Normalized Difference Vegetation Index Time Research Articles

Weight Optimization for missing data prediction of Landslide Susceptibility Mapping in Remote sensing Analysis

To keep track of changes to the Earth's surface, extensive time series of data from remote sensing using image processing is required. This research is motivated by the effectiveness of computational modelling techniques; however, the problem of missing data is multifaceted. When data at numerous a-periodic timestamps are absent during multi-temporal analysis, the issue becomes increasingly problematic. To make remote sensing time series analysis easier, weight optimised machine learning is used in this study to rebuild lost data. Keeping the causality restriction in mind, this method makes use of data from previous and subsequent timestamps. The architecture is based on an ensemble of numerous forecasting modules, built on the observed data in the time-series order. Dummy data is used to connect the forecasting modules, which were previously linked by the earlier half of the sequence. After that, iterative improvements are made to the dummy data to make it better fit the next segment of the sequence. On the basis of Landsat-7 TM-5 satellite imagery, the work has been proven to be accurate in forecasting missing images in normalised difference vegetation index time series. In a performance evaluation, the proposed forecasting model was shown to be effective.

Abandoned Farmland Extraction and Feature Analysis Based on Multi-Sensor Fused Normalized Difference Vegetation Index Time Series—A Case Study in Western Mianchi County

Farmland abandonment monitoring is one of the key aspects of land use and land cover research, as well as being an important prerequisite for ecological environmental protection and food security. A Normalized Difference Vegetation Index (NDVI) time series analysis is a common method used for farmland abandonment data extraction; however, extracting this information using high-resolution data is still difficult due to the limitations caused by cloud influence and data of low temporal resolution. To address this problem, this study used STARFM for GF-6 and Landsat 8 data fusion to enhance the continuity of high-resolution and cloudless images. A dataset was constructed by combining the phenological cycle of crops in the study area and then extracting abandoned farmland data based on an NDVI time series analysis. The overall accuracy of the results based on the NDVI time series analysis using the STARFM-fused dataset was 93.42%, which was 15.5% higher than the accuracy of the results obtained using only GF-6 data and 28.52% higher than those obtained using only Landsat data. Improvements in accuracy were also achieved when using SVM for time series analysis based on the fused dataset, indicating that the method used in this study can effectively improve the accuracy of the results. Then, we analyzed the spatial distribution pattern of abandoned farmland by extracting the results and concluded that the abandonment rate increased with the increase in the road network density and decreased with the increase in the distance to residential areas. This study can provide decision-making guidance and scientific and technological support for the monitoring of farmland abandonment and can facilitate the analysis of abandonment mechanisms in the study area, which is conducive to the sustainable development of farmland.

Effectiveness of Spatiotemporal Data Fusion in Fine-Scale Land Surface Phenology Monitoring: A Simulation Study

Spatiotemporal data fusion technologies have been widely used for land surface phenology (LSP) monitoring since it is a low-cost solution to obtain fine-resolution satellite time series. However, the reliability of fused images is largely affected by land surface heterogeneity and input data. It is unclear whether data fusion can really benefit LSP studies at fine scales. To explore this research question, this study designed a sophisticated simulation experiment to quantify effectiveness of 2 representative data fusion algorithms, namely, pair-based Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and time series-based Spatiotemporal fusion method to Simultaneously generate Full-length normalized difference vegetation Index Time series (SSFIT) by fusing Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) data in extracting pixel-wise spring phenology (i.e., the start of the growing season, SOS) and its spatial gradient and temporal variation. Our results reveal that: (a) STARFM can improve the accuracy of pixel-wise SOS by up to 74.47% and temporal variation by up to 59.13%, respectively, compared with only using Landsat images, but it can hardly improve the retrieval of spatial gradient. For SSFIT, the accuracy of pixel-wise SOS, spatial gradient, and temporal variation can be improved by up to 139.20%, 26.36%, and 162.30%, respectively; (b) the accuracy improvement introduced by fusion algorithms decreases with the number of available Landsat images per year, and it has a large variation with the same number of available Landsat images, and (c) this large variation is highly related to the temporal distributions of available Landsat images, suggesting that fusion algorithms can improve SOS accuracy only when cloud-free Landsat images cannot capture key vegetation growth period. This study calls for caution with the use of data fusion in LSP studies at fine scales.

