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The outbreak of large-scale desert locust plague in 2020 has attracted wide attention in the world and caused serious damage to food security and livelihood of African and Asian people. Remote sensing techniques can provide indirect feedback on locust plagues, facilitating quick and real-time monitoring of the occurrence and development of locusts, which is of great significance for ensuring national and regional food security and stability. The Hidden Markov Model (HMM) is a classic machine learning model that has been widely applied in the fields of time-series data mining. In this study, we aim to predict the severity of locust plague in croplands using the time-series dynamic change features extracted from remote sensing data via HMM. In addition, we assess the damages on the croplands using change detection methods by comparing the crop spectrum before and after the locust plague from two-phase hyperspectral images covering sub-study area (northern Narok, Kenya). Evaluated by the ground truth data, the overall accuracies of predicted results of the plague severity in Apr, May, Jun, and Jul are 0.78, 0.71, 0.74, and 0.72, respectively. The land cover classification accuracy of the sub-study area of the two-phase images are 97.45 and 96.14, and the size of the changed croplands we detected is about 128.3km2. Our study demonstrates the validity of the HMM-based method using the remote sensing time-series data to predict locust plague. The results of the cropland change detection suggest that the damage of locusts can be quantitatively evaluated using hyperspectral images.
Zhenfeng Shao; Xiaoxiao Feng; Linze Bai; Haiming Jiao; Ya Zhang; Deren Li; Haisheng Fan; Xiao Huang; Yuqi Ding; Orhan Altan; Nayyer Saleem. Monitoring and Predicting Desert Locust Plague Severity in Asia-Africa using Multi-source Remote Sensing Time-series data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2021, PP, 1 -1.
AMA StyleZhenfeng Shao, Xiaoxiao Feng, Linze Bai, Haiming Jiao, Ya Zhang, Deren Li, Haisheng Fan, Xiao Huang, Yuqi Ding, Orhan Altan, Nayyer Saleem. Monitoring and Predicting Desert Locust Plague Severity in Asia-Africa using Multi-source Remote Sensing Time-series data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021; PP (99):1-1.
Chicago/Turabian StyleZhenfeng Shao; Xiaoxiao Feng; Linze Bai; Haiming Jiao; Ya Zhang; Deren Li; Haisheng Fan; Xiao Huang; Yuqi Ding; Orhan Altan; Nayyer Saleem. 2021. "Monitoring and Predicting Desert Locust Plague Severity in Asia-Africa using Multi-source Remote Sensing Time-series data." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing PP, no. 99: 1-1.
Building extraction from high-resolution remote sensing images is of great significance in urban planning, population statistics, and economic forecast. However, automatic building extraction from high-resolution remote sensing images remains challenging. On the one hand, the extraction results of buildings are partially missing and incomplete due to the variation of hue and texture within a building, especially when the building size is large. On the other hand, the building footprint extraction of buildings with complex shapes is often inaccurate. To this end, we propose a new deep learning network, termed Building Residual Refine Network (BRRNet), for accurate and complete building extraction. BRRNet consists of such two parts as the prediction module and the residual refinement module. The prediction module based on an encoder–decoder structure introduces atrous convolution of different dilation rates to extract more global features, by gradually increasing the receptive field during feature extraction. When the prediction module outputs the preliminary building extraction results of the input image, the residual refinement module takes the output of the prediction module as an input. It further refines the residual between the result of the prediction module and the real result, thus improving the accuracy of building extraction. In addition, we use Dice loss as the loss function during training, which effectively alleviates the problem of data imbalance and further improves the accuracy of building extraction. The experimental results on Massachusetts Building Dataset show that our method outperforms other five state-of-the-art methods in terms of the integrity of buildings and the accuracy of complex building footprints.
Zhenfeng Shao; Penghao Tang; Zhongyuan Wang; Nayyer Saleem; Sarath Yam; Chatpong Sommai. BRRNet: A Fully Convolutional Neural Network for Automatic Building Extraction From High-Resolution Remote Sensing Images. Remote Sensing 2020, 12, 1050 .
AMA StyleZhenfeng Shao, Penghao Tang, Zhongyuan Wang, Nayyer Saleem, Sarath Yam, Chatpong Sommai. BRRNet: A Fully Convolutional Neural Network for Automatic Building Extraction From High-Resolution Remote Sensing Images. Remote Sensing. 2020; 12 (6):1050.
Chicago/Turabian StyleZhenfeng Shao; Penghao Tang; Zhongyuan Wang; Nayyer Saleem; Sarath Yam; Chatpong Sommai. 2020. "BRRNet: A Fully Convolutional Neural Network for Automatic Building Extraction From High-Resolution Remote Sensing Images." Remote Sensing 12, no. 6: 1050.
In flood-prone areas, the delineation of the spatial pattern of historical flood extents, damage assessment, and flood durations allow planners to anticipate potential threats from floods and to formulate strategies to mitigate or abate these events. The Chenab plain in the Punjab region of Pakistan is particularly prone to flooding but is understudied. It experienced its worst riverine flood in recorded history in September 2014. The present study applies Remote Sensing (RS) and Geographical Information System (GIS) techniques to estimate the riverine flood extent and duration and assess the resulting damage using Landsat-8 data. The Landsat-8 images were acquired for the pre-flooding, co-flooding, and post-flooding periods for the comprehensive analysis and delineation of flood extent, damage assessment, and duration. We used supervised classification to determine land use/cover changes, and the satellite-derived modified normalized difference water index (MNDWI) to detect flooded areas and duration. The analysis permitted us to calculate flood inundation, damages to built-up areas, and agriculture, as well as the flood duration and recession. The results also reveal that the floodwaters remained in the study area for almost two months, which further affected cultivation and increased the financial cost. Our study provides an empirical basis for flood response assessment and rehabilitation efforts in future events. Thus, the integrated RS and GIS techniques with supporting datasets make substantial contributions to flood monitoring and damage assessment in Pakistan.
