Eyes in the sky and on the ground: enhanced dryland monitoring with remote sensing
New method combines time-series imagery of satellites with near-surface cameras to better monitor ecological states in drylands
Journal of Remote Sensing
image:
Comparison of grass-dominated (top) and shrub-dominated (bottom) dryland ecological states, with differences in growing-season phenology shown for dry and wet rainfall years. States are shown on the left using PhenoCam images.
view moreCredit: PhenoCam Network and Emily Myers, USDA-ARS.
While animals in drylands hone their natural senses to find vegetation, humans have developed “external eyes” to track these vital resources.
Scientists from the U.S. Department of Agriculture (USDA) Agricultural Research Service (ARS) have created an advanced method that integrates high-frequency near-surface camera data with broader satellite imagery to better monitor and assess dryland ecosystems. Their approach could aid in taking timely action to prevent land degradation, contributing to improved environmental management and conservation strategies.
Their results were published in the Journal of Remote Sensing on July 8.
Drylands, including arid, semi-arid and dry sub-humid areas, account for over 40% of the Earth’s land surface and are vital for supporting diverse wildlife, crop production and carbon regulation. These areas, however, are prone to abrupt ecological state changes, such as shifts from grass-dominated to shrub-dominated landscapes, driven by climate change and unsustainable land use.
Drylands pose significant challenges for ecological monitoring due to their sparse vegetation cover, high spatial variability, and year-to-year fluctuations in vegetation growth. Traditional methods often fall short in accurately classifying and detecting changes in ecological states. To address this, the USDA-ARS researchers melded space and ground approaches to eagle-eye these ecosystems.
“We wanted to understand whether we could use images collected remotely from near-surface cameras or satellites to measure differences in dryland vegetation greenness over time and in response to rainfall,” said Emily R. Myers, a SCINet postdoctoral fellow with the USDA-ARS and lead author of this study.
The researchers’ goal was to use these differences to distinguish between different dryland ecological states. The team combined daily data from near-surface cameras (PhenoCam) with satellite imagery (Harmonized Landsat 8 and Sentinel-2, or HLS) at a desert grassland site in southwest New Mexico, analyzing data from 12 different locations over multiple years between 2014 and 2022.
“Measurements from near-surface cameras and satellites were able to distinguish between different dryland vegetation responses to rainfall,” said Dawn M. Browning, a senior research ecologist at the USDA-ARS and co-lead author of the study.
Their findings showed that grass-dominated states were the most affected by rainfall, with rapid spikes in greenness and productivity during wet growing seasons. Shrub-dominated states were less affected by growing season rainfall.
The distinct phenological responses of grass-dominated states can serve as indicators of grass productivity and ecological state change in drylands.
“These differences in greenness responses may help us map and monitor dryland states and productivity using remotely sensed imagery,” Myers said.
By refining remote sensing techniques, the researchers hope to identify and monitor more effectively dryland areas with significant grass cover. This insight could guide management strategies for maintaining grass cover and preventing shrub encroachment in these increasingly vulnerable ecosystems.
“We would like to explore more ways to characterize the growing season, with a particular focus on measurements that are sensitive to the rapid increases in greenness that are indicative of grassy productivity,” Browning said.
Journal
Journal of Remote Sensing
Method of Research
Imaging analysis
Subject of Research
Not applicable
Article Title
Novel Use of Image Time Series to Distinguish Dryland Vegetation Responses to Wet and Dry Years
From plows to pixels: comprehensive rice mapping with satellite technology
Study integrates satellite data with rice growth stages for improved rice paddy map accuracy
image:
Support Vector Machine (SVM) and Random Forest (RF) classifiers with matched features produced more complete paddy rice mapping results than those with unmatched features, reducing misclassifications of fishponds in the center of the image and significantly outperforming the other three comparison methods in these regions.
view moreCredit: [Lingyu Sun, Wuhan University]; [Tianyao Yang, Peking University]; [Yuxin Lou, Beijing Normal University]; [Qian Shi, Sun Yat-Sen University]; [Liangpei Zhang, Wuhan University].
While satellite technology might seem distant and space-bound, its benefits can be seen everywhere.
Scientists from Sun Yat-sen University and their colleagues have developed an innovative framework that aligns advanced satellite data with rice growth stages to bolster paddy rice mapping accuracy. These results are expected to push the boundaries of mapping precision and contribute to global food security efforts.
Their findings were published in the Journal of Remote Sensing on July 8.
Rice is a staple grain in many countries and constitutes a significant share of the global food supply. Accurate paddy rice mapping is essential for coordinating agricultural production, ensuring food security and managing cultivation. Regions in the tropics and subtropics, such as Guangdong, China, are major rice producers. Mapping rice paddies in these areas, however, remains a challenge.
“There are two main difficulties in tropical and subtropical paddy rice mapping: the lack of high-quality optical images and differences in paddy rice planting times,” said Qian Shi, a professor at the School of Geography and Planning at Sun Yat-sen University and lead author of this study.
Tropical and subtropical regions often experience cloud cover, which obscures land surface information on satellite images. Additionally, varying climate conditions and planting habits mean that rice planting times differ, complicating accurate mapping.
To overcome these obstacles, the team developed a new mapping framework using phenology matching. This technique involves aligning the growth stages of rice plants with data from Sentinel-1 and Sentinel-2 satellites to improve mapping accuracy in Guangdong.
Leveraging Sentinel-1’s ability to penetrate cloud cover, the researchers identified rice growth periods, or phenological periods, and gathered radar and optical data features specific to these periods from both satellites. These features were then fed into machine learning classifiers, or algorithms that categorize data.
“By matching features derived from Sentinel-1 and Sentinel-2 to specific growth stages, our classifiers significantly enhanced mapping accuracy compared to conventional methods,” Shi said.
This multi-source data approach indicated a significant improvement in accuracy, ranging from 6.44% to 16.10%. These advancements were further validated through regression analysis that aligned mapped areas with statistical data.
This study also shed light on critical factors influencing rice phenology and spatial distribution in Guangdong. Thermal conditions, particularly cold severity during growth stages, emerged as primary determinants affecting rice development. The interplay of human and natural[L2] factors, including land slope and minimum temperature, also significantly influenced spatial rice patterns.
These findings provide a deeper understanding of rice ecosystem dynamics, offering valuable insights for formulating agricultural policies and underscoring the importance of integrating cutting-edge science with on-the-ground agriculture to optimize production and sustainability.
“We aim to apply this methodology to other tropical and subtropical regions, further refining accuracy and expanding insights into global rice production dynamics,” Shi said.
Journal
Journal of Remote Sensing
Method of Research
Imaging analysis
Subject of Research
Not applicable
Article Title
Paddy Rice Mapping Based on Phenology Matching and Cultivation Pattern Analysis Combining Multi-Source Data in Guangdong, China
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