Article
Geosciences, Multidisciplinary
Wei Yue, Zhihai Gao, Bin Sun, Yifu Li, Ziyu Yan
Summary: Shrub encroachment is a global concern, and the fraction of shrub coverage (FSC) is an important indicator. The commonly used line-point intercept (LPI) method for FSC measurement in field surveys has issues of poor accuracy and low measurement efficiency. This study focuses on Caragana microphylla shrub-encroached grassland and uses normalized difference vegetation index (NDVI) data from unmanned aerial vehicles (UAVs) to derive actual FSC values and simulate measurements using the LPI method. The results show significant systematic errors in measurements from circular plots and suggest that achieving an efficient measurement in square plots requires considering different coverage levels, ensuring an adequate number of lines, and setting a relatively smaller point spacing.
Article
Agriculture, Multidisciplinary
Sharareh Akbarian, Chengyuan Xu, Weijin Wang, Stephen Ginns, Samsung Lim
Summary: This study aims to improve early prediction of sugarcane yield by using high-resolution multispectral UAV imagery. The results show that the optimal growth stage for prediction is the middle stage from mid-March to early May, and March is the best month for predicting future sugarcane yields. A novel cross-validation methodology and feature selection techniques were used to improve model accuracy. The best performance was achieved by combining NDRE and GRNDVI indices in March. These results provide valuable insights for growers and decision-makers to benefit from early yield forecast.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2022)
Review
Agronomy
Mohammad Fatin Fatihur Rahman, Shurui Fan, Yan Zhang, Lei Chen
Summary: There is a significant demand for drone and UAS development in the agricultural sector, playing a crucial role in helping developing countries achieve economic prosperity. Financial investments in agriculture have been increasing, but the sector still faces significant losses. Utilizing drones for spraying pesticides and fertilizers can reduce health issues and the number of workers, enhancing agricultural productivity effectively.
Article
Agriculture, Multidisciplinary
Lang Qiao, Weijie Tang, Dehua Gao, Ruomei Zhao, Lulu An, Minzan Li, Hong Sun, Di Song
Summary: This study evaluated the response differences and robustness of 36 different vegetation indices (VIs) under different crop coverage conditions, selected some VIs as optimal spectral variables, and established a maize canopy chlorophyll content detection model using partial least squares regression and random forest algorithms.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2022)
Article
Agronomy
Wenyan Ge, Xiuxia Li, Linhai Jing, Jianqiao Han, Fei Wang
Summary: Investigation of deciduous forest phenology is significant for understanding temperate deciduous forest eco-systems. Fine-scale perspectives on vegetation phenology have recently been carried out with the development of near-surface sensors, particularly uncrewed aerial vehicles (UAV). However, the capability of UAV-derived indices for canopy-scale phenology monitoring remains under-studied.
AGRICULTURAL AND FOREST METEOROLOGY
(2023)
Article
Environmental Sciences
Fen Chen, Tim van de Voorde, Dar Roberts, Haojie Zhao, Jingbo Chen
Summary: This communication discusses the limitations and risks of a commonly used method for detecting ground materials, which relies on setting thresholds for normalized difference indices. The authors critically analyze this method and present experimental results on various spectral libraries and satellite images. They highlight the risk of commission errors and provide suggestions for reducing them.
Article
Forestry
Jose Luis Gallardo-Salazar, Marcela Rosas-Chavoya, Marin Pompa-Garcia, Pablito Marcelo Lopez-Serrano, Emily Garcia-Montiel, Arnulfo Melendez-Soto, Sergio Ivan Jimenez-Jimenez
Summary: The use of unmanned aerial vehicles (UAV) with multispectral sensors for forest monitoring has increased in recent years. This study analyzed the spectral responses and variations of the normalized difference vegetation index (NDVI) in tree crowns, as well as their correlation with climatic factors. Significant differences in NDVI values were found among different tree species, possibly due to their physiological features and phenology. Quercus grisea had the lowest NDVI values throughout the year, which may be attributed to its sensitivity to relative humidity and temperatures. In more complex forest analyses, the integration of hyperspectral and LiDAR sensors, as well as considering other vegetation indexes, should be considered.
JOURNAL OF FORESTRY RESEARCH
(2023)
Article
Agronomy
Hyunjin Jung, Ryosuke Tajima, Rongling Ye, Naoyuki Hashimoto, Yi Yang, Shuhei Yamamoto, Koki Homma
Summary: In this study, a new method for evaluating the leaf area index (LAI) of individual sweetcorn plants using an unmanned aerial vehicle (UAV) was proposed and tested in small-scale field experiments. The use of a plant-based method allowed for the detection of statistical differences in LAI and yield for different plant densities and fertilizer treatments. These findings demonstrate the potential and impact of plant-based evaluations using UAVs for future field experiments.
