Identification Of Land Use Research Articles

Characterization and evaluation of environmental units as a management and conservation strategy of the Palmira Desert (Ecuador)

Globally, deserts are considered as fragile and unique biomes. They have the capacity to provide various ecosystem services, and possess great tourism potential and scientific value, but are also susceptible to modifications or alterations in their geomorphology. Despite the strategic importance of these ecosystems at a global level, there is limited specialized research directed towards the study of deserts in Ecuador. The purpose of this research is to determine the susceptibility to degradation and carrying capacity of environmental units to support tourist and recreational activities, thereby offering the community a sustainable resource over time. The delimitation of environmental units was carried out based on the identification of land use and land cover through the supervised classification method. Multicriteria analysis was used to obtain the areas susceptible to degradation, where the environmental units were evaluated through expert evaluation. Once the results of the previous methodologies were obtained, areas with environmental and tourist potential were identified using the hosting capacity matrix, which is based on anthropic activities developed in the area. Subsequently, a proposal for conservation strategies and activities at the national and local levels was formulated. Five categories of land use and land cover were identified, resulting in 11 environmental units represented in spatial data. It was determined that approximately 31% of the territory is more susceptible to degradation, while the remaining 69% is less susceptible. Based on the susceptibility of these areas, 7 activities and 2 tourist routes were designated to help reduce anthropogenic pressure. Additionally, the community was provided with 5 proposals for conservation strategies at the national level, 4 at the local level, and 4 activities to be developed based on the findings identified during the investigation. This highlights the significant potential that the Palmira Desert has to become a conservation area.

ENDAU ROMPIN NATIONAL PARK LAND USE LAND COVER CHANGES USING REMOTE SENSING APPROACH

Identification of land use and land cover in forest areas can be challenging due to various land cover types within a forest can be similar, making it hard to differentiate between them using remote sensing techniques. We hypothesized that random forest classification (RF) would outperform maximum likelihood (ML) in the classification of land use and land cover (LULC) in forest areas compared to maximum likelihood (ML). To verify this hypothesis, we conducted a comparative analysis, assessing the accuracy of RF and ML in the classification of LULC within the Taman Negara Endau-Rompin (TNER) region, utilizing Landsat 8 imagery. An accuracy assessment demonstrated that the RF classifier (overall accuracy: 87% kappa coefficient: 0.778, performed better than ML classifying land cover (overall accuracy 77% with kappa accuracy: 0.473) Our results suggest that both methods are able to classify land cover of forest area, but RF is more accurate than ML. From the classification result of RF classification, we calculate the land cover changes of TNER from 2013 to 2022. results showed that there are small changes of forest area were found in TNER. The total forest area decreases from 163250.089 ha to 144765.46 ha during 2013 to 2022. This finding suggests that the effectiveness of the protected area in mitigating deforestation in its surrounding regions may be somewhat limited, as indicated by the observed minor changes.

Type Identification of Land Use in Metro Station Area Based on Spatial–Temporal Features Extraction of Human Activities

As a social carrier, a city is a place of human activities, and human activities also shape and influence the city through its dynamic demand. The in-depth understanding of urban functions is important for urban planning, and the full utilization of the spatial–temporal variation of human activities can be more effective in the identification of land functions. However, the complete extraction of time series features is one of the difficulties. To solve the above problems, the paper explores the identification of land use types based on human activity feature extraction, by taking Beijing as an example. Firstly, this paper constructs a time series that characterizing the change of passenger flow in the metro station area with AFC data, and realizes the feature extraction and type clustering of the time series. Secondly, this paper forms an index system for land use type identification by introducing POI-based indicators, which achieves a comprehensive representation of population activity data. Finally, this paper constructs a land use type identification model based on multi-source human activity data by using GBDT classifier. The results show that the model has high recognition accuracy. It is found that the fusion application of AFC and POI improves the land use recognition accuracy in the case of “Consistent change in time series but different types of demand”, and the different POI triggers the seemingly overall stable but random activity demand within the city. The research results promote the innovative application of open-source big data in the field of urban planning.

