Application of Normalized Difference Vegetation Index (NDVI) for the Detection of Extreme Precipitation Change

Evaluation of vegetation restoration effectiveness along the Yangtze River shoreline and its response to land use changes

Assessing the effectiveness of vegetation restoration along the Yangtze River shoreline and exploring its relationship with land use changes are imperative for providing recommendations for sustainable management and environmental protection. However, the impact of vegetation restoration post-implementation of the Yangtze River Conservation Project remains uncertain. In this study, utilizing Sentinel-2 satellite imagery and Dynamic World land use data from pre- (2016) and post- (2022) Yangtze River Conservation Project periods, pixel-based binary models, transition matrices, and geographically weighted regression models were employed to analyze the status and evolution of vegetation coverage along the Yangtze River shoreline. The results indicated that there had been an increase in the area covered by high and high-medium vegetation levels. The proportion of vegetation cover shifting to better was 4201.87 km2 (35.68%). Hotspots of vegetation coverage improvement were predominantly located along the Yangtze River. Moreover, areas witnessing enhanced vegetation coverage experienced notable land use changes, notably the conversion of water to crops (126.93 km2, 22.79%), trees to crops (59.93 km2, 10.76%), and crops to built area (59.93 km2, 10.76%). Notably, the conversion between crops and built area emerged as a significant factor influencing vegetation coverage improvement, with average regression coefficients of 0.68 and 0.50, respectively. These outcomes underscore the significance of this study in guiding ecological environmental protection and sustainable management along the Yangtze River shoreline.

Analysis and prediction of ground deformation in Yinxi Industrial Park based on time-series InSAR technology

Monitoring ground deformation in industrial parks is of great importance for the economic development of urban areas. However, limited research has been conducted on the deformation mechanism in industrial parks, and there is a lack of integrated monitoring and prediction models. Therefore, this study proposes a comprehensive monitoring and prediction model for industrial parks, utilizing time-series Interferometry Synthetic Aperture Radar (InSAR) technology and the Whale Optimization Algorithm-Back Propagation (WOA-BP) neural network algorithm. Taking Yinxi Industrial Park in Baiyin District as a case study, we used 68 scenes of Sentinel-1A ascending and descending orbit data from June 2018 to April 2021. The Stanford Method for Persistent Scatterers-Permanent Scatterers (StaMPS-PS) and the Small Baseline Subsets-Interferometry Synthetic Aperture Radar (SBAS-InSAR) technologies were employed to obtain the surface deformation information of the park. The deformation information obtained by the two technologies was cross-validated in terms of temporal and spatial distribution, and the vertical and east-west deformation of the park was obtained by combining the ascending and descending orbit data. The results show that the deformation feature points in the line of sight (LOS) direction obtained by the two technologies have a high consistency in spatial distribution, using the ascending orbit data as an example. Additionally, the SBAS-InSAR technology was used to obtain the east-west and vertical deformation results of the park after merging the ascending and descending orbit data for the same period. It was found that the park is mainly affected by vertical deformation, with a maximum subsidence rate of 14.67 mm/yr. The subsidence areas correspond to the deformation positions observed in field survey photos. Based on the ascending orbit deformation data, the two technologies were validated with 585 points of the same latitude and longitude, and the coefficient of determination R2 was found to be 0.82, with a root mean square error (RMSE) of 2.20 mm/a. The deformation rates were also highly consistent. Due to the 47% increase in the number of sampling points provided by the StaMPS-PS technique compared to the SBAS-InSAR technique, the former was found to be more applicable in the industrial park. Based on the ground deformation mechanism in the park, we combined the StaMPS-PS technique with the WOA-BP neural network to construct a deformation zone prediction model. We conducted predictive studies on the deformation zones of buildings and roads within the park, and the results showed that the WOA-optimized BP neural network achieved higher accuracy and lower overall error compared to the unoptimized network. Finally, we analyzed and discussed the geological conditions and inducing factors of ground deformation in the park, providing a reference for a better understanding of the deformation mechanism and early warning of disasters in the industrial park.

