Decrease In Vegetation Cover Research Articles

Intersectoral Approach for Mitigation of Threats and Pressures on Lakhari Valley Wildlife Sanctuary, Odisha, India

The Lakhari Valley Wildlife Sanctuary (LVWS) in Odisha, an abode for many life forms in its dense mixed deciduous forests, have been undergoing degradation in the last three to four decades primarily due to anthropogenic activities. The sanctuary has been prone to various pressures, viz., timber/wood logging, poaching, and hunting of animals, habitat destruction, agricultural intensification, construction activities, slash and burn cultivation and natural hazards. On account of this, the present study on the LVWS was attempted to i) review the reports and articles documented, ii) analyze the forest cover change, iii) enlist the prevailing threats, iv) suggest various measures for threat mitigation, and v) list out possible conservation strategies for LVWS on a short and long term basis. The data sets and literature (published and unpublished reports) on LVWS availed from the online portals, news reports, and personal communication with different stakeholders including officials from the forest department and non-government organizations were collected and compiled. Maps of Land Use Land Cover (LULC) and Normalised Difference in Vegetation Index (NDVI) were prepared using satellite imageries. The LVWS witnessed about a 28% (50 km2) decrease in vegetation cover from 1990 to 2000 itself, an overall reduction of 20 km2 vegetation cover from 1990 to 2010. During the last decade (2013-2020), the LVWS recorded a 10% reduction in forest cover, 140% upsurge of barren land, 12% increase of shrub/ agriculture land. Based on the natural and anthropogenic threats observed in the LVWS, threat mitigation measures, and intersectoral conservation strategies, ecosystem models for habitat management were compiled.

Multi-temporal Satellite Images Analysis for Assessing and Mapping Deforestation in Um Hataba Forest, South Kordofan, Sudan

. Sudan is a hot spot for deforestation, despite the increasing awareness of deforestation and its consequences. Consequences are related to increased emissions of greenhouse gases, water pollution, and loss of biodiversity. However, precise information on its forests' current state is very limited. Therefore, to intervene support of existing resources effectively, it is important to have a better understanding of the process to take place in the country and impact those resources. The objective of this study is assess and map Land use Land cover (LULC) change and analyze the anthropogenic factors causing it in Um Hataba forest, South Kordofan State. The study utilized two-free cloud images (TM 2000 and Sentinel-2 in 2018), field surveys, and questionnaires to analyze the decrease in forest cover. The results indicated there were a decrease in vegetation cover on wadis (clay soil) from 20.98% in 2000 to 15.85% in 2018 and vegetation on sandy soil decreased from 30.29% in 2000 to 30.13% in 2018. While mixed shrubs and grassland increased from 28.60% in 2000 to 33.20% in 2018 of the total area under study and the rainfed agricultural area increased from 20.13% in 2000 to 20.82% in 2018. The main factors of degradation and fragmentation as the expansion of mechanized are rain-fed agriculture, felling of trees and woodcutting, worse grazing activities, and construction of infrastructure. Information garnered from this study can provide a good basis for forest rehabilitation programs and can also be used for developing proper management plans that consider the needs of the communities utilizing the forest.

A Multi Criterion Decision Approach for Identification of Potential Flood Risk Zones in Hussainsagar Watershed using Geomatics- A Case Study

Urban flooding is becoming a major threat in the recent times due to changes in the land use land cover leading to chaos, loss of livelihood, damage to property, infrastructure and utilities. The main objective of the present study is to assess the current state of land use, assess the impact of these temporal changes on the basin hydrology over the past few years. The study was initiated to identify how increase in impervious layer due to construction activities over a decade have significantly increased the potential flood risk zones in and around the Hussainsagar catchment using multi criterion decision support system using GIS and RS techniques. From the analysis it was observed that there is an increase of almost 32.6% in the built-up area from 104.05 km2 to 154.39 km2,38.20% reduction in open space, 51.75% in agricultural and, 14.10% decrease in vegetation cover, of the total catchment area between the years 2005 and 2019. These changes in the land use have not only altered the basin hydrology but also augmented the extent of potentially high flood risk zones in the proposed study leading to frequent flooding over the past few years.

