Evaluating the NDVI–Rainfall Relationship in Bisha Watershed, Saudi Arabia Using Non-Stationary Modeling Technique

Spatiotemporal dynamics and driving factors of vegetation coverage around linear cultural heritage: A case study of the Beijing-Hangzhou Grand Canal

Linear cultural heritage, which plays significant roles in safeguarding the world's cultural and maintaining global civilization, has obtained rising concerns in purpose of sustainability. However, in view of existing publications, most attention has been paid on its values of recreation, history and culture. Its ecological environment is still poorly understood. As the longest linear cultural heritage in the world and spiritual symbol of China, the Beijing-Hangzhou Grand Canal (BHGC) was selected as the study area in this study. We focused on the vegetation coverage around the BHGC from 2000 to 2020 and aimed to practically investigate whether and why vegetation distributes imbalanced along the entire BHGC. The annual Normalized Difference Vegetation Index (NDVI), derived from Landsat images, was used to indicate the spatiotemporal dynamics of vegetation coverage. Based on ten natural and human interference factors, the geographic detector model was applied to analyze its driving mechanism. Results show that (1) vegetation coverage around the BHGC presented apparently spatial heterogeneity. Cities located at both ends of the BHGC showed lower vegetation coverage, whereas those in the middle were relatively higher. (2) Vegetation coverage in 23 cities around the BHGC was relatively stable over time, i.e., nearly 76.39% of the study area was measured unchanged trend. The slight degradation mainly occurred to the sub-urban and extra-urban areas. (3) The driving forces of human interferences on vegetation coverage dynamics around the BHGC surpassed natural factors from 2000 to 2020. Population density, GDP and cultural heritage density presented higher explanatory powers of vegetation growth compared to other seven factors. These findings provide a scientific basis for local governments to intervene in vegetation changes and ecological restoration through natural and human factors within the favorable scope.

Machine learning models for predicting vegetation conditions in Mahanadi River basin

The vegetation of a river basin is affected by various climate factors, such as precipitation and land surface temperature (LST). This study explores the best machine learning model for the prediction of normalized difference vegetation index (NDVI) with LST and precipitation as input parameters. The study also determines the correlation between NDVI, LST, and precipitation of the Mahanadi basin from 2003 to 2021. Monthly precipitation data was extracted from the Center for Hydrometeorology and Remote Sensing (CHRS) portal. The Moderate Resolution Imaging Spectroradiometer (MODIS) products were used to derive the LST and NDVI using Google Earth Engine (GEE). Four different machine learning models were used to predict the NDVI of the Mahanadi basin: linear regression (LR), random forest (RF), support vector regression (SVR), and k-nearest neighbors (KNN). The coefficient of determination (R2), root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE), and explained variance score (EVS) were calculated to evaluate the performance of the models. The results show that the RF model has the highest R2 value in both the training and testing sets among these models, indicating that it is the most optimal among these models for predicting NDVI. The SVR model has the lowest RMSE value in the training set, but the KNN model has the lowest RMSE value in the testing set. The results also show that there is a positive correlation between precipitation and NDVI, a negative correlation between precipitation and LST, and between NDVI and LST. This study provides insights into the relationship between NDVI, LST, and precipitation, and the best machine-learning model for predicting NDVI. The findings of this study can be used to improve the management of river basins and to predict the effects of climate change on vegetation.

Land-use change and forest cover depletion in Bhawal National Park, Gazipur, Bangladesh from 2005 to 2020

Despite being a historically significant and biologically diverse protected area in Bangladesh, Bhawal National Park (BNP) has been the victim of anthropogenic activities that have challenged its sustainability. This study aims to determine the extent of landscape change and forest area depletion within BNP territory from 2005 to 2020, as land use change is the most visible indicator of human footprint liable to biodiversity loss and land degradation. Landsat satellite images from four sensors are used in this research (MSS, TM, OLI, and TIRS). The complex spatial composition of Bhawal National Park was characterized using a supervised classification technique using ArcMap version 10.8. The findings show a steady decline in the dense and degraded forest classifications, but an increase in the other classed classes. Waterbody, settlement, and chala/baid are found to increase by approximately 105, 369, and 650% respectively from 2005 till 2020, while, the area of dense and degraded forest decreased by about 47 and 51%. Within 16 years, nearly 1000 ha of dense forest cover has been depleted. Comparing the latest classified image of BNP with the relevant map, Bhabanipur, B.K. Bari, and Boupara beat are identified to be the most degraded areas. The conclusion can be drawn from this study that owing to most suppressing factors, namely settlement and chala/baid, a forest cover of nearly 2284 ha has been depleted from this national park which is almost 49% of the total possessed forest cover in 2005.

Influence of Anthropogenic Activities and Major Natural Factors on Vegetation Changes in Global Alpine Regions

Understanding vegetation changes and their driving forces in global alpine areas is critical in the context of climate change. We aimed to reveal the changing trend in global alpine vegetation from 1981 to 2015 using the least squares regression method and Mann-Kendall (MK) test. The area-of-influence dominated by anthropogenic activity and natural factors was determined in an area with significant vegetation change by residual analysis; the primary driving force of vegetation change in the area-of-influence dominated by natural factors was identified using the partial correlation method. The results showed that (1) the vegetation in the global alpine area exhibited a browning trend from 1981 to 2015 on the annual scale; however, a greening trend was observed from May to July on the month scale. (2) The influence of natural factors was greater than that of anthropogenic activities, and the positive impact of natural factors was greater than the negative impact. (3) Among the factors that were often considered as the main natural factors, the contribution of albedo to significant changes in vegetation were greater than that of temperature, precipitation, soil moisture, and sunshine duration. This study provides a scientific basis for the protection of vegetation and sustainable development in alpine regions.

