Analysing land use/land cover changes and its dynamics using remote sensing and GIS in Gubalafito district, Northeastern Ethiopia

Assessing the effectiveness of integrated watershed management practices and suggesting innovative strategies in southern Ethiopia

Integrated watershed management plays a vital role in promoting sustainable water resource management and addressing environmental challenges. This study aims to analyze and assess the effectiveness of existing IWM practices and develop new strategies to improve watershed management. The data collection process encompassed comprehensive field observations, surveys, and consultations with the stakeholders. According to a hydrometer test, loam soil was the average dominant soil type in Elgo and Kola shell kebele. The assessment of existing soil water conservation initiatives adhered to the rigorous standards set by the Ministry of Agriculture. From 2016 to 2022, Elgo Kebele saw significant land use changes: agriculture expanded by 11.24 %, bare land by 2.05 %, water bodies by 1.79 %, and settlements by 0.54 %, while forests declined by 15.34 %. In Kola Shele, agriculture, water bodies, and settlements slightly increased by 0.5 %, 1.03 %, and 0.033 %, respectively, with decreases in bare land (1.82 %) and forest (0.05 %). Only 25 % of sampled plots met the criteria for effective soil water conservation systems, indicating challenges in current practices. For cultivated land with less than a 15 % slope and vertisol, recommended conservation practices include broad bed and furrow, conservation tillage, grass strips, grassland improvement, and mulching. For slopes greater than 50 %, hillside terracing, graded bunds, and trenches are advised. Additional measures, such as water harvesting, grass waterways, revegetation, and actions against illegal farming, were proposed. In summary, this study highlights the urgent need for improved IWM practices, and used to enhance watershed management, address environmental and socio-economic issues, and promote sustainable land use in the study.

Effects of participatory forest management programs on Land use/land cover change and its Determinants in Alle District, southwest Ethiopia

In order to create sustainable conservation policies for biodiversity, it is imperative that participatory forest management (PFM) be assessed. Forests contribute to the sustainability of the planet by controlling soil erosion in agricultural areas and by moderating the effects of climate change. However, Ethiopia's forest resources have been under intense pressure because of the increased demand for wood products and agricultural conversion. As one of the potential solutions, the PFM programme was implemented in 1990. This study set out to investigate the effects of the PFM programme on land use and land cover (LULC) in the Alle district of southwest Ethiopia, as well as the variables influencing community involvement and the obstacles to PFM implementation and community involvement. Changes in forest cover were detected using Landsat images from 1992, 2012, and 2022 obtained from Thematic Mapper (TM), Enhanced Thematic Mapper (ETM+), and Operational Land Imager (OLI). Images were obtained during the dry season and were cloud-free. A total of 240 respondents were chosen by means of a straightforward random sampling technique, and survey data were collected using questionnaires, interviews, and field observations. Data were analyzed using ArcGIS 10.5, ERDAS Imagine 2015, SPSS version 20, and Excel 2010. The change in forest cover shows an increasing trend from 2012 to 2022. Again, grassland and wetland coverage in this study decreased rapidly. In the years 2012-2022, forest land increased from 462.7ha (74.8 %), to 569.8ha (92.1 %), while, the agricultural land, grassland, and wetland were reduced from 109.5ha (17.7 %) to 37.8ha (6.1 %), 31.9ha (5.2 %) to 0.0ha (0.0 %); 14.1 ha (2.3 %), to 10.8 ha (1.7 %) respectively. There have been beneficial developments in the forests over the last 30 years. The binary logistic regression model disclose that, land ownership had a negative impact on forest management participation, while other factors such as gender, education level, family size, TLU, access to credit, training, and law enforcement had a positive and significant (p < 0.05) effect on PFM practices. LULC change in study area causes rapid wetland ecosystem deterioration, which may result in the extinction of the most significant and ecologically valuable species and a loss of biodiversity in the environment. In this context, developing an integrated participatory approach requires rapid attention, and all farmers and stakeholders must be actively involved in PFM programs.

