Phenological Indicators Research Articles

Mapping evergreen forests using new phenology index, time series Sentinel-1/2 and Google Earth Engine

Evergreen forests are sensitive to climate change and their role in the exchange of carbon, water, and energy in terrestrial ecosystems is irreplaceable. The temporal and spatial variation of the evergreen forest is closely related to forest monitoring and management in China, which has a far-reaching impact on forest protection and sustainable development. However, the lack of annual fine resolution maps of evergreen forests limits our exploration of the evolution of the spatiotemporal patterns of evergreen forests. Therefore, it is necessary to timely and accurately maps evergreen forests with high spatial resolution. We used a new phenology index of NDVImax-NDVIwinter_max to identify evergreen forests at the 10-m scale. First, we mapped annual 2019 land cover types to obtain the forest mask. Second, we calculated the classification phenology index by extracting the difference between evergreen forests and deciduous forests in phenological characteristics. Finally, we extracted the evergreen forest in 2019 from the annual forest map based on the constructed phenological indicators. The kappa coefficient of our annual forest map for 2019 was 0.92, and the overall, producer and user accuracies were 97.98%, 90.09%, and 97.46%, respectively. The kappa coefficient of the annual evergreen forest map in 2019 was 0.98, and the overall, producer and user accuracies were 99.73%, 97.84%, and 99.68%, respectively. Our study shows that the new phenological index can identify and map evergreen forests on complex landforms dominated by evergreen-deciduous mixed forests, which can be applied to other regions and years in China. The results of this study have reference value for evaluating the spatial distribution and resource management of evergreen forests.

Digital tools for agrobiological and phonological accounting of the Anapa ampelographic collection

Relevance. Digitalization in the monitoring system of vine plantations of agrobiological indicators is the most important resource for the development of viticulture. Digital tools for collecting, analyzing and visualizing data can improve the efficiency and accuracy of scientific research in the field of viticulture. The purpose of the work. Digitalization of methods for the rapid collection of scientific data on the state of grape plantations in real time based on mobile digital spatial positioning devices.Methods. Agrobiological and phenological accounting with recording data in the observation log on paper and entering data into spreadsheets from the keyboard and voice. Evaluation of errors and timing of transferring data from paper to a computer database when manually writing data to a log with recoding into a CSV-text format for the MySQL DBMS. AppSheet technology was used as a platform for collaborative work of mobile devices with Internet server databases.Results. The automated system for collecting, recording and processing data on agrobiological and phenological indicators of grape varieties is designed for route and field studies of the Anapa ampelographic collection during field data collection on grape bushes. It is used in agronomic and phenological accounting by specialists of research organizations. The main functions of the program are data registration using a mobile digital device in the field and their transfer to a cloud database on the Internet. The results of the calculation of agrobiological and phenological indicators of grapes are transmitted in real time to digital electronic devices connected to the Internet. The program records the date and time of the field observation, geographic coordinates, agrobiological and phenological indicators of the survey, and a photo of the bush.

Convergence and divergence emerging in climatic controls of polynomial trends for northern ecosystem productivity over 2000–2018

Recent rapid warming has caused uneven impacts on the composition, structure, and functioning of northern ecosystems. It remains unknown how climatic drivers control linear and non-linear trends in ecosystem productivity. Based on a plant phenology index (PPI) product at a spatial resolution of 0.05° over 2000–2018, we used an automated polynomial fitting scheme to detect and characterize trend types (i.e., polynomial trends and no-trends) in the yearly-integrated PPI (PPIINT) for northern (> 30°N) ecosystems and their dependence on climatic drivers and ecosystem types. The averaged slope for the linear trends (p < 0.05) of PPIINT was positive across all the ecosystems, among which deciduous broadleaved forests and evergreen needle-leaved forests (ENF) showed the highest and lowest mean slopes, respectively. More than 50% of the pixels in ENF, arctic and boreal shrublands, and permanent wetlands (PW) had linear trends. A large fraction of PW also showed quadratic and cubic trends. These trend patterns agreed well with estimates of global vegetation productivity based on solar-induced chlorophyll fluorescence. Across all the biomes, PPIINT in pixels with linear trends showed lower mean values and higher partial correlation coefficients with temperature or precipitation than in pixels without linear trends. Overall, our study revealed the emergence of latitudinal convergence and divergence in climatic controls on the linear and non-linear trends of PPIINT, implying that northern shifts of vegetation and climate change may potentially increase the non-linear nature of climatic controls on ecosystem productivity. These results can improve our understanding and prediction of climate-induced changes in plant phenology and productivity and facilitate sustainable management of ecosystems by accounting for their resilience and vulnerability to future climate change.

