Article
Environmental Sciences
Wanjuan Song, Xihan Mu, Tim R. McVicar, Yuri Knyazikhin, Xinli Liu, Li Wang, Zheng Niu, Guangjian Yan
Summary: Fractional Vegetation Cover (FVC) is important for understanding ecosystems and their response to climate change. The lack of global near-real-time satellite-based products limits the use of FVC in various studies. This study developed an algorithm using EPIC to estimate FVC daily at a global scale with a resolution of 10 km.
REMOTE SENSING OF ENVIRONMENT
(2022)
Article
Remote Sensing
Jiali Liu, Jianrong Fan, Chao Yang, Fubao Xu, Xiyu Zhang
Summary: The empirical retrieval method based on vegetation indices (VIs) is widely used for estimating vegetation cover, but has limitations. By utilizing the red edge bands on the Sentinel-2 satellite, new indices were developed to improve estimation accuracy. Through field observations and simulations, it was found that the new red edge indices can effectively estimate both photosynthetic and non-photosynthetic vegetation cover at different growth stages.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2022)
Article
Environmental Sciences
Lu Xie, Xiang Meng, Xiaodi Zhao, Liyong Fu, Ram P. Sharma, Hua Sun
Summary: Fractional vegetation cover (FVC) is an important indicator of ecosystem changes. This study used satellite remote sensing and machine learning algorithms to estimate FVC in desert regions. The results showed that RGB images are suitable for mapping FVC, and the vegetation cover in the study area changed significantly from 2006 to 2019. Understanding these changes is crucial for understanding the evolution of desert ecosystems.
Article
Environmental Sciences
Baohui Mu, Xiang Zhao, Jiacheng Zhao, Naijing Liu, Longping Si, Qian Wang, Na Sun, Mengmeng Sun, Yinkun Guo, Siqing Zhao
Summary: According to the study, China's major cities experienced an overall increase in vegetation cover from 2001 to 2018, although certain cities in the core area and expansion area showed a decrease. The expansion of urbanization, climate factors, and CO2 were identified as the main contributors to vegetation changes, with climate factors and CO2 having the largest contributions.
Article
Engineering, Electrical & Electronic
Tianjun Wu, Jiancheng Luo, Lijing Gao, Yingwei Sun, Yingpin Yang, Ya'nan Zhou, Wen Dong, Xin Zhang
Summary: This study proposes a geoparcel-based spatial prediction method to improve the reliability and accuracy of grassland resource information extraction using irregular geographic objects, i.e., grassland geoparcels. A case experiment in Abag Banner, Inner Mongolia, China, demonstrates that the proposed method achieves good FVC mapping results. Compared to traditional regular grid-based methods, the proposed method shows higher accuracy in predicting FVC, with advantages in sensing tiny spatial heterogeneity at the boundary of grassland change.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2021)
Article
Agriculture, Multidisciplinary
Michael J. Hill, Juan P. Guerschman
Summary: Vegetation Fractional Cover (VFC) is an important indicator of terrestrial surface condition, with dynamics of bare soil (BS), non-photosynthetic vegetation (NPV), and photosynthetic vegetation (PV) fractions revealing global land use management trends. Negative trends in BS could lead to risks of soil erosion, habitat change, and reduction in ecosystem health.
AGRICULTURE ECOSYSTEMS & ENVIRONMENT
(2022)
Article
Engineering, Electrical & Electronic
Wei Chen, Zhe Wang, Xuepeng Zhang, Guangchao Li, Fengjiao Zhang, Lan Yang, Haijing Tian, Gongqi Zhou
Summary: This study introduces a deep learning method based on high dynamic range images to reduce the influence of vegetation shadows on FVC estimation accuracy, achieving high accuracy in extracting FVC. The HDR REC-DL method performs well under various weather conditions.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2022)
Article
Environmental Sciences
Yuliang Wang, Mingshi Li
Summary: This paper proposed an improved FVC estimation model by fusing the optimized dynamic range vegetation index (ODRVI) model, which enhanced sensitivity and stability to changes in urban vegetation cover. The annual urban FVC dynamics were mapped using this model in Hefei, China, showing a decrease of 33.08% in total FVC area over the past 20 years.