Estimating stomatal conductance of citrus under water stress based on multispectral imagery and machine learning methods

INTRODUCTION

Canopy stomatal conductance (Sc) indicates the strength of photosynthesis and transpiration of plants. In addition, Sc is a physiological indicator that is widely employed to detect crop water stress. Unfortunately, existing methods for measuring canopy Sc are time-consuming, laborious, and poorly representative.

METHODS

To solve these problems, in this study, we combined multispectral vegetation index (VI) and texture features to predict the Sc values and used citrus trees in the fruit growth period as the research object. To achieve this, VI and texture feature data of the experimental area were obtained using a multispectral camera. The H (Hue), S (Saturation) and V (Value) segmentation algorithm and the determined threshold of VI were used to obtain the canopy area images, and the accuracy of the extraction results was evaluated. Subsequently, the gray level co-occurrence matrix (GLCM) was used to calculate the eight texture features of the image, and then the full subset filter was used to obtain the sensitive image texture features and VI. Support vector regression, random forest regression, and k-nearest neighbor regression (KNR) Sc prediction models were constructed, which were based on single and combined variables.

RESULTS

The analysis revealed the following: 1) the accuracy of the HSV segmentation algorithm was the highest, achieving more than 80%. The accuracy of the VI threshold algorithm using excess green was approximately 80%, which achieved accurate segmentation. 2) The citrus tree photosynthetic parameters were all affected by different water supply treatments. The greater the degree of water stress, the lower the net photosynthetic rate (Pn), transpiration rate (Tr), and Sc of the leaves. 3) In the three Sc prediction models, The KNR model, which was constructed by combining image texture features and VI had the optimum prediction effect (training set: R² = 0.91076, RMSE = 0.00070; validation set; R² = 0.77937, RMSE = 0.00165). Compared with the KNR model, which was only based on VI or image texture features, the R² of the validation set of the KNR model based on combined variables was improved respectively by 6.97% and 28.42%.

DISCUSSION

This study provides a reference for large-scale remote sensing monitoring of citrus Sc by multispectral technology. Moreover, it can be used to monitor the dynamic changes of Sc and provide a new technique for gaining a better understanding of the growth status and water stress of citrus crops.

Influence of Precipitation Characteristics and Vegetation on Runoff and Sediment: A Case on the Basin in the Three Gorges Reservoir Region

Sediment is the main carrier of pollutants in river channels. This study analyzed the distribution characteristics of precipitation, runoff, and sediment and their response characteristics in the Daning River basin. Based on daily precipitation (1979–2017), runoff (1989–2017), and sediment (1997–2017) time series, the Gini concentration index, precipitation concentration index (PCI), precipitation concentration degree (PCD), and precipitation concentration period were applied to assess the concentration characteristics of precipitation, runoff, and sediment on the daily, monthly, and seasonal scales. At each intensity level, precipitation was negatively correlated to the PCI and PCD. The normalized difference vegetation index (NDVI) values had strong negative correlations with rainy days with light precipitation (0.1–9.9 mm). The degrees of concentration were in the same order for the multiscale analysis: runoff < precipitation < sediment. Although the amount of daily precipitation of more than 25 mm displayed a significant increasing trend, suggesting an increased risk of flood and soil erosion, the significantly improved vegetation cover reduced the sediment-carrying capacity of the surface runoff, with significant decreases in the total amount and multiscale concentration degrees of sediment being observed. The results of the study provide a reference for the improvement of the potable water safety and ecological environment in the Three Gorges Reservoir region.