An evaluative technique for drought impact on variation in agricultural LULC using remote sensing and machine learning

Drought events threaten freshwater reservoirs and agricultural productivity, particularly in semi-arid regions characterized by erratic rainfall. This study evaluates a novel technique for assessing the impact of drought on LULC variations in the context of climate change from 2018 to 2022. Various data sources were harnessed, encompassing Sentinel-2 satellite imagery for LULC classification, climate data from the CHIRPS and AgERA5 databases, geomorphological data from JAXA's ALOS satellite, and a drought indicator (Vegetation Health Index (VHI)) derived from MODIS data. Two classifier models, namely gradient tree boost (GTB) and random forest (RF), were trained and assessed for LULC classification, with performance evaluated by overall accuracy (OA) and kappa coefficient (K). Notably, the GTB model exhibited superior performance, with OA > 90% and a K > 0.9. Over the period from 2018 to 2022, Fez experienced LULC changes of 19.92% expansion in built-up areas, a 34.86% increase in bare land, a 17.86% reduction in water bodies, and a 37.30% decrease in agricultural land. Positive correlations of 0.81 and 0.89 were observed between changes in agricultural LULC, rainfall, and VHI. Furthermore, mild drought conditions were identified in the years 2020 and 2022. This study emphasizes the importance of AI and remote sensing techniques in assessing drought and environmental changes, with potential applications for improving existing drought monitoring systems.

LULC Assessment and Green Infrastructure Conservation in residential neighborhoods: a case of FESTAC Town, Lagos, Nigeria

In addressing environmental challenges and ecosystem resilience, green networks are preserved, repaired, and rebuilt by green infrastructure. However, urbanization effects have seen urban land form undergo significant modifications over time due to different anthropogenic activities. The objective of this study is to evaluate the land use and land cover (LULC) change in FESTAC Town, a government-owned residential neighborhood in Lagos, with the goal of recommending interventions for conserving green infrastructure. The study mainly focuses on employing remote sensing and geographic information system (GIS) techniques to detect alterations in land use in FESTAC Town from 1984 to 2022. The ERDAS Imagine software was utilized, employing a supervised classification-maximum likelihood algorithm, to identify changes in LULC. Additionally, an accuracy assessment was conducted using ground truth data. Findings from this study show significant increase in built-up areas at the cost of loss in dense vegetation over a 38-year period thereby, putting pressure on available green spaces. In terms of the area under each LULC category, most significant changes have been observed in built-up area (410.86%), bare surface (- 79.79%), sparse vegetation (- 53.42%), and dense vegetation (- 31.83%). Effective conservation strategies should focus on promoting connectivity between green spaces, engaging stakeholders in the planning and implementation of green infrastructure projects.

A spatio-temporal analysis of the magnitude and trend of land use/land cover changes in Gilgel Gibe Catchment, Southwest Ethiopia

Analyzing alterations in land use/land cover is crucial for water Scientists, planners, and decision-makers in watershed management. This examination enables the development of effective solutions to mitigate the adverse impacts resulting from such changes. The focus of this research was analyzing alterations in land use/land cover within the Gilgel Gibe Catchment in 1991 - 2021. LULC data of 1991-2021 were derived from multispectral Landsat images. Data were also gathered using field observations and key informant interview. Data of LULC classes (1991-2021) were generated utilizing supervised classification with maximum likelihood algorithm of ENVI 5.1 and ArcGIS 10.5. Change detection analysis and accuracy assessment were done where accuracy levels all the study periods were > 85 %, and the overall Kappa statistics of the periods were > 0.89. Built-up area and cultivated land of the catchment are increasing with increasing magnitude of change; whereas, while forest cover and grazing land of the catchment are shrinking with declining magnitudes of change, shrubland covers and water body are declining with increasing magnitude of change in the catchment. The net increase in degraded land is a reflection of the increasing degradation of natural resources in the catchment. Swift escalation of population and the subsequent raising demand for farmland and forest and shrub (e.g. fuel-wood and construction) products, decline yield, unemployment and lack of alternative income source, and open access and limited conservation of resources are the principal factors for the dramatic shrinkages of grazing, forest, water body and shrubland resources. Thus, concerned bodies should take rehabilitation measures to restore degraded lands, improve production and yield of farmland by increasing improved farm-inputs and irrigation, and create employment and alternative income sources for the youth, women and the poor so as to ensure sustainable rural livelihoods and to curb the impacts on forest, shrubland and other resources.