Evaluation and quantitative characterization for the ecological environment impact of open pit mining on vegetation destruction from landsat time series: A case study of Wulishan limestone mine

Quantitatively evaluating the ecological environment impacts of vegetation destruction due to open-pit mining activities is vital for enhancing the green mining standard and cost management capabilities of mining enterprises. Based on the Landsat time series, this study proposes an ecological environment impact assessment and quantitative characterization method for vegetation destruction in mining areas resulting from open-pit mining activities. First, the modified normalized difference water index and the normalized difference vegetation index time series data were calculated. The water body thresholds and the fraction of vegetation coverage were ascertained using the K-means clustering algorithm and the dimidiate pixel model, respectively, to determine the area of direct vegetation destruction in mining areas. Second, utilizing the Theil-Sen Median trend analysis and the Mann-Kendall test, the indirect impact area of vegetation in the mining region was identified. Lastly, by integrating vegetation's net primary productivity with the Chinese Emission Allowance price index, the total carbon emission cost of vegetation destruction due to mining activities over 20 years was calculated to be about 2.122 million yuan. The findings indicated that the ecological environmental impact of open-pit mining activities on vegetation destruction cannot be ignored. From 2000 to 2020, open-pit mining at the Wulishan limestone mine in Anhui Province, China, increased the area of direct vegetation destruction by 9.072 × 105 m2, and the indirect impact area on vegetation was 7.371 × 105 m2. The carbon emission cost of vegetation destruction in the direct destruction area was about 104,000 yuan per year, and the carbon emission cost of vegetation damage in the indirect impact area was approximately 2,082.53 yuan per year. This research provides a scientific foundation for ecological environmental protection, regulations, green mining, and cost management for mining enterprises, promoting the harmonious progress of both the economy and environmental protection.

An Elastic-Window-Based Method for the Underdetermined Problem in Linear Spectral Unmixing to Enhance the Spatial Resolution of the Normalized Difference Vegetation Index Time Series

Inverting land cover reflectance or derived indices from low-spatial-resolution images to refine the spatial resolution of this data is cost-effective for land surface monitoring applications that face technical or budget limitations. Based on the linear spectral mixing model, many approaches have successfully unmixed coarse mixed pixels using high-spatial-resolution land cover maps in the past decades. However, in some cases, the solutions of linear systems composed of several mixed pixels may not be acquired due to the underdetermined problem. This study presents the causes of this problem and proposes an iterative approximation strategy to address it. An elastic-window-based algorithm was developed, where the initial size of the window was calculated based on the land cover of the mixed pixel. Mixed pixels of neighborhoods with similar land covers were then selected to form the unmixing linear system, which was examined through a simulation test to ensure it was not underdetermined. Otherwise, the window would expand to include more adjacent pixels. This process was repeated until a successful solution was obtained. A statistical analysis of sixty land cover maps from around the globe shows that the underdetermined problem exists at a low level but becomes more serious with an increase in mixed scale. The results demonstrate that the proposed algorithm effectively prevents the underdetermined problem for mixed pixels of different scales and can be integrated into the coarse NDVI downscaling procedure to refine spatial resolution. This study provides a reference for estimating underdetermined mixed pixels and benefits applications that require dealing with the inversion of land cover values directly.

Assessing the effect of rotational grazing adoption in Iberian silvopastoral systems with Normalized Difference Vegetation Index time series

Adaptive Multi-Paddock (AMP) is a grazing system which combines intensive, rapid grazing livestock rotation with relatively short grazing periods and long recovery time after grazing. The study assesses, under Mediterranean silvopastoral systems, changes in pasture phenology and spatial variability after adopting the AMP under contrasting land cover (Wooded Grassland vs Grassland ) with a remote sensing approach based on the time-series analysis of Normalized Difference Vegetation Index (NDVI) from remote sensing through Landsat satellite. The study revealed an overall positive effect of rotational grazing on pasture phenology and NDVI spatial variability. The AMP adoption resulted in higher estimated values of NDVI at the beginning (under grassland land cover), the end, and the peak of the growing season, while no differences were observed in parameters estimating the length of the growing season. The spatial variability of NDVI was always lower under AMP than in continuously grazed areas, except in the early stages of the growing season under grassland land cover. The results suggested that in a relatively short period (4-5 years), the AMP grazing system can represent a strategy to improve forage availability and exploitation by grazing animals under low stocking rates in extensively managed Mediterranean silvopastoral systems.