Article
Agronomy
Petri R. Forsstrom, Jussi Juola, Miina Rautiainen
Summary: This study analyzed the spectral reflectance factors and fractional covers of understory vegetation in different forest stands in a southern boreal forest area in Finland. The results showed specific spectral features of the understory related to site fertility type and fractional cover. The findings suggest that remote sensing can differentiate forest site fertility types and estimate understory green fractional cover in northern European boreal forests.
AGRICULTURAL AND FOREST METEOROLOGY
(2021)
Article
Remote Sensing
Cuicui Zhu, Jia Tian, Qingjiu Tian, Xiaoqiong Wang, Qianjing Li
Summary: By comparing and analyzing the NDVI-NSSI and EVI-NSSI triangular spaces and their application on Sentinel-2A/B images, we found that EVI-NSSI is better suited for pixel three-decomposition in high PV cover scenarios. NDVI-NSSI underestimates (overestimates) fPV when PV cover is low (high). NDVI-NSSI and EVI-NSSI produce significantly different estimates for fPV and fBS, but similar estimates for fNPV, and can be combined to estimate a wider range of NPV cover in different seasons, including sparse or dense green vegetation scenarios.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2023)
Article
Remote Sensing
Guofeng Tao, Kun Jia, Xiangqin Wei, Mu Xia, Bing Wang, Xianhong Xie, Bo Jiang, Yunjun Yao, Xiaotong Zhang
Summary: The study proposed a Spatial and Temporal Fusion method combining with Vegetation Growth Models (STF-VGM) to address the underestimation issue of high fractional vegetation cover (FVC) values in agricultural regions. By establishing vegetation growth models with time series data, STF-VGM significantly improved the prediction accuracy for high FVC values.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2021)
Article
Agriculture, Multidisciplinary
Hongru Bi, Wei Chen, Yi Yang
Summary: Polarization information is introduced in this study to extract both illuminated and shadowed vegetation, as well as background, using deep learning algorithms. The experimental results show that adding polarization information can significantly improve the extraction accuracy, especially for shadowed vegetation.
PRECISION AGRICULTURE
(2023)
Article
Agriculture, Multidisciplinary
Liu Da-Zhong, Yang Fei-Fei, Liu Sheng-Ping
Summary: The study proposed a new method for extracting fractional vegetation cover from wheat hyperspectral images using the density peak k-means algorithm, which showed to be more accurate and robust compared to traditional pixel dichotomy methods under various conditions.
JOURNAL OF INTEGRATIVE AGRICULTURE
(2021)
Article
Environmental Sciences
Renjie Huang, Jianjun Chen, Zihao Feng, Yanping Yang, Haotian You, Xiaowen Han
Summary: Long-term series global fractional vegetation cover (FVC) products are crucial for eco-monitoring and simulation study, but their accuracy in certain regions, such as the Qinghai-Tibet Plateau, is still unknown due to the lack of high-precision ground-measured data. This uncertainty affects eco-environment monitoring and simulation.
Article
Environmental Sciences
Haoshuang Han, Yunhe Yin, Yan Zhao, Feng Qin
Summary: The alpine vegetation of the Qinghai-Tibet Plateau is vulnerable to climatic fluctuations, and this study investigated the spatiotemporal variations of vegetation cover and its response to climatic changes. Results showed an overall increase in vegetation cover during the growing season, with greening in the north and browning in the south of the plateau. Precipitation was found to be the primary controlling factor for vegetation growth. This research provides new insights into the vegetation response to climate change in the Qinghai-Tibet Plateau.
Article
Geography, Physical
Godfrey Mutowo, Onisimo Mutanga, Mhosisi Masocha
Summary: This study used high resolution broadband satellite technology to determine the optimal spatial resolution for estimating foliar nitrogen in dry miombo woodlands. The results found that the optimum size for estimating nitrogen is 50m.