Multi-decadal coastal change detection using remote sensing: the Mediterranean coast of Egypt between El-Dabaa and Ras El-Hekma

A key source of information for many decision support systems is identifying land use and land cover (LULC) based on remote sensing data. Land conservation, sustainable development, and water resource management all benefit from the knowledge obtained from detecting changes in land use and land cover. The present study aims to investigate the multi-decadal coastal change detection for Ras El-Hekma and El-Dabaa area along the Mediterranean coast of Egypt, a multi-sectoral development area. Besides, the superiority of the area is highly dependent on its proximity to three development projects: the tourism and urban growth pole at Ras El-Hekma, the beachfront Alamain New Mega City, and the Nuclear Power Plant at El Dabaa. This study utilized multi-spectral Landsat satellite images covering 1990, 2010, and 2020 to perceive the post-classification change detection analysis of the land use and land cover changes (LULCC) over 30 years. The results of the supervised classification from 1990 to 2020 showed a 47.33 km2 (4.13%) expansion of the agricultural land area, whereas the bare soil land area shrunk to 73.13 km2 (6.24%). On the other hand, the built-up activities in the area launched in 2010 and escalated to 20.51 km2(1.77%) in 2020. The change in land use reveals the shift in the economic growth pattern in the last decade toward tourism and urban development. Meanwhile, it indicates that no conflict has yet arisen regarding the land use between the expanded socioeconomic main sectors (i.e., agriculture, and tourism). Therefore, the best practices of land use management and active participation of the stakeholders and the local community should be enhanced to achieve sustainability and avoid future conflicts. An area-specific plan including resource conservation measures and the provision of livelihood alternatives should be formulated within the National Integrated Coastal Zone Management (ICZM) plan with the participation of the main stakeholders and beneficiaries. The findings of the present work may be considered useful for sustainable management and supportive to the decision-making process for the sustainable development of this area.

Spatial and temporal classification and prediction of LULC in Brahmani and Baitarni basin using integrated cellular automata models

Monitoring the dynamics of land use and land cover (LULC) is imperative in the changing climate and evolving urbanization patterns worldwide. The shifts in land use have a significant impact on the hydrological response of watersheds across the globe. Several studies have applied machine learning (ML) algorithms using historical LULC maps along with elevation data and slope for predicting future LULC projections. However, the influence of other driving factors such as socio-economic and climatological factors has not been thoroughly explored. In the present study, a sensitivity analysis approach was adopted to understand the effect of both physical (elevation, slope, aspect, etc.) and socio-economic factors such as population density, distance to built-up, and distance to road and rail, as well as climatic factors (mean precipitation) on the accuracy of LULC prediction in the Brahmani and Baitarni (BB) basin of Eastern India. Additionally, in the absence of the recent LULC maps of the basin, three ML algorithms, i.e., random forest (RF), classified and regression trees (CART), and support vector machine (SVM) were utilized for LULC classification for the years 2007, 2014, and 2021 on Google earth engine (GEE) cloud computing platform. Among the three algorithms, RF performed best for classifying built-up areas along with all the other classes as compared to CART and SVM. The prediction results revealed that the proximity to built-up and population growth dominates in modeling LULC over physical factors such as elevation and slope. The analysis of historical data revealed an increase of 351% in built-up areas over the past years (2007-2021), with a corresponding decline in forest and water areas by 12% and 36% respectively. While the future predictions highlighted an increase in built-up class ranging from 11 to 38% during the years 2028-2070, the forested areas are anticipated to decline by 4 to 16%. The overall findings of the present study suggested that the BB basin, despite being primarily agricultural with a significant forest cover, is undergoing rapid expansion of built-up areas through the encroachment of agricultural and forested lands, which could have far-reaching implications for the region's ecosystem services and sustainability.