Identification of land use and land cover using the image Landsat 8 in upstream Lematang sub-watershed by support vector machine and random trees methods

Accuracy of data and information on various types of land use and land cover (LULC) is crucial in watershed management planning. The development of remote sensing technology in predicting LULC has been widely used, such as the Support Vector Machine (SVM) and Random Trees (RT) methods. Both methods are part of the model of machine learning and belong to the category of supervised learning. This method can predict, study, and recognize data patterns from the results of training data. The aim of this research is the use of SVM and RT methods for the identification of LULC. Based on the SVM method analysis results, the forest area is 211.61 km2, coffee plantations are 32.92 km2, shrubs are 16.85 km2, built-up/bare land is 3.40 km2, the water body is 3.17 km2, and cloud/shadow is 6.79 km2. While the RT method obtained a forest area of 215.51 km2, coffee plantations covering an area of 29.32 km2, shrubs are covering an area of 16.32 km2, built-up/bare land surrounding an area of 7.22 km2, water body covering an area of 2.62 km2, and cloud/shadow covering an area of 3.75 km2. Overall, the percentage accuracy of the SVM method of 87%, and the RT of 86%. The success rate of modeling SVM (AUC = 0.73) and RT (AUC = 0.75) based on the results of data validation using the area under the receiver operating characteristic curve (AUC) was considered fair good.

Identification of Land Use and Land Change due to its impact on Groundwater Level in Surakarta city

Surakarta City is the center of economic activity in the great Solo area. As a result, land use changes occur very quickly. This study aims to identify land use and land changes due to their effects on groundwater levels in Surakarta City. The method used in this research is a descriptive-quantitative method. The analysis technique used is time series, by comparing image data for 2003, 2008, and 2019. The image is obtained from Google Earth and then visually interpreted in a GIS application to get the land use area. Land use in Surakarta City includes buildings, agriculture, vacant land, tourist land, waters, settlements, green open spaces, rice fields, shrubs, and dryland agriculture. The results of image processing show that there is a change in land use in Surakarta City. The results show that there is a relationship between land use/change with an increase in population and ground water use. The areas most affected by the decline in groundwater are in the central part of the city of Surakarta, especially in the villages of Sangkrah and Semanggi.

STRATEGY TO PLACE AIR QUALITY MONITORING STATIONS IN AREQUIPA INDUSTRIAL PARK

Industrial development in urban areas is a continuing problem for citizens and industries because of the constant air pollution complaints and respiratory illness. There is a lack of data in Arequipa, Peru for the environmental guidance of air quality and public health protection. The strategy proposed in this report is to determine the optimal locations of monitoring stations in Arequipa Industrial Park. Using the Analytic Hierarchy Process (AHP), we evaluated their relative preferences for locating each monitoring station. The strategy includes three main activities: 1) identification of land use (i.e., industrial, bus station, urban), and data collection (meteorology, chemical pollutants), 2) development and a hierarchy of criteria by AHP, and 3) design of the monitoring network by weighted overlay and geographic information system methods. First, three sustainable criteria (environmental, social, and economic) are evaluated with nine sub-criteria. Afterward, with the assigned weights, we obtained a random index of 0.58, a consistency index of 0.04, and a consistency ratio of 0.07. With this confirmation, sustainability was prioritized as follows: environmental (61.9%), social (28.4%), and economic (9.6%). Finally, we propose a monitoring network sustainability; it includes three monitoring stations. Competent parties can use this proposal to develop a rapid diagnostic capacity for both pollution episodes and impact the health of the industrial park Arequipa population.

Performance testing of selected spectral indices in automated extraction of impervious built-up surface features using Resourcesat LISS-III image

