Land cover change detection using GIS and remote sensing techniques: A spatio-temporal study on Tanguar Haor, Sunamganj, Bangladesh

Haque, Md. Inzamul; Basak, Rony

doi:10.1016/j.ejrs.2016.12.003

Cited by 171 publications

(100 citation statements)

References 14 publications

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“…These land use classes served as an implicit measure of the drivers of deforestation and forest degradation (Paneque‐Gálvez et al. ; Haque and Basak ). Training sites were identified randomly with the aid of high resolution Quick Bird images (Google Earth) in ERDAS Imagine (Vieilledent et al.…”

Section: Methodsmentioning

confidence: 99%

“…Moat and Smith (2007) classification was modified to form four distinct classes: forest, vegetation matrix (includes grassland, wooded grassland and highly degraded forest), cultivated land and exposed surface (includes urban areas). These land use classes served as an implicit measure of the drivers of deforestation and forest degradation (Paneque-G alvez et al 2013;Haque and Basak 2017). Training sites were identified randomly with the aid of high resolution Quick Bird images (Google Earth) in ERDAS Imagine Devries et al 2015), as well as sites selected from multiple land cover categories directly observed by Brown et al (2013) during their field research in Madagascar.…”

Section: Mapping Land Use Land Cover Changementioning

confidence: 99%

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Assessing regional‐scale variability in deforestation and forest degradation rates in a tropical biodiversity hotspot

Yesuf

Brown

Walford

2019

Remote Sens Ecol Conserv

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Deforestation and forest degradation are major drivers of global environmental change and tropical forests are subjected to unprecedented pressures from both. For most tropical zones, deforestation rates are averaged across entire countries, often without highlighting regional differentiation. There are also very few estimates of forest degradation, either averaged or localized for the tropics. We quantified regional and country‐wide changes in deforestation and forest degradation rates for Madagascar from Landsat temporal data (in two intervals, 1994–2002 and 2002–2014). To our knowledge, this is the first country‐wide estimate of forest degradation for Madagascar. We also performed an intensity analysis to categorize the magnitude and speed of transitions between forest, vegetation matrix, cultivated land and exposed surface. We found significant regional heterogeneity in deforestation and forest degradation. Deforestation rates decreased annually in lowland evergreen moist forest by −0.24% and in all other vegetation zones. Forest degradation rates had annual increases in the same period in lowland evergreen moist forest (0.09%), littoral forest (0.06%) but decreased in medium altitude moist evergreen forest (−0.25%), dry deciduous forest (−0.23%) and scelrophyllous woodland (−0.61%) in the same period. Despite these regional differences, higher rates of deforestation and forest degradation were consistently driven by rapid and large‐sized conversions of largely intact forest to cultivated lands and exposed surfaces, most of which occurred between 1994 and 2002. These results suggest that while targeted conservation projects may have reduced forest degradation rates in some areas (e.g. medium altitude moist evergreen forest), the drivers of land cover change remain intense in relatively neglected regions. We advocate a more balanced approach to future conservation initiatives, one recognizing that deforestation and forest degradation, particularly in tropical Africa, are often driven by region‐specific conditions and therefore require conservation policies tailored for local conditions.

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Section: Methodsmentioning

confidence: 99%

Section: Mapping Land Use Land Cover Changementioning

confidence: 99%

Assessing regional‐scale variability in deforestation and forest degradation rates in a tropical biodiversity hotspot

Yesuf

Brown

Walford

2019

Remote Sens Ecol Conserv

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“…Hay que destacar que estas técnicas constituyen una forma de medición directa en campo, de características biofísicas a partir de las propiedades ópticas de los tipos de cobertura terrestre en términos de su reflexión de la luz incidente. Las citas antes presentadas (Beuchle et al 2015, Wasige et al 2013, Haque & Basak 2016, Sanhouse-Garcia, 2016, Estrada et al 2013, Osuna-Osuna et al 2015 son constancia de la eficacia y pertinencia de los estudios de cambio de cobertura terrestre en cuencas usando análisis de imágenes satelitales.…”

Section: Aportes De La Geomática Y Limitantes En El Estudiounclassified

Variabilidad espacio-temporal de la cobertura terrestre en la cuenca del río Tecolutla, México