Article
Agriculture, Multidisciplinary
Radhwane Derraz, Farrah Melissa Muharam, Khairudin Nurulhuda, Noraini Ahmad Jaafar, Ng Keng Yap
Summary: This study compares the model performance, variance, stability, and confidence of base and ensemble machine learning models in the context of multicollinearity and non-multicollinearity for predicting rice biomass. The experiment shows that ensemble machine learning models outperform base models for predicting all rice biomass traits in the multicollinearity context. Base and ensemble machine learning models exhibit inconsistent patterns of R2 and RMSE variances in both multicollinearity and non-multicollinearity contexts. Multicollinearity and the base-ensemble machine learning concept do not affect model confidence, which is subject to the cross-effects of machine learning and dataset characteristics.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2023)
Article
Environmental Sciences
Arthur Bayle, Erwan Roussel, Bradley Z. Carlson, Franck Vautier, Claire Brossard, Elise Fovet, Geraud de Bouchard d'Aubeterre, Dov Corenblit
Summary: Scientists utilized UAV imagery in the Glacier noir foreland in France to evaluate the sensitivity of Landsat NDVI to subpixel vegetation and topographic components. They found a linear relationship between fractional vegetation cover and Landsat NDVI, but noted that vegetation height and subpixel topographic heterogeneity could lead to misestimation of vegetation cover.
JOURNAL OF APPLIED REMOTE SENSING
(2021)
Article
Plant Sciences
Yanyan Lv, Menghong Shen, Baoping Meng, Huifang Zhang, Yi Sun, Jianguo Zhang, Li Chang, Jingrong Li, Shuhua Yi
Summary: The study found that the asymmetric response of productivity to precipitation is related to the similarity between vegetation species composition and soil seed bank accumulation. This suggests that the variation in vegetation species composition is closely tied to the composition of seeds stored in the soil seed bank.
Article
Plant Sciences
Stephan Getzin, Todd E. Erickson, Hezi Yizhaq, Miriam Munoz-Rojas, Andreas Huth, Kerstin Wiegand
Summary: The study in spinifex-grassland ecosystem of Western Australia demonstrates that the fairy circles (FCs) pattern influences the vitality of grass, with high-vitality grasses being more associated with FCs. The feedback exhibited by grass at different scales not only indicates facilitation or competition, but also supports the infiltration feedback as proposed in theoretical models. These findings further validate the importance of scale-dependent feedbacks in explaining the emergent grassland pattern.
JOURNAL OF ECOLOGY
(2021)
Article
Remote Sensing
Xiaoping Yao, Qiuxiang Yi, Fumin Wang, Tianyue Xu, Jueyi Zheng, Zhou Shi
Summary: Monitoring the growth of rice is crucial for ensuring food security. This research developed flower intensity estimation models using stepwise multiple linear regression (SMLR) and random forest (RF) to monitor the status of rice flowers. The results showed that the accuracy of flower intensity estimation models based on flower indices (FI) was comparable to models based on vegetation indices (VI). These promising results provide an alternative way to obtain information about rice yield.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2023)
Article
Environmental Sciences
Sajjad Hussain, Muhammad Mubeen, Ashfaq Ahmad, Hamid Majeed, Saeed Ahmad Qaisrani, Hafiz Mohkum Hammad, Muhammad Amjad, Iftikhar Ahmad, Shah Fahad, Naveed Ahmad, Wajid Nasim
Summary: This research examines the impact of changes in land use/land cover on land surface temperature (LST) in Southern Punjab, Pakistan, using remote sensing data. The study finds that the expansion of buildings and conversion of vegetation land into commercial and residential areas has accelerated the increase of LST. The study also shows a negative correlation between NDVI and NDWI with lower LST, and a positive correlation between NDBI and higher LST.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Article
Ecology
Simon Taugourdeau, Antoine Diedhiou, Cofelas Fassinou, Marina Bossoukpe, Ousmane Diatta, Ange N'Goran, Alain Auderbert, Ousmane Ndiaye, Abdoul Aziz Diouf, Torbern Tagesson, Rasmus Fensholt, Emile Faye
Summary: This study tested the potential of Structure from Motion (SfM) using Unmanned Aerial Vehicle (UAV) and ground camera measurements to estimate herbaceous aboveground biomass (HAB) in Sahelian rangelands. The results showed that estimates based on camera data were slightly more accurate, and combining datasets across scales for the same type of tool could be useful for monitoring HAB in grassy ecosystems.
ECOLOGY AND EVOLUTION
(2022)