Tectonic controls on the ecosystem of the Mara River basin, East Africa, from geomorphological and spectral index analysis

Abstract. Tectonic activity impacts the environment; therefore, identifying the influence of active faulting on environmental factors, such as soil development and vegetation growth patterns, is valuable in better understanding ecosystem functions. Here, we illustrate how tectonic activity and the lithology of bedrock influence temporal and spatial patterns of vegetation and soil parameters in a fault-controlled river basin. The Mara River basin lies in a region of previously unrecognised active normal faulting, dominated by the Utimbara and Isuria faults, resulting in areas of relative uplift, subsidence and tilting. Faulting leads to spatially variable erosion and soil formation rates as well as disruption and modification of drainage systems. On a small scale, steep escarpments cast shade and provide shelter. All of these factors might be expected to exert controls on ecosystem dynamics on a range of lengths and timescales. Here, we investigate tectonic controls on ecological processes in the Mara River basin using TanDEM-X and Sentinel-2 data. We use high-resolution digital elevation models (DEMs) to map the Utimbara and Isuria faults and to measure the height of the escarpments (up to 400 m) along the length of the faults. Total fault offset can be estimated by correlating Neogene phonolite lavas (thought to be 3.5–4.5 Myr old) on either side of the faults. If the age is correct, slip rates can be estimated to be on the order of 0.1 mm yr−1. Analysis of DEMs also reveals the presence of recent earthquake scarps in the hanging-wall sediments of the main faults and extensive alluvial fan formation on the hanging wall. Low mountain front sinuosity values and the presence of steep escarpments also suggest recent activity. Drainage is displaced across the fault traces, and, in one area, it is possible to map the lateral channel migration of the Mara River due to hanging-wall tilting. We used a 5-year normalised difference vegetation index (NDVI) time series, the clay mineral ratio (CMR) and a moisture stress index (MSI) to investigate spatiotemporal vegetation patterns and soil formation. Whilst lithology does exert some control, as expected, we observed that the downthrown hanging wall of the faults, especially directly adjacent to the escarpment, is consistently associated with a higher degree of vegetation, wetland formation and clay distribution. Analysis of spectral indices shows that the overall spatial pattern of vegetation cover is seasonally low in the flat plains and perennially high in the vicinity of more complex, tectonically influenced structures. The NDVI highlights several locations with permanently healthy vegetation along the escarpment which extend downslope for several kilometres. Our study shows that in the Mara River basin, active normal faulting is an important stabiliser of vegetation growth patterns, likely caused by favourable hydrological and pedological conditions along the escarpments; tectonic activity has a direct beneficial influence on ecological processes in this climatically sensitive region.

SAR2NDVI: a pix2pix generative adversarial network for reconstructing field-level normalized difference vegetation index time series using Sentinel-1 synthetic aperture radar data

The normalized difference vegetation index (NDVI) is essential for monitoring urban green space, forest cover, and crop growth from sowing to harvesting. High-resolution images from Sentinel-2 and Landsat-8 satellites are available nowadays to get field-level phenological information at short intervals. However, cloud cover is one of the biggest hindrances in vegetation monitoring using NDVI, resulting in data gaps. To address this issue, we propose an NDVI gap-filling methodology to generate cloud-free NDVI time series from Sentinel-1 SAR data using the pix2pix generative adversarial network (GAN) model. Pix2pix GANs with generators based on U-Net and ResNet were designed, and the performance of the models was compared for both the VV and VH polarizations. The generalization capability of the models was studied using synthetic aperture radar (SAR) and NDVI image pairs with different vegetation, field sizes, and shapes. Experimental results show that the generated synthetic NDVI images can effectively substitute the cloudy images for gap-filling. Compared to ResNet, the U-Net generator has given the best results with PSNR = 29.447 dB, RMSE = 0.055, and Pearson correlation coefficient ρ = + 0.909 for VH polarization.