Asif Sajjad; Jianzhong Lu; Xiaoling Chen; Chikondi Chisenga; Nayyer Saleem; Hammad Hassan. Operational Monitoring and Damage Assessment of Riverine Flood-2014 in the Lower Chenab Plain, Punjab, Pakistan, Using Remote Sensing and GIS Techniques. Remote Sensing 2020, 12, 714 .
AMA StyleAsif Sajjad, Jianzhong Lu, Xiaoling Chen, Chikondi Chisenga, Nayyer Saleem, Hammad Hassan. Operational Monitoring and Damage Assessment of Riverine Flood-2014 in the Lower Chenab Plain, Punjab, Pakistan, Using Remote Sensing and GIS Techniques. Remote Sensing. 2020; 12 (4):714.
Chicago/Turabian StyleAsif Sajjad; Jianzhong Lu; Xiaoling Chen; Chikondi Chisenga; Nayyer Saleem; Hammad Hassan. 2020. "Operational Monitoring and Damage Assessment of Riverine Flood-2014 in the Lower Chenab Plain, Punjab, Pakistan, Using Remote Sensing and GIS Techniques." Remote Sensing 12, no. 4: 714.
Decentralization problems in Africa have caused some infrastructure disparity between country capitals and distant districts. In Ghana, less public investment has created a gap between implementation results and theoretical benefits. Spectral indices are a good approach to extracting impervious surfaces, which is a good method of measuring urbanization. These are restricted by complexity, sensor limitation, threshold values, and high computational time. In this study, we measure the urbanization dynamics of Wa District in Ghana by applying a proposed method of impervious surface extraction index (ISEI), to evaluate the decentralization policy using Landsat images from 1984–2018 and a single S2A data. Comparing our proposed method with five other existing indexes, ISEI provided good discriminated results between target feature and background, with pixel values ranging between 0 and +1. Other indexes produced negative values. ISEI accuracy varied from 84.62–94.00% while existing indexes varied from 73.85–90.00%. Our results also showed increased impervious surface areas of 83.26 km2, which is about 7.72% of total area while the average annual urban growth was recorded as 4.42%. These figures proved that the quantification of decentralization is very positive. The study provides a foundation for urban environment research in the context of decentralization policy.
Nana Yaw Danquah Twumasi; Chikondi Chisenga; Nayyer Saleem; Neema Nicodemus Lyimo; Orhan Altan. Impact Quantification of Decentralization in Urban Growth by Extracting Impervious Surfaces Using ISEI in Model Maker. Sustainability 2020, 12, 380 .
AMA StyleNana Yaw Danquah Twumasi, Chikondi Chisenga, Nayyer Saleem, Neema Nicodemus Lyimo, Orhan Altan. Impact Quantification of Decentralization in Urban Growth by Extracting Impervious Surfaces Using ISEI in Model Maker. Sustainability. 2020; 12 (1):380.
Chicago/Turabian StyleNana Yaw Danquah Twumasi; Chikondi Chisenga; Nayyer Saleem; Neema Nicodemus Lyimo; Orhan Altan. 2020. "Impact Quantification of Decentralization in Urban Growth by Extracting Impervious Surfaces Using ISEI in Model Maker." Sustainability 12, no. 1: 380.
Digital elevation models (DEMs) are considered an imperative tool for many 3D visualization applications; however, for applications related to topography, they are exploited mostly as a basic source of information. In the study of landslide susceptibility mapping, parameters or landslide conditioning factors are deduced from the information related to DEMs, especially elevation. In this paper conditioning factors related with topography are analyzed and the impact of resolution and accuracy of DEMs on these factors is discussed. Previously conducted research on landslide susceptibility mapping using these factors or parameters through exploiting different methods or models in the last two decades is reviewed, and modern trends in this field are presented in a tabulated form. Two factors or parameters are proposed for inclusion in landslide inventory list as a conditioning factor and a risk assessment parameter for future studies.
Nayyer Saleem; Enamul Huq; Nana Yaw Danquah Twumasi; Akib Javed; Asif Sajjad. Parameters Derived from and/or Used with Digital Elevation Models (DEMs) for Landslide Susceptibility Mapping and Landslide Risk Assessment: A Review. ISPRS International Journal of Geo-Information 2019, 8, 545 .
AMA StyleNayyer Saleem, Enamul Huq, Nana Yaw Danquah Twumasi, Akib Javed, Asif Sajjad. Parameters Derived from and/or Used with Digital Elevation Models (DEMs) for Landslide Susceptibility Mapping and Landslide Risk Assessment: A Review. ISPRS International Journal of Geo-Information. 2019; 8 (12):545.
Chicago/Turabian StyleNayyer Saleem; Enamul Huq; Nana Yaw Danquah Twumasi; Akib Javed; Asif Sajjad. 2019. "Parameters Derived from and/or Used with Digital Elevation Models (DEMs) for Landslide Susceptibility Mapping and Landslide Risk Assessment: A Review." ISPRS International Journal of Geo-Information 8, no. 12: 545.