Hyperspectral image-based vegetation index (HSVI): A new vegetation index for urban ecological research

Fan

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

et al. 2023

Self Cite

Soil salinization leads to dehydration of plants, which seriously threatens ecologically sustainable development and food security guarantee. In the complex and diverse coastal wetland environment, the impervious surface and bare soil have similar spectral features with salinized soil, which make it difficult for traditional satellite data and algorithms to accurately and timely monitor the small surface features of salinization. This paper presents a Baseline-based Soil Salinity Index (BSSI) for soil salinization monitoring using medium-resolution data. In BSSI, we construct a virtual salinization baseline by connecting the nearinfrared (NIR) band and the short-wave infrared-2 (SWIR2) band to enhance the spectral feature of salinized soils which border on the impervious surface. In addition, we calculate the distance between the short-wave infrared-1 (SWIR1) band and the virtual salinization baseline as the BSSI, which can effectively improve the stability of salinity inversion for different soils. Through data comparison and model simulations, BSSI has shown advantages over a series of the traditional salinization spectral indices (SSIs). The results show that the saline soil extraction accuracy of BSSI exceeds 85% and the correlation coefficient of the BSSI and the degree of soil salinization exceeds 0.90. Since the related spectral bands, such as NIR, SWIR1, and SWIR2, are available on many existing satellite sensors such as Landsat TM/ETM+, OLI, and sentinel 2, the BSSI concept can be extended to establish long-term records for soil salinization monitoring.

Section: ) Verification Methods For Extraction Of Saline Soilmentioning

confidence: 99%

“…In addition, BSSI consists of NIR, SWIR1, and SWIR2 bands. Landsat TM/ETM+, OLI, and sentinel 2 all contain these three bands and their wavelength bands have similar wavelength ranges [50]. Therefore, BSSI can be extended to these satellite sensors to enable long time series monitoring of soil salinity.…”

Section: B Capacity Of Extrapolation Analysis Of the Bssimentioning

confidence: 99%

Baseline-Based Soil Salinity Index (BSSI): A Novel Remote Sensing Monitoring Method of Soil Salinization

Fan

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

et al. 2023

Self Cite

“…It has been well known that vegetation indices often saturate in high-density vegetation canopies [95,96]. As the crop grows, canopy vegetation coverage gradually increases until it reaches the maximum or closure in the reproductive growth stage.…”

Section: The Optimal Time Window For the Agb Monitoringmentioning

confidence: 99%

Estimation of Above-Ground Biomass of Winter Wheat Based on Consumer-Grade Multi-Spectral UAV

Wang

Yang

et al. 2022

Remote Sensing

One of the problems of optical remote sensing of crop above-ground biomass (AGB) is that vegetation indices (VIs) often saturate from the middle to late growth stages. This study focuses on combining VIs acquired by a consumer-grade multiple-spectral UAV and machine learning regression techniques to (i) determine the optimal time window for AGB estimation of winter wheat and to (ii) determine the optimal combination of multi-spectral VIs and regression algorithms. UAV-based multi-spectral data and manually measured AGB of winter wheat, under five nitrogen rates, were obtained from the jointing stage until 25 days after flowering in the growing season 2020/2021. Forty-four multi-spectral VIs were used in the linear regression (LR), partial least squares regression (PLSR), and random forest (RF) models in this study. Results of LR models showed that the heading stage was the most suitable stage for AGB prediction, with R2 values varying from 0.48 to 0.93. Three PLSR models based on different datasets performed differently in estimating AGB in the training dataset (R2 = 0.74~0.92, RMSE = 0.95~2.87 t/ha, MAE = 0.75~2.18 t/ha, and RPD = 2.00~3.67) and validation dataset (R2 = 0.50~0.75, RMSE = 1.56~2.57 t/ha, MAE = 1.44~2.05 t/ha, RPD = 1.45~1.89). Compared with PLSR models, the performance of the RF models was more stable in the prediction of AGB in the training dataset (R2 = 0.95~0.97, RMSE = 0.58~1.08 t/ha, MAE = 0.46~0.89 t/ha, and RPD = 3.95~6.35) and validation dataset (R2 = 0.83~0.93, RMSE = 0.93~2.34 t/ha, MAE = 0.72~2.01 t/ha, RPD = 1.36~3.79). Monitoring AGB prior to flowering was found to be more effective than post-flowering. Moreover, this study demonstrates that it is feasible to estimate AGB for multiple growth stages of winter wheat by combining the optimal VIs and PLSR and RF models, which overcomes the saturation problem of using individual VI-based linear regression models.

“…Drones may be useful for capturing floral data relevant to pollination research as they can be operated over spatial areas that roughly match the distances covered by many foraging pollinators (Osborne et al, 2008;Danner et al, 2016), with maximum operating distances of up to 10 km, depending on the aircraft model, and limited by legislative restrictions (Duffy et al, 2018). Their proximity to the Earth's • Developed by Sun et al, 2021 surface means that the grain of such observations has capacity to capture flower-scale information (e.g., Wan et al, 2018;Xavier et al, 2018;Chen et al, 2019;López-Granados et al, 2019). Furthermore, drones with high-quality inbuilt cameras are becoming more affordable (£500-£2000) for the everyday field ecologist, and there is the possibility to further modify these with customized sensors.…”

Section: Structure From Motion (Sfm) Photogrammetrymentioning

confidence: 99%

Remote Sensing of Floral Resources for Pollinators – New Horizons From Satellites to Drones

2022

Insect pollinators are affected by the spatio-temporal distribution of floral resources, which are dynamic across time and space, and also influenced heavily by anthropogenic activities. There is a need for spatial data describing the time-varying spatial distribution of flowers, which can be used within behavioral and ecological studies. However, this information is challenging to obtain. Traditional field techniques for mapping flowers are often laborious and limited to relatively small areas, making it difficult to assess how floral resources are perceived by pollinators to guide their behaviors. Conversely, remote sensing of plant traits is a relatively mature technique now, and such technologies have delivered valuable data for identifying and measuring non-floral dynamics in plant systems, particularly leaves, stems and woody biomass in a wide range of ecosystems from local to global scales. However, monitoring the spatial and temporal dynamics of plant floral resources has been notably scarce in remote sensing studies. Recently, lightweight drone technology has been adopted by the ecological community, offering a capability for flexible deployment in the field, and delivery of centimetric resolution data, providing a clear opportunity for capturing fine-grained information on floral resources at key times of the flowering season. In this review, we answer three key questions of relevance to pollination science – can remote sensing deliver information on (a) how isolated are floral resources? (b) What resources are available within a flower patch? And (c) how do floral patches change over time? We explain how such information has potential to deepen ecological understanding of the distribution of floral resources that feed pollinators and the parameters that determine their navigational and foraging choices based on the sensory information they extract at different spatial scales. We provide examples of how such data can be used to generate new insights into pollinator behaviors in distinct landscape types and their resilience to environmental change.