Generating High-Resolution and Long-Term SPEI Dataset over Southwest China through Downscaling EEAD Product by Machine Learning

Drought is an event of shortages in the water supply, whether atmospheric, surface water or ground water. Prolonged droughts have negative impacts on ecosystems, agriculture, society, and the economy. Although existing drought index products are widely utilized in drought monitoring, the coarse spatial resolution greatly limits their applications on regional or local scales. Machine learning driven by remote sensing observations offers an opportunity to monitor regional scale droughts. However, the limited time range of remote sensing observations such as vegetation index (VI) resulted in a substantial gap in generating high resolution drought index products before 2000. This study generated spatiotemporally continuous Standardized Precipitation Evapotranspiration Index (SPEI) data spanning from 1901–2018 in southwestern China by machine learning. It indicated that four Classification and Regression Tree (CART) approaches, decision trees (DT), random forest (RF), gradient boosted regression trees (GBRT) and extra trees (ET), can provide valid local drought information by downscaling the Estación Experimental de Aula Dei (EEAD) data. The in-situ SPEI dataset produced by the Penman–Monteith method was used as a benchmark to evaluate the temporal and spatial performance of the downscaled SPEI. In addition, the necessity of VI in SPEI downscaling was also assessed. The results showed that: (1) the ET-based product has the best performance (R2 = 0.889, MAE = 0.232, RMSE = 0.432); (2) the VI provides no significant improvement for SPEI re-construction; (3) topography exerts an obvious influence on the downscaling process, and (4) the downscaled SPEI shows more consistency with the in-situ SPEI compared with EEAD SPEI. The proposed method can be easily extended to other areas without in-situ data and enhance the ability of long-term drought monitoring.

Influences of Climate Change and Human Activities on NDVI Changes in China

The spatiotemporal evolution of vegetation and its influencing factors can be used to explore the relationships among vegetation, climate change, and human activities, which are of great importance for guiding scientific management of regional ecological environments. In recent years, remote sensing technology has been widely used in dynamic monitoring of vegetation. In this study, the normalized difference vegetation index (NDVI) and standardized precipitation–evapotranspiration index (SPEI) from 1998 to 2017 were used to study the spatiotemporal variation of NDVI in China. The influences of climate change and human activities on NDVI variation were investigated based on the Mann–Kendall test, correlation analysis, and other methods. The results show that the growth rate of NDVI in China was 0.003 year−1. Regions with improved and degraded vegetation accounted for 71.02% and 22.97% of the national territorial area, respectively. The SPEI decreased in 60.08% of the area and exhibited an insignificant drought trend overall. Human activities affected the vegetation cover in the directions of both destruction and restoration. As the elevation and slope increased, the correlation between NDVI and SPEI gradually increased, whereas the impact of human activities on vegetation decreased. Further studies should focus on vegetation changes in the Continental Basin, Southwest Rivers, and Liaohe River Basin.

The Identification and Classification of Arid Zones through Multicriteria Evaluation and Geographic Information Systems—Case Study: Arid Regions of Northwest Mexico

Arid and semiarid regions are geographic units that cover approximately 43% of the earth’s surface worldwide, and conditions of extreme drought and reduced vegetation cover predominate in these regions. In Mexico, arid and semiarid ecosystems cover more than half of the territory, with desertification, mainly caused by anthropogenic activities and climatic events, as the main problem in these regions. The present research aimed to assess, identify, and classify arid and semiarid zones by employing a methodology based on multicriteria evaluation analysis (MCA) using the weighted linear combination (WLC) technique and geographic information systems (GIS) in the hydrological administrative regions (HARs) of the North Pacific, Northwest, and Baja California Peninsula, located in Northwest Mexico. Data related to aridity, desertification, degradation, and drought were investigated, and the main factors involved in the aridity process, such as surface temperature, soil humidity, precipitation, slopes, orientations, the normalized difference vegetation index (NDVI), and evapotranspiration, were obtained. For the standardization of factors, a fuzzy inference system was used. The weight of each factor was then determined with the analytical hierarchy process (AHP). To delimit arid regions, the classification of arid zones proposed by the United Nations Environment Program (UNEP) was used, and the result was an aridity suitability map. To validate the results, the sensitivity analysis method was applied. Quantitative and geospatial aridity indicators were obtained at the administrative hydrological level and by state. The main results indicated that semiarid and dry subhumid zones predominated, representing 40% and 43% of the surface of the study area, respectively, while arid regions represented 17%, and humid regions represented less than 1%. In addition, of the states for which 100% of the surface lay in the study area, it was observed that Baja California and Baja California Sur had the largest arid and semiarid zones, while subhumid regions predominated in Sonora and Sinaloa.