Mapping as instrument to support public authorities in risk analysis and promotion of climate justice

Urbanization is one of the most powerful anthropogenic causes of Earth change. The report by the Intergovernmental Panel on Climate Change claims that human influence on the warming of the soil, ocean and atmosphere is unequivocal. People who are socially, economically, culturally, politically, institutionally or otherwise marginalized are especially vulnerable to climate change and also to adaptation and mitigation responses. Climate injustice refers to the fact that groups in a greater situation of vulnerability present challenges that accumulate as a result of less political power and great social inequality and, therefore, are disproportionately harmed by the impacts of climate change. Thus, this article seeks to address, from indices such as the NDBI, NDVI and census data from the IBGE Census, for the year 2010 and data from the analysis of Climate Risks and Vulnerabilities in Recife, the climate (in)justice in the city of Recife. For this purpose, 3 images from the TM sensor and one from the OLI sensor aboard the Landsat satellites were used. Results show that NDBI values above 0.21 were extended to various areas of the city of Recife over the years, while the NDVI showed a decrease in vegetation cover, indicating greater soil sealing and construction of buildings. These results, in accordance with the mapping of vulnerabilities to climate change, corroborate that a public policy focused on reducing climate injustices is essential, especially when it is concluded that the areas with the greatest social vulnerability are those with the greatest climate vulnerability.

Impacts of anthropogenic activities on vegetation cover changes in the Circum-Bohai-Sea region, China

Besides climate change, intensive anthropogenic activities affect vegetation cover variations, particularly in highly populated regions. Herein, we investigated the impacts of anthropogenic activities in the Circum-Bohai-Sea (CBS) region in China using correlation analysis between the normalized difference vegetation index (NDVI) and indicators related to urban development, agricultural practices, and afforestation efforts; multivariate linear regression and residual analyses were used to quantify how anthropogenic activities contribute to NDVI change. We found that the vegetation cover in most of the study area significantly increased from 1982 to 2015. In major cities, the decrease in vegetation cover was not as expected because of the opposing effects of rapid urbanization and greening. Afforestation and improved agricultural practices dominated the increase in vegetation cover in different regions. Anthropogenic activities accounted for 42% of the increase in vegetation cover in the CBS region. Therefore, to achieve regional sustainable development, the effects of human activities in highly populated area must be heeded.

The Spatiotemporal Implications of Urbanization for Urban Heat Islands in Beijing: A Predictive Approach Based on CA–Markov Modeling (2004–2050)

The rapid increase in infrastructural development in populated areas has had numerous adverse impacts. The rise in land surface temperature (LST) and its associated damage to urban ecological systems result from urban development. Understanding the current and future LST phenomenon and its relationship to landscape composition and land use/cover (LUC) changes is critical to developing policies to mitigate the disastrous impacts of urban heat islands (UHIs) on urban ecosystems. Using remote sensing and GIS data, this study assessed the multi-scale relationship of LUCC and LST of the cosmopolitan exponentially growing area of Beijing, China. We investigated the impacts of LUC on LST in urban agglomeration for a time series (2004–2019) of Landsat data using Classification and Regression Trees (CART) and a single channel algorithm (SCA), respectively. We built a CA–Markov model to forecast future (2025 and 2050) LUCC and LST spatial patterns. Our results indicate that the cumulative changes in an urban area (UA) increased by about 908.15 km2 (5%), and 11% of vegetation area (VA) decreased from 2004 to 2019. The correlation coefficient of LUCC including vegetation, water bodies, and built-up areas with LST had values of r = −0.155 (p > 0.419), −0.809 (p = 0.000), and 0.526 (p = 0.003), respectively. The results surrounding future forecasts revealed an estimated 2309.55 km2 (14%) decrease in vegetation (urban and forest), while an expansion of 1194.78 km2 (8%) was predicted for a built-up area from 2019 to 2050. This decrease in vegetation cover and expansion of settlements would likely cause a rise of about ~5.74 °C to ~9.66 °C in temperature. These findings strongly support the hypothesis that LST is directly related to the vegetation index. In conclusion, the estimated overall increase of 7.5 °C in LST was predicted from 2019–2050, which is alarming for the urban community’s environmental health. The present results provide insight into sustainable environmental development through effective urban planning of Beijing and other urban hotspots.

