Land change modeler for predicting land cover change in Banjarmasin City, South Borneo (2014 - 2022)

Abstract. The establishment of Batam City as a Free Trade Zone (FTZ) encourages the city’s growth, as manifested in massive built-up area expansion. The aim of this paper was to analyze the pattern of built-up area expansion in FTZ Batam in 2035 based on the corresponding pattern from 2000 to 2015. Land Change Modeler (LCM) was the instrument used to determine and analyze land cover changes in 2000–2015, from which future changes or built-up area expansion in 2035 were predicted using the validated 2020 land cover map as reference. The validation test based on the Kappa Index of Agreement yielded 96%. The prediction results showed that, compared with 2020, the built-up area in 2035 would have increased by 31.8% and expanded towards the outskirts of FTZ Batam. This sprawl follows the location of the primary activity centers in the FTZ, as allocated in Presidential Regulation of the Republic of Indonesia No. 87 of 2011. A new expansion is expected to continue into existing open space and extensive untouched forest areas. These research findings provide a concept that can be utilized to formulate certain policies and regional planning in the future.

Download Full-text

Implementation of CCDC to produce the LCMAP Collection 1.0 annual land surface change product

10.5194/essd-2021-202 ◽

2021 ◽

Author(s):

George Xian ◽

Kelcy Smith ◽

Danika Wellington ◽

Josephine Horton ◽

Qiang Zhou ◽

...

Keyword(s):

United States ◽

Land Cover ◽

Land Cover Change ◽

Land Surface ◽

The United States ◽

Land Resource ◽

Land Change ◽

Continuous Change ◽

Ancillary Data ◽

Surface Change

Abstract. The increasing availability of high-quality remote sensing data and advanced technologies have spurred land cover mapping to characterize land change from local to global scales. However, most land change datasets either span multiple decades at a local scale or cover limited time over a larger geographic extent. Here, we present a new land cover and land surface change dataset created by the Land Change Monitoring, Assessment, and Projection (LCMAP) program over the conterminous United States (CONUS). The LCMAP land cover change dataset consists of annual land cover and land cover change products over the period 1985–2017 at 30-meter resolution using Landsat and other ancillary data via the Continuous Change Detection and Classification (CCDC) algorithm. In this paper, we describe our novel approach to implement the CCDC algorithm to produce the LCMAP product suite composed of five land cover and five land surface change related products. The LCMAP land cover products were validated using a collection of ~25,000 reference samples collected independently across CONUS. The overall agreement for all years of the LCMAP primary land cover product reached 82.5 %. The LCMAP products are produced through the LCMAP Information Warehouse and Data Store (IW+DS) and Shared Mesos Cluster systems that can process, store, and deliver all datasets for public access. To our knowledge, this is the first set of published 30 m annual land cover and land cover change datasets that span from the 1980s to the present for the United States. The LCMAP product suite provides useful information for land resource management and facilitates studies to improve the understanding of terrestrial ecosystems and the complex dynamics of the Earth system. The LCMAP system could be implemented to produce global land change products in the future.

Download Full-text

Land use/land cover change and statistical modelling of cultivated land change drivers in Nigeria

Regional Environmental Change ◽

10.1007/s10113-017-1186-5 ◽

2017 ◽

Vol 18 (1) ◽

pp. 247-259 ◽

Cited By ~ 25

Author(s):

Aisha Olushola Arowolo ◽

Xiangzheng Deng

Keyword(s):

Land Use ◽

Land Cover ◽

Land Cover Change ◽

Statistical Modelling ◽

Land Use Land Cover ◽

Cultivated Land ◽

Land Change

Download Full-text

Análise espaço-temporal das mudanças no uso e cobertura do solo no município de São Thomé das Letras / Spatio-temporal land use land and cover changes analysis in the São Thomé das Letras municipality

Caderno de Geografia ◽

10.5752/p.2318-2962.2016v26n45p79 ◽

2015 ◽

Vol 26 (45) ◽

pp. 79

Author(s):

Nayara Lage Silva ◽

Bráulio Magalhães Fonseca

Keyword(s):

