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Coherent change detection is a technology that utilizes phase information in complex-valued synthetic aperture radar images. It is mostly used to detect subtle changes that cannot be detected by amplitude images on the ground, and it also has excellent detection performance when it comes to low-coherence typical changed areas. However, due to its high sensitivity to changes, this technology will falsely detect areas of natural change such as vegetation disturbance, river flow, and low signal-to-noise ratio areas (e.g., uninteresting areas) as changes, resulting in false-alarm interference areas. In order to tackle this problem, this paper studies a coherent change detection method based on multi-scale analysis to extract typical changed areas in complicated scenes. The method uses an equal variance coherence estimator to calculate the coherence value, separates the interference areas and the typical changed areas using a multi-scale method, and then extracts a binary image of the typical changed areas through noise filtering and threshold segmentation. The method in this paper is experimentally verified with publicly available Airbus spaceborne SAR data, and ESAR airborne data, which is provided by the ESA. The experimental results are visualized and quantitatively evaluated. Through the results, by calculating the probability of correct classification and false-positive and other performance parameters, as well as drawing the receiver operating characteristic curve and the kappa coefficient curve of different threshold values, we find that the method has the capability to suppress the interference areas and the high detection performance of the typical changed areas. The experimental data are complicated scenes that include various types of ground object changes. The results show that the method is effective and universal and can provide reference value for the application of coherent change detection. [ABSTRACT FROM AUTHOR]

Coastal cliffs erode in response to short- and long-term environmental changes, but predicting these changes continues to be a challenge. In addition to a chronic lack of data on the cliff face, vegetation presence and growth can bias our erosion measurements and limit our ability to detect geomorphic erosion by obscuring the cliff face. This paper builds on past research segmenting vegetation in three-band red, green, blue (RGB) imagery and presents two approaches to segmenting and filtering vegetation from the bare cliff face in dense point clouds constructed from RGB images and structure-from-motion (SfM) software. Vegetation indices were computed from previously published research and their utility in segmenting vegetation from bare cliff face was compared against machine learning (ML) models for point cloud segmentation. Results demonstrate that, while existing vegetation indices and ML models are both capable of segmenting vegetation and bare cliff face sediments, ML models can be more efficient and robust across different growing seasons. ML model accuracy quickly reached an asymptote with only two layers and RGB images only (i.e., no vegetation indices), suggesting that these more parsimonious models may be more robust to a range of environmental conditions than existing vegetation indices which vary substantially from one growing season to another with changes in vegetation phenology. [ABSTRACT FROM AUTHOR]

Floral phenology as a special indicator of climate change and vegetation dynamics is drawing more attention. The long-term observations of flowering events collected at scattered ground sites have accumulated valuable priority on the understanding of floral phenology, but with insufficient investigation on the spatio-temporal dynamics at regional scale, which is mainly induced by the lack of effective ways to capture the pixel-based flower events from remote sensing images. The existing yellowness indices are constructed for rape (Brassica napus L.) with less suppression to the bright background and dark green vegetation, and further with inadequate consideration on physiological characteristics and the temporal spectral signature of investigated vegetation. In this paper, we examined rape and several other representative vegetation types to determine spectral features of yellow-flower period within the growing season, then selected the visible and near-infrared bands to construct a Novel Yellowness Index (NYI) with an enhancement on the physiological mechanism of plants. The proposed NYI were discussed on the variation of mathematical properties with representative instances, cross-compared with three typical yellowness indices—Ratio Yellowness Index (RYI), Normalized Difference Yellowness Index (NDYI), and Ashourloo Canola Index (ACI) —over various yellow-flowering vegetation species at multiple scales, and validated with ground observations of three available PhenoCam network stations and field phenological observations at Görlitz, Sachsen, and Germany. In addition, we applied NYI to detect the rape field using Sentinel-2 image at Görlitz with typical rape area as a case study. Results show that the proposed NYI exhibits the potential to capture yellow-flowering events with increased sensitivity to the variation of flower density, and reduction of noise introduced by bright background or dark green vegetation of multiple vegetation species at different scales. As the flower density increases from 33% to 78%, the relative differences of NYI captured can reach up to 74%, compared with other three indices which have the relative differences no more than 57%. The cross-comparison indicates NYI performs better with higher consistent with PhenoCam observation and Deutscher Wetterdienst phenological station than other yellowness indices in capturing the variation of yellow flower density. The case study of NYI application in the identification of rape field exhibits good accuracy with the overall accuracy up to 97.5%, the Kappa coefficient of 0.94, and F score of 0.96. Consequently, the satellite-derived yellowness index will be a potential means to investigate the flowering dynamics and planting range of yellow-flowering vegetation such as rape. [ABSTRACT FROM AUTHOR]

Understanding the effects of natural and human disturbance factors on fractional vegetation coverage (FVC) is significant in the promotion of ecological and environmental protection. However, most of the relevant studies neglect to consider differences in the effect of driving factors on areas with different vegetation change characteristics. In this paper, we have combined Theil-Sen median trend analysis and Mann-Kendall testing to identify degraded and restored areas. Differences in the impact of various factors on FVC in terms of degradation, restoration, and the whole region were distinguished quantitatively using the geodetector model. Additionally, the constraint line approach was used to detect the influence thresholds of factors on FVC. The results are shown as below: (1) FVC showed an overall improving trend, and vegetation restoration and degradation areas accounted for 69.2% and 22.0%, respectively. (2) The two dominant factors affecting FVC were Digital Elevation Model (DEM) and temperature for both degraded and restored regions. However, the explanatory power of precipitation was noticeably different between regions. (3) Most natural factors had a "convex" constraint effect on FVC, which gradually weakened with an increase in the variable below the threshold and vice versa. Human disturbance factors negatively constrained FVC, and the constraint effect increased with increased human activity. This study can help decision-makers optimize specific implementation policies relating to ecological restoration and sustainable development. [ABSTRACT FROM AUTHOR]

This paper presents a multi-temporal comparison of high-resolution 3D digital models from two urban areas susceptible to landslides in three time periods. The study areas belong to the São Paulo landslide risk mapping database and are named "CEU Paz" (CP) and "Parque Santa Madalena I" (PSM). For each area, a lidar digital surface model (DSM) (2017) and two structure-from-motion multi-view stereo DSMs (2019 and 2022) built from drone imagery were combined using raster algebra to generate three digital surface models of differences (DoDs). The DoDs were able to highlight changes in vegetation cover and buildings, which are important characteristics for evaluating geological risks in an urban context. Still, they were unable to highlight changes in the ground surface. The results demonstrate that the method greatly supports monitoring, allowing for greater detail and ease of detecting large-scale changes. Even with promising results, this technique should be understood as one more tool for mapping risk areas without replacing fieldwork. [ABSTRACT FROM AUTHOR]

In this paper, we propose a processing chain jointly employing Sentinel-1 and Sentinel-2 data, aiming to monitor changes in the status of the vegetation cover by integrating the four 10 m visible and near-infrared (VNIR) bands with the three red-edge (RE) bands of Sentinel-2. The latter approximately span the gap between red and NIR bands (700 nm–800 nm), with bandwidths of 15/20 nm and 20 m pixel spacing. The RE bands are sharpened to 10 m, following the hyper-sharpening protocol, which holds, unlike pansharpening, when the sharpening band is not unique. The resulting 10 m fusion product may be integrated with polarimetric features calculated from the Interferometric Wide (IW) Ground Range Detected (GRD) product of Sentinel-1, available at 10 m pixel spacing, before the fused data are analyzed for change detection. A key point of the proposed scheme is that the fusion of optical and synthetic aperture radar (SAR) data is accomplished at level of change, through modulation of the optical change feature, namely the difference in normalized area over (reflectance) curve (NAOC), calculated from the sharpened RE bands, by the polarimetric SAR change feature, achieved as the temporal ratio of polarimetric features, where the latter is the pixel ratio between the co-polar and the cross-polar channels. Hyper-sharpening of Sentinel-2 RE bands, calculation of NAOC and modulation-based integration of Sentinel-1 polarimetric change features are applied to multitemporal datasets acquired before and after a fire event, over Mount Serra, in Italy. The optical change feature captures variations in the content of chlorophyll. The polarimetric SAR temporal change feature describes depolarization effects and changes in volumetric scattering of canopies. Their fusion shows an increased ability to highlight changes in vegetation status. In a performance comparison achieved by means of receiver operating characteristic (ROC) curves, the proposed change feature-based fusion approach surpasses a traditional area-based approach and the normalized burned ratio (NBR) index, which is widespread in the detection of burnt vegetation. [ABSTRACT FROM AUTHOR]