ASHFALL DISPERSAL MAPPING OF THE 2020 TAAL VOLCANO ERUPTION USING DIWATA-2 IMAGERY FOR DISASTER ASSESSMENT

Abstract. Natural disasters incur many fatalities and economic losses for vulnerable and developing countries such as the Philippines. It is crucial that during calamities, on-ground surveillance is supplemented by low-cost and time-efficient methods such as satellite remote sensing. Diwata-2 is a Philippine microsatellite specifically equipped for disaster assessment. In this study, the capabilities of this satellite in ashfall detection were explored by closely examining the case of the Taal volcano eruption on January 12, 2020. Satellite images covering parts of CALABARZON and Metropolitan Manila before and after the phreatomagmatic eruption were compared. The presence and extent of heavy ash over the study area were identified after the image classification using the Support Vector Machine (SVM) algorithm. A decrease in vegetation cover and built-up areas was also observed. Upon validation, an overall accuracy of 91.4562 and Kappa coefficient of 0.8833 were achieved for the post-eruption ashfall extent map, exhibiting the potential of Diwata-2 imagery in monitoring volcanic eruptions and similar phenomena.

Habbaniyah Land Cover Change Detection Using Geoinformatics

The study area was chosen within the district of Habbaniyah city with an area of 12320 hectares. In order to detect the change in the predominant ground covers and classify the soils of the study area. Using remote sensing and geographic information systems. For the Landsat 7 and 8 satellites, path 169 and Row 37, for the ETM+ sensor for the year 2007 and 2011, and for the OLI sensor for the year 2015 and 2020. All parts of the study area were covered by semi-detailed soil surveying with Free Lance method, And nine pedons were determined and determined by GPS. The results showed that the classification of soils in the study area included two order of soils: Entisols and Aridisols. Four main classes of land cover were identified: barren land, vegetation cover, Urban Lands and water. As the area of barren lands in 2007 was 6417.5 hectares, 54.09%, accompanied by a decrease in vegetation cover during 2007, with an area of 4360.6 hectares, 35.39%. It was also noted that there was an increase in the areas of Urban Land represented by Urban sprawl, as it was during 2007 with an area of 1104.8 hectares, or 8.97% While it increased during the year 2020, reaching 1927.4 hectares, at a rate of 15.64%, and this is a serious indicator of the desertification of agricultural lands due to Urban sprawl and the transformation of agricultural lands into residential areas. Water bodies decreased during the year 2011 at a rate of 6.3 hectares/year, while the amount of the decrease during the year 2020 was about 2.3 hectares/year due to water scarcity, the construction of dams and the reduction of water flow towards Iraqi lands. It also notes a slight increase in the areas of vegetation cover during the year 2020, at a rate of 5.6 hectares/year, which is very small compared to the area of the study area. While it reached its peak at a rate of 27.1 hectares/year during the year 2011. We also note an increase in Urban sprawl during the year 2020, as it was an increase of 5.7 hectares/year and on a continuous basis.

Monitoring of Vegetation Cover Changes With Geomorphological Forms using Google Earth Engine in Kendari City

Vegetation cover plays an important role in controlling the view, boundaries, air temperature, living place and aesthetics in an area. Vegetation cover changes can be caused by changes in temperature, rainfall and human activities. Google Earth Engine (GEE) provides machine learning algorithms such as NDVI which are very useful in extracting vegetation density levels from imagery. The purpose of this study was to analyze vegetation cover changes by human activities in relation to the geomorphological form of Kendari City. The imagery used in multi-temporal monitoring are Landsat-7 ETM in 2000, Landsat-5 TM in 2005 and 2010 and Landsat 8 OLI in 2015 and 2020. Input machine learning using near infrared (NIR) and red (Red) for the NDVI Algorithm while the geomorphological form uses SRTM imagery. The classification of vegetation cover consists of water bodies, open field, built areas and roads covered with asphalt, paving or soil, plantations/agriculture, bushes, grass, reeds, green open space and forests. Each sub-district experienced a decrease in vegetation cover in the form of plantations/agriculture, bushes, grass, reeds, green open space except for the West Kendari District which tended to be varied. The forest area is getting better every year. The existence of protected forests and geomorphological forms such as lowlands are the driving factors for changes in vegetation cover, while low hills and high hills are flat to steep are contrainst factors. Machine learning in GEE is very helpful in monitoring vegetation cover changes and has an NDVI algorithm that is quite easy to apply.

