Land Cover Change Detection Research Articles

Relationship between the character and the changes of the landscape in the Sopron-Fertő area (Hungary)

Assessment of landscape dynamics is a prominent topic of landscape research. These studies refer mainly to administrative areas, given the availability of the data and because of the planning areas. Whereas we can often find areas with significantly different character within these administrative boundaries, showing land-cover changes in the opposite direction. According to our hypothesis, the land-cover change assessments of the administrative area units do not reflect those special conversations, that are bound to the landscape character areas. Change tendencies of the mountains, piedmonts, and lowlands, or intensively developing urban and slow-changing rural areas are often opposite. The real measure and weight of the transformations cannot be presented by the summarised statistics of certain study areas, where diverse landscape character units can be found, even though the maps show the main differences Our case study proved that significantly more exact and differentiated results can be gained by the land-cover change detection referring to the landscape character units.

An integrative climate and land cover change detection unveils extensive range contraction in mountain newts

The global decline of amphibian populations, driven by anthropogenic activities, is a pressing conservation issue, with salamanders being of particular concern, as these species serve as ecological indicators vulnerable to environmental change. Mountain newts of the Neurergus genus, which are endemic to the Zagros Mountain chain from southeastern Turkey to northern Iraq and southwestern Iran, face a multitude of threats. Among these threats, climate change and land use alterations have been identified as major contributors to the decline of these species. Given the varying spatiotemporal scales at which these factors operate, in this study we aimed to assess the impacts of climate and land use/land cover (LULC) changes (LULCC) on the distribution of the Neurergus genus. We employed MaxEnt model to predict their habitat suitability under current climatic conditions. We projected the predicted model to the future, i.e., 2050, under two climate change scenarios. We then proceeded to map the LULC patterns of the identified suitable habitats for each species using Landsat satellite images, and conducted a hindcast of LULCC within these habitats for three time-slices 1988, 2005, and 2020. Finally, we evaluated the efficiency of current network of protected areas (PAs) and key biodiversity areas (KBAs) in Iran, Iraq, and Turkey to cover suitable habitats of the species. Our results revealed that climate changes would negatively influence all Neurergus species, with southern species in Iran and Iraq, i.e., N. derjugini, N. kaiseri, and N. crocatus exhibiting the greatest range loss. Conversely, LULC change detection indicated that northern species in Turkey, i.e., N. strauchii and N. barani, are more exposed to cropland developments and have experienced greatest habitat changes over the past 30 years. Ultimately, our findings underscore the insufficiency of extant conservation areas in protecting Neurergus habitats and urge the need for comprehensive conservation measures. We recommend promoting less strictly conserved areas, e.g., KBAs, implementing trans-boundary conservation plans, and designating new reserves to ensure long-term preservation of amphibians in the regions.

Solving photogrammetric cold cases using AI-based image matching: New potential for monitoring the past with historical aerial images

With the ongoing digitization in archives, an increasing number of historical data becomes available for research. This includes historical aerial images which provide detailed information about the depicted area. Among the applications enabled by these images are change detection of land use, land cover, glaciers, and coastal environments as well as the observation of land degradation, and natural hazards. Studying the depicted areas and occurring 3D deformations requires the generation of a digital surface model (DSM) which is usually obtained via photogrammetric Structure-from-Motion (SfM). However, conventional SfM workflows often fail in registering historical aerial images due to their radiometric characteristics introduced by digitization, original image quality, or vast temporal changes between epochs. We demonstrate that the feature matching step in the Structure from Motion (SfM) pipeline is particularly crucial. To address this issue, we apply the two synergetic neural network methods SuperGlue and DISK, improving feature matching for historical aerial images. This requires several modifications to enable rotational invariance and leveraging the high resolution of aerial images. In contrast to other studies our workflow does not require any prior information such as DSMs, flight height, focal lengths, or scan resolution which are often no more extent in archives. It is shown that our methods using adapted parameter settings are even able to deal with quasi texture-less images. This enables the simultaneous processing of various kind of mono-temporal and multi-temporal data handled in a single workflow from data preparation over feature matching through to camera parameter estimation and the generation of a sparse point cloud. It outperforms conventional strategies in the number of correct feature matches, number of registered images and calculated 3D points and allows the generation of multi-temporal DSMs with high quality.With the flexibility of the method, it enables the automatic processing of formerly unusable or only to be interactively processed data, e.g. aerial images where the flight route is unknown, or with difficult radiometric properties. This makes it possible to go back even further in time, where the data quality usually decreases, and enables a holistic monitoring and comparison of environments of high interest. The code is made publicly available at https://github.com/tudipffmgt/HAI-SFM.