For vegetation monitoring, it is crucial to understand which changes are caused by the measurement setup and which changes are true representations of vegetation dynamics. UAV–LiDAR offers great possibilities to measure vegetation structural parameters; however, UAV–LiDAR sensors are undergoing rapid developments, and the characteristics are expected to keep changing over the years, which will introduce data inter-operability issues. Therefore, it is important to determine whether datasets acquired by different UAV–LiDAR sensors can be interchanged and if changes through time can accurately be derived from UAV–LiDAR time series. With this study, we present insights into the magnitude of differences in derived forest metrics in savanna woodland when three different UAV–LiDAR systems are being used for data acquisition. Our findings show that all three systems can be used to derive plot characteristics such as canopy height, canopy cover, and gap fractions. However, there are clear differences between the metrics derived with different sensors, which are most apparent in the lower parts of the canopy. On an individual tree level, all UAV–LiDAR systems are able to accurately capture the tree height in a savanna woodland system, but significant differences occur when crown parameters are measured with different systems. Less precise systems result in underestimations of crown areas and crown volumes. When comparing UAV–LiDAR data of forest areas through time, it is important to be aware of these differences and ensure that data inter-operability issues do not influence the change analysis. In this paper, we want to stress that it is of utmost importance to realise this and take it into consideration when combining datasets obtained with different sensors. [ABSTRACT FROM AUTHOR]

Vegetation in arid central Asia (ACA) has been experiencing significant changes due to substantial warming and humidification since the 1980s. These changes are inhomogeneous due to the ecological vulnerability and topographic complexity of ACA. However, the heterogeneity of vegetation changes has received limited attention in the literature, which has focused more on the region's overall general features. Thus, this paper analyzes the regional heterogeneity of vegetation changes during the growing season in ACA and further explores their underlying drivers. The results reveal an antiphase trend of vegetation, with an increase in eastern ACA and a decrease in western ACA. This antiphase pattern is primarily constrained by the divergent hydrothermal and climatic contexts of different elevation gradients. At elevations higher than 300 m (in the eastern ACA), increased growing season precipitation dominates vegetation greening. Conversely, vegetation at elevations lower than 300 m (in western ACA) is influenced by growing season soil water, which is driven by winter precipitation (pre-growing season precipitation). Additionally, the temperature could indirectly impact vegetation trends by altering precipitation, soil water, glaciers, snow cover, and runoff. Our findings have implications for restoring the ecosystem and sustainable development in ACA. [ABSTRACT FROM AUTHOR]

High-resolution crop type mapping is of importance for site-specific agricultural management and food security in smallholder farming regions, but is challenging due to limited data availability and the need for image-based algorithms. In this paper, we developed an efficient object- and pixel-based mapping algorithm to generate a 10 m resolution crop type map over large spatial domains by integrating time series optical images (Sentinel-2) and synthetic aperture radar (SAR) images (Sentinel-1) using the Google Earth Engine (GEE) platform. The results showed that the proposed method was reliable for crop type mapping in the study area with an overall accuracy (OA) of 93.22% and a kappa coefficient (KC) of 0.89. Through experiments, we also found that the monthly median values of the vertical transmit/vertical receive (VV) and vertical transmit/horizontal receive (VH) bands were insensitive to crop type mapping itself, but adding this information to supplement the optical images improved the classification accuracy, with an OA increase of 0.09–2.98%. Adding the slope of vegetation index change (VIslope) at the critical period to crop type classification was obviously better than that of relative change ratio of vegetation index (VIratio), both of which could make an OA improvement of 2.58%. These findings not only highlighted the potential of the VIslope and VIratio indices during the critical period for crop type mapping in small plots, but suggested that SAR images could be included to supplement optical images for crop type classification. [ABSTRACT FROM AUTHOR]

Vegetation degeneration has become a serious ecological problem for karst regions in the Anthropocene. According to the deficiency of long serial and high-resolution analysis of karst vegetation, this paper reconstructed the variation of vegetation landscape changes from 1987 to 2020 in a typical karst region of China. Using Landsat time series data, the dynamic changes and driving factors of natural karst vegetation were identified at the landscape scale. On the premise of considering the time-lag effect, the main climatic factors that influence vegetation growth were presented at the interannual timescale. Then, the approach of residual analysis was adopted to distinguish the dominant factors affecting vegetation growth. Results of trend analysis revealed that 21.5% of the forestland showed an overall significant decline in vegetation growth, while only 1.5% showed an increase in vegetation growth during the study period. Precipitation and radiation were the dominant meteorological factors influencing vegetation at the interannual timescale, as opposed to temperature. More than 70% of the natural vegetation growth was dominated by climatic factors. The area percentage of negative human impact has increased gradually since 2009 and reached 18.5% in 2020, indicating the currently serious situation of vegetation protection; fortunately, in recent years, human disturbances on vegetation have been mitigated in karst areas with the promotion of ecological conservation and restoration projects. [ABSTRACT FROM AUTHOR]

Vegetation cover change is one of the key indicators used for monitoring environmental quality. It can accurately reflect changes in hydrology, climate, and human activities, especially in arid and semi-arid regions. The main goal of this paper is to review the remote sensing satellite sensors and the methods used for monitoring and mapping vegetation cover changes in arid and semi-arid. Arid and semi-arid lands are eco-sensitive environments with limited water resources and vegetation cover. Monitoring vegetation changes are especially important in arid and semi-arid regions due to the scarce and sensitive nature of the plant cover. Due to expected changes in vegetation cover, land productivity and biodiversity might be affected. Thus, early detection of vegetation cover changes and the assessment of their extent and severity at the local and regional scales become very important in preventing future biodiversity loss. Remote sensing data are useful for monitoring and mapping vegetation cover changes and have been used extensively for identifying, assessing, and mapping such changes in different regions. Remote sensing data, such as satellite images, can be obtained from satellite-based and aircraft-based sensors to monitor and detect vegetation cover changes. By combining remotely sensed images, e.g., from satellites and aircraft, with ground truth data, it is possible to improve the accuracy of monitoring and mapping techniques. Additionally, satellite imagery data combined with ancillary data such as slope, elevation, aspect, water bodies, and soil characteristics can detect vegetation cover changes at the species level. Using analytical methods, the data can then be used to derive vegetation indices for mapping and monitoring vegetation. [ABSTRACT FROM AUTHOR]

A high-quality leaf-area index (LAI) is important for land surface process modeling and vegetation growth monitoring. Although multiple satellite LAI products have been generated, they usually show spatio-temporal discontinuities and are sometimes inconsistent with vegetation growth patterns. A deep-learning model was proposed to retrieve time-series LAIs from multiple satellite data in this paper. The fusion of three global LAI products (i.e., VIIRS, GLASS, and MODIS LAI) was first carried out through a double logistic function (DLF). Then, the DLF LAI, together with MODIS reflectance (MOD09A1) data, served as the training samples of the deep-learning long short-term memory (LSTM) model for the sequential LAI estimations. In addition, the LSTM models trained by a single LAI product were considered as indirect references for the further evaluation of our proposed approach. The validation results showed that our proposed LSTMfusion LAI provided the best performance (R2 = 0.83, RMSE = 0.82) when compared to LSTMGLASS (R2 = 0.79, RMSE = 0.93), LSTMMODIS (R2 = 0.78, RMSE = 1.25), LSTMVIIRS (R2 = 0.70, RMSE = 0.94), GLASS (R2 = 0.68, RMSE = 1.05), MODIS (R2 = 0.26, RMSE = 1.75), VIIRS (R2 = 0.44, RMSE = 1.37) and DLF LAI (R2 = 0.67, RMSE = 0.98). A temporal comparison among LSTMfusion and three LAI products demonstrated that the LSTMfusion model efficiently generated a time-series LAI that was smoother and more continuous than the VIIRS and MODIS LAIs. At the crop peak growth stage, the LSTMfusion LAI values were closer to the reference maps than the GLASS LAI. Furthermore, our proposed method was proved to be effective and robust in maintaining the spatio-temporal continuity of the LAI when noisy reflectance data were used as the LSTM input. These findings highlighted that the DLF method helped to enhance the quality of the original satellite products, and the LSTM model trained by the coupled satellite products can provide reliable and robust estimations of the time-series LAI. [ABSTRACT FROM AUTHOR]

In terrestrial ecosystems, vegetation is sensitive to climate change and human activities. Its spatial-temporal changes also affect the ecological and social environment. In this paper, we considered the Beijing–Tianjin–Hebei region to study the spatio-temporal vegetation patterns. The detailed analysis of a moderate-resolution imaging spectroradiometer (MODIS) data were carried out through the Google Earth Engine (GEE) platform. Our results show a slow and tortuous upward trend in the average leaf area index (LAI) in the study region for the periods 2001–2020. Specifically, Beijing had the highest LAI value, with an average of 1.64 over twenty years, followed by Hebei (1.30) and Tianjin (1.04). Among different vegetation types, forests had the highest normalized difference vegetation index (NDVI) with the range of 0.62–0.78, followed by shrubland (0.58–0.75), grassland (0.34–0.66), and cropland (0.38–0.54) over the years. Spatially, compared to the whole study area, index value in the northwestern part of the Beijing–Tianjin–Hebei region increased greatly in many areas, such as northwest Beijing, Chengde, and Zhangjiakou, indicating a significant ecological optimization. Meanwhile, there was ecological degradation in the middle and southeast regions, from Tangshan southeastward to Handan, crossing Tianjin, Langfang, the east part of Baoding, Shijiazhuang, and the west part of Cangzhou. Air temperature and precipitation were positively and significantly correlated with net primary production (NPP) and precipitation stood out as a key driver. Additionally, an intensification of the urbanization rate will negatively impact the vegetation NPP, with the shrubland and forest being affected most relative to the cropland. [ABSTRACT FROM AUTHOR]