Impact of STARFM on Crop Yield Predictions: Fusing MODIS with Landsat 5, 7, and 8 NDVIs in Bavaria Germany

Rapid and accurate yield estimates at both field and regional levels remain the goal of sustainable agriculture and food security. Hereby, the identification of consistent and reliable methodologies providing accurate yield predictions is one of the hot topics in agricultural research. This study investigated the relationship of spatiotemporal fusion modelling using STRAFM on crop yield prediction for winter wheat (WW) and oil-seed rape (OSR) using a semi-empirical light use efficiency (LUE) model for the Free State of Bavaria (70,550 km2), Germany, from 2001 to 2019. A synthetic normalised difference vegetation index (NDVI) time series was generated and validated by fusing the high spatial resolution (30 m, 16 days) Landsat 5 Thematic Mapper (TM) (2001 to 2012), Landsat 7 Enhanced Thematic Mapper Plus (ETM+) (2012), and Landsat 8 Operational Land Imager (OLI) (2013 to 2019) with the coarse resolution of MOD13Q1 (250 m, 16 days) from 2001 to 2019. Except for some temporal periods (i.e., 2001, 2002, and 2012), the study obtained an R2 of more than 0.65 and a RMSE of less than 0.11, which proves that the Landsat 8 OLI fused products are of higher accuracy than the Landsat 5 TM products. Moreover, the accuracies of the NDVI fusion data have been found to correlate with the total number of available Landsat scenes every year (N), with a correlation coefficient (R) of +0.83 (between R2 of yearly synthetic NDVIs and N) and −0.84 (between RMSEs and N). For crop yield prediction, the synthetic NDVI time series and climate elements (such as minimum temperature, maximum temperature, relative humidity, evaporation, transpiration, and solar radiation) are inputted to the LUE model, resulting in an average R2 of 0.75 (WW) and 0.73 (OSR), and RMSEs of 4.33 dt/ha and 2.19 dt/ha. The yield prediction results prove the consistency and stability of the LUE model for yield estimation. Using the LUE model, accurate crop yield predictions were obtained for WW (R2 = 0.88) and OSR (R2 = 0.74). Lastly, the study observed a high positive correlation of R = 0.81 and R = 0.77 between the yearly R2 of synthetic accuracy and modelled yield accuracy for WW and OSR, respectively.

Mapping burn severity and monitoring CO content in Türkiye's 2021 Wildfires, using Sentinel-2 and Sentinel-5P satellite data on the GEE platform.

This study investigated forest fires in the Mediterranean of Türkiye between July 28, 2021, and August 11, 2021. Burn severity maps were produced with the difference normalised burned ratio index (dNBR) and difference normalised difference vegetation index (dNDVI) using Sentinel-2 images on the Google Earth Engine (GEE) cloud platform. The burned areas were estimated based on the determined burning severity degrees. Vegetation density losses in burned areas were analysed using the normalised difference vegetation index (NDVI) time series. At the same time, the post-fire Carbon Monoxide (CO) column number densities were determined using the Sentinel-5P satellite data. According to the burn severity maps obtained with dNBR, the sum of high and moderate severity areas constitutes 34.64%, 20.57%, 46.43%, 51.50% and 18.88% of the entire area in Manavgat, Gündoğmuş, Marmaris, Bodrum and Köyceğiz districts, respectively. Likewise, according to the burn severity maps obtained with dNDVI, the sum of the areas of very high severity and high severity constitutes 41.17%, 30.16%, 30.50%, 42.35%, and 10.40% of the entire region, respectively. In post-fire NDVI time series analyses, sharp decreases were observed in NDVI values from 0.8 to 0.1 in all burned areas. While the Tropospheric CO column number density was 0.03mol/m2 in all regions burned before the fire, it was observed that this value increased to 0.14mol/m2 after the fire. Moreover, when the area was examined more broadly with Sentinel 5P data, it was observed that the amount of CO increased up to a maximum value of 0.333mol/m2. The results of this study present significant information in terms of determining the severity of forest fires in the Mediterranean region in 2021 and the determination of the CO column number density after the fire. In addition, monitoring polluting gases with RS techniques after forest fires is essential in understanding the extent of the damage they can cause to the environment.