Change detection in VHR Remote Sensing Images by automatic sample selection and progressive classification

ABSTRACT This paper proposes an automatic method for land cover change detection in very-high-spatial-resolution optical remote sensing images based on the automatic selection of training samples using expectation-maximization (EM) and an extreme learning machine classifier, which has two key characteristics: (1) combining the advantages of supervised and unsupervised methods with progressive mask classification. (2) training samples of three classes (changed, unchanged, and unknown) were automatically selected by K-Means and EM, and then further refined by the likelihood function. The method was validated by one dataset of SuperView-1 imagery with a spatial resolution of 2.0 m, two datasets of TripleSat-2 imagery with a spatial resolution of 3.2 m, and one open dataset of Zi-Yuan-3 imagery with a spatial resolution of 5.8 m, and the results were compared with that of three unsupervised methods (iterative slow feature analysis, multivariate alteration detection, and adaptive object-oriented spatial-contextual extraction algorithm), two deep learning methods (convolutional-wavelet neural networks and dual-domain networks), and three supervised classifiers (support vector machine, random forest, and Naive Bayes), showing the effectiveness of this method in decrease of false-positive rates and increase of change detection accuracy. The average and maximum of the accuracy metric F1 score of our method are 0.6842 and 0.8708, respectively; the average F1 score of the unsupervised, deep learning, and supervised methods are 0.5049, 0.4943, and 0.633, respectively.

Index-Based Change Detection, Spatial Zoning and Environmental Criticality of Urban Land Cover: A Spatiotemporal Study on Gazipur Sadar Upazila of Bangladesh

Urbanization has become a comprehensive phenomenon worldwide as well as in Bangladesh. This study has tried to analyze the urban land cover change pattern of Gazipur Sadar Upazila between 2001 and 2021. This study also has made an investigation to classify the urban land cover of Gazipur Sadar Upazila based on categorical spatial zoning and analyze how urbanization affects nearby green spaces to monitor environmental criticality. Multispectral Landsat satellite imageries, population census data, air quality index, and open-ended questionnaire data were used as the operational data. Spectral indices such as NDVI, and IBI, were performed from remotely sensed satellite data. NDVI was used to extract green cover and IBI was used to extract the built-up area. Spatial zoning was done based on existing literature, expert interviews, and RS analysis. Environmental criticality was analyzed by correlating population density and air quality data with the extracted green cover. Between 2001 and 2021, the built-up area expanded by around 77.7 km2 and green cover by about 74.79 km2. Although the green cover flourished, it decreased in Tongi and Konabari. Besides, the south-western region of Gazipur experiences rapid growth in built-up areas. Three classified zones e.g., industrial and manufacturing zone, forested zone, and neutral zone were extracted from criteria-based spatial zoning. The results of green space per capita analysis showed that from 2001 to 2021, green space in the study area decreased by 10-713 m2. Environmental criticality analysis illustrated that in 2001, two regions (Tongi and Konabari) were classified as highly critical, and by 2021, this number had escalated to five (Tongi, Konabari, Kasimpur, Basan, Gaccha). Furthermore, the study uncovered that the criticality in urbanized areas is growing rapidly.

Land Cover Change Detection Based on Lenet-5 by Using Very-High-Resolution Remote Sensing Images

On the basis of remote sensing images, land cover change detection (LCCD) has gained popularity. Even though several techniques have been promoted lately, their effectiveness and usability still need to be improved. To assist urban sustainable development, evaluate natural disasters, and analyze the environment, very high-resolution (VHR) remote sensing images can be used to detect land cover change. Planning professionals can evaluate changes in land cover using remote sensing and geographic information systems, which have a reputation for excellence. A fully convolutional two-stream architecture is used in this paper to first extract highly representative deep features from bi-temporal images. After that, a Lenet5 is given the deep features to segment the land changes and detect changes. With the help of Faster-RCNN the land change classes are classified. We implement the proposed model in Python and conduct experiments on the land cover dataset. The outcomes of the experiments demonstrate that the proposed method outperformed previous deep learning algorithms, achieving the best accuracy, precision, recall, and F1-score.