Vegetation is the main component of the terrestrial Earth, and it plays an imperative role in carbon cycle regulation and surface water/energy exchange/balance. The coupled effects of climate change and anthropogenic forcing have undoubtfully impacted the vegetation cover in linear/non-linear manners. Considering the essential benefits of vegetation to the environment, it is vital to investigate the vegetation dynamics through spatially and temporally consistent workflows. In this regard, remote sensing, especially Normalized Difference Vegetation Index (NDVI), has offered a reliable data source for vegetation monitoring and trend analysis. In this paper, two decades (2000 to 2020) of Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI datasets (MOD13Q1) were used for vegetation trend analysis throughout Iran. First, the per-pixel annual NDVI dataset was prepared using the Google Earth Engine (GEE) by averaging all available NDVI values within the growing season and was then fed into the PolyTrend algorithm for linear/non-linear trend identification. In total, nearly 14 million pixels (44% of Iran) were subjected to trend analysis, and the results indicated a higher rate of greening than browning across the country. Regarding the trend types, linear was the dominant trend type with 14%, followed by concealed (11%), cubic (8%), and quadratic (2%), while 9% of the vegetation area remained stable (no trend). Both positive and negative directions were observed in all trend types, with the slope magnitudes ranging between −0.048 and 0.047 (NDVI units) per year. Later, precipitation and land cover datasets were employed to further investigate the vegetation dynamics. The correlation coefficient between precipitation and vegetation (NDVI) was 0.54 based on all corresponding observations (n = 1785). The comparison between vegetation and precipitation trends revealed matched trend directions in 60% of cases, suggesting the potential impact of precipitation dynamics on vegetation covers. Further incorporation of land cover data showed that grassland areas experienced significant dynamics with the highest proportion compared to other vegetation land cover types. Moreover, forest and cropland had the highest positive and negative trend direction proportions. Finally, independent (from trend analysis) sources were used to examine the vegetation dynamics (greening/browning) from other perspectives, confirming Iran's greening process and agreeing with the trend analysis results. It is believed that the results could support achieving Sustainable Development Goals (SDGs) by serving as an initial stage study for establishing conservation and restoration practices. [ABSTRACT FROM AUTHOR]

Vegetation greenness change is the result of the combination of natural and anthropogenic factors. Understanding how these factors individually and collectively affect vegetation dynamics and whether their spatial heterogeneity has any effect on vegetation greenness change is the crucial investigation area. Previous studies revealed the distinct characteristics of spatial and temporal heterogeneity in the impact factors influencing vegetation greenness change across various regions, often assuming a linear contribution mechanism between vegetation greenness change and these drivers. However, such a simplistic assumption fails to adequately capture the real-world dynamics of vegetation greenness change. Thus, this study firstly used geographical detector (Geodetector) to quantitatively measure the contribution of each factor to vegetation greenness change considering spatial heterogeneity in the Yangtze River Economic Belt (YREB) during the growing season from 2000 to 2020, then selecting significant factors from numerous drivers with the recursive feature elimination algorithm combined with a random forest model (RFE-RF), which is able to reduce redundant features in the data and prevent overfitting. Finally, four stable impact factors and the spatial heterogeneity of some factors contributing to vegetation greenness change were identified. The results show that approximately 83% of the regional vegetation has shown an overall increasing trend, while areas undergoing rapid development predominantly experienced a decline in greenness. Single factor screened by Geodetector with the explanatory power greater than 10% for vegetation greenness change included temperature (Tem), population density (PD), the land-use/land-cover (LULC), DEM, wind speed, and slope. The RFE-RF method identified precipitation (Pre) and CO2 emissions as additional influential factors for vegetation greenness change, in addition to the first four factors mentioned previously. These findings suggest that the four stable factors consistently influence vegetation greenness change. Combined with the principles of the algorithms and the above results, it was found that the spatial heterogeneity of wind speed and slope has an effect on vegetation greenness change, whereas the spatial heterogeneity of Pre and CO2 emissions has minimal effect. [ABSTRACT FROM AUTHOR]

Vegetation index (VI) time series derived from satellite sensors have been widely used in the estimation of vegetation parameters, but the quality of VI time series is easily affected by clouds and poor atmospheric conditions. The function fitting method is a widely used effective noise reduction technique for VI time series, but it is vulnerable to noise. Thus, ancillary data about VI quality are utilized to alleviate the interference of noise. However, this approach is limited by the availability, accuracy, and application rules of ancillary data. In this paper, we aimed to develop a new reconstruction method that does not require ancillary data. Based on the assumptions that VI time series follow the gradual growth and decline pattern of vegetation dynamics, and that clouds or poor atmospheric conditions usually depress VI values, we proposed a reconstruction method for VI time series based on self-weighting function fitting from curve features (SWCF). SWCF consists of two major procedures: (1) determining a fitting weight for each VI point based on the curve features of the VI time series and (2) implementing the weighted function fitting to reconstruct the VI time series. The double logistic function, double Gaussian function, and polynomial function were tested in SWCF based on a simulated dataset. The results indicate that the weighted function fitting with SWCF outperformed the corresponding unweighted function fitting with the root-mean-square error (RMSE) significantly reduced by 26.82–52.44% (p < 0.05), and it also outperformed the Savitzky–Golay filtering with the RMSE significantly reduced by 13.98–54.04% (p < 0.05) for 270 sample points selected in mid-high latitudes of the Northern Hemisphere. Moreover, SWCF showed excellent robustness and applicability in regional applications. [ABSTRACT FROM AUTHOR]

The normalized difference vegetation index (NDVI) is an important indicator for evaluating vegetation change, monitoring land surface fluxes or predicting crop models. Due to the great availability of images provided by different satellites in recent years, much attention has been devoted to testing trend changes with a time series of NDVI individual pixels. However, the spatial dependence inherent in these data is usually lost unless global scales are analyzed. In this paper, we propose incorporating both the spatial and the temporal dependence among pixels using a stochastic spatio-temporal model for estimating the NDVI distribution thoroughly. The stochastic model is a state-space model that uses meteorological data of the Climatic Research Unit (CRU TS3.10) as auxiliary information. The model will be estimated with the Expectation-Maximization (EM) algorithm. The result is a set of smoothed images providing an overall analysis of the NDVI distribution across space and time, where fluctuations generated by atmospheric disturbances, fire events, land-use/cover changes or engineering problems from image capture are treated as random fluctuations. The illustration is carried out with the third generation of NDVI images, termed NDVI3g, of the Global Inventory Modeling and Mapping Studies (GIMMS) in continental Spain. This data are taken in bymonthly periods from January 2011 to December 2013, but the model can be applied to many other variables, countries or regions with different resolutions. [ABSTRACT FROM AUTHOR]

Wetland ecosystems are experiencing rapid degradation due to human activities, particularly the diversion of natural flows for various purposes, leading to significant alterations in wetland hydrology and their ecological functions. However, understanding and quantifying these eco-hydrological changes, especially concerning inundation dynamics, presents a formidable challenge due to the lack of long-term, observation-based spatiotemporal inundation information. In this study, we classified wetland areas into ten equal-interval classes based on inundation probability derived from a dense, 30-year time series of Landsat-based inundation maps over an Australian dryland riparian wetland, Macquarie Marshes. These maps were then compared with three simplified vegetation patches in the area: river red gum forest, river red gum woodland, and shrubland. Our findings reveal a higher inundation probability over a small area covered by river red gum forest, exhibiting persistent inundation over time. In contrast, river red gum woodland and shrubland areas show fluctuating inundation patterns. When comparing percentage inundation with the Normalized Difference Vegetation Index (NDVI), we observed a notable agreement in peaks, with a lag time in NDVI response. A strong correlation between NDVI and the percentage of inundated area was found in the river red gum woodland patch. During dry, wet, and intermediate years, the shrubland patch consistently demonstrated similar inundation probabilities, while river red gum patches exhibited variable probabilities. During drying events, the shrubland patch dried faster, likely due to higher evaporation rates driven by exposure to solar radiation. However, long-term inundation probability exhibited agreement with the SAGA wetness index, highlighting the influence of topography on inundation probability. These findings provide crucial insights into the complex interactions between hydrological processes and vegetation dynamics in wetland ecosystems, underscoring the need for comprehensive monitoring and management strategies to mitigate degradation and preserve these vital ecosystems. [ABSTRACT FROM AUTHOR]