Study on Rice Phenological Differences under Heavy Metal Stress by Using Multi-source Remote Sensing Time-Series Images

Heavy metal pollution is surveyed rapidly and accurately, which is of great significance for soil pollution control. heavy metal pollution on farmland results in phenological changes, and we can use remote sensing technology to observe these changes. In the paper, Normalized Difference Vegetation Index (NDVI) time-series is built based on multi-source remote sensing images. Savitzky-Golay filter (S-G filter) was applied for denoising and reconstructing the time series. NDVI time series were used to calculate phenological indicators. Five phenological indicators were selected, which include seasonal amplitude, growth rate, seasonal length, seasonal integral and base level, to study on effect of heavy metal stress on rice phenology. The results showed that differences in phenological indexes at different levels of heavy metal stress as well as the phenological indicators under the stressed condition were generally lower than those under mild and moderate stress. From the findings, it can be concluded that the superiority of remote sensing phenological information in the monitoring of heavy metal stress in rice, and a new method for distinguishing heavy metal stress in rice will be provided.

Land surface phenology indicators retrieved across diverse ecosystems using a modified threshold algorithm

Land surface phenology (LSP), the study of the seasonal vegetation dynamics from remote sensing imagery, provides crucial information for plant monitoring and reflects the responses of ecosystems to climate change. The Moderate Resolution Imaging Spectroradiometer (MODIS) phenology product (MCD12Q2) provides global LSP information, but it has large spatial gaps in many regions, especially in ecosystems where rainfall influences phenology more than temperature. This study aimed to improve spatial coverage of LSP retrieval in these ecosystems. To do so, we used a regionally modified threshold algorithm for LSP retrievals, which were tested over continental Australia as it includes diverse landscapes of arid, mesic, and forest environments. We generated LSP metrics annually from 2003 to 2018 using satellite Enhanced Vegetation Index (EVI) time series at 500 m resolution, including the start, peak, end, and length of growing seasons, the minimum EVI value prior to and after the peak date, the seasonal maximum EVI value, the integral EVI value during the growing season (an approximation of productivity), and seasonal amplitude (maximum EVI value minus minimum EVI). Our regionally optimised algorithm improved the spatial coverage of LSP information in Australia from only 26 % of the continent to 70 % averaged across 16 years. Our results showed that the growing season amplitude was low (EVI < 0.1) over arid/semi-arid shrublands and savannas, tropical and subtropical savannas, and temperate evergreen forests, whose LSP metrics were captured by our regional algorithm and not by the global product. Some ecosystems, such as arid/semi-arid shrublands and savannas, showed more irregular phenology with low seasonal dynamics, and the growing seasons could skip a year or occur more than once in a year depending on climate conditions. Our algorithm was more sensitive to ecosystems with low seasonal amplitudes. We found that the detectability of LSP increases as the growing season amplitude increases, regardless of vegetation cover. Evaluation of the LSP metrics using eddy covariance flux tower measurements of gross primary productivity (GPP) demonstrated the reliability and accuracy of the algorithm. These improved LSP retrievals provide a greater understanding of the vegetation phenology across diverse ecosystems, especially savanna, shrubland, and evergreen forest ecosystems that cover more than 30 % of the land globally. The LSP provides essential information for ecological and agricultural studies such as quantifying bushfire fuel accumulation and forest carbon cycling, whilst enhancing our capacity for quantifying ecological responses to climate change.

