression Model, precipitation, land surface temperature and evapotranspiration were determined as the main climate factors controlling NDVI of grasslands along Right-of-Way of pipelines. In case of croplands, precipitation, evapotranspiration and annual minimum temperature were determined as the main factors controlling NDVI of croplands. The regression models predicting NDVI for grasslands and croplands were also formulated. The Geographically Weighted Regression analyses in comparison with the global regression models results clearly revealed that the relationship between NDVI of grasslands and croplands and the predictor variables was spatially non-stationary along the corridor of pipelines. Even though the observed R2 value between elevation and NDVI of grasslands was low (R2= 0.14), the accumulation of the largest NDVI patterns was observed higher than 150m elevation. This revealed that elevation has non-direct control of NDVI of grasslands through its control of precipitation and temperature along the grasslands of Right-of-Way. The spatial distribution percentage of NDVI classes within slope aspect categories was decreasing in the southern directions of slope faces. Land surface temperature was decreasing with elevation but no particular patterns of land surface temperature in the relationship with NDVI accumulation within the aspect categories were observed. Aspect categories have non-direct control of NDVI and there are some other factors apart from land surface temperature which require further investigations. Precipitation was determined to be controlling the formation of topsoil depth and the topsoil obviously controls the VC growth of grasslands as one of the main ground factors. The regression analysis between NDVI of grasslands and croplands with groundwater depth showed very low correlation. But the clustered patterns of vegetation cover were observed in the relationship with groundwater depth and soil moisture for both grasslands and croplands. The modeling of groundwater depth relative to soil moisture and MODIS NDVI of grasslands determined that the threshold of groundwater depth for vegetation growth is in the range of 1-5 m. MODIS NDVI and soil moisture did not reveal a significant correlation. Soil moisture revealed R2 equal to 0.34 with elevation, R2 equal to 0.23 with evapotranspiration, R2 equal to 0.57 with groundwater depth and R2 equal to 0.02 with precipitation. This allowed to suspect that precipitation is not the main factor controlling soil moisture whereas elevation, evapotranspiration and groundwater depth have non-direct control of soil moisture. Therefore, soil moisture has also non-direct control of vegetation cover growth along the corridor of pipelines. The variations of soil moisture in the 1-3 m soil depth range may have the threshold of depth controlling vegetation cover regrowth and this requires more detailed soil moisture data for further investigations. The reliability of the Global Spatial Regression Model and Geographically Weighted Regression predictions is limited by the MODIS images spatial resolution equal to 250 m and spectral characteristics. The Morgan-Morgan-Finney (MMF) and Universal Soil Loss Equation (USLE) predictions revealed non-similarity in the spatial distribution of soil loss rates along Right-of-Way. MMF model revealed more clustered patterns of predicted critical erosion classes with soil loss more than 10 ton/ha/year in particular ranges of pipelines rather than Universal Soil Loss Equation model with the widespread spatial distribution. Paired-Samples T-Test with p-value less than 0.05 and Bivariate correlation with the Pearson\'s correlation coefficient equal to 0.23 showed that the predictions of these two models were significantly different. Verification of USLE- and MMF- predicted erosion classes against in-situ 316 collected erosion occurrences collected in the period of 2005-2012 revealed that USLE performed better than MMF model along pipeline by identifying of 192 erosion occurrences out of 316, whereas MMF identified 117 erosion sites. USLE revealed higher ratio of frequencies of erosion occurrences within the critical erosion classes (Soil Loss > 10 t/ha), what also showed higher reliability of soil loss predictions by USLE. The validation of quantitative soil loss predictions using the measurements from 48 field erosion plots revealed higher R2 equal to 0.67 by USLE model than by MMF. This proved that USLE-predicted soil loss rates were more reliable than MMF not only in terms of spatial distributions of critical erosion classes but also in the quantitative terms of soil loss rates. The total number of erosion-prone pipeline segments with the identified erosion occurrences was 316 out of 38376. The number of erosion-prone pipeline segments realistically predicted by USLE model e.g. soil loss more than 10 t/ha was 97 whereas MMF predicted only 70 erosion-prone pipeline segments. The regression analysis between 354 USLE and MMF erosion-prone segments revealed R2 equal to 0.36 what means that the predictions by USLE and MMF erosion models are significantly different on the level of pipeline segments. The average coefficients of variation of predicted soil loss rates by USLE and MMF models and the number of accurately predicted erosion occurrences within the geomorphometric elements of terrain, vegetation cover and landuse categories were larger in the USLE model. This supported the hypothesis that larger spatial variations of erosion prediction models can contribute to the better soil loss prediction performance and reliability of erosion prediction models. This also supported the hypothesis that better understanding of spatial variations within geomorphometric elements of terrain, land-use and vegetation cover percentage classes can support in the selection of the appropriate erosion models with better performance in the particular areas of pipelines. Qualitative multi-criteria assessment for the determination of erosion-prone areas revealed stronger relations with the USLE predictions rather than with MMF. Multi-criteria assessment identified 35 of erosion occurrences but revealed more reliable predictions on the level of terrain units. Predicted erosion-prone areas by USLE revealed higher correlation coefficient with erosion occurrences than MMF model within terrain units what proved higher reliability of the USLE predictions and its stronger relation with the multi-criteria assessment.