Investigating the spatiotemporal dynamics of vegetation net primary productivity (NPP) and its influencing factors are crucial for green and low-carbon development and facilitate human well-being in the Yellow River Basin (YRB). Although the research on NPP has advanced rapidly, in view of the regional particularity of the YRB, the persistence of its NPP change trend needs to be further discussed and more comprehensive impact factors need to be included in the analysis. Meanwhile, the spatial non-stationarity and scale effects of the impact on NPP when multiple factors are involved remain uncertain. Here, we selected a total of twelve natural and anthropogenic factors and used multi-scale geographically weighted regression (MGWR) to disentangle the spatial non-stationary relationship between vegetation NPP and related factors and identify the impact scale difference in the YRB. Additionally, we analyze the spatiotemporal variation trend and persistence of NPP during 2000–2020. The results revealed the following: (1) The annual NPP showed a fluctuating increasing trend, and the vegetation NPP in most regions will exhibit a future trend of increasing to decreasing. (2) The effects of different factors show significant spatial non-stationarity. Among them, the intensity of the impact of most natural factors shows a clear strip-shaped distribution in the east-west direction. It is closely related to the spatial distribution characteristics of natural factors in the YRB. In contrast, the regularity of anthropogenic influences is less obvious. (3) The impact scales of different factors on vegetation NPP were significantly different, and this scale changed with time. The factors with small impact scales could better explain the change in vegetation NPP. Interestingly, the impact size and scale of relative humidity on NPP in the YRB are both larger. This may be due to the arid and semi-arid characteristics of the YRB. Our findings could provide policy makers with specific and quantitative insights for protecting the ecological environment in the YRB. [ABSTRACT FROM AUTHOR]

Afforestation is an important way to effectively reduce carbon emissions from human activities and increase carbon sinks in forest ecosystems. It also plays an important role in climate change mitigation. Currently, few studies have examined the spatiotemporal dynamics of future afforestation areas, which are crucial for assessing future carbon sequestration in forest ecosystems. In order to obtain the dynamic distribution of potential afforestation land over time under future climate change scenarios in China, we utilized the random forest method in this study to calculate weights for the selected influencing factors on potential afforestation land, such as natural vegetation attributes and environmental factors. The "weight hierarchy approach" was used to calculate the afforestation quality index of different regions in different 5-year intervals from 2021 to 2060 and extract high-quality potential afforestation lands in each period. By dynamically analyzing the distribution and quality of potential afforestation land from 2021 to 2060, we can identify optimal afforestation sites for each period and formulate a progressive afforestation plan. This approach allows for a more accurate application of the FCS model to evaluate the dynamic changes in the carbon sequestration capacity of newly afforested land from 2021 to 2060. The results indicate that the average potential afforestation land area will reach 75 Mha from 2021 to 2060. In the northern region, afforestation areas are mainly distributed on both sides of the "Hu Line", while in the southern region, they are primarily distributed in the Yunnan–Guizhou Plateau and some coastal provinces. By 2060, the potential calculated cumulative carbon storage of newly afforested lands was 11.68 Pg C, with a peak carbon sequestration rate during 2056–2060 of 0.166 Pg C per year. Incorporating information on the spatiotemporal dynamics of vegetation succession, climate production potential, and vegetation resilience while quantifying the weights of each influencing factor can enhance the accuracy of predictions for potential afforestation lands. The conclusions of this study can provide a reference for the formulation of future afforestation plans and the assessment of their carbon sequestration capacity. [ABSTRACT FROM AUTHOR]

The spatiotemporal evolution of vegetation and its influencing factors is crucial for understanding the relationship between vegetation and climate change, which helps guide the management of regional ecosystems effectively. Utilizing the Fractional Vegetation Cover (FVC) data and various meteorological elements from 1982 to 2021, this research employed methodologies, such as grey relational analysis, path analysis, and the time-lag effect, to examine the impact of climate change on FVC fluctuations. It introduced a comprehensive qualitative and quantitative analysis of the multi-factor climate–vegetation relationship, enhancing the understanding of the interaction between the climate and vegetation growth. The findings indicate that 77.41% of the wetland vegetation cover in the Yellow River Basin (YRB) has significantly decreased. Precipitation and evapotranspiration emerged as the primary factors affecting FVC, with soil moisture and temperature having a lesser impact. Given the crucial influence of climate factors' time lag on vegetation dynamics, especially the notable cumulative lag effects observed in arid regions, such as precipitation accumulating over approximately 1.963 months (on average) and evapotranspiration lagging by about 1.727 months (on average), this study offers valuable theoretical insights on vegetation restoration efforts amidst the challenges posed by climate change. [ABSTRACT FROM AUTHOR]

Revealing the response of vegetation on the Qinghai-Tibet Plateau (QTP) to climate change and human activities is crucial for ensuring East Asian ecological security and regulating the global climate. However, the current research rarely explores the time-lag effects of climate on vegetation growth, leading to considerable uncertainty in analyzing the driving mechanisms of vegetation changes. This study identified the main driving factors of vegetation greenness (vegetation index, EVI) changes after investigating the lag effects of climate. By analyzing the trends of interannual variation in vegetation and climate, the study explored the driving mechanisms behind vegetation changes on the QTP from 2000 to 2020. The results indicate that temperature and precipitation have significant time-lag effects on vegetation growth. When considering the lag effects, the explanatory power of climate on vegetation changes is significantly enhanced for 29% of the vegetated areas. About 31% of the vegetation on the QTP exhibited significant "greening", primarily in the northern plateau. This greening was attributed not only to improvements in climate-induced hydrothermal conditions but also to the effective implementation of ecological projects, which account for roughly half of the significant greening. Only 2% of the vegetation on the QTP showed significant "browning", sporadically distributed in the southern plateau and the Sanjiangyuan region. In these areas, besides climate-induced drought intensification, approximately 78% of the significant browning was due to unreasonable grassland utilization and intense human activities. The area where precipitation dominates vegetation improvement was larger than the area dominated by temperature, whereas the area where precipitation dominates vegetation degradation is smaller than that where temperature dominates degradation. The implementation of a series of ecological projects has resulted in a much larger area where human activities positively promoted vegetation compared to the area where they negatively inhibited it. [ABSTRACT FROM AUTHOR]

The selection and representation of remote sensing image classification features play crucial roles in image classification accuracy. To effectively improve the classification accuracy of features, an improved U-Net network framework based on multi-feature fusion perception is proposed in this paper. This framework adds the channel attention module (CAM-UNet) to the original U-Net framework and cascades the shallow features with the deep semantic features, replaces the classification layer in the original U-Net network with a support vector machine, and finally uses the majority voting game theory algorithm to fuse the multifeature classification results and obtain the final classification results. This study used the forest distribution in Xingbin District, Laibin City, Guangxi Zhuang Autonomous Region as the research object, which is based on Landsat 8 multispectral remote sensing images, and, by combining spectral features, spatial features, and advanced semantic features, overcame the influence of the reduction in spatial resolution that occurs with the deepening of the network on the classification results. The experimental results showed that the improved algorithm can improve classification accuracy. Before the improvement, the overall segmentation accuracy and segmentation accuracy of the forestland increased from 90.50% to 92.82% and from 95.66% to 97.16%, respectively. The forest cover results obtained by the algorithm proposed in this paper can be used as input data for regional ecological models, which is conducive to the development of accurate and real-time vegetation growth change models. [ABSTRACT FROM AUTHOR]

The Middle Route of the South-to-North Water Diversion Project is a critical infrastructure that ensures optimal water resource distribution across river basins and safeguards the livelihood of people in China. This study investigated its effects on the land surface temperature (LST) and fractional vegetation coverage (FVC) in the Danjiang River Basin. Moreover, it examined the spatial and temporal patterns of this project, providing a scientific basis for the safe supply of water and ecological preservation. We used the improved interpolation of mean anomaly (IMA) method based on the digital elevation model (DEM) to reconstruct LST while FVC was estimated using the image element dichotomous model. Our findings indicated a general increase in the average LST in the Danjiang River Basin post-project implementation. During both wet and dry seasons, the cooling effect was primarily observed in the south-central region during the daytime, with extreme values of 6.1 °C and 5.9 °C. Conversely, during the nighttime, the cooling effect was more prevalent in the northern region, with extreme values of 3.0 °C and 2.3 °C. In contrast, the warming effect during both seasons was predominantly located in the northern region during the daytime, with extreme values of 5.3 °C and 5.5 °C. At night, the warming effect was chiefly observed in the south-central region, with extreme values of 5.8 °C and 5.9 °C. FVC displayed a seasonal trend, with higher values in the wet season and overall improvement over time. Statistical analysis revealed a negative correlation between vegetation change and daytime temperature variations in both periods (r = −0.184, r = −0.195). Furthermore, a significant positive correlation existed between vegetation change and nighttime temperature changes (r = 0.315, r = 0.328). Overall, the project contributed to regulating LST, fostering FVC development, and enhancing ecological stability in the Danjiang River Basin. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau has experienced profound climate change with significant implication for spatial vegetation greenness. However, the spatiotemporal disparities of long-term vegetation trends in response to observed climate change remain unclear. Based on remote-sensing vegetation images indicated by the normalized difference vegetation index (NDVI) from two long-term combined datasets, GIMMS and MODIS, we identified two spatiotemporal evolution patterns (SEPs) in long-term vegetation anomalies across the Tibetan Plateau. This new perspective integrates spatial and temporal NDVI changes during the growing seasons over the past four decades. Notably, the dipole evolution pattern that rotates counterclockwise from May to September accounted for 62.8% of the spatial mean amplitude of vegetation trends, dominating the spatiotemporal disparities. This dominant pattern trend is attributed to simultaneous effects of spatial warming and rising CO2, which accounted for 75% and 15%, respectively, along with a lagged effect of dipole precipitation, accounting for 6%. Overall, wetting and warming promote greening evolution in the northern Tibetan Plateau, while slight drying and warming favor browning evolution in the southern Tibetan Plateau. These findings provide insights into the combined effects of climate change on spatiotemporal vegetation trends and inform future adaptive strategies in fragile regions. [ABSTRACT FROM AUTHOR]

