Hybrid Model for Forecasting of Changes in Land Use and Land Cover Using Satellite Techniques

Márquez‐Romance, Adriana Mercedes; Pérez, Enrique J. Martínez; Rey, Demetrio

doi:10.1109/jstars.2018.2885612

Cited by 32 publications

(36 citation statements)

References 28 publications

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“…The predicted maps 2020-2030 showed acceptable values within the ranges reported by the 1986-2000 series, confirming that the forecasted stochastic component (semivariance coeffcients) was able to reproduce the spatiotemporal pattern corresponding to the time series in period 1986-2000. Similar results have been obtained in the studies of the San Diego River basin, Venezuela, [25,27], and the state of Anzoátegui, Venezuela, [37]; as well, as the studies of Cortez 2016, in the Guárico state. The annual variability of precipitation in Venezuela is highly influenced by prevailing geographic conditions such as the presence of the La Costa mountain range and its interaction with the Caribbean Sea [36].…”

Section: Precipitation and Evaporation Forecastssupporting

confidence: 88%

“…The procedure is based on those carried out for studies made by the Center of Hydrological and Environmental Research of the University of Carabobo, Venezuela, in the basins of the San Diego and Pao Rivers, Carabobo state, Venezuela, [24][25][26][27]. It consists of two stages: 1) Compilation of information: a) Meteorology, b) Landsat satellite images and c) Digital elevation model.…”

Section: Methodsmentioning

confidence: 99%

“…The analysis of point data were carried out based on the derivation and layout of the so-called semivariances obtained by [31][32][33] for the differences between the neighboring values (Eq.2): Where z (si) is the value of the target variable at some sampled location and z (si + h) is the value of the neighbor at the distance si + h. For n observations of points, n (n -1) / 2 pairs are produced for which a semivariance can be calculated. An experimental variogram is adjusted using linear, spherical, exponential, circular, Gaussian, Bessel, power and similar models [25,27].…”

Section: Modeling the Spatial Statistical Predictionmentioning

confidence: 99%

“…Another variable influenced by the occurrence of extreme hydrological events is land use and land cover change. A hybrid model for forecasting changes in land use and land cover using satellite techniques helps to reproduce the spatial-temporal variation picked up by the reception of the reflectance variable, [26][27].…”

Section: Introductionmentioning

confidence: 99%

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Predicción espacio-temporal del balance hídrico en la cuenca del río Urama, Venezuela

Lopez

Márquez‐Romance

Pérez

2021

DYNA

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En este artículo se presenta un novedoso método de predicción espacio-temporal de variables de balance hídrico en el humedal de la cuenca del río Urama, Venezuela. La metodología consta de dos etapas: 1. Compilación de información utilizando datos meteorológicos de precipitación y evaporación de 73 estaciones climáticas; imágenes Landsat y modelo de elevación digital para la cuenca en estudio. 2. Procesamiento de información: a) modelización de predicción estadística espacial; b) modelo de pronóstico; y, c) pronóstico de series temporales de variables de balance hídrico. La ventaja del método está en utilizar datos para pronosticar variables de balance hídrico mediante modelos de predicción estadística espacial seleccionando aquellas con tendencias estacionales que se aproximan a las observaciones. Se obtuvieron coeficientes de correlación mayores que 0.99, indicando predicciones satisfactorias, por lo que el método, es adecuado para reproducir el perfil espacio-temporal de períodos futuros para ser aplicados en gestión sostenible de humedales.

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Section: Precipitation and Evaporation Forecastssupporting

confidence: 88%

Section: Methodsmentioning

confidence: 99%

Section: Modeling the Spatial Statistical Predictionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Predicción espacio-temporal del balance hídrico en la cuenca del río Urama, Venezuela

Lopez

Márquez‐Romance

Pérez

2021

DYNA

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“…Numerous studies are carried out on LU prediction and several models have been proposed for projecting future LU (Marquez et al, 2019;Verburg & Overmars, 2009). Most of the models are Geographic Information System (GIS)-based models, machine learning models, and a combination of GIS and machine learning models known as hybrid models (Gaur et al, 2020;Shrestha et al, 2018;Venkatesh et al, 2020a;Verburg & Overmars, 2009;.…”

Section: Introductionmentioning

confidence: 99%

Effects of land use and climate change on water scarcity in rivers of the Western Ghats of India

Sharannya

Venkatesh

Mudbhatkal

et al. 2021

Environ Monit Assess

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CC on regional hydrology, a scenario approach was adopted and three scenarios depicting near-future (2006-2040), mid-future (2041-2070), and farfuture (2071-2100) based on climate were established. The present rate of LU change indicated a reduction in forest cover by 20% and an increase in urbanized areas by 9.5% between 1988 and 2016. It was estimated that forest cover in the catchments may be expected to halve compared to the presentday LU (55% in 2016 to 23% in 2075), along with large-scale conversion to agricultural lands (13.5% in 2016 to 49.5% in 2075). As a result of changes to LU and forecasted climate, it was found that rivers in the Western Ghats of India might face scarcity of fresh water in the next two decades until the year 2040. However, because of large-scale LU conversion toward the year 2050, streamflow in rivers might increase as high as 70.94% at certain times of the year. Although an increase in streamflow is perceived favorable, the streamflow changes during summer and winter may be expected to affect the cropping calendar and crop yield. The changes to streamflow were also linked to a 4.2% increase in ecologically sensitive wetlands of the Aghanashini river catchment.

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An approach for management modeling of a tropical wetland

López-Calatayud

Márquez‐Romance

Pérez

et al. 2021

Environmental Quality Mgmt

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This article has proposed an approach for management modeling of a tropical wetland. The model is based on the combination of the components and criteria established in the 4th Strategic Plan (SP4) 2016–2024 of the Ramsar Convention on Wetlands with Bloom's Taxonomy. The management modeling of a tropical wetland consists of the formulation of a multiple criteria matrix of wetland management strategic plan (WMSP) for 18 Latin countries based an integral wetland management model index (WMMI‐integral) associated with Bloom's Taxonomy. The WMMI‐integral is categorized into five levels that include knowledge, comprehension, application, evaluation, and creation, which are influenced by the implementation in the Latin‐countries of targets linked to the goals of SP4. The results are fitting a multiple linear regression model to describe the relationship between WMMI‐Integral and five parameters. The proposed wetland management model constitutes a tool to contribute a decision for assessing sustainability by coupling an approach that considers wetland management components and attributes for wetland inventory, diagnosis, assessment, creation.

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Hybrid Model for Forecasting of Changes in Land Use and Land Cover Using Satellite Techniques

Cited by 32 publications

References 28 publications

Predicción espacio-temporal del balance hídrico en la cuenca del río Urama, Venezuela

Predicción espacio-temporal del balance hídrico en la cuenca del río Urama, Venezuela

Effects of land use and climate change on water scarcity in rivers of the Western Ghats of India

An approach for management modeling of a tropical wetland

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