Trends in normalized difference vegetation index time series in differently regulated cascade reservoirs in Wujiang catchment, China

Dam construction is economically and ecologically important for power generation and water capture. However, the effects of cascade dams with various regulation types (i.e., daily, weekly, seasonal, yearly) on vegetation coverage are still need to be clarified. In the current study, we investigated the effects of dam construction on vegetation coverage and predicted the dynamic trends of vegetation at the sub-catchment (i.e., the catchment removing the overlapping catchment of the upstream reservoir) and buffer scale of each reservoir. MODIS Terra normalized difference vegetation index (NDVI) composites (MOD13Q1, 2000–2019) were used to study spatial and temporal changes in riparian vegetation coverage. At the sub-catchment scale, the long-term mean NDVI increased significantly from upstream to downstream from 2000 to 2019, and the NDVI mutation point (i.e., turning point) was consistent with the construction year of the corresponding dam. At the temporal scale, the vegetation trend predictions indicated a greater degree of degradation (23 %–61 %) than in the past (2000–2019; 3 %–32 %). Our findings also showed that reservoir vegetation degradation was associated with regulation type, i.e., daily reservoirs experienced a higher degree of vegetation degradation. Each cascade reservoir exhibited higher past vegetation degradation within the 0–1 km buffer zone compared to the sub-catchment. However, future degradation was predicted to be much higher in the sub-catchment than in the 0–1 km buffer zone, indicating the potential impact of damming on vegetation from adjacent areas (i.e., 0–1 km buffer) with gradual expansion to more distant areas (i.e., sub-catchment) over time. A potential cascade effect of upstream dams on sub-catchment vegetation of downstream dams was also observed. This study provides a reference for watershed management and ecological civilization construction. Relevant laws and policies should be established in areas surrounding reservoirs to help mitigate dam-induced degradation of vegetation communities, especially in reservoirs with short retention times.

Assessing Climate Influence on Spatiotemporal Dynamics of Macrophytes in Eutrophicated Reservoirs by Remotely Sensed Time Series

The overgrowth of macrophytes is a recurrent problem within reservoirs of urbanized and industrialized areas, a condition triggered by the damming of rivers and other human activities. Although the occurrence of aquatic plants in waterbodies has been widely monitored using remote sensing, the influence of climate variables on macrophyte spatiotemporal dynamics is rarely considered in studies developed for medium scales to long periods of time. We hypothesize that the spatial dispersion of macrophytes has its natural rhythms influenced by climate fluctuations, and, as such, its effects on the heterogeneous spatial distribution of this vegetation should be considered in the monitoring of water bodies. A eutrophic reservoir is selected for study, which uses the Normalized Difference Vegetation Index (NDVI) as a proxy for macrophytes. Landsat’s NDVI long-term time series are constructed and matched with the Climate Variable (CV) from the National Oceanic and Atmospheric Administration (NOAA) to assess the spatiotemporal dynamics of aquatic plants and their associated climate triggers. The NDVI and CV time series and their seasonal and trend components are correlated for the entire reservoir, compartments, and segmented areas of the water body. Granger-causality of these climate variables show that they contribute to describe and predict the spatial dispersion of macrophytes.

Characterising the Land Surface Phenology of Middle Eastern Countries Using Moderate Resolution Landsat Data