The water diversion project in Central Yunnan Province (WDP-YN) is the largest water diversion project under construction in China. However, the ecological effects of this water diversion project are still unclear. This study utilized Sentinel-2 remote sensing data to estimate fractional vegetation cover (FVC), maps spatiotemporal variations of FVC in construction areas from 2017 to 2022, and evaluates the impact of the WDP-YN on regional vegetation coverage using buffer analysis and vegetation type transition matrix methods. The study led to the following findings: (1) From 2017 to 2022, FVC within 10 km of the tunnel construction route showed a slightly downward trend or remained relatively stable with no significant changes in the spatial pattern of FVC. (2) Before and after the construction of WDP-YN, over 60% of the area within 10 km of the tunnel construction route showed no change in FVC. On Construction Route Section I (CRS-I), vegetation improved and/or degraded within 12.90% (14.10%) of the area and the regions with degraded FVC concentrated in the northern CRS-I. For Construction Route Section II (CRS-II), 11.96% and 27.51% of the regions were dominated by improved and/or degraded FVC. Vegetation changes near Groundwater Monitoring Point a (GMPa) were relatively stable. (3) The WDP-YN degraded vegetation within 2 km of both sides of CRS-I, slowing down the increase in FVC, while the WDP-YN improved vegetation within 2–6 km of both sides of CRS-II, the closer the distance to CRS-II, the faster the increase in FVC and the decrease in FVC slowed down within 0–2 km of both sides of CRS-II. This study sheds light on the impacts of water diversion infrastructure on vegetation coverage and provides practical guidance and reference for eco-environment protection and ecological restoration given water conservancy projects in China and other regions of the world. [ABSTRACT FROM AUTHOR]

The necessity for precise and current data concerning the dynamics of land cover change in Indonesia is crucial for efforts to reduce natural vegetation cover due to agricultural expansion. The functionality of monitoring systems that incorporate Terra-MODIS is currently compromised by the limited availability of data for the immediate future. This study seeks to assess the potential of VIIRS satellite imagery in developing an early warning system for monitoring vegetation cover change in Indonesia. The normalized differential open-area index (NDOAI) computed from 8-day VIIRS data was employed to detect changes in vegetation cover based on pixel-by-pixel subtraction in the NDOAI data time series. Evaluating the pixel-level accuracy of change detection is complicated due to the fact that we evaluate a change map at a coarser resolution than the Landsat-based reference map. The results revealed that increasing the threshold percentage is associated with improved accuracy. In change detection, there is often a trade-off between accuracy and sensitivity. A threshold that is too low may result in false positives, while a threshold that is too high may lead to missed changes. This study demonstrates that when a threshold value of less than 20% is applied, Landsat can identify vegetation cover changes at an earlier stage. Conversely, when a threshold value greater than 20% is employed, the VIIRS will detect the change 4.5 days earlier than Landsat. Additionally, the VIIRS is capable of detecting changes 25.4 days and 54.8 days faster than Landsat, respectively, when using thresholds of 40% and 70%. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau (TP) is a climate-sensitive and ecologically fragile area. Studying drought and its effects on vegetation over the TP is of great significance for ecological conservation. However, there were large uncertainties in previous studies on the drought characteristics and their impacts on alpine vegetation in this region. This study explored the drought changes and their impacts on alpine vegetation during the growing season over the TP in 1982–2018. The results showed that the TP has experienced a wetting trend in most regions of the TP. Correspondingly, the vegetation has become greener in most areas. The wetting and drying trend in the growing season changed around 1995. Before 1995, the TP experienced an overall drying trend with a spatial pattern of a drying trend in the northern regions and a wetting trend in the southern regions, while it showed an overall wetting trend after 1995, with a reversed spatial pattern to that before 1995. After 1995, wetting and drying trends affected the vegetation in 61% of the TP. However, before 1995, the NDVI presented an increasing trend in most areas of the TP under a drying trend. Therefore, a drying trend was not the primary factor affecting vegetation growth in this period. Instead, changes in the cryosphere induced by warming could be the main factor. In addition, the distribution of vegetation across the TP was primarily influenced by drought intensity, which had the greatest impact on sparse vegetation, followed by meadow and grassland. This study enhances our understanding of the impact of drought changes on alpine vegetation on the TP. [ABSTRACT FROM AUTHOR]

The West Sahel is facing significant threats to its vegetation and wildlife due to the land degradation and habitat fragmentation. It is crucial to assess the regional vegetation greenness dynamics in order to comprehensively evaluate the effectiveness of protection in the natural reserves. This study analyzes the vegetation greenness trends and the driving factors in the Dosso Partial Faunal Reserve in Niger and nearby unprotected regions—one of the most important habitats for endemic African fauna—using satellite time series data from 2001 to 2020. An overall vegetation browning trend was observed throughout the entire region with significant spatial variability. Vegetation browning dominated in the Dosso Reserve with 17.7% of the area showing a significant trend, while the area with significant greening was 6.8%. In a comparison, the nearby unprotected regions to the north and the east were found to be dominated by vegetation browning and greening, respectively. These results suggest that the vegetation protection practice was not fully effective throughout the Dosso Reserve. The dominant drivers were also diagnosed using the Random Forest model-based method and the Partial Dependence Plot tool, showing that water availability (expressed as soil moisture) and land use/land cover change were the most critical factors affecting vegetation greenness in the study region. Specifically, soil moisture stress and specific land management practices associated with logging, grazing, and land clearing appeared to dominate vegetation browning in the Dosso Reserve. In contrast, the vegetation greening in the central Dosso Reserve and the nearby unprotected region to the east was probably caused by the increase in shrubland/forest, which was related to the effective implementation of protection. These findings improve our understanding of how regional vegetation greenness dynamics respond to environmental changes in the Dosso Reserve and also highlight the need for more effective conservation planning and implementation to ensure sustainable socio-ecological development in the West Sahel. [ABSTRACT FROM AUTHOR]

Anthropogenic climate change is increasing the occurrence of wildfires, especially in northern high latitudes, leading to a shift in land surface climate. This study aims to determine the predominant climatic effects of fires in boreal forests to assess their impact on vegetation composition, surface albedo, and snow dynamics. The influence of fire-induced changes on Earth's radiative forcing is investigated, while considering variations in burn severity and postfire vegetation structure. Six burn sites are explored in central Alaska's boreal region, alongside six control sites, by utilizing Moderate Resolution Imaging Spectroradiometer (MODIS)-derived albedo, Leaf Area Index (LAI), snowmelt timing data, AmeriFlux radiation, National Land Cover Database (NLCD) land cover, and Monitoring Trends in Burn Severity (MTBS) data. Key findings reveal significant postfire shifts in land cover at each site, mainly from high- to low-stature vegetation. A continuous increase in postfire surface albedo and negative surface shortwave forcing was noted even after 12 years postfire, particularly during the spring and at high-severity burn areas. Results indicate that the cooling effect from increased albedo during the snow season may surpass the warming effects of earlier snowmelt. The overall climate impact of fires depends on burn severity and vegetation composition. [ABSTRACT FROM AUTHOR]

Exploring the evolution of vegetation cover and its drivers in the Ferghana Basin helps to understand the current ecological status of the Ferghana Basin and to analyze the vegetation changes and drivers, with a view to providing a scientific basis for regional ecological and environmental management and planning. Based on GIMMS NDVI3g and meteorological data, the spatial and temporal evolution characteristics of NDVI were analyzed from multiple perspectives with the help of linear trend and Mann–Kendall (MK) test methods using arcgis and the R language spatial analysis module, combined with partial correlation coefficients and residual analysis methods to analyze the impacts of climate change and human activities on the regional vegetation cover of the Ferghana Basin from 1982 to 2015. NDVI driving forces. The results showed the following: (1) The growing season of vegetation NDVI in the Ferghana Basin showed an increasing trend in the 34-year period, with an increase rate of 0.0044/10a, and the spatial distribution was significantly different, which was high in the central part of the country and low in the northern and southern parts of the country. (2) Temperature and precipitation simultaneously co-influenced the vegetation NDVI growth season, with most of the temperature and precipitation contributing in the spring, most of the temperature in the summer being negatively phased and the precipitation positively correlated, and most of the temperature and precipitation in the fall inhibiting vegetation NDVI growth. (3) The combined effect of climate change and human activities was the main reason for the overall rapid increase and great spatial variations in vegetation NDVI in China, and the spatial distribution of drivers, namely human activities and climate change, contributed 44.6% to vegetation NDVI in the growing season. The contribution of climate change and human activities to vegetation NDVI in the Ferghana Basin was 62.32% and 93.29%, respectively. The study suggests that more attention should be paid to the role of human activities and climate change in vegetation restoration to inform ecosystem management and green development. [ABSTRACT FROM AUTHOR]

