scholarly journals Local Perception of Drivers of Land-Use and Land-Cover Change Dynamics across Dedza District, Central Malawi Region

2019 ◽  
Vol 11 (3) ◽  
pp. 832 ◽  
Author(s):  
Maggie G. Munthali ◽  
Nerhene Davis ◽  
Abiodun M. Adeola ◽  
Joel O. Botai ◽  
Jonathan M. Kamwi ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Management ◽  
Agricultural Land ◽  
Management Strategies ◽  
Rural Livelihoods ◽  
Remotely Sensed ◽  
Remotely Sensed Data ◽  
Barren Land ◽  
Lulc Changes

Research on Land Use and Land Cover (LULC) dynamics, and an understanding of the drivers responsible for these changes, are very crucial for modelling future LULC changes and the formulation of sustainable and robust land-management strategies and policy decisions. This study adopted a mixed method consisting of remote sensing and Geographic Information System (GIS)-based analysis, focus-group discussions, key informant interviews, and semi-structured interviews covering 586 households to assess LULC dynamics and associated LULC change drivers across the Dedza district, a central region of Malawi. GIS-based analysis of remotely sensed data revealed that barren land and built-up areas extensively increased at the expense of agricultural and forest land between 1991 and 2015. Analysis of the household-survey results revealed that the perceptions of respondents tended to validate the observed patterns during the remotely sensed data-analysis phase of the research, with 57.3% (n = 586) of the respondents reporting a decline in agricultural land use, and 87.4% (n = 586) observing a decline in forest areas in the district. Furthermore, firewood collection, charcoal production, population growth, and poverty were identified as the key drivers of these observed LULC changes in the study area. Undoubtedly, education has emerged as a significant factor influencing respondents’ perceptions of these drivers of LULC changes. However, unsustainable LULC changes observed in this study have negative implications on rural livelihoods and natural-resource management. Owing to the critical role that LULC dynamics play to rural livelihoods and the ecosystem, this study recommends further research to establish the consequences of these changes. The present study and future research will support decision makers and planners in the design of tenable and coherent land-management strategies.

scholarly journals Integrated Approach to Quantify the Impact of Land Use and Land Cover Changes on Water Quality of Surma River, Sylhet, Bangladesh

Water ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 17
Author(s):  
Abdul Kadir ◽  
Zia Ahmed ◽  
Md. Misbah Uddin ◽  
Zhixiao Xie ◽  
Pankaj Kumar
Keyword(s):  
Water Quality ◽  
Land Use ◽  
Land Cover ◽  
Agricultural Land ◽  
Regression Result ◽  
Barren Land ◽  
Lulc Changes ◽  
The Impact ◽  
Surma River

This study aims to assess the impacts of land use and land cover (LULC) changes on the water quality of the Surma river in Bangladesh. For this, seasonal water quality changes were assessed in comparison to the LULC changes recorded from 2010 to 2019. Obtained results from this study indicated that pH, electrical conductivity (EC), and total dissolved solids (TDS) concentrations were higher during the dry season, while dissolved oxygen (DO), 5-day biological oxygen demand (BOD5), temperature, total suspended solids (TSS), and total solids (TS) concentrations also changed with the season. The analysis of LULC changes within 1000-m buffer zones around the sampling stations revealed that agricultural and vegetation classes decreased; while built-up, waterbody and barren lands increased. Correlation analyses showed that BOD5, temperature, EC, TDS, and TSS had a significant relationship (5% level) with LULC types. The regression result indicated that BOD5 was sensitive to changing waterbody (predictors, R2 = 0.645), temperature was sensitive to changing waterbodies and agricultural land (R2 = 0.889); and EC was sensitive to built-up, vegetation, and barren land (R2 = 0.833). Waterbody, built-up, and agricultural LULC were predictors for TDS (R2 = 0.993); and waterbody, built-up, and barren LULC were predictors for TSS (R2 = 0.922). Built-up areas and waterbodies appeared to have the strongest effect on different water quality parameters. Scientific finding from this study will be vital for decision makers in developing more robust land use management plan at the local level.

scholarly journals Investigating impact of land-use and land cover changes on hydro-ecological balance using GIS: insights from IIT Bombay, India