Monitoring and Assessment of Land Use/Land Cover Changes in Elgezira-Aba Locality, White Nile State, Sudan using geospatial technology

Desertification as major environmental problem in Sudan seriously affected the livelihood and food security of population. Poor land use and climatic variability are the major causes of the desertification. This study focused on assessing and mapping of chanes land use and land cover (LU/LC) as drivers to desertification processes in the ElGezira-Aba Locality, White Nile State, Sudan. Two images (173/51) acquired by ETM+ and OLI/TIRS for years 2000 and 2016 were analyzed. Standard digital image processes were performed including layer stacking, supervised image classification, area calculation, accuracy assessment and post-classification change detection. In the years 2000 and 2016, respectively, forest covered 3838 Ha(15 %) and 1449 Ha (5.9 %), open forest covered 2749 Ha (11 %) and 1085 Ha (4.5%), grassland covered 2354 Ha (10 %) and 1315 Ha (5.4 %), rainfed mechanized agriculture covered 377 Ha (2 %) and 1914 Ha (8 %), irrigated scheme covered 2255 Ha (9 %) and 5051 Ha (20.8 %), fallow land covered 3791 Ha (5 %) and 1577 Ha (6 %), bareland covered 3938 Ha (16 %) and 1425 Ha (5.9 %), sand dunes covered 998 Ha (4 %) and 1165 Ha (4.8 %), water bodies covered 2219 Ha (9 %) and 3605 Ha (14 %) and urbanization covered 1794 Ha (7 %) and 5698 Ha (23 %). Overall classification accuracies for years 2000 and 2016, respectively were 86.39 % and 87.89 %, while overall kappa statistics were 0.8452 and 0.8594. Pronounced decrease in vegetation cover (forest, open forest and grassland) was recorded in favors of agriculture (irrigated scheme, rainfed mechanized agriculture)and urbanization. Sand dunes and Water bodies were increased. The dominant desertification processes were vegetation deterioration and wind erosion as a result of inappropriate agricultural activities, overgrazing and expansion of urban areas. The study concluded the necessity of protection natural resources by adoption of proper urban and land use planning in addition to use of geospatial technologies in monitoring of these resources.

Mapping and assessment of vegetation cover change and species variation in Medan, North Sumatra

Vegetation existence contributes to environmental quality in urban areas. The increase in population and development of cities has led to land conversion with lesser vegetated areas. Information on land cover change is needed, especially for urban regional planning with green open space consideration. The research aims to analyze urban vegetation cover and its changes in two sub-districts of Medan between the years 1999 and 2019. Normalized difference vegetation index (NDVI) and change analysis were conducted in the research. The diversity of plants within these areas was observed. The results showed changes in vegetation cover areas in the mentioned years. In 1999, most areas were under a highly dense vegetation class, while in 2019, they were under a low-density vegetation class. This finding indicates a decrease in vegetation cover as a result of increasing built-up areas. Within the vegetation cover, it was found many tree species and agricultural plants. Those vegetations existed in some areas: city parks, house yards, gardens, agricultural fields, etc. A special emphasis should be placed on riverside areas with less vegetation in order to provide a higher level of protection, particularly in the event of a flood. To increase the vegetated areas and maintain the environmental quality, optimizing the land by replanting in the area with no or less vegetation should be done.

