Land‐Use and Land‐Cover Change in the Páramo of South‐Central Ecuador, 1979–2014

Ross, Cristina; Fildes, Stephen; Millington, Andrew

doi:10.3390/land6030046

Cited by 29 publications

(51 citation statements)

References 40 publications

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“…Over the years, the remote sensing applications for rural areas have become extremely important [61], since they support the monitoring and mapping of the LULC changes of agricultural land. Some studies have emphasized the usefulness of using remote sensing data for agricultural land change classification [62,63], cropland estimation/scenario forecast [64][65][66], and vegetation health and crop production monitoring [67,68]. The ongoing abandonment of agricultural land [69][70][71], and the fact that these areas have been threatened by socioeconomic and biophysical factors [72,73] demonstrates the need to identify and characterize the spatiotemporal changes.…”

Section: Discussionmentioning

confidence: 99%

Long-Term Satellite Image Time-Series for Land Use/Land Cover Change Detection Using Refined Open Source Data in a Rural Region

2019

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The increasing availability and volume of remote sensing data, such as Landsat satellite images, have allowed the multidimensional analysis of land use/land cover (LULC) changes. However, the performance of image classification is highly dependent on the quality and quantity of the training set and its temporal continuity, which may affect the accuracy of the classification and bias the analysis of the LULC changes. In this study, we intended to apply a long-term LULC analysis in a rural region based on a Landsat time series of 21 years (1995 to 2015). Here, we investigated the use of open LULC source data to provide training samples and the application of the K-means clustering technique to refine the broad range of spectral signatures for each LULC class. Experiments were conducted on a predominantly rural region characterized by a mixed agro-silvo-pastoral environment. The open source data of the official Portuguese LULC map (Carta de Uso e Ocupação do Solo, COS) from 1995, 2007, 2010, and 2015 were integrated to generate the training samples for the entire period of analysis. The time series was computed from Landsat data based on the normalized difference vegetation index and normalized difference water index, using 221 Landsat images. The Time-Weighted Dynamic Time Warping (TWDTW) classifier was used, since it accounts for LULC-type seasonality and has already achieved promising overall accuracy values for classifications based on time series. The results revealed that the proposed method was efficient in classifying a long-term satellite time-series with an overall accuracy of 76%, providing insights into the main LULC changes that occurred over 21 years.

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Section: Discussionmentioning

confidence: 99%

Long-Term Satellite Image Time-Series for Land Use/Land Cover Change Detection Using Refined Open Source Data in a Rural Region

2019

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“…Satellite imagery has contributed to studies on the spatial analysis of urban/built-up areas. These satellites include Landsat Thematic Mapper (TM) and Operational Land Imager (OLI) [24][25][26][27]. Multitemporal and multi-spectral spatial datasets have proven to be economical and efficient tools for quantifying, monitoring and predicting future land cover change [28,29].…”

Section: Introductionmentioning

confidence: 99%

Tracking Urban Expansion Using Random Forests for the Classification of Landsat Imagery (1986–2015) and Predicting Urban/Built-Up Areas for 2025: A Study of the Kumasi Metropolis, Ghana

Frimpong

Molkenthin

2021

Land

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Kumasi is a nodal city and functions as the administrative and economic capital of the Ashanti region in Ghana. Rapid urbanization has been experienced inducing the transformation of various Land Use Land Cover (LULC) types into urban/built-up areas in Kumasi. This paper aims at tracking spatio-temporal LULC changes utilizing Landsat imagery from 1986, 2013 and 2015 of Kumasi. The unique contribution of this research is its focus on urban expansion analysis and the utilization of Random Forest (RF) Classifier for satellite image classification. Change detection, urban land modelling and urban expansion in the sub-metropolitan zones, buffers, density decay curve and correlation analysis were methodologies adopted for our study. The classifier yielded better accuracy compared to earlier works in Ghana. The evaluation of LULC changes indicated that urban/built-up areas are continually increasing at the expense of agricultural and forestlands. The urban/built-up areas occupied 4622.49 hectares (ha) (23.78%), 13,447.50 ha (69.18%) and 14,004.60 ha (72.05%) in 1986, 2013 and 2015, respectively of the 19,438 ha area of Kumasi. Projection indicated that urban/built-up areas will occupy 15,490 ha (79.70%) in 2025. The urban expansion was statistically significant. The results revealed the importance of spatial modeling for environmental management and city planning.