Change Detection of Land Use and Land Cover Over Ghataprabha River Basin

Change Detection of Land Use and Land Cover Over Ghataprabha River Basin

Assessing the impact of villagization program on land use land cover dynamics in Benishangul-Gumuz, Western Ethiopia

<p>Planning for continuing natural resource management requires current information on the dynamics of land use and land cover. The aim of this paper was to analyze the impacts of the villagization program on land use land cover dynamics in<strong> </strong>Benishangul-Gumuz region, western Ethiopia. The study has employed a mixed-method research design using both primary and secondary sources. Multispectral LANDSAT satellite images with a 30 m resolution were acquired for land use land cover change detection between the years 1999, 2009, and 2022. Arc GIS 10.8, QGIS 3.28, ERDAS Imagine 2014, and Microsoft Excel software were used for image classification, accuracy assessment, and change detection. Six different land use land cover types: forest land, shrub and grassland, cultivated land, residential, bare land, and water bodies were identified between 1999 and 2022. The trends indicated a dramatic decrease at the rate of 27.2 ha of forestland, 17.1 ha of shrub and grassland, and 4.6 ha of water bodies per year, while the share of cultivated land, residential, and bare land has expanded at an average rate of 34.3 ha, 11.7 ha, and 2.9 ha per year respectively between 1999 and 2022. The phenomenon was caused by added population pressure due to villagization program, which in turn triggered farmland expansion and deforestation. It is recommended that raising local community awareness, reforestation, practicing land use plans, and promoting successful livelihood diversification could help to alleviate the issue and reroute the course of events in order to achieve sustainable natural resource management.</p>

Deep Learning-based Land-cover Change Detection in Remote-sensing Imagery

With the significant advancement in deep-learning methods and their feature representation, deep-learning methods are more prevalent in solving change-detection tasks. The prime purpose of change detection is to detect the changes on the surface of the earth. In this work, an end-to-end encoder-decoder architecture is used to detect the changes in the land cover. The proposed method uses residual U-Net to find land-cover image changes. The UNet structure is used as the backbone of the network. The effectiveness of the proposed method has been experimented through LEVIR-CD datasets. The results showed that the proposed method outperforms the state-of-the-art techniques and gives reliable results. These techniques can be used to examine changes in the earth's crest due to natural events, such as landslides, earthquakes, erosion and geo-hazards or human activity, like mining and development. KEYWORDS: Change detection, Remote sensing, Residual UNet, Deep learning, Land cover, Climate.

Development of Machine Learning Algorithms for Riverside Land Cover Classification Using Synthetic Aperture Radar Satellite Imagery and Terrain Data

Riverine environments play a crucial role in maintaining the stability of river ecosystems as well as biodiversity. Furthermore, the appropriate management of small rivers has a significant impact not only on stable water supplies but also on water resource management. Wide monitoring of the riverside environment including land covers and their changes is an important issue in water resource management. This study aims to develop a high-resolution (10 m) model for classifying riverside land cover by integrating Sentinel-1 synthetic aperture radar (SAR) data and terrestrial characteristics using machine learning algorithms. We constructed a total of 3,284 landcover reference point datasets near the four major rivers of South Korea with five classes: water, barren, grass, forest, and built-up. The Random Forest and Light Gradient Boosting Machine classification models were developed using eight input variables derived from SAR signal and digital terrain data. The models showed an overall cross-validation accuracy exceeding 80% while maintaining consistent spatial distributions, except for the barren class. The false alarms on barren would be corrected through additional sampling processes and incorporating optical characteristics in further study. The high-resolution riverside land cover maps are expected to contribute to the establishment of a comprehensive management system for water resources such as riverside land cover change detection, river ecosystem monitoring, and flood hazard management. Furthermore, the utilization of the next generation medium satellite 5 (C-band SAR) would improve the performance of riverside land cover classification algorithm in the future.