An analysis of LULC changes for understanding the impact of anthropogenic activities on food security: a case study of Dudhganga watershed, India

Although the Dudhganga watershed is the primary water and food resource of the Kashmir Valley, it has undergone significant changes in food resources and strategies due to rampant urbanization in the area over the past 20 years. This urbanization has had a profound impact on the watershed and has also affected land use and land cover (LULC) patterns and environmental changes. The objective of this study is to investigate the effects of urban development on food security parameters in the Dudhganga watershed area, India, from 2000 to 2020, by evaluating LULC changes. Additionally, the study aims to examine the relationship between climate changes and LULC indices, such as the Modified Normalized Difference Water Index (MNDWI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Built-up Index (NDBI). The results indicate a 21.66% increase in barren areas, at the expense of snow-covered lands, during the 2000-2020 period. The primary land cover transition observed is towards barren areas. The predictions for LULC in 2030 highlight the need for careful management of land use and climate changes in the study area. This study can assist local government officials in reassessing food strategies by identifying areas where urban expansion should be controlled and climate impacts minimized, to prevent local hunger and ecological degradation. Therefore, the development of systematic urban planning approaches and mitigation of climate change sources are crucial. Furthermore, the adoption of advanced agricultural technology should be considered to mitigate the impact of urban expansion.

LULC change detection using support vector machines and cellular automata-based ANN models in Guna Tana watershed of Abay basin, Ethiopia

Recurrent changes recorded in LULC in Guna Tana watershed are a long-standing problem due to the increase in urbanization and agricultural lands. This research aims at identifying and predicting frequent changes observed using support vector machines (SVM) for supervised classification and cellular automata-based artificial neural network (CA-ANN) models for prediction in the quantum geographic information systems (QGIS) plugin MOLUSCE. Multi-temporal spatial Landsat 5 Thematic Mapper (TM) imageries, Enhanced Thematic Mapper plus 7 (ETM+), and Landsat 8 Operational Land Imager (OLI) images were used to find the acute problem the watershed is facing. Accuracy was assessed using the confusion matrix in ArcGIS 10.4 produced from ground truth data and Google Earth Pro. The results acquired from kappa statistics for 1991, 2007, and 2021 were 0.78, 0.83, and 0.88 respectively. The change detection trend indicates that urban land cover has an increasing trend throughout the entire period. In the future trend, agriculture land may shoot up to 86.79% and 86.78% of land use class in 2035 and 2049. Grassland may attenuate by 0.03% but the forest land will substantially diminish by 0.01% from 2035 to 2049. The increase of land specifically was observed in agriculture from 3128.4 to 3130 km2. Judicious planning and proper execution may resolve the water management issues incurred in the basin to secure the watershed.

Impacts of anthropogenic disturbance on forest vegetation cover, health, and diversity within Doma forest reserve, Nigeria

Forest encroachment is a common practice that has led to the destruction of canopy trees in the Guinea savanna part of Nigeria. This study investigated the influence of human activities on vegetation health and species composition of Doma forest reserve located in Nasarawa State, Nigeria. Landsat satellite data from 1986 to 2021 were utilized to assess forest cover change, land surface temperature (LST), and vegetation indices (VIs). The results show that dense woodland vegetation in the Doma forest reserve depreciated between 1991 and 1999 by 17.82% before increasing by 7.37% between 1999 and 2021. Similarly, vegetation greenness (measured by the Normalized Difference Vegetation Index (NDVI), Green Chlorophyll Vegetation Index (GCVI), and leaf area index (LAI)) of the forest mirrored the changes observed in the forest cover. The LST extracted for each year was correlated with all VIs, and an inverse relationship was observed in all relationships analyzed. The decline in greenness between 1999 and 2011 was attributed to increasing lumbering, bush burning, and sand dredging activities. Results also showed the current diversity state (H1 = 0.23), evenness (0.63), and the volume of tree (1.31 m3) species in the heart of the Doma forest reserve. However, a high (25%) native tree species in the Fabaceae family correlated with a dramatic increase in the VIs and an increase in dense woodland cover indicating the importance of Fabaceae in forest ecosystem regeneration.