Remote Sensing ◽

Land Use ◽

Land Cover ◽

Temporal Analysis ◽

Minas Gerais ◽

Time Range ◽

Land Change ◽

Land Change Modeler ◽

Rupestrian Fields ◽

Multi Temporal

O mapeamento do uso e cobertura do solo por meio da utilização de dados de sensoriamento remoto e técnicas de processamento digital de imagens tem se difundindo globalmente por permitir uma análise espacial e dinâmica das tipologias de uso e cobertura. A mineração é uma das atividades transformadoras do meio que mais causa impactos aos ambientes naturais, mesmo que de maneira concentrada, devido ao fator de rigidez locacional da atividade. É uma atividade que demanda controle ambiental em todo processo para reduzir os impactos negativos e garantir o equilíbrio dos processos ambientais. Neste contexto o trabalho objetivou: 1 - realizar uma análise multitemporal da cobertura do solo no município de São Thomé das Letras, no estado de Minas Gerais; 2 - quantificar e espacializar as alterações no período determinado entre 1984 a 2011. Buscou-se visualizar o comportamento da atividade de mineração desde seu início até os dias atuais, e consequentemente, observar a dinamicidade das mudanças ocorridas na cobertura do solo das outras classes mapeadas. Para o mapeamento do uso e cobertura do solo foi utilizado o programa SPRING/INPE e para a análise temporal/espacial de mudanças utilizou-se o modelo Land Change Modeler acoplado ao programa IDRISI. A partir da análise dos resultados foi possível quantificar e espacializar o avanço da mineração sob o campo rupestre/afloramento rochoso; a perda substancial da vegetação densa no intervalo do período analisado; o crescimento exponencial da ocupação urbana; e o surgimento da atividade reflorestamento.Palavras-chave: Análise multitemporal. Uso e Cobertura do Solo. Mineração. Sensoriamento Remoto. AbstractThe land use and land cover mapping using remote sensing data and techniques of digital image processing has been widely used by enabling a dynamic spatial analysis of land use and land cover types. Mining is a human activity that transforms the landscape and is one of the most impactful for natural environments, even in a concentrated way, due to locational rigidity factor of activity. It is an activity that requires environmental control throughout the process to reduce the negative impacts and ensure a balance of environmental processes. In that context the study aimed to: 1 - conduct a multi-temporal analysis of land use and land cover in São Thomé das Letras municipality, in Minas Gerais State, Brazil; 2- quantify and map changes from 1984 to 2011 in the area studied. We attempted to visualize the behavior of mining activity from its inception to the present day, and therefore observe the dynamics of change in land use and land cover of other mapped classes. To map land use and land cover was used SPRING/INPE software and to analyze the changes used the Land Change Modeler model, coupled to the IDRISI software. From the analysis of the results was possible to quantify and spatialize the advancement of mining under the outcrop and Rupestrian Fields; occurred substantial loss of dense vegetation in the analyzed time range; the exponential growth of urban occupation; and the emergence of reforestation activity. Keywords: Multi-temporal analysis. Land Use and Land Cover. Mining. Remote sensing.

Download Full-text

A Suite of Tools for Continuous Land Change Monitoring in Google Earth Engine

Frontiers in Climate ◽

10.3389/fclim.2020.576740 ◽

2020 ◽

Vol 2 ◽

Author(s):

Paulo Arévalo ◽

Eric L. Bullock ◽

Curtis E. Woodcock ◽

Pontus Olofsson

Keyword(s):

Time Series ◽

Land Cover ◽

Land Cover Change ◽

Google Earth ◽

Data Availability ◽

Ecosystem Change ◽

Series Data ◽

Land Change ◽

Continuous Change ◽

Google Earth Engine

Land cover has been designated by the Global Climate Observing System (GCOS) as an Essential Climate Variable due to its integral role in many climate and environmental processes. Land cover and change affect regional precipitation patterns, surface energy balance, the carbon cycle and biodiversity. Accurate information on land cover and change is essential for climate change mitigation programs such as UN-REDD+. Still, uncertainties related to land change are large, in part due to the use of traditional land cover and change mapping techniques that use one or a few remotely sensed images, preventing a comprehensive analysis of ecosystem change processes. The opening of the Landsat archive and the initiation of the Copernicus Program have enabled analyses based on time series data, allowing the scientific community to explore global land cover dynamics in ways that were previously limited by data availability. One such method is the Continuous Change Detection and Classification algorithm (CCDC), which uses all available Landsat data to model temporal-spectral features that include seasonality, trends, and spectral variability. Until recently, the CCDC algorithm was restricted to academic environments due to computational requirements and complexity, preventing its use by local practitioners. The situation has changed with the recent implementation of CCDC in the Google Earth Engine, which enables analyses at global scales. What is still missing are tools that allow users to explore, analyze and process CCDC outputs in a simplified way. In this paper, we present a suite of free tools that facilitate interaction with CCDC outputs, including: (1) time series viewers of CCDC-generated time segments; (2) a spatial data viewer to explore CCDC model coefficients and derivatives, and visualize change information; (3) tools to create land cover and land cover change maps from CCDC outputs; (4) a tool for unbiased area estimation of key climate-related variables like deforestation extent; and (5) an API for accessing the functionality underlying these tools. We illustrate the usage of these tools at different locations with examples that explore Landsat time series and CCDC coefficients, and a land cover change mapping example in the Southeastern USA that includes area and accuracy estimates.

Download Full-text