Estimating local-scale forest GPP in Northern Europe using Sentinel-2: Model comparisons with LUE, APAR, the plant phenology index, and a light response function

Northern forest ecosystems make up an important part of the global carbon cycle. Hence, monitoring local-scale gross primary production (GPP) of northern forest is essential for understanding climatic change impacts on terrestrial carbon sequestration and for assessing and planning management practices. Here we evaluate and compare four methods for estimating GPP using Sentinel-2 data in order to improve current available GPP estimates: four empirical regression models based on either the 2-band Enhanced Vegetation Index (EVI2) or the plant phenology index (PPI), an asymptotic light response function (LRF) model, and a light-use efficiency (LUE) model using the MOD17 algorithm. These approaches were based on remote sensing vegetation indices, air temperature (Tair), vapor pressure deficit (VPD), and photosynthetically active radiation (PAR). The models were parametrized and evaluated using in-situ data from eleven forest sites in North Europe, covering two common forest types, evergreen needleleaf forest and deciduous broadleaf forest. Most of the models gave good agreement with eddy covariance-derived GPP. The VI-based regression models performed well in evergreen needleleaf forest (R2 = 0.69–0.78, RMSE = 1.97–2.28 g C m−2 d−1, and NRMSE = 9–11.0%, eight sites), whereas the LRF and MOD17 performed slightly worse (R2 = 0.65 and 0.57, RMSE = 2.49 and 2.72 g C m−2 d−1, NRMSE = 12 and 13.0%, respectively). In deciduous broadleaf forest all models, except the LRF, showed close agreements with the observed GPP (R2 = 0.75–0.80, RMSE = 2.23–2.46 g C m−2 d−1, NRMSE = 11–12%, three sites). For the LRF model, R2 = 0.57, RMSE = 3.21 g C m−2 d−1, NRMSE = 16%. The results highlighted the necessity of improved models in evergreen needleleaf forest where the LUE approach gave poorer results., The simplest regression model using only PPI performed well beside more complex models, suggesting PPI to be a process indicator directly linked with GPP. All models were able to capture the seasonal dynamics of GPP well, but underestimation of the growing season peaks were a common issue. The LRF was the only model tending to overestimate GPP. Estimation of interannual variability in cumulative GPP was less accurate than the single-year models and will need further development. In general, all models performed well on local scale and demonstrated their feasibility for upscaling GPP in northern forest ecosystems using Sentinel-2 data.

Use of morphometric and phenological indicators of Betula pendula Roth for environmental health assessment

The article presents the results of long-term biomonitoring of the quality of the environmental of the city of Yoshkar-Ola (Russia) based on the analysis of morphological and phenological indicators of Betula pendula Roth. The authors studied Betula pendula trees at various stages of ontogenesis in the generative period. The authors also found out the heterogeneity of ontogenetic groups of trees by morphological and phenological characteristics in different ecotopes of the city, differing in the degree of pollution by industrial and transport emissions, using principal component analysis A decrease in the morphometric parameters of the Betula pendula leaf blade and leaf damage by phyllophagous insects do not always indicate a deterioration in the quality of the habitat. Moderate doses of atmospheric toxicants can accelerate the growth of B. pendula leaves. However, in conditions of urban pollution, the stability of B. pendula development is disrupted, which is manifested in an increase in the leaf asymmetry index. There are the most informative indicators of the state of the environment in individuals B. pendula: morphometric – an indicator of fluctuating asymmetry of leaves, phenological – the timing and duration of phenophases. The young generative trees of B. pendula are most sensitive to habitat pollution.

Comparison of Phenological Parameters Extracted from SIF, NDVI and NIRv Data on the Mongolian Plateau