Novel Land Cover Change Detection Deep Learning Framework with Very Small Initial Samples Using Heterogeneous Remote Sensing Images

Change detection with heterogeneous remote sensing images (Hete-CD) plays a significant role in practical applications, particularly in cases where homogenous remote sensing images are unavailable. However, directly comparing bitemporal heterogeneous remote sensing images (HRSIs) to measure the change magnitude is unfeasible. Numerous deep learning methods require substantial samples to train the module adequately. Moreover, the process of labeling a large number of samples for land cover change detection using HRSIs is time-consuming and labor-intensive. Consequently, deep learning networks face challenges in achieving satisfactory performance in Hete-CD due to the limited number of training samples. This study proposes a novel deep-learning framework for Hete-CD to achieve satisfactory performance even with a limited number of initial samples. We developed a multiscale network with a selected kernel-attention module. This design allows us to effectively capture different change targets characterized by diverse sizes and shapes. In addition, a simple yet effective non-parameter sample-enhanced algorithm that utilizes the Pearson correlation coefficient is proposed to explore the potential samples surrounding every initial sample. The proposed network and sample-enhanced algorithm are integrated into an iterative framework to improve change detection performance with a limited number of small samples. The experimental results were achieved based on four pairs of real HRSIs, which were acquired with Landsat-5, Radarsat-2, and Sentinel-2 satellites with optical and SAR sensors. Results indicated that the proposed framework could achieve competitive accuracy with a small number of samples compared with some state-of-the-art methods, including three traditional methods and nine state-of-the-art deep learning methods. For example, the improvement rates are approximately 3.38% and 1.99% compared with the selected traditional methods and deep learning methods, respectively.

Land Use and Land Cover Change Detection Using the Random Forest Approach: The Case of The Upper Blue Nile River Basin, Ethiopia.

Monitoring land use change dynamics is critical for tackling food security, climate change, and biodiversity loss on a global scale. This study is designed to classify land use and land cover in the upper Blue Nile River Basin (BNRB) using a random forest (RF) algorithm. The Landsat images for Landsat 45, Landsat 7, and Landsat 8 are used for classification purposes. The study area is classified into seven land use/land cover classes: cultivated lands, bare lands, built-ups, forests, grazing lands, shrublands, and waterbodies. The accuracy of classified images is 83%, 85%, and 91% using the Kappa index of agreements. From 1983 to 2022 periods, cultivated lands and built-up areas increased by 47541 and 1777 km2, respectively, at the expense of grazing lands, shrublands, and forests. Furthermore, the area of water bodies has increased by 662 km2 due to the construction of small and large-scale irrigation and hydroelectric power generation dams. The main factors that determine agricultural land expansion are related to population growth. Therefore, land use and land cover change detection using a random forest is an important technique for multispectral satellite data classification to understand the optimal use of natural resources, conservation practices, and decision-making for sustainable development.

MULTI-TEMPORAL URBAN LAND-USE CHANGE DETECTION AND PREDICTION USING CNN-BASED CA-MARKOV MODEL FROM GAOFEN SATELLITE IMAGES

Abstract. The intelligent interpretation of land-use change has become a research frontier. Reasonably and effectively utilizing limited land resources and making scientific predictions to promote sustainable utilization of land resources is significant for establishing a resource-saving and environmentally friendly society. Remote sensing technology can efficiently complete multi-temporal and dynamic land-use change detection, especially using high-spatial resolution remote sensing images. However, the existing land-use change and prediction have not been combined. In addition, land-use change detection mainly relies on shallow feature design, resulting in low prediction accuracy and weak generalization performance. To solve the above problems, we proposed a CNN-based CA Markov model using multi-temporal GaoFen satellite remote sensing images for the change detection and prediction of land cover. Taking the city of Panzhihua in China as an example, the study constructed training sample data that includes a multi-temporal remote sensing training dataset from 2006, 2010, 2015, and 2021 using GaoFen satellite remote sensing images. Meanwhile, a multitemporal CNN land-use detection model was constructed to generate a land-use transfer matrix by training the dataset. Furthermore, the comprehensive driving factors were selected, including terrain factors (height and slope) and social factors (economic and population density). Then, the CA-Markov model was constructed to predict the land-use development trend in Panzhihua City after ten years. Compared with the traditional methods, experimental results demonstrate that the proposed model can improve the model's automatic interpretation ability and prediction accuracy with an increase of 24.6% in the FoM index and 4.37% in the Kappa coefficient.