Trend analysis, past dynamics and future prediction of land use and land cover change in upper Wabe-Shebele river basin

A growing population has led to extensive farming at the expense of a natural environment. Changes in land use and cover have caused land degradation, and problematic groundwater recharge. The objective of this study was to evaluate the historical trend, simulations, and predictions of land use land cover change in the Upper Wabe-Shebele River Basin. The study accounted for 1992, 2007 and 2022 as well as it will predict the change for 2037 and 2052. Landsat TM for 1992, ETM + for 2007, and Landsat-8 OLI for 2022 were used. In QGIS 3.16, the maximum likelihood method was utilized for supervised image classification. Using CA-Markov and the Land Change Modeler land use and land cover change for 2037 and 2052 were predicted. Validity and accuracy of the model was evaluated using actual and predicted land use and land cover changes of 2022. Topography, proximity to a town, stream, roads, and population density were used as input for the model. The results showed that between 1992 and 2007, cultivated land increased by 17.07% on average at a rate of 1.05%, while settlement increased by 17.51% at a rate of 1.08% per year. Agricultural and settlement land increased by 22.97% and 30.12%, respectively. Between 1992 and 2022, the transition area matrix showed 2,330.25 and 1,145.77 km2 of forest and grazing land were changed to settlement and cultivated land, respectively. Meanwhile, from 2022 to 2037, the quantity of land used for cultivated, grazing, and settlement is predicted to increase by 0.19, 3.66, and 23.8% in order. For 2037 and 2052, settlement and cultivated land were increased by 1.3 and 7.32% respectively. Finally, since natural ecosystem had been significantly disturbed by change in the study area, comprehensive rehabilitation and management is demanded.

Modeling of land use/land cover dynamics using artificial neural network and cellular automata Markov chain algorithms in Goang watershed, Ethiopia

Land Use/Land Cover (LULC) change has inhibited sustainable development for the last millennia by affecting climate, biological cycles, and ecosystem services and functions. In this regard, understanding the historical and future patterns of LULC change plays a crucial role in implementing effective natural resource management. This study aimed to model and characterize the spatiotemporal trajectories of landscape change between the 1984 and 2060 periods. The satellite image spectral information was segmented into seven LULC classes using a hybrid approach of image spectral recognition. The supervised classification technique of Support Vector Machine (SVM) was used to classify the satellite images, whilst the Land Change Modeler (LCM) Module in TerrSet software was used to assess the historical trend and future simulation of LULC dynamics. To predict future landscape changes, transition potential maps were generated using a Multi-layer Perceptron (MLP) neural network algorithm. The findings of the study demonstrated that the Goang Watershed has experienced significant LULC change since 1984. During the 1984-2001, 2001-2022, and 1984-2022 periods, farmland showed a dramatic increasing trend with 7.5 km2/yr-1, 110.3 km2/yr-1, and 64.3 km2/yr-1, respectively. A similar trend was also observed in built-up areas with 0.5 km2/yr-1, 3.2 km2/yr-1, and 2 km2/yr-1. The expansion of farmland and built-up area was at the expense of forest, shrubland, and grasslands. With a business-as-usual scenario, the extent of farmland will continue to increase between 2022 and 2060 while rapid reduction is expected by forest, shrubland, and grasslands. The alarming rate of farmland and built-up area expansion will put significant pressure on biodiversity and ecosystem services in the area. As a result, eco-friendly conservation approaches should be implemented as soon as possible to maintain ecosystem health and encourage sustainable development.