The response of vegetation phenology to urbanization has become a growing concern. As impervious surfaces change as urbanization advances, the variation in vegetation phenology at the dynamic urbanization level was analyzed to significantly quantify the impact of urbanization processes on vegetation phenology. Based on the MOD13Q1 vegetation index product from 2001 to 2020, vegetation phenology parameters, including the start of the growing season (SOS), the end of the growing season (EOS), and the length of the growing season (GSL), were extracted, and the spatial–temporal variation in vegetation phenology, as well as its response to urbanization, was comprehensively analyzed. The results reveal that (1) from 2001 to 2020, the average rates of change for the SOS, EOS, and GSL were 0.41, 0.16, and 0.57 days, respectively. (2) The vegetation phenology changes showed significant spatial–temporal differences at the urbanization level. With each 10% increase in the urbanization level, the SOS and EOS were advanced and delayed by 0.38 and 0.34 days, respectively. (3) The urban thermal environment was a major factor in the impact of urbanization on the SOS and EOS. Overall, this study elucidated the dynamic reflection of urbanization in phenology and revealed the complex effects of urbanization on vegetation phenology, thus helping policymakers to develop effective strategies to improve urban ecological management. [ABSTRACT FROM AUTHOR]

As the most important city in China, Beijing has experienced an economic soar, large-scale population growth and eco-environment changes in the last 20 years. Evaluating climate- and human-induced vegetation changes could reveal the relationship of vegetation-climate-human activities and provide important insights for the coordination of economic growth and environmental protection. Based on a long-term MODIS vegetation index dataset, meteorological data (temperature, precipitation) and impervious surface data, the Theil-Sen regression and the Mann-Kendall method are used to estimate vegetation change trends in this study and the residual analysis is utilized to distinguish the impacts of climate factors and human activities on vegetation restoration and degradation from 2000 to 2019 in Beijing. Our results show that the increasing vegetation areas account for 80.2% of Beijing. The restoration of vegetation is concentrated in the urban core area and mountainous area, while the degradation of vegetation is mainly concentrated in the suburbs. In recent years, the vegetation in most mountainous areas has changed from restoration to significant restoration, indicating that the growth of mountain vegetation has continued to restore. We also found that in the process of urban expansion, vegetation browning occurred in 53.1% of the urban built-up area, while vegetation greening occurred in the remaining area. We concluded that precipitation is the main climatic factor affecting the growth of vegetation in Beijing's mountainous areas through correlation analysis. Human activities have significantly promoted the vegetation growth in the northern mountainous area thanks to the establishment of environmental protection areas. The negative correlation between vegetation and the impervious surface tends to gradually expand outwards, which is consistent with the trend of urban expansion. The positive correlation region remains stable, but the positive correlation is gradually enhanced. The response of vegetation to urbanization demonstrated a high degree of spatial heterogeneity. These findings indicated that human activities played an increasingly important role in influencing vegetation changes in Beijing. [ABSTRACT FROM AUTHOR]

Rapid global changes are altering regional hydrothermal conditions, especially in ecologically vulnerable areas such as coastal regions, subsequently influencing the dynamics of vegetation growth. However, there is limited research investigating the response of vegetation in these regions to extreme climates and the associated time lag-accumulation relationships. This study utilized a combined approach of gradual and abrupt analysis to examine the spatiotemporal patterns of vegetation dynamics in the coastal provinces of China from 2000 to 2019. Additionally, we evaluated the time lag-accumulation response of vegetation to extreme climate events. The results showed that (1) extreme high temperatures and extreme precipitation had increased over the past two decades, with greater warming observed in high latitudes and concentrated precipitation increases in water-rich southern regions; (2) both gradual and abrupt analyses indicate significant vegetation improvement in coastal provinces; (3) significant lag-accumulation relationships were observed between vegetation and extreme climate in the coastal regions of China, and the time-accumulation effects were stronger than the time lag effects. The accumulation time of extreme temperatures was typically less than one month, and the accumulation time of extreme precipitation was 2–3 months. These findings are important for predicting the growth trend of coastal vegetation, understanding environmental changes, and anticipating ecosystem evolution. [ABSTRACT FROM AUTHOR]

Information about the growth, productivity, and distribution of vegetation, which are highly relied on and sensitive to natural and anthropogenic factors, is essential for agricultural production management and eco-environmental sustainability in the Amur River Basin (ARB). In this paper, the spatial–temporal trends of vegetation dynamics were analyzed at the pixel scale in the ARB for the period of 1982–2013 using remotely sensed data of long-term leaf area index (LAI), fractional vegetation cover (FVC), and terrestrial gross primary productivity (GPP). The spatial autocorrelation characteristics of the vegetation indexes were further explored with global and local Moran's I techniques. The spatial–temporal relationships between vegetation and climatic factors, land use/cover types and hydrological variables in the ARB were determined using a geographical and temporal weighted regression (GTWR) model based on the observed meteorological data, remotely sensed vegetation information, while the simulated hydrological variables were determined with the soil and water assessment tool (SWAT) model. The results suggest that the variation in area-average annual FVC was significant with an increase rate of 0.0004/year, and LAI, FVC, and GPP all exhibited strong spatial heterogeneity trends in the ARB. For LAI and FVC, the most significant changes in local spatial autocorrelation were recognized over the Sanjiang Plain, and the low–low agglomeration in the Sanjiang Plain decreased continuously. The GTWR model results indicate that natural and anthropogenic factors jointly took effect and interacted with each other to affect the vegetated regime of the region. The decrease in the impact of precipitation to vegetation growth over the Songnen Plain was determined as having started around 1991, which was most likely attributed to dramatic changes in water use styles induced by local land use changes, and corresponded to the negative correlation between pasture areas and vegetation indexes during the same period. The analysis results presented in this paper can provide vital information to decision-makers for use in managing vegetation resources. [ABSTRACT FROM AUTHOR]

Warming in the Arctic has resulted in changes in the distribution and composition of vegetation communities. Many of these changes are occurring at fine spatial scales and at the level of individual species. Broad-band, coarse-scale remote sensing methods are commonly used to assess vegetation changes in the Arctic, and may not be appropriate for detecting these fine-scale changes; however, the use of hyperspectral, high resolution data for assessing vegetation dynamics remains scarce. The aim of this paper is to assess the ability of field spectroscopy to differentiate among four vegetation communities in the Low Arctic of Alaska. Primary data were collected from the North Slope site of Ivotuk, Alaska (68.49°N, 155.74°W) and analyzed using spectrally resampled hyperspectral narrowbands (HNBs). A two-step sparse partial least squares (SPLS) and linear discriminant analysis (LDA) was used for community separation. Results from Ivotuk were then used to predict community membership at five other sites along the Dalton Highway in Arctic Alaska. Overall classification accuracy at Ivotuk ranged from 84%-94% and from 55%-91% for the Dalton Highway test sites. The results of this study suggest that hyperspectral data acquired at the field level, along with the SPLS and LDA methodology, can be used to successfully discriminate among Arctic tundra vegetation communities in Alaska, and present an improvement over broad-band, coarse-scale methods for community classification. [ABSTRACT FROM AUTHOR]

Wetlands are ranked as very diverse ecosystems, covering about 4–6% of the global land surface. They occupy the transition zones between aquatic and terrestrial environments, and share characteristics of both zones. Wetlands play critical roles in the hydrological cycle, sustaining livelihoods and aquatic life, and biodiversity. Poor management of wetlands results in the loss of critical ecosystems goods and services. Globally, wetlands are degrading at a fast rate due to global environmental change and anthropogenic activities. This requires holistic monitoring, assessment, and management of wetlands to prevent further degradation and losses. Remote-sensing data offer an opportunity to assess changes in the status of wetlands including their spatial coverage. So far, a number of studies have been conducted using remotely sensed data to assess and monitor wetland status in semi-arid and arid regions. A literature search shows a significant increase in the number of papers published during the 2000–2020 period, with most of these studies being in semi-arid regions in Australia and China, and few in the sub-Saharan Africa. This paper reviews progress made in the use of remote sensing in detecting and monitoring of the semi-arid and arid wetlands, and focuses particularly on new insights in detection and monitoring of wetlands using freely available multispectral sensors. The paper firstly describes important characteristics of wetlands in semi-arid and arid regions that require monitoring in order to improve their management. Secondly, the use of freely available multispectral imagery for compiling wetland inventories is reviewed. Thirdly, the challenges of using freely available multispectral imagery in mapping and monitoring wetlands dynamics like inundation, vegetation cover and extent, are examined. Lastly, algorithms for image classification as well as challenges associated with their uses and possible future research are summarised. However, there are concerns regarding whether the spatial and temporal resolutions of some of the remote-sensing data enable accurate monitoring of wetlands of varying sizes. Furthermore, it was noted that there were challenges associated with the both spatial and spectral resolutions of data used when mapping and monitoring wetlands. However, advancements in remote-sensing and data analytics provides new opportunities for further research on wetland monitoring and assessment across various scales. [ABSTRACT FROM AUTHOR]