Global change impacts including climate change, increased CO2 and nitrogen deposition can be determined through a more precise characterisation of Land Surface Phenology (LSP) parameters. In addition, accurate estimation of LSP dates is being increasingly used in applications such as mapping vegetation types, yield forecasting, and irrigation management. However, there has not been any attempt to characterise Middle East vegetation phenology at the fine spatial resolution appropriate for such applications. Remote-sensing based approaches have proved to be a useful tool in such regions since access is restricted in some areas due to security issues and their inter-annual vegetation phenology parameters vary considerably because of high uncertainty in rainfall. This study aims to establish for the first time a comprehensive characterisation of the vegetation phenological characteristics of the major vegetation types in the Middle East at a fine spatial resolution of 30 m using Landsat Normalized Difference Vegetation Index (NDVI) time series data over a temporal range of 20 years (2000–2020). Overall, a progressive pattern in phenophases was observed from low to high latitude. The earliest start of the season was concentrated in the central and east of the region associated mainly with grassland and cultivated land, while the significantly delayed end of the season was mainly distributed in northern Turkey and Iran corresponding to the forest, resulting in the prolonged length of the season in the study area. There was a significant positive correlation between LSP parameters and latitude, which indicates a delay in the start of the season of 4.83 days (R2 = 0.86, p < 0.001) and a delay in the end of the season of 6.54 days (R2 = 0.83, p < 0.001) per degree of latitude increase. In addition, we have discussed the advantages of fine resolution LSP parameters over the available coarse datasets and showed how such outputs can improve many applications in the region. This study shows the potential of Landsat data to quantify the LSP of major land cover types in heterogeneous landscapes of the Middle East which enhances our understanding of the spatial-temporal dynamics of vegetation dynamics in arid and semi-arid settings in the world.

Function fitting for modeling seasonal normalized difference vegetation index time series and early forecasting of soybean yield

Forecasting crop yields based on remote sensing data is one of the most important tasks in agriculture. Soybean is the main crop in the Russian Far East. It is desirable to forecast soybean yield as early as possible while maintaining high accuracy. This study aimed to investigate seasonal time series of the normalized difference vegetation index (NDVI) to achieve early forecasting of soybean yield. This research used data from the Moderate Resolution Image Spectroradiometer (MODIS), an arable-land mask obtained from the VEGA-Science web service, and soybean yield data for 2008–2017 for the Jewish Autonomous Region (JAR) districts. Four approximating functions were fitted to model the NDVI time series: Gaussian, double logistic (DL), and quadratic and cubic polynomials. In the period from calendar weeks 22–42 (end of May to mid-October), averaged over two districts, the model using the DL function showed the highest accuracy (mean absolute percentage error –4.0%, root mean square error (RMSE) –0.029, P < 0.01). The yield forecast accuracy of prediction in the period of weeks 25–30 in JAR municipalities using the parameters of the Gaussian function was higher (P < 0.05) than that using the other functions. The mean forecast error for the Gaussian function was 14.9% in week 25 (RMSE was 0.21 t ha−1) and 5.1%−12.9% in weeks 26–30 (RMSE varied from 0.06 to 0.15 t ha−1) according to the 2013–2017 data. In weeks 31–32, the error was 5.0%−5.4% (RMSE was 0.07 t ha−1) using the Gaussian parameters and 7.4%−7.7% (RMSE was 0.09–0.11 t ha−1) for the DL function. When the method was applied to municipal districts of other soy-producing regions of the Russian Far East. RMSE was 0.14–0.32 t ha−1 in weeks 25–26 and did not exceed 0.20 t ha−1 in subsequent weeks.

Study of plant configuration pattern in guided vegetation restoration: A case study of semiarid underground mining areas in Western China

The large-scale and high intensity mining of coal resources has further worsened the already fragile plant ecosystem in Western China. On the basis of strictly following the developmental law of regional natural ecosystems, guiding the restoration of damaged vegetation is one of the important measures for ecological restoration in mining areas. However, there are still some problems to achieve the guided restoration of damaged vegetation in semiarid mining areas: how to configure different plant species, and what is the control threshold of vegetation coverage under different site conditions among others. In this paper, based on DEM, slope, aspect and soil type data, 12 different site conditions were obtained by using overlay analysis function of ArcGIS software. The trend of interannual variation of vegetation coverage was analyzed using Landsat Normalized Difference Vegetation Index (NDVI) time series data, and the spatial distribution of areas of degraded vegetation under 12 site conditions was identified. Based on the Fully Constrained Least Square (FCLS) unmixing algorithm, typical native plants were classified using Aerial Hyperspectral data. Finally, through the comparative analysis of plant composition and abundance under 12 site conditions, the control thresholds of vegetation coverage ranged from 36.60% to 45.30% in the early stage of vegetation restoration. The thresholds of steep slope are lower than that in gentle and flat slope, which is mainly related to the fact that steep slope is more prone to soil erosion and is not conducive to the conservation of soil water and nutrients. According to the calculation results of different plant allocation proportion under 12 site conditions, the proportion of herbs and shrubs is higher than that of trees. When selecting species for vegetation configuration, native herbs and shrubs with strong adaptability, fast growth and strong drought resistance should be preferred. It is suggested that trees should be planted properly after the site conditions of vegetation growth are effectively restored or the vegetation community structure tends to be stable. In the process of vegetation restoration in semiarid mining area, not only the appropriate threshold of vegetation coverage should be considered, but also the proportion of trees, shrubs, and herbs has an important impact on the effect of vegetation restoration. The results of this paper will provide guidance for the orderly restoration of damaged vegetation in semiarid mining areas.