2021 ◽  
Vol 3 (3) ◽  
Author(s):  
Aman Srivastava ◽  
Pennan Chinnasamy
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Surface Runoff ◽  
Forest Cover ◽  
Google Earth ◽  
Negative Consequences ◽  
Ecological Stability ◽  
Barren Land ◽  
Ecological Balance ◽  
Lulc Changes

AbstractThe present study, for the first time, examined land-use land cover (LULC), changes using GIS, between 2000 and 2018 for the IIT Bombay campus, India. Objective was to evaluate hydro-ecological balance inside campus by determining spatio-temporal disparity between hydrological parameters (rainfall-runoff processes), ecological components (forest, vegetation, lake, barren land), and anthropogenic stressors (urbanization and encroachments). High-resolution satellite imageries were generated for the campus using Google Earth Pro, by manual supervised classification method. Rainfall patterns were studied using secondary data sources, and surface runoff was estimated using SCS-CN method. Additionally, reconnaissance surveys, ground-truthing, and qualitative investigations were conducted to validate LULC changes and hydro-ecological stability. LULC of 2018 showed forest, having an area cover of 52%, as the most dominating land use followed by built-up (43%). Results indicated that the area under built-up increased by 40% and playground by 7%. Despite rapid construction activities, forest cover and Powai lake remained unaffected. This anomaly was attributed to the drastically declining barren land area (up to ~ 98%) encompassing additional construction activities. Sustainability of the campus was demonstrated with appropriate measures undertaken to mitigate negative consequences of unwarranted floods owing to the rise of 6% in the forest cover and a decline of 21% in water hyacinth cover over Powai lake. Due to this, surface runoff (~ 61% of the rainfall) was observed approximately consistent and being managed appropriately despite major alterations in the LULC. Study concluded that systematic campus design with effective implementation of green initiatives can maintain a hydro-ecological balance without distressing the environmental services.

Land Use and Land Cover Changes in Northern Kordofan State of Sudan: A Remotely Sensed Data Analysis

2014 ◽  
pp. 269-283 ◽  
Author(s):  
Mohamed S. Dafalla ◽  
Elfatih M. Abdel-Rahman ◽  
Khalid H. A. Siddig ◽  
Ibrahim S. Ibrahim ◽  
Elmar Csaplovics
Keyword(s):  
Land Use ◽  
Data Analysis ◽  
Land Cover ◽  
Remotely Sensed ◽  
Land Cover Changes ◽  
Remotely Sensed Data

Measuring and monitoring land cover, land use, and vegetation characteristics

2010 ◽  
Author(s):  
Ned Horning ◽  
Julie A. Robinson ◽  
Eleanor J. Sterling ◽  
Woody Turner ◽  
Sacha Spector
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Deciduous Forest ◽  
Land Cover Classification ◽  
Remotely Sensed ◽  
Land Cover Mapping ◽  
Remotely Sensed Data ◽  
Soil P ◽  
Vegetation Characteristics ◽  
Classification Project

In terrestrial biomes, ecologists and conservation biologists commonly need to understand vegetation characteristics such as structure, primary productivity, and spatial distribution and extent. Fortunately, there are a number of airborne and satellite sensors capable of providing data from which you can derive this information. We will begin this chapter with a discussion on mapping land cover and land use. This is followed by text on monitoring changes in land cover and concludes with a section on vegetation characteristics and how we can measure these using remotely sensed data. We provide a detailed example to illustrate the process of creating a land cover map from remotely sensed data to make management decisions for a protected area. This section provides an overview of land cover classification using remotely sensed data. We will describe different options for conducting land cover classification, including types of imagery, methods and algorithms, and classification schemes. Land cover mapping is not as difficult as it may appear, but you will need to make several decisions, choices, and compromises regarding image selection and analysis methods. Although it is beyond the scope of this chapter to provide details for all situations, after reading it you will be able to better assess your own needs and requirements. You will also learn the steps to carry out a land cover classification project while gaining an appreciation for the image classification process. That said, if you lack experience with land cover mapping, it always wise to seek appropriate training and, if possible, collaborate with someone who has land cover mapping experience (Section 2.3). Although the terms “land cover” and “land use” are sometimes used interchangeably they are different in important ways. Simply put, land cover is what covers the surface of the Earth and land use describes how people use the land (or water). Examples of land cover classes are: water, snow, grassland, deciduous forest, or bare soil.