The monitoring of crop development can benefit from the increased frequency of observation provided by modern geostationary satellites. This paper describes a four-year testing period from 2010 to 2014, during which satellite images from the world's first Geostationary Ocean Color Imager (GOCI) were used for spectral analyses of paddy rice in South Korea. A vegetation index was calculated from GOCI data based on the bidirectional reflectance distribution function (BRDF)-adjusted reflectance, which was then used to visually analyze the seasonal crop dynamics. These vegetation indices were then compared with those calculated using the Moderate-resolution Imaging Spectroradiometer (MODIS)-normalized difference vegetation index (NDVI) based on Nadir BRDF-adjusted reflectance. The results show clear advantages of GOCI, which provided four times better temporal resolution than the combined MODIS sensors, interpreting subtle characteristics of the vegetation development. Particularly in the rainy season, when data acquisition under clear weather conditions was very limited, it was possible to find cloudless pixels within the study sites by compiling GOCI images obtained from eight acquisition periods per day, from which the vegetation index could be calculated. In this study, ground spectral measurements from CROPSCAN were also compared with satellite-based vegetation products, despite their different index magnitude, according to systematic discrepancy, showing a similar crop development pattern to the GOCI products. Consequently, we conclude that the very high temporal resolution of GOCI is very beneficial for monitoring crop development, and has potential for providing improved information on phenology. [ABSTRACT FROM AUTHOR]

Understanding the spatial and temporal dynamics of vegetation is essential in drylands. In this paper, we evaluated three vegetation indices, namely the Normalized Difference Vegetation Index (NDVI), the Soil-Adjusted Vegetation Index (SAVI) and the Enhanced Vegetation Index (EVI), derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) Surface-Reflectance Product in the Xinjiang Uygur Autonomous Region, China (XUAR), to assess index time series' suitability for monitoring vegetation dynamics in a dryland environment. The mean annual VI and its variability were generated and analyzed from the three VI time series for the period 2001-2012 across XUAR. Two phenological metrics, start of the season (SOS) and end of the season (EOS), were detected and compared for each vegetation type. The mean annual VI images showed similar spatial patterns of vegetation conditions with varying magnitudes. The EVI exhibited high uncertainties in sparsely vegetated lands and forests. The phenological metrics derived from the three VIs are consistent for most vegetation types, with SOS and EOS generated from NDVI showing the largest deviation. [ABSTRACT FROM AUTHOR]

Monitoring changes in vegetation cover over time is crucial for understanding the spatial distribution of rainfall, as well as the dynamics of plants and animals in the Namib desert. Traditional vegetation indices have limitations in capturing changes in vegetation cover within water-limited ecosystems like the Namib gravel plains. Spectral emissivity derived from thermal infrared remote sensing has recently emerged as a promising tool for distinguishing between bare ground and non-green vegetation in arid environments. This study investigates the potential of satellite-derived emissivities for mapping changes in fractional vegetation cover across the Namib gravel plains. Analyzing Moderate Resolution Imaging Spectroradiometer (MODIS) band 29 (λ = 8.55 µm) emissivity time series from 2001 to 2021, our findings demonstrate the ability of both Normalized Difference Vegetation Index (NDVI) and emissivity to detect sudden vegetation growth on the gravel plains. Emissivity additionally allows monitoring the extent of desiccated grass over several years after a rainfall event. Our results support a relationship between the change in fractional vegetation cover, the amount of rainfall and emissivity change magnitude. Information from NDVI and emissivity therefore provide complementary information for assessing vegetation in arid environments. [ABSTRACT FROM AUTHOR]

Coal mining and ecological restoration activities significantly affect land surfaces, particularly vegetation. Long-term quantitative analyses of vegetation disturbance and restoration are crucial for effective mining management and ecological environmental supervision. In this study, using the Google Earth Engine and all available Landsat images from 1987 to 2020, we employed the Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) algorithm and Support Vector Machine (SVM) to conduct a comprehensive analysis of the year, intensity, duration, and pattern of vegetation disturbance and restoration in the Heidaigou and Haerwusu open-pit coal mines (H-HOCMs) in the Jungar Coalfield of China. Our findings indicate that the overall accuracy for extractions of disturbance and restoration events in the H-HOCMs area is 83% and 84.5%, respectively, with kappa coefficients of 0.82 for both. Mining in Heidaigou has continued since its beginning in the 1990s, advancing toward the south and then eastward directions, and mining in the Haerwusu has advanced from west to east since 2010. The disturbance magnitude of the vegetation greenness in the mining area is relatively low, with a duration of about 4–5 years, and the restoration magnitude and duration vary considerably. The trajectory types show that vegetation restoration (R, 44%) occupies the largest area, followed by disturbance (D, 31%), restoration–disturbance (RD, 16%), disturbance–restoration (DR, 8%), restoration–disturbance–restoration (RDR), and no change (NC). The LandTrendr algorithm effectively detected changes in vegetation disturbance and restoration in H-HOCMs. Vegetation disturbance and restoration occurred in the study area, with a cumulative disturbance-to-restoration ratio of 61.79% since 1988. Significant restoration occurred primarily in the external dumps and continued ecological recovery occurred in the surrounding area. [ABSTRACT FROM AUTHOR]

How to distinguish the relative role of climate change and human activities in vegetation dynamics has attracted increasing attention. However, most of the current studies concentrate on arid and semiarid regions, while the relative contributions of climate change and human activities to vegetation changes remain unclear in warm-humid regions. Based on the normalized difference vegetation index (NDVI) and climatic variables (temperature, precipitation, radiation) during 2001–2020, this study used the Theil–Sen median trend analysis, partial correlation analysis, and residual trend analysis to analyze the spatiotemporal pattern of vegetation trends, the response of vegetation to climate variations, and the climatic and anthropogenic contributions to vegetation dynamics in the warm and humid Guangdong Province of south China. Results showed that the NDVI in most areas exhibited an increasing trend. Changes in climatic variables displayed different spatial variations which, however, were not significant in most areas. Vegetation responded diversely to climate change with temperature as the most important climatic factor for vegetation improvement in most areas, while precipitation was the dominant climatic factor in the southern edge region and radiation was the dominant climatic factor in the central and western regions. Vegetation in most areas was influenced by both climate change and human activities, but the contribution rate of human activities was commonly much higher than climate change. The findings of this study are expected to enhance our understanding of the relative climatic and anthropogenic contributions to vegetation changes in warm-humid regions and provide a scientific basis for future ecological policies and ecosystem management in highly urbanized regions. [ABSTRACT FROM AUTHOR]

Digital elevation models derived from airborne laser scanning have found worldwide application in archaeology and other disciplines. A key feature that makes these models so valuable lies in their capacity to represent micro-relief features indicating traces of past human activity. While detection of these often faint traces in vegetated areas benefits from maximum leaf-off conditions during data acquisition, countrywide collection of data must make compromises and often cannot take place in the most appropriate seasons. In this paper, we identify the impact of leaf-on conditions on the distribution of ground returns and present what types of archaeological objects might remain unnoticeable if the flight date is outside the desirable time window. Comparing five ALS data acquisition campaigns from both leaf-off (April and November) and leaf-on conditions (May and June), we demonstrate how foliage affects the morphology of relief features as recorded in ALS derivatives, and we identify other effects on archaeological interpretation caused by changing vegetation conditions. The results encourage evaluation of countrywide general-purpose data for their applicability in archaeology. [ABSTRACT FROM AUTHOR]

Open and analysis-ready data, as well as methodological and technical advancements have resulted in an unprecedented capability for observing the Earth's land surfaces. Over 10 years ago, Landsat time series analyses were inevitably limited to a few expensive images from carefully selected acquisition dates. Yet, such a static selection may have introduced uncertainties when spatial or inter-annual variability in seasonal vegetation growth were large. As seminal pre-open-data-era papers are still heavily cited, variations of their workflows are still widely used, too. Thus, here we quantitatively assessed the level of agreement between an approach using carefully selected images and a state-of-the-art analysis that uses all available images. We reproduced a representative case study from the year 2003 that for the first time used annual Landsat time series to assess long-term vegetation dynamics in a semi-arid Mediterranean ecosystem in Crete, Greece. We replicated this assessment using all available data paired with a time series method based on land surface phenology metrics. Results differed fundamentally because the volatile timing of statically selected images relative to the phenological cycle introduced systematic uncertainty. We further applied lessons learned to arrive at a more nuanced and information-enriched vegetation dynamics description by decomposing vegetation cover into woody and herbaceous components, followed by a syndrome-based classification of change and trend parameters. This allowed for a more reliable interpretation of vegetation changes and even permitted us to disentangle certain land-use change processes with opposite trajectories in the vegetation components that were not observable when solely analyzing total vegetation cover. The long-term budget of net cover change revealed that vegetation cover of both components has increased at large and that this process was mainly driven by gradual processes. We conclude that study designs based on static image selection strategies should be critically evaluated in the light of current data availability, analytical capabilities, and with regards to the ecosystem under investigation. We recommend using all available data and taking advantage of phenology-based approaches that remove the selection bias and hence reduce uncertainties in results. [ABSTRACT FROM AUTHOR]