Detection of Land Use and Land Cover Change in Jammu District using Remote Sensing and GIS Techniques

Abstract: Jammu District has under gone a massive change in its land use and land cover in last 30 years. It has grown 2.5 times in its population in past 3 decades and simultaneously the infrastructure supporting the population growth has increased altering the overall land cover. With the growing population the demand for arable land, infrastructure development, land resource management becomes an obligation for effective optimization of resources and policy formulation and implementation. In this research the land use land cover change detection and analysis in last 30 years from 1993 to 2023 is attempted for the first time. The classification is achieved by supervised image classification of Landsat 5 and Landsat 9 Level 1 imagery acquired from USGS. The classification scheme is developed for five classes viz. Water Bodies, Built-Up Area, Agriculture, Barren Land and Forests. Change detected is -3% for water bodies, +5% for built-up area, +29 % for agricultural land, -22% for barren land, -9% for forest cover. The percentages are calculated of the total area. The overall accuracy is maintained at 87% for year 1993 and 78% for year 2023.

Implementation of Super Resolution Techniques in Geospatial Satellite Imagery

The potential for more precise land cover classifications and pattern analysis is provided by technological advancements and the growing accessibility of high-resolution satellite images, which might significantly improve the detection and quantification of land cover change for conservation. A group of methods known as "super-resolution imaging" use generative modelling to increase the resolution of an imaging system. Super-Resolution Imaging, which falls under the category of sophisticated computer vision and image processing, has a variety of practical uses, including astronomical imaging, surveillance and security, medical imaging, and satellite imaging. As computer vision is where deep learning algorithms for super-resolution first appeared, they were mostly created on RGB images in 8-bit colour depth, where the sensor and camera are separated by a few meters. But no evaluation of these methods has been done.

A Review of Practical AI for Remote Sensing in Earth Sciences

Integrating Artificial Intelligence (AI) techniques with remote sensing holds great potential for revolutionizing data analysis and applications in many domains of Earth sciences. This review paper synthesizes the existing literature on AI applications in remote sensing, consolidating and analyzing AI methodologies, outcomes, and limitations. The primary objectives are to identify research gaps, assess the effectiveness of AI approaches in practice, and highlight emerging trends and challenges. We explore diverse applications of AI in remote sensing, including image classification, land cover mapping, object detection, change detection, hyperspectral and radar data analysis, and data fusion. We present an overview of the remote sensing technologies, methods employed, and relevant use cases. We further explore challenges associated with practical AI in remote sensing, such as data quality and availability, model uncertainty and interpretability, and integration with domain expertise as well as potential solutions, advancements, and future directions. We provide a comprehensive overview for researchers, practitioners, and decision makers, informing future research and applications at the exciting intersection of AI and remote sensing.

U-Net-STN: A Novel End-to-End Lake Boundary Prediction Model

Detecting changes in land cover is a critical task in remote sensing image interpretation, with particular significance placed on accurately determining the boundaries of lakes. Lake boundaries are closely tied to land resources, and any alterations can have substantial implications for the surrounding environment and ecosystem. This paper introduces an innovative end-to-end model that combines U-Net and spatial transformation network (STN) to predict changes in lake boundaries and investigate the evolution of the Lake Urmia boundary. The proposed approach involves pre-processing annual panoramic remote sensing images of Lake Urmia, obtained from 1996 to 2014 through Google Earth Pro Version 7.3 software, using image segmentation and grayscale filling techniques. The results of the experiments demonstrate the model’s ability to accurately forecast the evolution of lake boundaries in remote sensing images. Additionally, the model exhibits a high degree of adaptability, effectively learning and adjusting to changing patterns over time. The study also evaluates the influence of varying time series lengths on prediction accuracy and reveals that longer time series provide a larger number of samples, resulting in more precise predictions. The maximum achieved accuracy reaches 89.3%. The findings and methodologies presented in this study offer valuable insights into the utilization of deep learning techniques for investigating and managing lake boundary changes, thereby contributing to the effective management and conservation of this significant ecosystem.