Cropland Extraction Using Water Mask and Phenological Features based on Landsat-8 Imagery

Accurate cropland information is crucial for food security assessment. At present, Landsat-8 remote sensing imagery and Google Earth Engine (GEE) cloud platform provide favorable conditions for mapping cropland. Despite the improved mapping conditions, the existence of mixed pixels is still one of the main difficulties in the cropland extraction. Especially in paddy fields, the influence of water bodies on farmland extraction should not be ignored. Also, the lack of time series data with temporal-spatial resolution impedes the accurate cultivated land mapping. Aiming at these problems, we proposed an effective method to extract cropland precisely based on the Landsat-8 imagery and GEE platform. First, we created cloud-free images and masked out water based on Landsat-8 images. Second, we constructed the normalized difference vegetation index time series data with 30-m resolution from Landsat-8 and MODIS images to obtain accurate phenological information. Third, other cropland features such as texture features, tasseled cap transformation features, terrain features and spectral features were acquired. Finally, we applied the Random Forest algorithm and obtained the cropland for 2015 in Qinzhou. The results show that terrain characteristics and phenological characteristics have the greatest influence on cropland extraction. Based on water mask, the overall accuracy of cropland improved by 6.9%. Overall, the product has high accuracy with the mean overall accuracy (OA) of 97.9%. Compared with MCD12Q1 and GFSAD30 cropland products, it is 6.2% and 24.3% higher in OA respectively. In brief, our method is helpful for the cropland extraction.

Spatiotemporal fusion method to simultaneously generate full-length normalized difference vegetation index time series (SSFIT)

High spatiotemporal resolution normalized difference vegetation index (NDVI) time-series imagery is required for monitoring vegetation dynamics with dense observations and spatial details, and in recent years, many spatiotemporal data fusion methods have been proposed to fulfill this need. However, strict data requirements and inappropriate modeling strategies often limit their performance, particularly under poor conditions of available input data. In this study, we proposed a Spatiotemporal fusion method to Simultaneously generate Full-length normalized difference vegetation Index Time series (SSFIT) with a high spatial resolution and frequent coverage. The utilization of temporal information across sensors contributes to its two distinct features: (1) no cloud-free high-spatial-resolution image is required and (2) high-spatial-resolution images on multiple prediction dates are generated at the same time. Comparison experiments were conducted by simulating ideal and challenging conditions of input time-series data in two characteristic areas, and the proposed methods were also compared with four typical methods: The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Flexible Spatiotemporal DAta Fusion (FSDAF), Fit-FC (regression model Fitting, spatial Filtering and residual Compensation), and Improved FSDAF. The results demonstrate that SSFIT yields a better overall prediction accuracy and efficiency under ideal input conditions (average root mean square error of 0.1037 and 0.0713, average correlation coefficient of 0.9180 and 0.7875, and computation time of 109 and 120 s, computed in the two test areas) and is more robust against the decrease in available input data under challenging conditions. SSFIT is thus expected to be extended to various remote sensing products and support applications for monitoring land surface dynamics.