MONITORING DESERTIFICATION IN SOME REGIONS OF IRAQ USING GIS TECHNIQUES

Desertification is one of the phenomena that threatening the environmental, economic, and social systems. This study aims to evaluate and monitor desertification in the central parts of Iraq between the Tigris and Euphrates rivers through the use of remote sensing techniques and geographic information systems. The Normalized difference vegetation index NDVI and the crust index CI were used, which were applied to two of the Landsat ETM + and OLI satellite imagery during the years 1990 and 2019. The research results showed that the total area of ​​the vegetation cover was 2620 km2 in 1990, while there was a marked decrease in the area Vegetation cover 764 km2 in 2019, accounting for 34.8% (medium desertification) and 10.2% (high desertification), respectively. Also, the results showed that sand dunes occupied an area of ​​767 km2 in 1990, while the area of ​​sand dunes increased to 1723 km2 in 2019, with a rate of 10.2%) medium desertification (and 22.9% (severe desertification), respectively. It was noted that the overall rate of decrease in vegetation cover was 21.33 km2year-1 while the overall rate of increase in ground erosion in the area is 10.99 km2year-1.

ASSESSMENT OF URBAN HEAT ISLAND IN KADUNA METROPOLIS BETWEEN 2000 AND 2018

Urbanization is a major event in human history and there is no doubt the world is urbanizing rapidly and this is resulting in changes in temperature. This research aims is to identify changes in land surface temperature (LST) between 2000 and 2018. The technique used is the Landsat TM. The result of the land cover classification revealed a +15.93% increase in built-up areas, -27.21% decrease in vegetative cover, +11.19% increase in bare land and +0.09 increase in water bodies between 2000 and 2018. NDVI analysis showed a +0.02 and +0.17 increase in the maximum and minimum range and a +2.16 and -7.76 increase and decrease in maximum and minimum temperature respectively for LST. The finding revealed that the repelling of vegetative cover by built-up is an evidence of rapid urbanization taking place in Kaduna metropolis and a major driver of UHI in Kaduna metropolis. Conclusively, there is a correlation between the various land covers of study i.e built-up, vegetative cover, bare land and water body and LST in Kaduna metropolis between 2000 and 2018. Likewise, urban development has a major impact on urban climate particularly in the increase in land surface temperature which in turn contributes to the development of urban heat. This is true because changes in urban land cover tend to alter the spectral signature and emissivity of the urban surface as observed using remote sensing. The researcher suggests further studies should focus on the assessment of UHI under different land cover and micro-climate in Kaduna metropolis.

Modeling the Hydrological Impacts of Vegetation Cover Changes in the Upper Oum Er-Rbia Watershed (Morocco)

In Morocco, the mountainous areas are often exposed to bulky and vicious flows of water and sediment. This process is exacerbated by the decrease in vegetation cover and the disruption in rainfall-runoff conditions that frequently cause significant flooding. By exploring the main hydrologic elements of these processes, it is possible to understand the behavior and hydrological response of watersheds and thus plan accordingly. In this study, the authors focused on determining the morphometric characteristics of the upper Oum Er-Rbia River basin (UOERRB by assessing/ evaluating the land use and land cover changes for a period of 32 years (1984–2016). Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) was applied to simulate four daily hydrological events. The concentration time was 7.7 hours. The four storm events examined to calibrate and validate the simulated outflow at the outlet indicated a good agreement between the hydrographs of the measured and simulated flows, with an average Nash-Sutcliffe efficiency (NSE) value ranging from 0.63 to 0.76. Between 2002 and 2016, an average 6.21 percent increase in vegetation cover of with annual rainfall increasing from 690 to 714.1 mm/year was observed. These results can contribute to a better understanding of the hydrologic processes and better estimation of the return flows and thus guiding management decisions and developments in the UOERRB.

ASSESSMENT OF LAND COVER CHANGES DUE TO ANTHROPOGENIC CAUSES IN THE MOUNTAINOUS AREA OF ISHKOMAN WATERSHED, GILGIT, PAKISTAN