Remotely-sensed Leaf Area Index (LAI) is a useful metric for assessing changes in vegetation cover and greeness over time and space. Satellite-derived LAI measurements can be used to assess these intra- and inter-annual vegetation dynamics and how they correlate with changing regional and local climate conditions. The detection of such changes at local and regional levels is challenged by the underlying continuity and extensive missing values of high-resolution spatio-temporal vegetation data. Here, the feasibility of functional data analysis methods was evaluated to improve the exploration of such data. In this paper, an investigation of multidecadal variation in LAI is conducted in the Columbia River Watershed, as detected by NOAA Advanced Very High-Resolution Radiometer (AVHRR) satellite imaging. The inter- and intra-annual correlation of LAI with temperature and precipitation were then investigated using data from the European Centre for Medium-Range Weather Forecasts global atmospheric re-analysis (ERA-Interim) in the period 1996–2017. A functional cluster analysis model was implemented to identify regions in the Columbia River Watershed that exhibit similar long-term greening trends. Across this region, a multidecadal trend toward earlier and higher annual LAI peaks was detected, and strong correlations were found between earlier and higher LAI peaks and warmer temperatures in late winter and early spring. Although strongly correlated to LAI, maximum temperature and precipitation do not demonstrate a similar strong multidecadal trend over the studied time period. The modeling approach is proficient for analyzing tens or hundreds of thousands of sampled sites without parallel processing or high-performance computing (HPC). [ABSTRACT FROM AUTHOR]

Latest remote sensing sensors are capable of acquiring high spatial and spectral Satellite Image Time Series (SITS) of the world. These image series are a key component of classification systems that aim at obtaining up-to-date and accurate land cover maps of the Earth's surfaces. More specifically, current SITS combine high temporal, spectral and spatial resolutions, which makes it possible to closely monitor vegetation dynamics. Although traditional classification algorithms, such as Random Forest (RF), have been successfully applied to create land cover maps from SITS, these algorithms do not make the most of the temporal domain. This paper proposes a comprehensive study of Temporal Convolutional Neural Networks (TempCNNs), a deep learning approach which applies convolutions in the temporal dimension in order to automatically learn temporal (and spectral) features. The goal of this paper is to quantitatively and qualitatively evaluate the contribution of TempCNNs for SITS classification, as compared to RF and Recurrent Neural Networks (RNNs) —a standard deep learning approach that is particularly suited to temporal data. We carry out experiments on Formosat-2 scene with 46 images and one million labelled time series. The experimental results show that TempCNNs are more accurate than the current state of the art for SITS classification. We provide some general guidelines on the network architecture, common regularization mechanisms, and hyper-parameter values such as batch size; we also draw out some differences with standard results in computer vision (e.g., about pooling layers). Finally, we assess the visual quality of the land cover maps produced by TempCNNs. [ABSTRACT FROM AUTHOR]

The analysis of vegetation dynamics is essential in semi-arid regions, in particular because of the frequent occurrence of long periods of drought. In this paper, multi-temporal series of the Normalized Difference of Vegetation Index (NDVI), derived from SPOT-VEGETATION satellite data between September 1998 and June 2010, were used to analyze the vegetation dynamics over the semi-arid central region of Tunisia. A study of the persistence of three types of vegetation (pastures, annual agriculture and olive trees) is proposed using fractal analysis, in order to gain insight into the stability/instability of vegetation dynamics. In order to estimate the state of vegetation cover stress, we propose evaluating the properties of an index referred to as the Vegetation Anomaly Index (VAI). A positive VAI indicates high vegetation dynamics, whereas a negative VAI indicates the presence of vegetation stress. The VAI is tested for the above three types of vegetation, during the study period from 1998 to 2010, and is compared with other drought indices. The VAI is found to be strongly correlated with precipitation. [ABSTRACT FROM AUTHOR]

Vegetation biophysical products offer unique opportunities to examine long-term vegetation dynamics and land surface phenology (LSP). It is important to understand the time-series performances of various global biophysical products for global change research. However, few endeavors have been dedicated to assessing the performances of long-term change characteristics or LSP extraction derived from different satellite products, especially in mountainous areas with highly fragmented and rugged surfaces. In this paper, we assessed the time-series characteristics and LSP detections of Global LAnd Surface Satellite (GLASS) leaf area index (LAI), fractional vegetation cover (FVC), and gross primary production (GPP) products across the Three-River Source Region (TRSR). The performances of products' temporal agreements and their statistical relationship as a function of topographic indices and heterogeneous pixels, respectively, were investigated through intercomparison among three products during the period 2000 to 2018. The results show that the phenological differences between FVC and two other products are beyond 10 days over more than 35% of the pixels in TRSR. The long-term trend of FVC diverges significantly from GPP and LAI for 13.96% of the total pixels, and the percentages of mismatched pixels between FVC and two other products are 33.24% in the correlation comparison. Moreover, good agreements are observed between GPP and LAI, both in terms of LSP and interannual variations. Finally, the LSP and long-term dynamics of the three products exhibit poor performances on heterogeneous surfaces and complex topographic areas, which reflects the potential impacts of environmental factors and algorithmic imperfections on the quality and performances of different products. Our study highlights the spatiotemporal disparities in detections of surface vegetation activity in mountainous areas by using different biophysical products. Future global change studies may require multiple high-quality satellite products with long-term stability as data support. [ABSTRACT FROM AUTHOR]

Vegetation is an essential component of the terrestrial ecosystem and has changed significantly over the last two decades in the Northwestern Highlands of Ethiopia. However, previous studies have focused on the detection of bitemporal change and lacked the incorporation of entire vegetation time series changes, which are considered significant indicators of ecosystem conditions. The Normalized Difference Vegetation Index (NDVI) time series dataset from the Moderate-Resolution Imaging Spectroradiometer (MODIS) is an efficient method for analyzing the dynamics of vegetation change over a lengthy period using remote sensing techniques. This study aimed to utilize time series satellite data to detect vegetation changes from 2000 to 2020 and investigate their links with ecosystem conditions. The time-series satellite processing package (TIMESAT) was used to estimate the seasonal parameter values of NDVI and their correlation across the seasons during the study period. Break Detection for Additive Season and Trend (BFAST) was applied to identify the year of breakpoints, the direction of magnitude, and the number of breakpoints. The results were reported, analyzed, and linked to ecosystem conditions. The overall trend in the study area increased from 0.58 (2000–2004) to 0.65 (2015–2020). As a result, ecosystem condition indicators such as peak value (PV), base value (BV), amplitude (Amp), and large integral (LI) exhibited significant positive trends, particularly for Acacia decurrens plantations, Eucalyptus plantations, and grasslands, but phenology indicator parameters such as start of season (SOS), end of season (EOS), and length of season (LOS) did not show significant trends for almost any vegetation type. The most abrupt changes were recorded in 2015 (24.7%), 2012 (18.6%), and 2014 (9.8%). Approximately 30% of the vegetation changes were positive in magnitude. The results of this study imply that there was an improvement in the ecosystem's condition following the establishment of the Acacia decurrens plantation. The findings are considered relevant inputs for policymakers and serve as an initial stage for the assessment of the other environmental and climatic implications of Acacia decurrens plantations at the local scale. [ABSTRACT FROM AUTHOR]

In recent years, vegetation on the Qinghai–Tibet Plateau (QTP) has undergone significant greening. However, the causal factors underpinning this phenomenon, whether attributable to temperature fluctuations, precipitation patterns, or anthropogenic interventions, remain a subject of extensive scholarly debate. This study conducted a comprehensive analysis of the evolving vegetation across the QTP. The National Oceanic and Atmospheric Administration Climate Data Record Advanced Very High Resolution Radiometer Normalized Vegetation Difference Index (NOAA CDR AVHRR NDVI) dataset was employed to elucidate the intricate relationship between climatic variables and human activities driving vegetative transformations. The findings were as follows: The NDVI on the QTP has exhibited a significant greening trend at a rate of 0.0013/a (per year). A minor decline, accounting for only 17.6% of grasslands, was observed, which was primarily concentrated in the northwestern and northern regions. Through residual analysis, climate change was found to be the predominant driver, explaining 70.6% of the vegetation variability across the plateau. Concurrently, noticeable trends in temperature and precipitation increases were observed on the QTP, with the southern region demonstrating improved sensitivity to precipitation alterations. In summary, these results substantiate that a confluence of climatic warming, enhanced moisture availability, and a reduction in livestock population collectively creates an environment conducive to enhanced vegetation vigor on the QTP. This study highlights the significance of acknowledging the dual influence of climate and human agency in shaping vegetative dynamics, which is a critical consideration for informed land management strategies and sustainable development initiatives on this ecologically pivotal plateau. [ABSTRACT FROM AUTHOR]