Vegetation Trends, Drought Severity and Land Use-Land Cover Change during the Growing Season in Semi-Arid Contexts

Drought severity and impact assessments are necessary to effectively monitor droughts in semi-arid contexts. However, little is known about the influence land use-land cover (LULC) has—in terms of the differences in annual sizes and configurations—on drought effects. Coupling remote sensing and Geographic Information System techniques, drought evolution was assessed and mapped. During the growing season, drought severity and the effects on LULC were examined and whether these differed between areas of land change and persistence. This study used areas of economic importance to Botswana as case studies. Vegetation Condition Index, derived from Normalised Difference Vegetation Index time series for the growing seasons (2000–2018 in comparison to 2020–2021), was used to assess droughts for 17 constituencies (Botswana’s fourth administrative level) in the Central District of Botswana. Further analyses by LULC types and land change highlighted the vulnerability of both human and natural systems to drought. Identified drought periods in the time series correspond to declared drought years by the Botswana government. Drought severity (extreme, severe, moderate and mild) and the percentage of land areas affected varied in both space and time. The growing seasons of 2002–2003, 2003–2004 and 2015–2016 were the most drought-stricken in the entire time series, coinciding with the El Niño southern oscillation (ENSO). The lower-than-normal vegetation productivity during these growing seasons was evident from the analysis. With the above-normal vegetation productivity in the ongoing season (2020–2021), the results suggest the reversal of the negative vegetation trends observed in the preceding growing seasons. However, the extent of this reversal cannot be confidently ascertained with the season still ongoing. Relating drought severity and intensities to LULC and change in selected drought years revealed that most lands affected by extreme and severe drought (in descending order) were in tree-covered areas (forests and woodlands), grassland/rangelands and croplands. These LULC types were the most affected as extreme drought intersected vegetation productivity decline. The most impacted constituencies according to drought severity and the number of drought events were Mahalapye west (eight), Mahalapye east (seven) and Boteti west (seven). Other constituencies experienced between six and two drought events of varying durations throughout the time series. Since not all constituencies were affected similarly during declared droughts, studies such as this contribute to devising appropriate context-specific responses aimed at minimising drought impacts on social-ecological systems. The methodology utilised can apply to other drylands where climatic and socioeconomic contexts are similar to those of Botswana.

Evaluation of MODIS-based Vegetation Restoration After the 2008 Wenchuan Earthquake

Mountainous vegetation recovery after major earthquakes has been significant for preventing post-seismic soil erosion and geo-hazards. Magnitude 7.9 Wenchuan earthquake struck western Sichuan, China in 2008, caused salient number of geological hazards and caused major vegetation damage. This recovery process could be a very long and fluctuating. And Remote sensing has been an important method of vegetation restoration monitoring. This study aims to use remote sensing technology data to analyze the post-seismic vegetation damage and recovery situation of the 2008 Wenchuan earthquake over years to 2020, and find the relevant factors affecting the restoration of ecological vegetation. This paper examined the vegetation recovery processes following the 2008 Wenchuan earthquake using 16-day interval MODIS normalized difference vegetation index time series from 2000 to 2020. It has been found that the vegetation recovery rate generally increased by years, the entire study area has recovered 49.89% by 2020. In addition, by combining remote sensing imagery and geographic information data, we also found that the heavily affected vegetation areas are mainly located along the southern part of the earthquake surface rupture, where have a very high slope which mainly over 60 degrees. It makes this part having higher probabilities to experiences secondary natural hazards and a fluctuating vegetation recovery rate. Through this research, it can be concluded that remote sensing is an effective method for monitoring vegetation dynamics in a long series. For soil and soil retention and ecological vegetation protection of landslides after the earthquake, it should be more concerned about the areas where have steep slope that over 60 degrees.

A New Index for Assessing Tree Vigour Decline Based on Sentinel-2 Multitemporal Data. Application to Tree Failure Risk Management

ABSTRACT Detection and monitoring of vigour decline are certainly useful for forest managers to support future interventions. In this work a new index, from Sentinel-2 retrieved NDVI (Normalized Difference Vegetation Index) time series, is proposed, hereafter called ‘normalized Vegetation Vigour Index (nVVI)’, which is specifically designed to quantify and map tree vigour decline. Results proved that nVVI is negatively correlated to tree age (r = −0.815, p < 0.001): older or unhealthy trees showed low nVVI values; younger trees showed high nVVI possibly related to vigour increase. nVVI was, finally used within a Decision Support System (DSS) specifically designed to support tree failure risk management in wide areas, like the natural parks. It was found that such approach can result in an economic saving of about 52% compared to more traditional tree stability surveys. If present, a nVVI map can highlight incipient vegetative anomalies permitting tree stability surveys to be better prioritized.