Historical spatiotemporal analysis of land-use/land-cover changes and carbon budget in a temperate peatland (Turkey) using remotely sensed data

2011 ◽  
Vol 31 (3) ◽  
pp. 1166-1172 ◽  
Author(s):  
Fatih Evrendilek ◽  
Suha Berberoglu ◽  
Nusret Karakaya ◽  
Ahmet Cilek ◽  
Guler Aslan ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Carbon Budget ◽  
Spatiotemporal Analysis ◽  
Remotely Sensed ◽  
Land Cover Changes ◽  
Land Use Land Cover ◽  
Remotely Sensed Data

Changes in Land Use Influenced by Anthropogenic Activity

2020 ◽  
Author(s):  
Lang Wang ◽  
Zong-Liang Yang
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Management ◽  
Land Surface ◽  
Anthropogenic Activities ◽  
Urban Expansion ◽  
Management Strategies ◽  
Terrestrial Ecosystems ◽  
Anthropogenic Activity ◽  
Management Actions

The terms “land cover” and “land use” are often used interchangeably, although they have different meanings. Land cover is the biophysical material at the surface of the Earth, whereas land use refers to how people use the land surface. Land use concerns the resources of the land, their products, and benefits, in addition to land management actions and activities. The history of changes in land use has passed through several major stages driven by developments in science and technology and demands for food, fiber, energy, and shelter. Modern changes in land use have been increasingly affected by anthropogenic activities at a scale and magnitude that have not been seen. These changes in land use are largely driven by population growth, urban expansion, increasing demands for energy and food, changes in diets and lifestyles, and changing socioeconomic conditions. About 70% of the Earth’s ice-free land surface has been altered by changes in land use, and these changes have had environmental impacts worldwide, ranging from effects on the composition of the Earth’s atmosphere and climate to the extensive modification of terrestrial ecosystems, habitats, and biodiversity. A number of different methods have been developed give a thorough understanding of these changes in land use and the multiple effects and feedbacks involved. Earth system observations and models are examples of two crucial technologies, although there are considerable uncertainties in both techniques. Cross-disciplinary collaborations are highly desirable in future studies of land use and management. The goals of mitigating climate change and maintaining sustainability should always be considered before implementing any new land management strategies.

scholarly journals Estimating Land Use and Land Cover Change in North Central Georgia: Can Remote Sensing Observations Augment Traditional Forest Inventory Data?

Forests ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 856 ◽  
Author(s):  
Gretchen G. Moisen ◽  
Kelly S. McConville ◽  
Todd A. Schroeder ◽  
Sean P. Healey ◽  
Mark V. Finco ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Forest Inventory ◽  
Remotely Sensed ◽  
Data Sets ◽  
Inventory Data ◽  
Remotely Sensed Data ◽  
North Central ◽  
Forest Inventory Data ◽  
Long Time

Throughout the last three decades, north central Georgia has experienced significant loss in forest land and tree cover. This study revealed the temporal patterns and thematic transitions associated with this loss by augmenting traditional forest inventory data with remotely sensed observations. In the US, there is a network of field plots measured consistently through time from the USDA Forest Service’s Forest Inventory and Analysis (FIA) Program, serial photo-based observations collected through image-based change estimation (ICE) methodology, and historical Landsat-based observations collected through TimeSync. The objective here was to evaluate how these three data sources could be used to best estimate land use and land cover (LULC) change. Using data collected in north central Georgia, we compared agreement between the three data sets, assessed the ability of each to yield adequately precise and temporally coherent estimates of land class status as well as detect net and transitional change, and we evaluated the effectiveness of using remotely sensed data in an auxiliary capacity to improve detection of statistically significant changes. With the exception of land cover from FIA plots, agreement between paired data sets for land use and cover was nearly 85%, and estimates of land class proportion were not significantly different for overlapping time intervals. Only the long time series of TimeSync data revealed significant change when conducting analyses over five-year intervals and aggregated land categories. Using ICE and TimeSync data through a two-phase estimator improved precision in estimates but did not achieve temporal coherence. We also show analytically that using auxiliary remotely sensed data for post-stratification for binary responses must be based on maps that are extremely accurate in order to see gains in precision. We conclude that, in order to report LULC trends in north central Georgia with adequate precision and temporal coherence, we need data collected on all the FIA plots each year over a long time series and broadly collapsed LULC classes.

Sign in / Sign up

Export Citation Format

Share Document