The phenological parameters estimated from different data may vary, especially in response to climatic factors. Therefore, we estimated the start of the growing season (SOS) and the end of the growing season (EOS) based on sunlight-induced chlorophyll fluorescence (SIF), the normalized difference vegetation index (NDVI) and the near-infrared reflectance of vegetation (NIRv). The SIF, NDVI and NIRv breakpoints were detected, and the trends and change-points of phenological parameters based on these data were analyzed. The correlations between the phenological parameters and snow-related factors, precipitation, temperature, soil moisture and population density were also analyzed. The results showed that SIF and NIRv could identify breakpoints early. SIF could estimate the latest SOS and the earliest EOS. NDVI could estimate the earliest SOS and the latest EOS. The change-points of SOSSIF were mostly concentrated from 2001 to 2003, and those of SOSNDVI and SOSNIRv occurred later. The change-points of EOSSIF and EOSNIRv were mostly concentrated from 2001 to 2007, and those of EOSSIF occurred later. Differently from the weak correlation with SOSSIF, SOSNDVI and SOSNIRv were significantly correlated with snow-related factors. The correlation between the meteorological factors in the summer and autumn and EOSSIF was the most significant. The population density showed the highest degree of interpretation for SOSNIRv and EOSNDVI. The results reveal the differences and potentials of different remote-sensing parameters in estimating phenological indicators, which is helpful for better understanding the dynamic changes in phenology and the response to changes in various influencing factors.

Optimization of Characteristic Phenological Periods for Winter Wheat Extraction Using Remote Sensing in Plateau Valley Agricultural Areas in Hualong, China

It is important to develop or validate remote sensing methods to explore agricultural management and food self-sufficiency in the agricultural areas of the Qinghai–Tibet Plateau under the influence of global change, ecological protection, and socio-economic development. Studies on the use of remote sensing to monitor crop planting on the Qinghai-Tibetan Plateau are limited, with inconclusive results. Therefore, in this study, we analyzed Sentinel-2A/B images and field survey data in Hualong, China (located in Hehuang Valley, Qinghai-Tibetan Plateau) for winter wheat identification and verification at different spatial scales based on the time series of the normalized difference phenology index (NDPI) and dynamic time warping (DTW) algorithm. The characteristic phenological period and the corresponding DTW threshold were optimized using remote sensing data extracted for winter wheat. The results showed that NDPI corresponding to the jointing-heading stage, grouting-harvesting stage, and jointing-harvesting stage with DTW could identify winter wheat regardless of whether the spatial scale was a single quadrat, a combination of two quadrats, or the entire study area. The NDPI corresponding to the jointing-heading stage (corresponding DTW threshold T = 0.158) could generate a relatively rational winter wheat map; the NDPI corresponding to the time series of the grouting-harvesting stage (combined with DTW threshold T = 0.195) could detect a planting area with relatively high accuracy when supported by cultivated land, which matches the statistical reporting of the winter wheat area data. Similarly, with the support of cultivated land data, the planted area could be identified early based on the phenological characteristics of winter wheat before overwintering; however, the extraction scheme needs to be optimized further.

Ice phenology dataset reconstructed from remote sensing and modelling for lakes over the Tibetan Plateau

The Tibetan Plateau (TP) is a region sensitive to global climate change and has been experiencing substantial environmental changes in the past decades. Lake ice phenology (LIP) is a perceptible indicator reflecting changes of lake thermodynamics in response to global warming. Lake ice phenology over the Tibetan Plateau is however rarely observed and recorded. This research presents a dataset containing 39-year (1978–2016) lake ice phenology data of 132 lakes (each with area >40 km2) over the Tibetan Plateau by combining the strengths of both remote sensing (MOD11A2, MOD10A1) and numerical modelling (air2water). Data validation shows that the ice phenology data derived by our method is highly consistent with that based on existing approaches (with R2 > 0.75 for all phenology index and RMSE < 5d). The dataset is valuable to investigate the lake-atmosphere interactions and long-term hydrothermal change of lakes across the Tibetan Plateau.

RESULTS OF THE STUDY OF SETS OF SWEET PEPPER VARIETY SAMPLES AND F1 HYBRIDS IN THE REPEATED TERM

The article describes the results of research on phenological indicators and productivity of 28 varieties of sweet pepper and 10 F1 hybrids, which were repeatedly studied in the conditions of the central region of Uzbekistan. The total productivity of the high-yielding varieties was 110.6-124.8% higher than the standard variety. The varieties Tong, Podarok Moldovy, Bolgarsky 79, Pamir, Kaliforniyskoe chudo, PP10674, PP10678, D11000, D11520, S7103, whose productivity is almost equal to the standard variety, were also isolated. Their yield was 27,7-29,0 t/ha. In the hybrids of the first generation, the yield was slightly higher. Standard F1 Jaihun hybrid yield was 40,9 t/ha. This is a 145% higher result compared to the standard Dar Tashkenta variety.

