Toward Efficient Land Cover Mapping: An Overview of the National Land Representation System and Land Cover Map 2015 of Bangladesh

Jalal, Rashed; Mahboob, M. Golam; Udita, Tasnuva Shabnam; Aziz, Tariq; Masum, S M Injamamul Haque; Costello, Liam; Saha, Champa R.; Chowdhury, Abdullah A. M.; Salam, Abdus; Shahrin, Farzana; Sumon, Fazle R.; Zahid, Iqbal; Rahman, Mahbubur; Siddique, Mohammad Anwar; Rahman, M. Mahmudur; Jahan, Nasreen; Shaunak, Mir Fahim; Rahman, Mohammad Shafiur; Islam, Mohammad RedowanSharmin AkterNusrat; Mosca, Nicola; d’Annunzio, Rémi; Hira, Shrabanti; Henry, Matieu; Gregorio, A. Di; Franceschini, Gianluca; Islam, Mohammad Shariful; Akhter, Mariam; Khan, Zarin; Hadi, M. A.; Hossain, Mohammed Abed

doi:10.1109/jstars.2019.2903642

Cited by 21 publications

(18 citation statements)

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“…In a similar situation, the use of fine-resolution and multi-dated satellite data is recognized to result in better accuracy (Uddin et al 2015). In the context of the broad ranges of classspecific accuracies of the remote sensing-based land cover map, we note that the reference classes were labelled using a pseudo-ground truth validation technique (Jalal et al 2019) and were assumed to be correct considering the fact that reference classification error often occurs and may have implications for analysis of accuracy of land cover by remote sensing (Foody 2010). Another approach was tested using object-based classification of the land cover based on the descriptors collected by field crews.…”

Section: Discussionmentioning

confidence: 99%

“…The accuracy assessment analysis of the land cover map was performed through visual interpretation of fine resolution imagery (pseudo-ground truth validation technique), with stratified random sampling by district and by land cover class. The most commonly used measures of accuracy (i.e., overall accuracy, user's accuracy, producer's accuracy) were estimated following the approach suggested in Olofsson et al (2013) and presented in Jalal et al (2019). The overall accuracy, through visual interpretation, was estimated at 89%.…”

Section: The National Land Cover Mapmentioning

confidence: 99%

“…User's accuracy ranged from 20% to 99% while producer's accuracy ranged from 13% to 100%. The detailed methodological process and results (including the accuracy, uncertainty and adjusted area estimates) of land cover 2015 are presented in Jalal et al (2019).…”

Section: The National Land Cover Mapmentioning

confidence: 99%

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A multi-purpose National Forest Inventory in Bangladesh: design, operationalisation and key results

Henry

Zahid²,

Johnson

et al. 2021

For. Ecosyst.

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Background National forest inventory and forest monitoring systems are more important than ever considering continued global degradation of trees and forests. These systems are especially important in a country like Bangladesh, which is characterised by a large population density, climate change vulnerability and dependence on natural resources. With the aim of supporting the Government’s actions towards sustainable forest management through reliable information, the Bangladesh Forest Inventory (BFI) was designed and implemented through three components: biophysical inventory, socio-economic survey and remote sensing-based land cover mapping. This article documents the approach undertaken by the Forest Department under the Ministry of Environment, Forests and Climate Change to establish the BFI as a multipurpose, efficient, accurate and replicable national forest assessment. The design, operationalization and some key results of the process are presented. Methods The BFI takes advantage of the latest and most well-accepted technological and methodological approaches. Importantly, it was designed through a collaborative process which drew from the experience and knowledge of multiple national and international entities. Overall, 1781 field plots were visited, 6400 households were surveyed, and a national land cover map for the year 2015 was produced. Innovative technological enhancements include a semi-automated segmentation approach for developing the wall-to-wall land cover map, an object-based national land characterisation system, consistent estimates between sample-based and mapped land cover areas, use of mobile apps for tree species identification and data collection, and use of differential global positioning system for referencing plot centres. Results Seven criteria, and multiple associated indicators, were developed for monitoring progress towards sustainable forest management goals, informing management decisions, and national and international reporting needs. A wide range of biophysical and socioeconomic data were collected, and in some cases integrated, for estimating the indicators. Conclusions The BFI is a new information source tool for helping guide Bangladesh towards a sustainable future. Reliable information on the status of tree and forest resources, as well as land use, empowers evidence-based decision making across multiple stakeholders and at different levels for protecting natural resources. The integrated socio-economic data collected provides information about the interactions between people and their tree and forest resources, and the valuation of ecosystem services. The BFI is designed to be a permanent assessment of these resources, and future data collection will enable monitoring of trends against the current baseline. However, additional institutional support as well as continuation of collaboration among national partners is crucial for sustaining the BFI process in future.