Remote Sensing (RS) provides the best ways to monitor temporal changes and to understand land use dynamics. Remote sensing analysis can be further enhanced when community perception regarding major drivers of change is integrated. The present study was an attempt to assess the land use land cover changes in the Ishkoman watershed in the Ghizer district. The study explored Landsat-5 and Landsat-8 images to assess the LULC dynamics from 1998 to 2018, and also used questionnaires for community perception regarding LULC changes in the past two decades. Supervised classification was used to monitor changes between 1998 and 2018 and the maximum likelihood technique was used to categorize the pixels into six classes: vegetation/forest area, bare rocks, water bodies, glaciers/snow area, rivers, water, and agriculture. Regarding the questionnaires, the correlation matrix and regression models were developed between independent variables (population, land type cleared, and extra land required for new family members) and dependent variables (land use dynamics factors and socio-economic variables). The results showed that all six land cover classes have shown temporal changes between 1998-2018 and the most significant change was observed in forests and pastures (which decreased from 18.7% to 5.9 %). Similarly, glaciers, water, rivers, and agriculture have changed from 13.1, 6.5, 9.3, 1.5 to 15.8, 4.0, 11.32, 3.1, respectively between 1998-2018. The largest change was observed in bare rocks which increased from 50.2 % to 60.06%. Moreover, temporal NDVI analysis showed a decrease in vegetation cover (conversion to bare rocks) between 1998-2018. The questionnaire results revealed that the highest correlation was shown between population increase and decrease in crop production (R2 = -0.348), whereas the lowest correlation was found in population increase and population access to bus stops (R2 = -0.167). Similarly, the highest correlation was found between access to roads and markets (R2 = 0.349) and dependent variable (land type cleared), whereas the lowest correlobserved in access to water resources (R2 = -0.021). The study concluded that land use land cover has been significantly changed from 1998 to 2018 in the Ishkoman Watershed. The study suggested more in-depth research to examine land use land cover changes at finer scales by using high resolution satellite imagery, and conducting details surveys regarding the underlying anthropogenic causes of land use dynamics.

Tropical Indo-Pacific SST influences on vegetation variability in eastern Africa

Mechanisms by which tropical Pacific and Indian Ocean sea surface temperatures (SST) influence vegetation in eastern Africa have not been fully explored. Here, we use a suite of idealized Earth system model simulations to elucidate the governing processes for eastern African interannual vegetation changes. Our analysis focuses on Tanzania. In the absence of ENSO-induced sea surface temperature anomalies in the Tropical Indian Ocean (TIO), El Niño causes during its peak phase negative precipitation anomalies over Tanzania due to a weakening of the tropical-wide Walker circulation and anomalous descending motion over the Indian Ocean and southeastern Africa. Resulting drought conditions increase the occurrence of wildfires, which leads to a marked decrease in vegetation cover. Subsequent wetter La Niña conditions in boreal winter reverse the phase in vegetation anomalies, causing a gradual 1-year-long recovery phase. The 2-year-long vegetation decline in Tanzania during an ENSO cycle can be explained as a double-integration of the local rainfall anomalies, which originate from the seasonally-modulated ENSO Pacific-SST forcing (Combination mode). In the presence of interannual TIO SST forcing, the southeast African precipitation and vegetation responses to ENSO are muted due to Indian Ocean warming and the resulting anomalous upward motion in the atmosphere.

Time-series analysis of open data for studying urban heat island phenomenon: a geospatial approach

Urbanization is increasing with a faster pace in almost all the cities of India, which has somehow prompted the anthropogenic activities eventually influencing the environment in this climate change scenario. This has led to the more prominent urban heat island (UHI) effect in urban areas, significantly raising the surface temperature of urban built-up. To focus on this issue, a spatio-temporal analysis of UHI over the Jaipur site of Rajasthan has been performed using moderate-resolution imaging spectroradiometer (MODIS) land surface temperature (LST) and MODIS Normalized Difference Vegetation Index (NDVI) for 16 years i.e., from 2003 to 2018 wherein NDVI has been used to analyze the status of vegetation in the urban and non-urban area w.r.t. UHI phenomenon. Study of UHI phenomenon has been done by computing the difference of mean LST between urban area and urban periphery (at a buffer distance of 2 km from urban boundary) both for day and night time. The difference of LST day time observed between urban and non-urban (buffer) is 0.06 °C in May, 2003 and 0.51 °C in May, 2018 whereas, difference of LST night time between urban and area surrounding to urban is 0.58 °C in May, 2003 and 1.41 °C in May, 2018. From the quantitative analysis of land use land cover it has been observed that there is a rapid increase of urban area from 63% in 2003 to 69% in 2018 and vegetation cover decreased from 36% in 2003 to 31% in 2018. The percentage decrease of vegetation cover from 2003 to 2018 is 5% and the percentage increase in urban area is 6% which is still continuing at an even faster rate. Data preparation tasks can be greatly minimized by using analysis ready open data available in the public domain for carrying out such studies. The approach followed in the study will assist researchers to carry out a quick spatio-temporal analysis for the identification of areas susceptible to increasing variability of LST. Also, it provides a basis to understand and manage urban stress, one of the major causes of unalterable damages to the environment.