The increasing urban density and spatial expansion of urbanized areas result in changes from the natural landscape to impervious surface features. Remote sensing–based spectral indices provide an efficient method in the automated identification of land use and cover classes. However, a common challenge is the accurate extraction of built-up features from satellite images. The original Normalized Difference Built-up Index (NDBI) has been modified by several researchers in the anticipation of improvement of the built-up area classification. The indices adopted in the study are Index-based Built-up Index (IBI), Built Up Index (BUI), NDBI, Modified Built-up Index (MBI), and the newly developed Impervious Built-up Index (IBUI). These indices work on automated kernel-based probabilistic thresholding algorithm to group the index values into built-up and non-built-up areas. This study investigates the performance of the above mentioned five spectral indices on Resourcesat LISS III imageries over the Kolkata-Howrah urban agglomeration, India. The indices were compared and found based on the overall accuracy that the novel IBUI provided the maximum accuracy (94%) in extracting impervious built-up areas. Based on the overall accuracy and kappa statistic the IBUI was chosen for estimating spatio-temporal urban growth of the study area from 2003 to 2018 based on the same LISS-III sensor data. It was calculated that the urban built-up area has increased from 144 to 177 km2.

Quantitative identification and spatial analysis of land use ecological-production-living functions in rural areas on China's southeast coast

The quantitative identification of land use functions (LUFs) forms the basis of land use planning and management. Based on the widely recognized “ecological-production-living” function in sustainable development, a uniform classification and value evaluation system of LUFs for China's rural land use planning and management is established. To highlight the functions of ecological regulation, product supply and living security, the LUFs were divided into 8 primary functions and 20 subfunctions. LUF value evaluation function groups were established based on the indirect or direct value of land use. For empirical research, Fengzhou Town, a typical rural coastal area in southeastern China, was selected as an example. The results show that the values of the ecological, production and living functions account for 14.31%, 44.54% and 41.15% of the total value, respectively. This finding indicates that the primary direction of land use in the study area is oriented toward pursuing the production and living functions. The spatial distribution of the values of the production and living functions shows obvious consistency, and they are also clearly complementary with the ecological function. More than 90% of the land area is assigned double or triple functions, which indicates that the multifunctional characteristics of land use are significant, while the spatial function zoning is disordered. These findings are consistent with the socioeconomic development of the study area, demonstrating that the established classification and value evaluation system of LUFs can reliably reflect realistic land use and will provide scientific support for the multifunctional utilization and effective management of rural land in China.

Process for the identification of land use in environment preservation areas in the municipality of São José dos Campos – SP

The present article deals with the urbanization process in the Paraíba do Sul river basin, covering social occupation under consideration of the legal provisions for areas surrounding water resources. The study has the purpose of assessing the Environment Preservation Area, called APA Banhado, in the city of São José dos Campos, a municipality belonging to the Metropolitan Region of the Paraíba Valley and the Northern Sea Shore (RMVPLN) in the state of São Paulo, Brazil. The research carried out made use of materials and information collected from aerial images obtained from remote sensing databases maintained by public offices, and the provisions of the APA Banhado implementation law. In order to extract information from satellite images, the methodology proposed by Moreira (2011) was applied, which suggests an automatic software classification in the Geographical Information Systems (SIG), with visual interpretation. The maps generated from the classification of the APA Banhado were based on reference data from the years of 1995, 2000, 2005, 2010 and 2015. The results revealed conflicts between the environmental legislation and the land use, which prompted the possibility of generating basic data capable of incorporation into public planning policies and the preserved area environmental management, including a revision of concepts under the environmental preservation, as a future safety water reserve for the metropolitan region of the Paraíba Valley – SP.

MULTIVARIATE STATISTICAL ANALYSIS AND USE OF GEOGRAPHIC INFORMATION SYSTEMS IN RAW WATER QUALITY ASSESSMENT

This study aimed to apply a methodology for evaluating raw water qualityand its relationship with land uses and occupations through multivariatestatistical analysis and Geographic Information System. Hydrogenic potential,water temperature, dissolved oxygen, biochemical oxygen demand,chemical oxygen demand, total nitrogen, total phosphorus, and E. coli weremonitored from August 2012 until March 2013. The geoprocessing toolenabled delimiting the contribution areas of each sampling site, as well asthe individual identification of land use of each area. Principal ComponentAnalysis resulted in: domestic sewage, domestic sewage/agriculture, andindustrial discharge. Significant correlations were identified between thevariable urban area and hydrogenionic potential (ρ = 0.446; p = 0.049),dissolved oxygen (ρ = -0.625; p = 0.003), total nitrogen (ρ = 0.649; p = 0.002),and E. coli (ρ = 0.932; p < 0.001). The methodology enabled to identify thecontribution of land use factors as to water quality.