Improving the Prediction of Soil Organic Matter in Arable Land Using Human Activity Factors

Detailed spatial distribution of soil organic matter (SOM) in arable land is essential for agricultural management and decision making. Based on digital soil mapping (DSM) theory, much attention has been focused on the selection of environmental covariates. However, the importance of human activity factors in SOM prediction has not received enough attention, especially in arable soil. Moreover, due to the insufficient amount of soil sampling data used to train and validate the DSM model, the prediction results may be questionable, and some even contradictory. This paper explores the effectiveness of the human footprint, amount of fertilizer application, agronomic management level, crop planting type, and irrigation guarantee degree in SOM mapping of arable land in Heilongjiang Province. The results show that the model only including environmental covariates accounts for 41% of the variation in SOM distribution. The model combining the five human activity factors increases the SOM spatial prediction by 39% in terms of R2 (coefficient of determination), 12% in terms of RMSE (root mean square error), 15% in terms of MAE (mean absolute error), and 11% in terms of LCCC (Lin’s concordance correlation coefficient), showing better prediction accuracy and performance. This indicates that human activity factors play a crucial role in determining SOM distribution in arable land. In the SOM prediction, soil moisture is the most important environmental covariate, and the amount of fertilizer application with a relative importance of 11.36% (ranking 3rd) is the most important human activity factor, higher than the annual average precipitation and elevation. From a spatial point of view, the Sanjiang Plain is a difficult area for prediction.

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.

Soil Moisture and Salinity Inversion Based on New Remote Sensing Index and Neural Network at a Salina-Alkaline Wetland

In arid and semi-arid regions, soil moisture and salinity are important elements to control regional ecology and climate, vegetation growth and land function. Soil moisture and salt content are more important in arid wetlands. The Ebinur Lake wetland is an important part of the ecological barrier of Junggar Basin in Xinjiang, China. The Ebinur Lake Basin is a representative area of the arid climate and ecological degradation in central Asia. It is of great significance to study the spatial distribution of soil moisture and salinity and its causes for land and wetland ecological restoration in the Ebinur Lake Basin. Based on the field measurement and Landsat 8 satellite data, a variety of remote sensing indexes related to soil moisture and salinity were tested and compared, and the prediction models of soil moisture and salinity were established, and the accuracy of the models was assessed. Among them, the salinity indexes D1 and D2 were the latest ones that we proposed according to the research area and data. The distribution maps of soil moisture and salinity in the Ebinur Lake Basin were retrieved from remote sensing data, and the correlation analysis between soil moisture and salinity was performed. Among several soil moisture and salinity prediction indexes, the normalized moisture index NDWI had the highest correlation with soil moisture, and the salinity index D2 had the highest correlation with soil salinity, reaching 0.600 and 0.637, respectively. The accuracy of the BP neural network model for estimating soil salinity was higher than the one of other models; R2 = 0.624, RMSE = 0.083 S/m. The effect of the cubic function prediction model for estimating soil moisture was also higher than that of the BP neural network, support vector machine and other models; R2 = 0.538, RMSE = 0.230. The regularity of soil moisture and salinity changes seemed to be consistent, the correlation degree was 0.817, and the synchronous change degree was higher. The soil salinity in the Ebinur Lake Basin was generally low in the surrounding area, high in the middle area, high in the lake area and low in the vegetation coverage area. The soil moisture in the Ebinur Lake Basin slightly decreased outward with the Ebinur Lake as the center and was higher in the west and lower in the east. However, the spatial distribution of soil moisture had a higher mutation rate and stronger heterogeneity than that of soil salinity.

Quantitatively Estimating of InSAR Decorrelation Based on Landsat-Derived NDVI

As a by-product of Interferometric Synthetic Aperture Radar (SAR, InSAR) technique, interferometric coherence is a measure of the decorrelation noise for InSAR observation, where the lower the coherence value, the more serious the decorrelation noise. In the densely vegetated area, the coherence value could be too low to obtain any valuable signals, leading to the degradation of InSAR performance and the possible waste of expensive SAR data. Normalized Difference Vegetation Index (NDVI) value is a measure of the vegetation coverage and can be estimated from the freely available optical satellite images. In this paper, a multi-stage model is established to quantitatively estimate the decorrelation noise for vegetable areas based on Landsat-derived NDVI prior to the acquisition of SAR data. The modeling process is being investigated with the L-band ALOS-1/PALSAR-1 data and the Landsat-5 optical data acquired in the Meitanba area of Hunan Province, China. Furthermore, the reliability of the established model is verified in the Longhui area, which is situated near the Meitanba area. The results demonstrate that the established model can quantitatively estimate InSAR decorrelation associated with the vegetation coverage.