Multispectral Analysis of Small Plots Based on Field and Remote Sensing Surveys—A Comparative Evaluation

In-Season Monitoring of Maize Leaf Water Content Using Ground-Based and UAV-Based Hyperspectral Data

China is one the largest maize (Zea mays L.) producer worldwide. Considering water deficit as one of the most important limiting factors for crop yield stability, remote sensing technology has been successfully used to monitor water relations in the soil–plant–atmosphere system through canopy and leaf reflectance, contributing to the better management of water under precision agriculture practices and the quantification of dynamic traits. This research was aimed to evaluate the relation between maize leaf water content (LWC) and ground-based and unoccupied aerial vehicle (UAV)-based hyperspectral data using the following approaches: (I) single wavelengths, (II) broadband reflectance and vegetation indices, (III) optimum hyperspectral vegetation indices (HVIs), and (IV) partial least squares regression (PLSR). A field experiment was undertaken at the Chinese Academy of Agricultural Sciences, Beijing, China, during the 2020 cropping season following a split plot model in a randomized complete block design with three blocks. Three maize varieties were subjected to three differential irrigation schedules. Leaf-based reflectance (400–2500 nm) was measured with a FieldSpec 4 spectroradiometer, and canopy-based reflectance (400–1000 nm) was collected with a Pika-L hyperspectral camera mounted on a UAV at three assessment days. Both sensors demonstrated similar shapes in the spectral response from the leaves and canopy, with differences in reflectance intensity across near-infrared wavelengths. Ground-based hyperspectral data outperformed UAV-based data for LWC monitoring, especially when using the full spectra (Vis–NIR–SWIR). The HVI and the PLSR models were demonstrated to be more suitable for LWC monitoring, with a higher HVI accuracy. The optimal band combinations for HVI were centered between 628 and 824 nm (R2 from 0.28 to 0.49) using the UAV-based sensor and were consistently located around 1431–1464 nm and 2115–2331 nm (R2 from 0.59 to 0.80) using the ground-based sensor on the three assessment days. The obtained results indicate the potential for the complementary use of ground-based and UAV-based hyperspectral data for maize LWC monitoring.

A UAS and Machine Learning Classification Approach to Suitability Prediction of Expanding Natural Habitats for Endangered Flora Species

In this study, we propose integrating unmanned aerial systems (UASs) and machine learning classification for suitability prediction of expanding habitats for endangered flora species to prevent further extinction. Remote sensing imaging of the protected steppe-like grassland in Bilje using the DJI P4 Multispectral UAS ensured non-invasive data collection. A total of 129 individual flora units of five endangered flora species, including small pasque flower (Pulsatilla pratensis (L.) Miller ssp. nigricans (Störck) Zämelis), green-winged orchid (Orchis morio (L.)), Hungarian false leopardbane (Doronicum hungaricum Rchb.f.), bloody cranesbill (Geranium sanguineum (L.)) and Hungarian iris (Iris variegate (L.)) were detected and georeferenced. Habitat suitability in the projected area, designated for the expansion of the current area of steppe-like grassland in Bilje, was predicted using the binomial machine learning classification algorithm based on three groups of environmental abiotic criteria: vegetation, soil, and topography. Four machine learning classification methods were evaluated: random forest, XGBoost, neural network, and generalized linear model. The random forest method outperformed the other classification methods for all five flora species and achieved the highest receiver operating characteristic (ROC) values, ranging from 0.809 to 0.999. Soil compaction was the least favorable criterion for the habitat suitability of all five flora species, indicating the need to perform soil tillage operations to potentially enable the expansion of their coverage in the projected area. However, potential habitat suitability was detected for the critically endangered flora species of Hungarian false leopardbane, indicating its habitat-related potential for expanding and preventing further extinction. In addition to the current methods of predicting current coverage and population count of endangered species using UASs, the proposed method could serve as a basis for decision making in nature conservation and land management.

Aerial Monitorization—A Vector for Ensuring the Agroecosystems Sustainability

This paper is based on the modernization of work processes in agriculture by ensuring the efficient management of land and equipment and the acquisition of inputs given the specific natural variation in environmental conditions. Specifically, the paper highlights research from a dual perspective, descriptive and explanatory, according to the methodology of the case study conducted in the field of the agricultural enterprise SC AgriConsorțium SRL, located in the S–W of Romania, by adopting the spatial technology for the aerial monitorization of agricultural crops and for signalizing, in real time, the changes and vulnerabilities of the agroecosystem in order to function and develop sustainably. The research aims to promote spatial technologies to monitor crop growth resources, crop vegetation conditions, the real-time signaling of changes, and vulnerabilities in the agroecosystem. The research study’s results highlight the role of the aerial monitoring of crops and rapid signaling of changes in the agroecosystem, such as vegetation conditions, plant density, quality of applied work, and the destruction of crops by overgrazing for the rapid and relevant assessment of affected areas and damage. The case study of the paper is a modern, innovative, and sustainable tool for digitizing agricultural enterprises to obtain accurate information on changes in the agroecosystem and to adopt a geographical information system for recording and managing data specific to cultivated areas and their use in providing studies and reports necessary for state institutions, respectively, in order to support and guide the decision-making process. The obtained results are the basis for future research on the interpretation and use of information obtained by drones.