"When the Wild Roses Bloom": Indigenous Knowledge and Environmental Change in Northwestern North America.

Indigenous Peoples in Northwestern North America have always worked with predictable cycles of day and night, tides, moon phases, seasons, and species growth and reproduction, including such phenological indicators as the blooming of flowers and the songs of birds. Negotiating variability has been constant in people's lives. Long-term monitoring and detailed knowledge of other lifeforms and landscapes of people's home territories have assisted in responding and adapting to change. Aspects of cultural knowledge and practice that have helped Indigenous Peoples navigate nature's cycles at different scales of time and space include kin ties and social relationships, experiential learning, language, storytelling and timing of ceremonies such as "First Foods" celebrations. Working with ecological processes, Indigenous Peoples have been able to maintain optimal conditions for preferred species, reducing variability and uncertainty through taking care of productive habitats, leaving ecosystems intact, and allowing other species to change in their own cycles. Since the onset of colonization, however, Indigenous Peoples' lifeways have been changed drastically, culminating with the current impacts of global climate change and biodiversity loss. This paper, based on contributions of numerous Indigenous Knowledge holders from across Northwestern North America, outlines some of the key ways in which Indigenous Peoples have embraced predictability and change in their environments and lifeways, and addresses the particular threat of climate change: its recognition, ways of adapting to it, and, ultimately, how it might be reversed through developing more careful, respectful relationships with and responsibilities for the other-than-human world.

Gap-filling of ocean color over the tropical Indian Ocean using Monte-Carlo method

Continuous remote-sensed daily fields of ocean color now span over two decades; however, it still remains a challenge to examine the ocean ecosystem processes, e.g., phenology, at temporal frequencies of less than a month. This is due to the presence of significantly large gaps in satellite data caused by clouds, sun-glint, and hardware failure; thus, making gap-filling a prerequisite. Commonly used techniques of gap-filling are limited to single value imputation, thus ignoring the error estimates. Though convenient for datasets with fewer missing pixels, these techniques introduce potential biases in datasets having a higher percentage of gaps, such as in the tropical Indian Ocean during the summer monsoon, the satellite coverage is reduced up to 40% due to the seasonally varying cloud cover. In this study, we fill the missing values in the tropical Indian Ocean with a set of plausible values (here, 10,000) using the classical Monte-Carlo method and prepare 10,000 gap-filled datasets of ocean color. Using the Monte-Carlo method for gap-filling provides the advantage to estimate the phenological indicators with an uncertainty range, to indicate the likelihood of estimates. Quantification of uncertainty arising due to missing values is critical to address the importance of underlying datasets and hence, motivating future observations.

Sentinel-2 Images Based Modeling of Grassland Above-Ground Biomass Using Random Forest Algorithm: A Case Study on the Tibetan Plateau

Accurate information on grassland above-ground biomass (AGB) is critical to better understanding the carbon cycle and conserve grassland resources. As a climate-sensitive key ecological function area, it is important to accurately estimate the grassland AGB of the Tibetan Plateau. Sentinel-2 (S2) images have advantages in reducing mixed pixels and the scale effect for remote sensing, while the data volume is correspondingly larger. In order to improve the estimation accuracy while reducing the data volume required for AGB estimation and improving the computational efficiency, this study used the Recursive Feature Elimination (RFE) algorithm to find the optimal feature set and compared the performance of the Cubist, Gradient Boosting Regression Tree (GBRT), random forest (RF) and eXtreme Gradient Boosting (XGBoost) algorithms for estimating AGB. In this study, ten S2 bands, ten S2-derived vegetation indexes, 218 pieces of AGB field survey data, four types of meteorological data and three types of topographic data were used as the alternative input features for the AGB estimation model. The impurity and permutation importance were used as the feature importance calculation method input to the RFE, and the Cubist, GBRT, RF and XGBoost algorithms were used to construct the AGB estimation models. The results showed that the RF algorithm based on the monthly average temperature (T), elevation, Normalized Difference Phenology Index (NDPI), Normalized Difference Infrared Index (NDII) and Palmer Drought Severity Index (PDSI) performed best (R2 = 0.8838, RMSE = 35.05 g/m2, LCCC = 2.44, RPPD = 0.91). The above findings suggest that the RF model based on the features related to temperature, altitude, humidity and leaf water content is beneficial to estimate the grassland AGB on the Tibetan Plateau.