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“…Table 1 shows the list of Landsat images used in the study. [59,60] and have been used appropriately to map long-term LULC changes (e.g., [61][62][63][64]). Since the LULC is only classified into broad categories, the use of these images is considered appropriate for this study.…”

Section: Remote Sensing Datamentioning

confidence: 99%

The Influence of Rainfall and Land Use/Land Cover Changes on River Discharge Variability in the Mountainous Catchment of the Bagmati River

Tuladhar

Dewan

Kühn

et al. 2019

Water

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Changes in rainfall and land use/land cover (LULC) can influence river discharge from a catchment in many ways. Homogenized river discharge data from three stations and average rainfall records, interpolated from 13 stations, were examined for long-term trends and decadal variations in the headwater, upper and middle catchments of the Bagmati River. LULC changes over five decades were quantified using multitemporal Landsat images. Mann-Kendall tests on annual time series showed a significant decrease in river discharge (0.61% per year) from the entire Bagmati catchment, although the decrease in rainfall was statistically insignificant. However, declines in river discharge and rainfall were both significant in upper catchment. Decadal departures from long-term means support these trend results. Over tenfold growth in urban area and a decrease in agricultural land were observed in the upper catchment, while forest cover slightly increased in the entire catchment between 1975 and 2015. Correlation analysis showed a strong association between surface runoff, estimated using the curve number method, observed river discharge and rainfall in the upper catchment, while the relationship was weaker in the headwater catchment. These results were also supported by multiple regression analysis, suggesting that human activities together with climate change have contributed to river discharge changes in the Bagmati catchment.Bosch and Hewlett [15] suggested that clearing of forests increases river discharge while recovery of vegetation or afforestation decreases the discharge. Growth in urban areas can increase surface runoff while evaporation and ground water recharge may have varying response [16,17]. Increasing river discharge is also linked with expansion of agricultural land and decrease in shrubs and forests [18,19]. In the Ganges basin, natural and climatic variability in precipitation and river discharge are further affected by rapid population growth and subsequent LULC changes, including urbanization and deforestation [20]. Nevertheless, a few studies also reported no significant changes in rainfall, evapotranspiration and river discharge in relation to forest-cover changes [21,22].Within Nepal, significant LULC changes have occurred, especially with conversion from forest to agriculture. Forest cover in Nepal decreased from 45% in 1966 to 29% in 1994 [23,24]. However, recent forest assessments over 20 districts of Terai region suggest that, the rate of decrease has slowed down to 0.06% per year during 1990-2010 [24]. Forest conditions are estimated to be improving with community-based forest management and government regulations [23][24][25]. However, increasing frequency of weather-related extreme events in the region [26] such as excessive rainfall, long drought spells, landslides and floods have already had negative impacts on agriculture, forestry and biodiversity in Nepal [23].The Bagmati River is not only important in relation to the initial settlement of Kathmandu valley, its tributary systems are also crucia...

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Land‐Use and Land‐Cover Change in the Páramo of South‐Central Ecuador, 1979–2014

Cited by 29 publications

References 40 publications

Long-Term Satellite Image Time-Series for Land Use/Land Cover Change Detection Using Refined Open Source Data in a Rural Region

Long-Term Satellite Image Time-Series for Land Use/Land Cover Change Detection Using Refined Open Source Data in a Rural Region

Tracking Urban Expansion Using Random Forests for the Classification of Landsat Imagery (1986–2015) and Predicting Urban/Built-Up Areas for 2025: A Study of the Kumasi Metropolis, Ghana

The Influence of Rainfall and Land Use/Land Cover Changes on River Discharge Variability in the Mountainous Catchment of the Bagmati River

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