Tapia-Silva

Flores

2017

GeoFocus

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RESUMENEn este estudio se caracteriza el cambio de coberturas de suelo de una cuenca en México desde la Geomática. Se parte de aplicar un modelado conceptual que permitió efectuar una abstracción espacial útil para identificar los elementos fundamentales del sistema territorial analizado y sus interrelaciones. Se analizaron imágenes satelitales Landsat (5, 7, 8), correspondientes a los años 1985-1986, 1994, 2002-2003 y 2013, cubriendo un periodo de 28 años. Se aplicaron correcciones radiométricas, y se obtuvieron clasificaciones supervisadas. Como resultado se alcanzaron índices Kappa de 0.74-0.85, indicando buena exactitud. Los principales resultados en la cuenca en los 28 años analizados son: la cobertura agrícola pasó de 24 % a 42 % y su tasa de cambio anual (TCA) fue de 1.86 %. Poblado fue la clase más dinámica (TCA 8.8 %). Forestal pasó a agrícola en 54 %, matorral a pastizal en 46 % y a agrícola en 41 %.Palabras clave: Geomática, Modelo Conceptual Geoespacial, imágenes Landsat, cuenca, cambio de cobertura terrestre. SPACE-TEMPORAL VARIABILITY OF TERRESTRIAL COVERAGE IN THE RIVER BASIN TECOLUTLA, MEXICO ABSTRACTLand cover change in a Mexican river basin was characterized by Geomatics, applying a conceptual modeling method that allowed a spatial abstraction useful to identify the fundamental elements of the analyzed territorial system and their interrelationships. Landsat satellite images (5, 7 and 8) of 1985-1986, 1994, 2002-2003 and 2013, were used, covering a period of 28 years. Radiometric corrections were applied and supervised classifications were obtained. As result Kappa indices of 0.74-0.85 were reached, indicating good accuracy. The main results in the 28-year analyzed are: agricultural coverage increased from 24 % to 42 % and its annual change rate (TCA) was 1.86 %. Urban was the most dynamic class (TCA 8.8 %). Forest cover changes to agriculture in 54 %, scrub to pasture in 46 % and to agriculture in 41 %.

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“…Theoretically, if the NDVI approaches a value of 1, the existing vegetation in that area is greener and denser; if it approaches a value of 0, it means that the vegetation is dry or not green; and if the NDVI value is between 0 and -1, then it is land cover that is not a type of vegetation (Haque and Basak, 2017). The index can be used to detect changes in land cover (Haque and Basak, 2017). (9) i.…”

Section: H Transformation To Ndvimentioning

confidence: 99%

The Relationship between the Mixed Pixel Spectral Value of Landsat 8 OLI Data and LAPAN Surveillance Aircraft (LSA) Aerial-Photo Data

et al. 2017

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Abstract. Medium-resolution satellite data such as data from Landsat has a lot of potential for a mixed pixel (mixel) to occur. Indonesian land use is diverse, especially in urban areas, which causes a high potential for mixels in the Landsat pixel size of 30 x 30m, based on the actual conditions. Multispectral aerial photo data from LAPAN Surveillance Aircraft (LSA) with a spatial resolution reaching 58cm can display objects in more detail in these sizes. The purpose of this research is to study mixels in Landsat 8 Operational Land Imager (OLI) data, with multispectral data from an LSA as a complement to the Landsat 8 OLI data. The method proposed in this study is a visual interpretation using the object-based image analysis (OBIA) method for the classification of land cover, testing the validity of the sample to be used in the research, and then using the normalized difference vegetation index (NDVI) to see the relationship between the vegetation index of the LSA data and the Landsat 8 OLI data. The results showed that the regression equation obtained from the regression between the NDVI of the Landsat 8 OLI data and the NDVI of the LSA with a significance of less than 0.05 is y = 0.732x -0102 with a value of R 2 = 0.887. Through these results we can conclude that the NDVI values for both sets of data are related to one another.Keywords: mixed pixel, aerial remote sensing, LSA, Landsat, OBIA, NDVI. Abstrak. Data satelit beresolusi sedang seperti data dari Landsat memiliki banyak potensi untuk terjadinya piksel campuran (mixel). Penggunaan lahan di Indonesia beragam, terutama di daerah perkotaan, yang menyebabkan potensi mixel tinggi pada ukuran piksel

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Land cover change detection using GIS and remote sensing techniques: A spatio-temporal study on Tanguar Haor, Sunamganj, Bangladesh

Cited by 171 publications

References 14 publications

Assessing regional‐scale variability in deforestation and forest degradation rates in a tropical biodiversity hotspot

Assessing regional‐scale variability in deforestation and forest degradation rates in a tropical biodiversity hotspot

Variabilidad espacio-temporal de la cobertura terrestre en la cuenca del río Tecolutla, México

The Relationship between the Mixed Pixel Spectral Value of Landsat 8 OLI Data and LAPAN Surveillance Aircraft (LSA) Aerial-Photo Data

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