JOURNAL OF SPATIAL SCIENCE
(2023)
Article
Green & Sustainable Science & Technology
Dadirai Matarira, Onisimo Mutanga, Maheshvari Naidu, Terence Darlington Mushore, Marco Vizzari
Summary: This study systematically analyzes and quantitatively investigates the patterns, dynamics, and processes of informal settlement growth, as well as related land use/land cover transitions in the Durban Metropolitan area from 2015 to 2021 using remote sensing imagery and Google Earth Engine platform. The results show that informal settlements have spatial growth with a net gain of 3% land area. Intensity analysis reveals that informal settlements experience both land gain and loss, with annual gain and loss intensity of 72% and 54% respectively, compared to the overall intensity of 26%. The observed systematic transition between informal settlements and other urban land suggests the influence of government policies on informal housing upgrading. This study demonstrates the efficacy of intensity analysis in understanding land change patterns and processes, and provides decision support for suitable urban land upgrading plans in the Durban Metropolitan area.
Review
Geography, Physical
Onisimo Mutanga, Anita Masenyama, Mbulisi Sibanda
Summary: This article provides a comprehensive assessment of the spectral reflectance saturation problem and its impact on vegetation monitoring. The study reveals the influence of vegetation type, structure, and species composition on signal saturation, as well as the importance of wavelength position and polarization in different sensors. Further research is needed to understand the fundamental relationship between spectral reflectance measurements and vegetation characteristics, and to develop appropriate sensors and retrieval methods for different vegetation types.
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
(2023)
Review
Environmental Sciences
Tsitsi Bangira, Onisimo Mutanga, Mbulisi Sibanda, Timothy Dube, Tafadzwanashe Mabhaudhi
Summary: One-third of the Earth's land is grasslands used mainly for forage, and efforts are being made to develop tools to estimate grassland productivity (GP) in relation to climate change. Grassland productivity is an important indicator of ecosystem functioning and is commonly assessed using proxies such as aboveground biomass, leaf area index, and chlorophyll content. Remote sensing techniques, particularly high-resolution sensors, play a crucial role in calculating these proxies. A systematic review of published articles showed a growing demand for high-resolution sensors and computational image-processing techniques for accurate GP prediction at different scales. Future research should focus on integrating optical and radar data, selecting appropriate techniques for GP prediction, and reducing uncertainties associated with different algorithms.
Article
Environmental Sciences
Tsikai Solomon Chinembiri, Onisimo Mutanga, Timothy Dube
Summary: This study compares the performance of two geostatistical approaches for predicting C stock. The results show that the Bayesian approach is more accurate when using Sentinel-2 data compared to the frequentist approach.
Article
Chemistry, Multidisciplinary
Anita Masenyama, Onisimo Mutanga, Timothy Dube, Mbulisi Sibanda, Omosalewa Odebiri, Tafadzwanashe Mabhaudhi
Summary: Indicators of grass water content (GWC) have a significant impact on eco-hydrological processes. In this study, Sentinel-2 MSI bands, spectral derivatives combined with topographic and climatic variables, were used to estimate GWC indicators within communal grasslands. The results showed that the use of combined spectral and topo-climatic variables, coupled with random forest (RF) in the Google Earth Engine (GEE), improved the prediction accuracies of GWC variables across wet and dry seasons.
APPLIED SCIENCES-BASEL
(2023)
Article
Chemistry, Multidisciplinary
Rowan Naicker, Onisimo Mutanga, Kabir Peerbhay, Naeem Agjee
Summary: Unregulated agricultural activities have caused significant damage to grasslands in the past decade. Although nitrogen-based fertilization is used to rehabilitate affected grasslands, excessive fertilization can lead to nitrogen saturation and environmental damage. This study aims to use Worldview-3 satellite imagery and decision tree techniques to identify nitrogen-saturated tropical grasses and provide guidance for rangeland managers to adjust fertilization treatments in near-real-time. The results show significant differences in net nitrate concentrations and pH levels between saturated and non-saturated plots, and the random forest model outperforms the classification and regression tree method in classifying saturated plots. The identified variables, including Red-Edge, Coastal, Near-Infrared 3, Soil-Adjusted Vegetation Index (SAVI), and Normalized Difference Vegetation Index 3 (NDVI3), can assist in identifying grasslands in the early stages of nitrogen saturation and maintain the health of Southern African grasslands.