Monitoring Meteorological Drought in Southern China Using Remote Sensing Data

Severe meteorological drought is generally considered to lead to crop damage and loss. In this study, we created a new standard value by averaging the values distributed in the middle 30–70% instead of the traditional mean value, and we proposed a new index calculation method named Normalized Indices (NI) for meteorological drought monitoring after normalized processing. The TRMM-derived precipitation data, GLDAS-derived soil moisture data, and MODIS-derived vegetation condition data from 2003 to 2019 were used, and we compared the NI with commonly used Condition Indices (CI) and Anomalies Percentage (AP). Taking the mid-to-lower reaches of the Yangtze River (MLRYR) as an example, the drought monitoring results for paddy rice and winter wheat showed that (1) NI can monitor well the relative changes in real precipitation/soil moisture/vegetation conditions in both arid and humid regions, while meteorological drought was overestimated with CI and AP, and (2) due to the monitoring results of NI, the well-known drought event that occurred in the MLRYR from August to October 2019 had a much less severe impact on vegetation than expected. In contrast, precipitation deficiency induced an increase in sunshine and adequate heat resources, which improved crop growth in 78.8% of the area. This study discusses some restrictions of CI and AP and suggests that the new NI index calculation provides better meteorological drought monitoring in the MLRYR, thus offering a new approach for future drought monitoring studies.

Monitoring Vegetation Change and Its Potential Drivers in Inner Mongolia from 2000 to 2019

Inner Mongolia in China is a typically arid and semi-arid region with vegetation prominently affected by global warming and human activities. Therefore, investigating the past and future vegetation change and its impact mechanism is important for assessing the stability of the ecosystem and the ecological policy formulation. Vegetation changes, sustainability characteristics, and the mechanism of natural and anthropogenic effects in Inner Mongolia during 2000–2019 were examined using moderate resolution imaging spectroradiometer normalized difference vegetation index (NDVI) data. Theil–Sen trend analysis, Mann–Kendall method, and the coefficient of variation method were used to analyze the spatiotemporal variability characteristics and sustained stability of the NDVI. Furthermore, a trend estimation method based on a Seasonal Trend Model (STM), and the Hurst index was used to analyze breakpoints and change trends, and predict the likely future direction of vegetation, respectively. Additionally, the mechanisms of the compound influence of natural and anthropogenic activities on the vegetation dynamics in Inner Mongolia were explored using a Geodetector Model. The results show that the NDVI of Inner Mongolia shows an upward trend with a rate of 0.0028/year (p < 0.05) from 2000 to 2019. Spatially, the NDVI values showed a decreasing trend from the northeast to the southwest, and the interannual variation fluctuated widely, with coefficients of variation greater than 0.15, for which the high-value areas were in the territory of the Alxa League. The areas with increased, decreased, and stable vegetation patterns were approximately equal in size, in which the improved areas were mainly distributed in the northeastern part of Inner Mongolia, the stable and unchanged areas were mostly in the desert, and the degraded areas were mainly in the central-eastern part of Inner Mongolia, it shows a trend of progressive degradation from east to west. Breakpoints in the vegetation dynamics occurred mainly in the northwestern part of Inner Mongolia and the northeastern part of Hulunbuir, most of which occurred during 2011–2014. The future NDVI trend in Inner Mongolia shows an increasing trend in most areas, with only approximately 10% of the areas showing a decreasing trend. Considering the drivers of the NDVI, we observed annual precipitation, soil type, mean annual temperature, and land use type to be the main driving factors in Inner Mongolia. Annual precipitation was the first dominant factor, and when these four dominant factors interacted to influence vegetation change, they all showed interactive enhancement relationships. The results of this study will assist in understanding the influence of natural elements and human activities on vegetation changes and their driving mechanisms, while providing a scientific basis for the rational and effective protection of the ecological environment in Inner Mongolia.