Mapping Urban Transitions Using Multi-Temporal Landsat and DMSP-OLS Night-Time Lights Imagery of the Red River Delta in Vietnam

Castrence, Miguel; Nong, Duong H.; Tran, Chinh C.; Young, Luisa; Fox, Jefferson

doi:10.3390/land3010148

Cited by 36 publications

(25 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…These edge pixels tend to show approximately one third of the amount of valid pixels compared to the centers of the scenes. This could affect the quality of the classification, but we expect this effect to be limited because more than 90%-95% of all pixels of each footprint show an average of more than three valid observations for each year [43,44].…”

Section: Data Sets and Initial Processingmentioning

confidence: 99%

See 1 more Smart Citation

Mapping the Expansion of Boom Crops in Mainland Southeast Asia Using Dense Time Stacks of Landsat Data

et al. 2017

Self Cite

View full text Add to dashboard Cite

Abstract:We performed a multi-date composite change detection technique using a dense-time stack of Landsat data to map land-use and land-cover change (LCLUC) in Mainland Southeast Asia (MSEA) with a focus on the expansion of boom crops, primarily tree crops. The supervised classification was performed using Support Vector Machines (SVM), which are supervised non-parametric statistical learning techniques. To select the most suitable SMV classifier and the related parameter settings, we used the training data and performed a two-dimensional grid search with a three-fold internal cross-validation. We worked in seven Landsat footprints and found the linear kernel to be the most suitable for all footprints, but the most suitable regularization parameter C varied across the footprints. We distinguished a total of 41 LCLUCs (13 to 31 classes per footprint) in very dynamic and heterogeneous landscapes. The approach proved useful for distinguishing subtle changes over time and to map a variety of land covers, tree crops, and transformations as long as sufficient training points could be collected for each class. While to date, this approach has only been applied to mapping urban extent and expansion, this study shows that it is also useful for mapping change in rural settings, especially when images from phenologically relevant acquisition dates are included.

show abstract

Section: Data Sets and Initial Processingmentioning

confidence: 99%

“…We therefore added samples in a purposeful manner for many of the change classes, ultimately collecting between 1500 and 3000 points for each footprint [43,44]. Locations for the additional samples were identified by comparing the 1000 random points with inventories of concessions and multi-seasonal NDVI composites.…”

mentioning

confidence: 99%

Mapping the Expansion of Boom Crops in Mainland Southeast Asia Using Dense Time Stacks of Landsat Data

et al. 2017

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, since 2003, all Landsat 7 ETM+ images have data gaps due to a malfunction in the scan line corrector (SLC) [33,34], resulting in about 22% image loss [35]. Despite these data gaps, Landsat 7 imagery is still widely used in land cover and land use change studies through a variety of gap-filling and dense time stack approaches [36][37][38][39]. Furthermore, many of the Landsat images are inevitably covered by clouds and cloud shadows, which limits their utility.…”

Section: Data and Pre-processingmentioning

confidence: 99%

“…The BPNNs activation function in the hidden layer was set to "tansig", the transfer function for the output layer was set to "purelin", and the training function was set to "trainlm". Because of its efficient convergence capacity, the Levenberg-Marquardt minimization algorithm was used to calibrate the synaptic coefficients [36].…”

Section: The Bpnns Modelmentioning

confidence: 99%

A Robust Algorithm for Estimating Surface Fractional Vegetation Cover from Landsat Data

et al. 2017

View full text Add to dashboard Cite

Abstract:Fractional vegetation cover (FVC) is an essential land surface parameter for Earth surface process simulations and global change studies. The currently existing FVC products are mostly obtained from low or medium resolution remotely sensed data, while many applications require the fine spatial resolution FVC product. The availability of well-calibrated coverage of Landsat imagery over large areas offers an opportunity for the production of FVC at fine spatial resolution. Therefore, the objective of this study is to develop a general and reliable land surface FVC estimation algorithm for Landsat surface reflectance data under various land surface conditions. Two machine learning methods multivariate adaptive regression splines (MARS) model and back-propagation neural networks (BPNNs) were trained using samples from PROSPECT leaf optical properties model and the scattering by arbitrarily inclined leaves (SAIL) model simulations, which included Landsat reflectance and corresponding FVC values, and evaluated to choose the method which had better performance. Thereafter, the MARS model, which had better performance in the independent validation, was evaluated using ground FVC measurements from two case study areas. The direct validation of the FVC estimated using the proposed algorithm (Heihe: R 2 = 0.8825, RMSE = 0.097; Chengde using Landsat 7 ETM+: R 2 = 0.8571, RMSE = 0.078, Chengde using Landsat 8 OLI: R 2 = 0.8598, RMSE = 0.078) showed the proposed method had good performance. Spatial-temporal assessment of the estimated FVC from Landsat 7 ETM+ and Landsat 8 OLI data confirmed the robustness and consistency of the proposed method. All these results indicated that the proposed algorithm could obtain satisfactory accuracy and had the potential for the production of high-quality FVC estimates from Landsat surface reflectance data.

show abstract

“…Urbanization is a feedback system that is economically motivated [12]. Urban areas have grown substantially in the last several decades worldwide, but urbanization varies considerably in different geographic regions and contexts [13].…”

Section: Introductionmentioning

confidence: 99%

Monitoring Urban Growth and the Nepal Earthquake 2015 for Sustainability of Kathmandu Valley, Nepal

Rimal

Zhang

et al. 2017

Land

View full text Add to dashboard Cite

Abstract:The exodus of people from rural areas to cities brings many detrimental environmental, social and cultural consequences. Monitoring spatiotemporal change by referencing the historical timeline or incidence has become an important way to analyze urbanization. This study has attempted to attain the cross-sectional analysis of Kathmandu valley that has been plagued by rampant urbanization over the last three decades. The research utilizes Landsat images of Kathmandu valley from 1976 to 2015 for the transition analysis of land use, land cover and urban sprawl for the last four decades. Results showed that the urban coverage of Kathmandu valley has tremendously increased from 20.19 km 2 in 1976 to 39.47 km 2 in 1989 to 78.96 km 2 in 2002 to 139.57 km 2 in 2015, at the cost of cultivated lands, with an average annual urban growth rate of 7.34%, 7.70% and 5.90% in each temporal interval, respectively. In addition, the urban expansion orientation analysis concludes the significant urban concentration in the eastern part, moderately medium in the southwest and relatively less in the western and northwest part of the valley. Urbanization was solely accountable for the exploitation of extant forests, fertile and arable lands and indigenous and cultural landscapes. Unattended fallow lands in suburban areas have compounded the problem by welcoming invasive alien species. Overlaying the highly affected geological formations within the major city centers displays that unless the trend of rapid, unplanned urbanization is discontinued, the future of Kathmandu is at the high risk. Since land use management is a fundamental part of development, we advocate for the appropriate land use planning and policies for sustainable and secure future development.

show abstract

Mapping Urban Transitions Using Multi-Temporal Landsat and DMSP-OLS Night-Time Lights Imagery of the Red River Delta in Vietnam

Cited by 36 publications

References 30 publications

Mapping the Expansion of Boom Crops in Mainland Southeast Asia Using Dense Time Stacks of Landsat Data

Mapping the Expansion of Boom Crops in Mainland Southeast Asia Using Dense Time Stacks of Landsat Data

A Robust Algorithm for Estimating Surface Fractional Vegetation Cover from Landsat Data

Monitoring Urban Growth and the Nepal Earthquake 2015 for Sustainability of Kathmandu Valley, Nepal

Contact Info

Product

Resources

About