APPLIED SCIENCES-BASEL
(2023)
Article
Multidisciplinary Sciences
Ezra Pedzisai, Onisimo Mutanga, John Odindi, Tsitsi Bangira
Summary: Flood disasters have severe impacts on infrastructure, ecosystems, and social activities, making flood extent mapping crucial for mitigation efforts. This study proposes a three-step process, called the ensemble of scenarios pyramid technique, using Sentinel-1 radar data to improve the reliability and accuracy of flood extent mapping. The results demonstrate that this technique significantly enhances the performance metrics of flood extent mapping, with an overall accuracy of 93.204% and other metrics such as Cohen's Kappa and recall reaching high values. The study also concludes that VV channels outperform VH channels in flood extent mapping.
Article
Geography
Chantel Chiloane, Timothy Dube, Cletah Shoko
Summary: This study assesses the distribution of groundwater dependent vegetation (GDV) within the Heuningnes Catchment using multispectral remotely sensed data, vegetation indices, and in-situ data. The findings show that spectral indices significantly influence the GDV classification performance, with S2-derived SAVI achieving the highest overall accuracy of 97%. The study demonstrates the capabilities of a combined remote sensing and GIS framework for improving our knowledge of GDV.
SOUTH AFRICAN GEOGRAPHICAL JOURNAL
(2023)
Article
Geography, Physical
Kudzai S. Mpakairi, Timothy Dube, Mbulisi Sibanda, Onisimo Mutanga
Summary: This study presents a comprehensive and spatially explicit methodology for identifying and mapping irrigated and rainfed croplands in South Africa. Utilizing low-cost earth observation technologies and highly accurate classification algorithms, the proposed framework provides accurate information on different types of croplands, which is essential for crop monitoring, yield forecasting, and enhancing agricultural efficiency.
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
(2023)
Review
Environmental Sciences
Mishkah Abrahams, Mbulisi Sibanda, Timothy Dube, Vimbayi G. P. Chimonyo, Tafadzwanashe Mabhaudhi
Summary: This study systematically reviewed the literature on the remote sensing of the spatial distribution and health of neglected and underutilised crop species (NUS). The findings showed slow progress in using unmanned aerial vehicles (UAVs) for mapping NUS, particularly in the Global South, due to high costs and restrictive regulations.
Article
Environmental Sciences
Trisha Deevia Bhaga, Timothy Dube, Munyaradzi Davis Shekede, Cletah Shoko
Summary: This study assesses the use of Landsat-8 OLI and Sentinel-2 MSI satellite data to characterize and monitor the impacts of drought on water resources in the Western Cape, South Africa. Different multispectral indices were computed to determine the most suitable method for surface water detection and drought monitoring. The findings provide valuable insights into surface water variability and the impacts of drought on water resources.
REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT
(2023)
Article
Environmental Sciences
Siphiwokuhle Buthelezi, Onisimo Mutanga, Mbulisi Sibanda, John Odindi, Alistair D. Clulow, Vimbayi G. P. Chimonyo, Tafadzwanashe Mabhaudhi
Summary: Maize is a valuable food crop in sub-Saharan Africa and plays a critical role in the local, national, and regional economies. However, the lack of appropriate technologies hinders the collection of accurate information on smallholder farm maize production. The use of Unmanned Aerial Vehicle (UAV) imagery and vegetation indices (VIs) provides a promising solution to determine maize leaf area index (LAI) at a farm scale. The study found that UAV-derived VIs, especially those in the blue, red edge, and NIR sections, can reliably predict maize LAI across the growing season.
Article
Environmental Sciences
Terence Darlington Mushore, John Odindi, Rob Slotow, Onisimo Mutanga
Summary: This study analyzed the variation of thermal comfort in different Land Cover Zone (LCZ) types in eThekwini Municipality, South Africa, using remotely sensed data. The study mapped the LCZs and retrieved land surface temperatures (LSTs) using Landsat data and a classification algorithm. The results showed that built-up LCZs were concentrated in the eastern parts of the municipality, influenced by the proximity to the sea. The average LSTs were lowest in dense forest, open low-rise, and water LCZs in cool and hot seasons, respectively. The compact high-rise LCZ had the highest temperatures in both seasons. The study highlighted the need for heat mitigation strategies in areas with strong heat stress and the importance of considering the thermal environment in urban development policies and plans.