Investigating Sentinel-1 and Sentinel-2 Data Efficiency in Studying the Temporal Behavior of Wheat Phenological Stages Using Google Earth Engine

Crop monitoring is critical for sustaining agriculture, preserving natural resources, and dealing with the effects of population growth and climate change. The Sentinel missions, Sentinel-1 and Sentinel-2, provide open imagery at a high spatial and temporal resolution. This research aimed (1) to evaluate the temporal profiles derived from Sentinel-1 and Sentinel-2 time series data in deducing the dates of the phenological stages of wheat from germination to the fully mature plant using the Google Earth Engine (GEE) JavaScript interface and (2) to assess the relationship between phenological stages and optical/ SAR remote sensing indices for developing an accurate phenology estimation model of wheat and extrapolate it to the regional scale. Firstly, the temporal profiles derived from Sentinel-1 and Sentinel-2 remote sensing indices were evaluated in terms of deducing the dates of the phenological stages of wheat. Secondly, the remote sensing indices were used to assess their relationship with phenological stages using the linear regression (LR) technique. Thirdly, the best performing optical and radar remote sensing indices were selected for phenological stage prediction. Fourthly, the spatial distribution of wheat in the TIP region was mapped by performing a Random Forest (RF) classification of the fusion of Sentinel-1 and Sentinel 2 images, with an overall accuracy of 95.02%. These results were used to characterize the growth of wheat on the TIP regional scale using the Temporal Normalized Phenology Index (TNPI) and the predicted models. The obtained results revealed that (1) the temporal profiles of the dense time series of Sentinel-1 and Sentinel-2 indices allowed the dates of the germination, tillering, jointing heading, maturity, and harvesting stages to be determined with the support of the crop calendar. (2) The TNPIincrease and TNPIdecrease revealed that the declining part of the NDVI profile from NDVIMax, to NDVIMin2 revealed higher TNPI values (from 0.58 to 1) than the rising part (from 0.08 to 0.58). (3) The most accurate models for predicting phenological stages were generated from the WDVI and VH–VV remote sensing indices, having an R2 equal to 0.70 from germination to jointing and an R2 equal to 0.84 from heading to maturity.

Combined Inhibitory Effect of Canada Goldenrod Invasion and Soil Microplastics on Rice Growth.

Alien plant invasion and residual soil microplastics (MPs) are growing threats to agricultural crop production. This study determined the adverse effects of Canadian goldenrod (Solidago canadensis L.) invasion and residual soil MPs on rice growth and development. The biomass, phenological indices, photosynthetic parameters, and antioxidant enzyme activities of rice were measured on the 50th and 80th day of post-plantation. Biomass and phenotypic results indicated the more harmful effects of the combination of S. canadensis invasion and residual soil MPs compared to S. canadensis invasion or residual soil MPs effects alone. Moreover, the interaction effect of S. canadensis invasion and residual soil MPs markedly reduced the ascorbate peroxidase and catalase belowground, while they increased in the aboveground parts of the rice. However, the S. canadensis invasion and residual soil MPs interactive treatments lowered the superoxide dismutase concentrations in the belowground parts of the rice plants while elevating the peroxidase and reactive oxygen species concentrations in both the belowground and aboveground parts compared to the other treatments. Among all treatments, S. canadensis invasion alone had the most negligible negative impact on rice biomass and growth indices. Our study suggests that soil MPs could negatively affect crop production with invasive alien plants, and the combined effects were more harmful than either of the single factors. Our findings will lay the groundwork for analyzing the impacts of invasive alien plants on rice crops.