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Section: Discussionmentioning

confidence: 99%

Section: The National Land Cover Mapmentioning

confidence: 99%

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A multi-purpose National Forest Inventory in Bangladesh: design, operationalisation and key results

Henry

Zahid²,

Johnson

et al. 2021

For. Ecosyst.

Self Cite

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“…This is sufficient for statistical but not for mapping purposes. Newly (semi) automatic solutions have been recently proposed (Inglada et al, 2017, Jalal et al, 2019 and pave the way for products with superior spatial coverage and updateness (dashed line, Figure 1).…”

Section: Current Productsmentioning

confidence: 99%

Current Challenges in Operational Very High Resolution Land-Cover Mapping

Mallet

Bris

2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Many land-cover products have been made available for a large range of end-users over the last ten years, even at global scales. In particular, remote sensing data analysis has proved to be the most feasible solution for automation purposes, at multiple spatial scales. However, current solutions are not sufficient for designing better products, adapted to real-case applications, operational constraints, and the generation of services, built upon these core layers. In this paper, we review the main requirements and the recent changes in remote sensing for the specific case of very high resolution land-cover mapping. We also comment current and evaluate challenges for the optimal exploitation of Earth Observation images with the aim of automatically generating maps tailored to specific end-users’ needs. We advocate for more challenging large-scale benchmarks and for human-in-the-loop solutions.

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“…The development of aerospace technology and remote sensing technology has promoted the application of hyperspectral image (HSI) based on remote sensing satellite [1][2][3][4], such as land monitoring [5,6], urban planning [7], road network layout [8], agricultural yield estimation [9] and disaster prevention and control [10]. However, due to the volume limitation of the imaging system and the need for system stability and time resolution, the acquisition of a large amount of spectral band information of hyperspectral images is often at the expense of spatial resolution [11].…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Feature Mapping Network for Hyperspectral Image Super-Resolution

et al. 2021

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Hyperspectral Image (HSI) can continuously cover tens or even hundreds of spectral segments for each spatial pixel. Limited by the cost and commercialization requirements of remote sensing satellites, HSIs often lose a lot of information due to insufficient image spatial resolution. For the high-dimensional nature of HSIs and the correlation between the spectra, the existing Super-Resolution (SR) methods for HSIs have the problems of excessive parameter amount and insufficient information complementarity between the spectra. This paper proposes a Multi-Scale Feature Mapping Network (MSFMNet) based on the cascaded residual learning to adaptively learn the prior information of HSIs. MSFMNet simplifies each part of the network into a few simple yet effective network modules. To learn the spatial-spectral characteristics among different spectral segments, a multi-scale feature generation and fusion Multi-Scale Feature Mapping Block (MSFMB) based on wavelet transform and spatial attention mechanism is designed in MSFMNet to learn the spectral features between different spectral segments. To effectively improve the multiplexing rate of multi-level spectral features, a Multi-Level Feature Fusion Block (MLFFB) is designed to fuse the multi-level spectral features. In the image reconstruction stage, an optimized sub-pixel convolution module is used for the up-sampling of different spectral segments. Through a large number of verifications on the three general hyperspectral datasets, the superiority of this method compared with the existing hyperspectral SR methods is proved. In subjective and objective experiments, its experimental performance is better than its competitors.

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Toward Efficient Land Cover Mapping: An Overview of the National Land Representation System and Land Cover Map 2015 of Bangladesh

Cited by 21 publications

References 18 publications

A multi-purpose National Forest Inventory in Bangladesh: design, operationalisation and key results

A multi-purpose National Forest Inventory in Bangladesh: design, operationalisation and key results

Current Challenges in Operational Very High Resolution Land-Cover Mapping

Multi-Scale Feature Mapping Network for Hyperspectral Image Super-Resolution

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