Effects of Quality Coffee Production by Smallholders on Local Land Use and Land Cover in Yirgacheffe, Southern Ethiopia

ABSTRACT The objectives of the study are to identify LULC types that have undergone changes due to quality coffee production and assess smallholders’ perceptions of the LULC changes associated with their coffee production practices in Yirgacheffe coffee area, southern Ethiopia. The study used Landsat satellite images of 1988, 2003, and 2018 to examine the LULC change. In addition, household surveys and focus group discussions were carried out to assess land management practices in the study area. The findings show that forest coffee (FC) cover decreased at a rate of 1.02% per year in the 30-year time, which was mostly changed to semi-forest and semi-plantation coffee cover. The study area has also experienced an increase in semi-plantation cover (0.07% per year), semi-forest coffee (0.9% per year) over the study period, showing a gradual decrease in vegetation cover. This also suggests that a major driving force for the local LULC change is the increasing demand for coffee in the global and national market, as evident from the increasing trend of coffee export from Ethiopia. Survey data show that quality coffee production drives coffee agroforest conversion while it has potitive effects on land management practices by smallholders. Reducing the current heavy dependence of livelihoods on coffee as the single most important commodity is likely to enhance sustainability of the coffee agroforests in the area.

Monitoring forest landcover changes in the Eastern Sundarban of Bangladesh from 1989 to 2019

The present study aims to estimate areal extent of the mangrove forest cover in the eastern Sundarban of Bangladesh from 1989 to 2019 to understand mangrove dynamic over the last 30 years. Freely available Landsat TM of 1989, 2014, and L8 OLI imagery were used to generate land use/land cover (LU/LC) map for the study area using maximum likelihood (MaxLike) algorithm. Results of previous investigations among different scientists and researchers were used to develop a conceptual background and also included in this paper to find out the causes that relate to forest cover change in the study area. Study results show that the vegetation cover of Sharankhola range in Sundarban has decreased by 0.44% over last 30 years (from 1989 to 2019). Water body has increased (1.30%) with the decrease in vegetation cover. Classified map of 2014 and 2019 shows that 2.66% vegetation cover of the study area was lost in 2014 based on 1989 while 2.22% vegetation cover was gained in 2019 based on 2014. The overall accuracy of Landsat TM (1989), TM (2014), and L8 OLI (2019) were 80%, 82.85%, and 84.28%, respectively. Its accuracy would increase if it is supplemented by extensive ground verification data and hybrid satellite data of different spectral and spatial resolution.

Monitoring Vegetation Patterns at the Syrdarya River Basin from 2000 to 2015 using MODIS Data

The dependence of vegetation condition dynamics as expressed by Normalized Difference Vegetation Index (NDVI) from hydro-climatic factors (Multiyear precipitation, land surface temperature) in the Syrdarya River Basin (SRB) was analyzed for the period of 16 years from 2000 to 2015. The analysis demonstrated a different correlation between NDVI and hydrometric parameters. According to experimental analyses, the average NDVI values reached a maximum in April and minimum in October, while the annual average values of land surface temperature were observed maximum in June and minimum in October. Correlation between precipitation and NDVI was positive and extraordinarily strong in Spring while the correlation between Land Surface Temperature (LST) and NDVI was found negative and strong. Correlation between LST and NDVI changed from positive in spring to negative in summer due to an increase in seasonal temperature and found a decrease of vegetation cover throughout the Syrdarya river basin. Desert vegetation area in plain part of SRB decreased while NDVI of cropland area in Syrdarya and Shu river basins remained the same or increased. Hydro-climatic factors negatively affected a decrease in vegetation cover, which leads to desertification processes.