Land use and land cover changes in Doume Communal Forest in eastern Cameroon: implications for conservation and sustainable management

Large-scale identification of land use and land cover change in a tropical forest is a challenge to landscape designers and forest ecologists. Here, Landsat images acquired during the years 2000, 2009, and 2018 were used to assess the spatial-dynamics of land use and land cover (LULC) during the last two decades (2000–2018). A classification system composed of six classes—dense forest with (high tree density and low tree density), swampy Raphia forest, swampy flooded forest and savanna were designed as LULC for this study. A maximum likelihood classification was used to classify Landsat images into thematic areas. Elsewhere, Landsat-based LULC mapping, post classification at the per-pixel scales and self-knowledge on the land cover change processes were combined to analyze LULC change, forest loss and change trajectories in Doume Communal Forest in eastern Cameroon. The results show that half of the study area changed in 2000–2009 and that the different types of LULC changes increased and involved more diverse and characteristic trajectories in 2009–2018 compared to 2000–2009. Degradation to a dense forest with low tree density and swampy Raphia forest was dominant, and the forest was mostly lost due to trajectories that involved conversion to agroforestry systems (10%), and a lesser extent due to trajectories that involved deforestation to grasslands (7%). The trajectory analyses did thus contribute to a more comprehensive analysis of LULC change and the drivers of forest loss and, therefore, is essential to improve the sustainable management and support spatial planning of the forest.

Land cover change and land use suitability analyses of coastal area in Bantul District, Yogyakarta, Indonesia

Abstract. Afsholnissa S, Hernawan E, Lastini T. 2019. Land cover change and land use suitability analyses of coastal area in Bantul District, Yogyakarta, Indonesia. Biodiversitas 20: 1475-1481. Shrimp farming in the southern coast of Bantul District is considered to be the cause of environmental degradation, decline of mangrove association due to logging for land conversion to shrimp pond and also considered to be the cause of agricultural land for crops around the shrimp pond. The purpose of this study was to identify land use change and analyze the land suitability for shrimp farming, agriculture, and mangrove plant associations. Identification of land use was done using ENVI 5.3. The study identified 34.88 ha (3.16%) empty ponds, 20.97 ha (1.90%) active ponds and as much as 91.93 ha (8.31%) land used for agriculture. The identification of land use change using Google Timelapse revealed a dominant land conversion of sandy soil and mangrove plant area into shrimp ponds in 2007 and continue to increase from 2011 to 2015. Land suitability was evaluated using ArcGIS 10.5 with an overlay method. The results revealed that 634.82 ha area of the land was suitable for shrimp ponds and ​​84.32 ha area were suitable for non-paddy agriculture. Lands suitable for paddy field were about 148.58 ha, and that for mangrove plant association was 616.42 ha. Spatially, coastal area of ​​Bantul District was dominantly suitable for shrimp farming.

Analysis Of Shoreline Changes On Krui Bay, West Coast Of Lampung Province

Shoreline change is a natural process caused by the transported sediment generated by nearshore current to preserve the mass rates. Shoreline change can be a shoreline loss called abrasion as well as shoreline expansion due to sedimentation called accretion. Abrasion and accretion phenomenon can be regarded as a threat if they cause damage or potentially disrupts human activity. Identification of land use as well as geographical and physical condition of a region can be approached by satellite image analysis. This research uses Landsat Satellite imagery to observe shoreline changes based on the position of shoreline recorded through satellite images of 2004, 2010, and 2016. The image of one period overlapped with other period images is used to calculate the increment or decrease of shoreline occurring at a particular location. The results of this study will show the trend of increasing or decreasing the shoreline based on certain time and position

Dynamic Land-Use Map Based on Twitter Data

Location-based social media allows people to communicate and share information on a popular landmark. With millions of data records generated, it provides new knowledge about a city. The identification of land use intends to uncover accurate positions for future urban development planning. The purpose of this research is to investigate the use of social networking check-in data as a source of information to characterize dynamic urban land use. The data from this study were obtained from the social media application i.e., Twitter. Three kinds of data that are prioritized in this research are check-ins (specific location), timestamps, and a user’s status text or post activities. In this study, we propose a grid-based aggregation method to divide the urban area. Two different approaches are compared—rank and clustering methods to group the place’s activities. Then we utilize time distribution frequency to attain the land-use function. In this case, Makassar City, Indonesia, has been selected as the case study. An analysis shows that the check-in activity and the method we proposed can be used to group the actual land-use types.