Analyze of spatial extent and current condition of land use land cover dynamics for the period 1990-2020 Wayu-Tuka district, western Ethiopia

LULC variation has increased in many parts of the world recent years. Analyzing LULC is valuable to ability to grasp for spatial extent, patterns and impacts of the dynamics. This research examines the magnitudes and trends of LULC dynamics of Wayu-Tuka District, Western Ethiopia for a period of 1990-2020. Data were acquired from Landsat images (i.e, TM from 1990 to 2000, ETM+ from 2010 and OLI 2020). LULC classes were classified (from Landsat images) to develop land use land cover change maps for the study area. Landsat images were grouped via supervised classification method and maximum likelihood classifier (MLC). Accuracy scores and kappa a coefficient was used to confirm the accuracy categorized for LULC classes. Forest, settlement area, cultivated area, water body, and bare land are the main land use land cover categories identified in the study area. At the study district, forest coverage decreased progressively within the past three decades (1990-2020) from 12.4% in 1990 to 2.6% in 2020. The settlements, cultivated lands and water bodies have been explained by a average rate of 0.41% per year and forest land has been reduced by 0.33% per year. The study identified the major drivers of land use/land cover dynamics such as expansion of agricultural land, extraction of fuel woodland, illegal settlements and illegal logging was the key factors of LULC changes in the field of the study. In expressions of historical and current LULC, the analysis indicated that in the three decades years' viewpoint; changes in agriculture land expansion and expansion of settlement land have had a strong impact on the LULC dynamics. The few remaining forest area coverage of the District shall be completely vanished unless measures are taken to curb these declining trends. Therefore, relevant stakeholders should take integrated actions to rehabilitate degraded landscapes through afforestation and reforestation programmes.

Effect of forest cover change on ecosystem services in central highlands of Ethiopia: A case of Wof-Washa forest

Forest provides a wide range of ecosystem services and is considered as one of the major sources of livelihood for the local people. In recent years, forest cover in developing countries has been declining due to expansion of agricultural land and increasing human demand for forest products. The declining of forest cover significantly reduces forest ecosystem services, impacting environmental health and community well-being. Although many studies have shown declining of forest cover, the impact of declining forest cover on ecosystem services is not getting much attention in Ethiopia. Therefore, this study aimed to assess the impact of forest cover change on ecosystem service values in the Wof-Washa forest over the past 47 years. This study combined geospatial techniques and socioeconomic survey methods to assess the impact of land use and land cover (LULC) change on the value of ecosystem services. Ecosystem services were estimated using the benefit transfer method and socioeconomic assessment. A total of 184 households were surveyed with structured and semi-structured questionnaires. The results revealed that the provisioning services increased, while the regulating, supportive, and cultural services decreased. We find that about US$ 2 million were reduced due to LULC change, especially due to forest cover change. As forest cover decreased, the monetary value of ecosystem services and their benefits to local people declined significantly. The results reveal that deforestation is a major challenge that can reduce the value of ecosystem services. The results of this study are vital for developing effective forest conservation strategies before irreversible damage to ecosystem services.

Grassland cover declined in Southern Africa but increased in other African subcontinents in early twenty-first century