A Systematic Review on Advancements in Remote Sensing for Assessing and Monitoring Land Use and Land Cover Changes Impacts on Surface Water Resources in Semi-Arid Tropical Environments

This study aimed to provide a systematic overview of the progress made in utilizing remote sensing for assessing the impacts of land use and land cover (LULC) changes on water resources (quality and quantity). This review also addresses research gaps, challenges, and opportunities associated with the use of remotely sensed data in assessment and monitoring. The progress of remote sensing applications in the assessment and monitoring of LULC, along with their impacts on water quality and quantity, has advanced significantly. The availability of high-resolution satellite imagery, the integration of multiple sensors, and advanced classification techniques have improved the accuracy of land cover mapping and change detection. Furthermore, the study highlights the vast potential for providing detailed information on the monitoring and assessment of the relationship between LULC and water resources through advancements in data science analytics, drones, web-based platforms, and balloons. It emphasizes the importance of promoting research efforts, and the integration of remote sensing data with spatial patterns, ecosystem services, and hydrological models enables a more comprehensive evaluation of water quantity and quality changes. Continued advancements in remote sensing technology and methodologies will further improve our ability to assess and monitor the impacts of LULC changes on water quality and quantity, ultimately leading to more informed decision making and effective water resource management. Such research endeavors are crucial for achieving the effective and sustainable management of water quality and quantity.

Automatic labelling for semantic segmentation of VHR satellite images: Application of airborne laser scanner data and object-based image analysis

The application of deep learning methods to remote sensing data has produced good results in recent studies. A promising application area is automatic land cover classification (semantic segmentation) from very high-resolution satellite imagery. However, the deep learning methods require large, labelled training datasets that are suitable for the study area. Map data can be used as training data, but it is often insufficiently detailed for very high-resolution satellite imagery. National airborne laser scanner (lidar) datasets provide additional details and are available in many countries. Successful land cover classifications from lidar datasets have been reached, e.g., by object-based image analysis. In the present study, we investigated the feasibility of using airborne laser scanner data and object-based image analysis to automatically generate labelled training data for a deep neural network -based land cover classification of a VHR satellite image. Input data for the object-based classification included digital surface models, intensity and pulse information derived from the lidar data. The resulting land cover classification was then utilized as training data for deep learning. A state-of-the-art deep learning architecture, UnetFormer, was trained and applied to the land cover classification of a WorldView-3 stereo dataset. For the semantic segmentation, three different input data composites were produced using the red, green, blue, NIR and digital surface model bands derived from the satellite data. The quality of the generated training data and the semantic segmentation results was estimated using an independent test set of ground truth points. The results show that final satellite image classification accuracy (94–96%) close to the training data accuracy (97%) was obtained. It was also demonstrated that the resulting maps could be used for land cover change detection.

Assessment of soil erosion hazard and its relation to land use land cover changes: Case study from alage watershed, central Rift Valley of Ethiopia

Soil erosion by water and wind is among the most crucial land degradation processes in Ethiopia. This is also the case for Alage watershed located in the cental Rift Valley system. This study aimed at assessment of soil erosion hazard and its relation to land use land cover change in the watershed during the period from 1984 to 2016 for a better land management. The study is based on application of Remote Sensing (RS) and Geographical Information System (GIS) to extract inputs factor values for the Revised Universal Soil Loss Equation (RUSLE). Time-series satellite imageries of Landsat TM 1984, ETM+ 2000 and OLI 2016 were used for land use land cover change detection and determination of cover management (C) factor of the RUSLE. Biophysical data such as rainfall, soil properties, land management practices including soil and water conservation measures within the watershed were collected using field survey and secondary data sources. Slope steepness and slope length factors were derived using Digital Elevaition Model (DEM). Long-term average annual soil loss rates were estimated by the RUSLE integrated with GIS for 1984, 2000 and 2016. Using satellite imageries, the land use land cover and changes within the watershed during the three periods were obtained through a supervised classification with maximum likelihood algorithim. The results of land use land cover change indicated that the proportion of rain-fed cropland, bare land and built up areas increased by 17.4%, 5.9% and 2.9% respectively over the three study period. In contrast the proportion of bush/shrub land, irrigated cropland, grass land, forested areas and waterbodies decresaed by 15.5%, 4.7%, 3.4%, 2.3% and 0.3% respectively during the same period. Estimated average annual soil loss rates showed an increasing trend from 24.3 ton ha−1 yr−1 in 1984 to 38 ton ha−1 yr−1 in 2016. Increasing trends of average annual soil loss rate is attributed to increased proportion of cropland, bare land and built up areas during those periods leading to decreased protective vegetation cover. Hotspot areas within the watershed require implementation of land management practices to prevent further degradation and expansion of gullies. This study is relevant to demonstrate environmental implication of land use land cover change for future land management practices and land use policy in the Rift Valley of central Ethiopia.