Analysis and Identification of Landuse on the Coastal Environment of South Sumatra using GIS

Analysis and identification of land use in coastal areas to study the coastal environment Sembilang National Park in South Sumatra using remote sensing system (Geographical Information System) has been conducted from June until November 2014. The objective to study the changes that occur in the coastal environment as well as to identify and estimate the area use change existing mangrove areas in Region I of the Section of Sembilang National Park Management, South Sumatra. Methods for the analysis and identification based on Landsat image data 8 in 2013 using ENVI 4.3 and Arc GIS 9.3 software as well as the conversion of ground check. The results showed the use of land in the mangrove areas in the region I of the Section of Sembilang National Park Management classified into 5 classes, namely mangrove, water area, mangrove shrubs, mainland mud, and ponds areas. The area of each class were observed in sequence as follows 8921.19 ha (41.69%) as mangrove area; 3934.3 ha (23.28%) as water areas; 3.608,52 ha (17.41%) as area of mangrove shrubs; 1.649,11 ha (9.05%) as mainland mud and settlement area; and 1.561,04 ha (8.57%) as the ponds area. The percentage of the class obtained as a result of the conversion of ground check, and it is based on the land use map data over the total area observed.

An Integrated Software Framework to Support Semantic Modeling and Reasoning of Spatiotemporal Change of Geographical Objects: A Use Case of Land Use and Land Cover Change Study

Evolving Earth observation and change detection techniques enable the automatic identification of Land Use and Land Cover Change (LULCC) over a large extent from massive amounts of remote sensing data. It at the same time poses a major challenge in effective organization, representation and modeling of such information. This study proposes and implements an integrated computational framework to support the modeling, semantic and spatial reasoning of change information with regard to space, time and topology. We first proposed a conceptual model to formally represent the spatiotemporal variation of change data, which is essential knowledge to support various environmental and social studies, such as deforestation and urbanization studies. Then, a spatial ontology was created to encode these semantic spatiotemporal data in a machine-understandable format. Based on the knowledge defined in the ontology and related reasoning rules, a semantic platform was developed to support the semantic query and change trajectory reasoning of areas with LULCC. This semantic platform is innovative, as it integrates semantic and spatial reasoning into a coherent computational and operational software framework to support automated semantic analysis of time series data that can go beyond LULC datasets. In addition, this system scales well as the amount of data increases, validated by a number of experimental results. This work contributes significantly to both the geospatial Semantic Web and GIScience communities in terms of the establishment of the (web-based) semantic platform for collaborative question answering and decision-making.

A Tale of Two Forest Carbon Assessments in the Eastern United States: Forest Use Versus Cover as a Metric of Change

The dynamics of land-use practices (for example, forest versus settlements) is often a major driver of changes in terrestrial carbon (C). As the management and conservation of forest land uses are considered a means of reducing future atmospheric CO2 concentrations, the monitoring of forest C stocks and stock change by categories of land-use change (for example, croplands converted to forest) is often a requirement of C monitoring protocols such as those espoused by the Intergovernmental Panel on Climate Change (that is, Good Practice Guidance and Guidelines). The identification of land use is often along a spectrum ranging from direct observation (for example, interpretation of owner intent via field visits) to interpretation of remotely sensed imagery (for example, land cover mapping) or some combination thereof. Given the potential for substantial differences across this spectrum of monitoring techniques, a region-wide, repeated forest inventory across the eastern U.S. was used to evaluate relationships between forest land-use change (derived from a forest inventory) and forest cover change (derived from Landsat modeling) in the context of forest C monitoring strategies. It was found that the correlation between forest land-use change and cover change was minimal (<0.08), with an increase in forest land use but a net decrease in forest cover being the most frequent observation. Cover assessments may be more sensitive to active forest management and/or conversion activities that can lead to confounded conclusions regarding the forest C sink (for example, decreasing forest cover but increasing C stocks in industrial timberlands). In contrast, the categorical nature of direct land-use field observations reduces their sensitivity to forest management activities (for example, clearcutting versus thinning) and recent disturbance events (for example, floods or wildfire) that may obscure interpretation of C dynamics over short time steps. While using direct land-use observations or cover mapping in forest C assessments, they should not be considered interchangeable as both approaches possess idiosyncratic qualities that should be considered when developing conclusions regarding forest C attributes and dynamics across large scales.