The African continent has the most extensive grassland cover in the world, providing valuable ecosystem services. African grasslands, like other continental grasslands, are prone to various anthropogenic disturbances and climate, and require data-driven monitoring for efficient functioning and service delivery. Yet, knowledge of how the African grassland cover has changed in the past years is lacking, especially at the subcontinent level, due to lack of relevant long-term, Africa-wide observations and experiments. In this study, we used Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Type (MCD12Q1) data spanning 2001 to 2017 to conduct land use land cover (LULC) change analyses and map grassland distribution in Africa. Specifically, we assessed the changes in grassland cover across and within African subcontinents over three periods (2001-2013, 2013-2017, and 2001-2017). We found that the African grassland cover was 16,777,765.5 km², 16,999,468.25 km², and 16,968,304.25 km² in 2001, 2013, and 2017, respectively. There were net gain (1.32%) and net loss (- 0.19%) during 2001-2013 and 2013-2017 periods, respectively, and the annual rate of change during these periods were 0.11% and - 0.05%, respectively. Generally, the African grassland cover increased by 1.14% (0.07% per annum) over the entire study period (2001-2017) at the expense of forestland, cropland, and built-up areas. The East and West African grassland cover reduced by 0.07% (- 0.02% per annum) and 1.35% (- 0.34% per annum), respectively from 2013 to 2017 but increased in other periods. On the other hand, the grassland cover in North and Central Africa increased throughout the three periods while that of Southern Africa decreased over the three periods. Overall, the net gains in the grassland cover of other African subcontinents offset the loss in Southern Africa and promoted the overall gain across Africa. This study underscores the need for continuous monitoring of African grasslands and the causes of their changes for efficient delivery of ecosystem services.

Impact of land use land cover change using remote sensing with integration of socio-economic data on Rural Livelihoods in the Nashe watershed, Ethiopia

Land use/land cover is an important component in understanding the interactions of human activities with the environment and is necessary to recognize the changes in order to monitor and maintain a sustainable environment. The main objectives of this study were to analyze changes in land cover in the Nashe-watershed for the period 2010-2020, analyze household demographic and livelihood characteristics and identify the impact of the construction of the DAM and changes in land cover on the environment. Since the dam of the Nashe watershed was built in 2012, the socioeconomic characteristics of the area were used to interpret the causes of land use and land cover changes, which cause changes in their life and environment. Purposively 156 households were selected who were more than 40 years old from the total households (1222) in three kebele and for land use land cover of 2010, Land sat 7 were used whereas for 2020, land sat 8 was used. The socioeconomic data were analyzed with Excel and integrated with biophysical data. The 2010-2020 ten-year period showed that cultivated land and forest land were reduced from 73% to 62% and 18%-14%, respectively, and swampy areas fully converted to Water Bodies, alternately increasing Water Bodies and grazing land also converted from 43.9% to 54.5% and 0.04%-17.96% respectively. The reason for this change was the construction of dams, human encroachment, and expansion of cultivated land which were bringing LULCC in study area. However, government could not gave these people adequate compensation for their lands, properties that conquered by water. Hence, the Nashe watershed is identified as an area highly affected by land use and land cover change, the livelihoods were suffered by Dam construction, also environmental sustainability is hindering still now. Therefore it is necessary to closely monitor land use/land cover, giving consideration for HHs who affected by Dam, and to maintain a sustainable environmental resource for the future sustainable development is a critical issue in the Ethiopia in general, particularly in the study area.

Monitoring invasive pines using remote sensing: a case study from Sri Lanka

Production plantation forestry has many economic benefits but can also have negative environmental impacts such as the spreading of invasive pines to native forest habitats. Monitoring forest for the presence of invasive pines helps with the management of this issue. However, detection of vegetation change over a large time period is difficult due to changes in image quality and sensor types, and by the spectral similarity of evergreen species and frequent cloud cover in the study area. The costs of high-resolution images are also prohibitive for routine monitoring in resource-constrained countries. This research investigated the use of remote sensing to identify the spread of Pinus caribaea over a 21-year period (2000 to 2021) in Belihuloya, Sri Lanka, using Landsat images. It applied a range of techniques to produce cloud free images, extract vegetation features, and improve vegetation classification accuracy, followed by the use of Geographical Information System to spatially analyze the spread of invasive pines. The results showed most invading pines were found within 100 m of the pine plantations' borders where broadleaved forests and grasslands are vulnerable to invasion. However, the extent of invasive pine had an overall decline of 4 ha over the 21 years. The study confirmed that remote sensing combined with spatial analysis are effective tools for monitoring invasive pines in countries with limited resources. This study also provides information to conservationists and forest managers to conduct strategic planning for sustainable forest management and conservation in Sri Lanka.