Analysis and Identification of Landuse on the East Coast of South Sumatera Using GIS

Analysis and identification of land use in coastal areas to study the coastal environment Sembilang National Park in South Sumatra using remote sensing system (Geoinformation System) has been conducted from June until November 2014. The objective to study the changes that occur in the coastal environment as well as to identify and estimate the area use change existing mangrove areas in Region I of the Section of Sembilang National Park Management, South Sumatra. Methods for the analysis and identification based on Landsat image data 8 in 2013 using ENVI 4.3 and Arc GIS 9.3 software as well as the conversion of ground check. The results showed the use of land in the mangrove areas in the region I of the Section of Sembilang National Park Management classified into 5 classes, namely mangrove, water area, mangrove shrubs, mainland mud, and ponds areas. The area of each class were observed in sequence as follows 8921.19 ha (41.69%) is a mangrove area; 3934.3 ha (23.28%) is water areas; 3.608,52ha (17.41%) is an area of mangrove shrubs; 1.649,11ha (9.05%) is mainland mud and settlement area; and 1.561,04ha (8.57%) is the ponds areas. The percentage of the class obtained as a result of the conversion of ground check is based on the land use map data with the total area observed.

Analysis and Identification of Landuse on the East Coast of South Sumatera Using GIS

Analysis and identification of land use in coastal areas to study the coastal environment Sembilang National Park in South Sumatra using remote sensing system (Geoinformation System) has been conducted from June until November 2014. The objective to study the changes that occur in the coastal environment as well as to identify and estimate the area use change existing mangrove areas in Region I of the Section of Sembilang National Park Management, South Sumatra. Methods for the analysis and identification based on Landsat image data 8 in 2013 using ENVI 4.3 and Arc GIS 9.3 software as well as the conversion of ground check. The results showed the use of land in the mangrove areas in the region I of the Section of Sembilang National Park Management classified into 5 classes, namely mangrove, water area, mangrove shrubs, mainland mud, and ponds areas. The area of each class were observed in sequence as follows 8921.19 ha (41.69%) is a mangrove area; 3934.3 ha (23.28%) is water areas; 3.608,52ha (17.41%) is an area of mangrove shrubs; 1.649,11ha (9.05%) is mainland mud and settlement area; and 1.561,04ha (8.57%) is the ponds areas. The percentage of the class obtained as a result of the conversion of ground check is based on the land use map data with the total area observed.

Continuous estimation of baseflow in snowmelt‐dominated streams and rivers in the Upper Colorado River Basin: A chemical hydrograph separation approach

AbstractEffective science‐based management of water resources in large basins requires a qualitative understanding of hydrologic conditions and quantitative measures of the various components of the water budget, including difficult to measure components such as baseflow discharge to streams. Using widely available discharge and continuously collected specific conductance (SC) data, we adapted and applied a long established chemical hydrograph separation approach to quantify daily and representative annual baseflow discharge at 14 streams and rivers at large spatial (&gt; 1000 km2 watersheds) and temporal (up to 37 years) scales in the Upper Colorado River Basin. On average, annual baseflow was 21–58% of annual stream discharge, 13–45% of discharge during snowmelt, and 40–86% of discharge during low‐flow conditions. Results suggest that reservoirs may act to store baseflow discharged to the stream during snowmelt and release that baseflow during low‐flow conditions, and that irrigation return flows may contribute to increases in fall baseflow in heavily irrigated watersheds. The chemical hydrograph separation approach, and associated conceptual model defined here provide a basis for the identification of land use, management, and climate effects on baseflow.