Assessment of Ecological Cumulative Effect due to Mining Disturbance Using Google Earth Engine

Open-pit mining and reclamation damage the land, resulting in unknown and significant changes to the regional ecology and ecosystem services. Surface mining restoration procedures necessitate a significant amount of money, typically at an unclear cost. Due to temporal and regional variability, few studies have focused on the cumulative impacts of mining activities. To investigate the ecological cumulative effects (ECE) of past mining and reclamation activities, this study continuously tracked land cover changes spatially and temporally based on phenological indices and focuses on the spatial and temporal evolution of past mining and reclamation areas using the LandTrendr algorithm. The cumulative trends of ecosystem services in the Pingshuo mining area from 1986 to 2021 were revealed using a uniform standard value equivalent coefficient. Meanwhile, the cumulative ecological effects due to essential ecosystem service functions were analyzed, including soil formation and protection, water containment, biodiversity maintenance, climate regulation, and food production. The synergistic effects and trade-offs among the functions were also explored using Spearman’s correlation coefficient. The results showed that (1) open-pit mining resulted in 93.51 km2 of natural land, 39.60 km2 of disturbed land, and 44.58 km2 of reclaimed land in the Pingshuo mine; (2) open-pit mining in the mine mainly resulted in the loss of 122.18 km2 (80.91%) of native grassland, but, through reclamation into grassland (31.30 km2), cropland (72.95 km2), and forest land (10.62 km2), the damaged area caused by mining only slightly increased; (3) the cumulative ecological value of the mining area declined by 128.78 million RMB; however, the real cumulative value per unit area was lower in the disturbance area (1483.47 million RMB) and the reclamation area (1297.00 million RMB) than in the natural area (2120.98 million RMB); (4) the cumulative value of the food production function in the study area increased, although the values of all individual functions in the study area decreased. Most of the cumulative values of services had a strong synergistic relationship. However, in the natural area, food production (FP) showed a trade-off relationship with the cumulative value of biodiversity maintenance (BM), soil formation and protection (SP), and water conservation (WC) service functions, respectively. This study constructed a methodology for analyzing mining-impacted ecosystem services using time-series processes, reproducing historically complete information for policymakers and environmental regulators.

Evaluation of the impact of crop residue on fractional vegetation cover estimation by vegetation indices over conservation tillage cropland: a simulation study

ABSTRACT Accurate estimation of fractional vegetation cover (FVC) is of great significance to agricultural production. Crop residue management affect crop residue cover (CRC) over croplands. Crop and crop residue on the soil surface both contribute to overall canopy reflectance. Few studies, however, have examined the effect of crop residue on vegetation indices (VIs) and estimated FVC. The present study evaluated the response of eight commonly used VIs to crop residues and FVC uncertainty caused by crop residue based on the dimidiate pixel model (DPM) by using simulated reflectance of low-tilled cropland via a three-dimensional radiative transfer model. The absolute difference (AD) was used to quantify the spectral difference between crop residues and soils in red and near infrared wavelengths. Increases in normalized difference VI (NDVI), ratio VI (RVI), transformed soil-adjusted VI (TSAVI), and normalized difference phenology index (NDPI) were observed when green crops were mixed with crop residue that had negative ADs with soils, but decreases in enhance VI (EVI), perpendicular VI (PVI), SAVI, and litter-soil-adjusted VI (L-SAVI) were observed when crop residue was present under medium and high vegetation cover. The presence of crop residue with a positive AD with soils reduced NDVI, RVI, TSAVI, and NDPI while increased the other VIs. Crop residue had the least impact on EVI- and SAVI-based DPMs, with FVC-estimated uncertainty less than 0.1, followed by the NDPI- and L-SAVI-based model, while DPMs based on NDVI- and RVI performed poorly. Each VI-based DPM’s estimated uncertainty was highly correlated with AD values. Furthermore, the majority of the VI-based models were sensitive to solar position except for the NDPI-based model. Our findings highlight the need of considering the impact of crop residue on FVC retrieval over low-tilled cropland in future research.

Using cloud computing techniques to monitor long-term variations in ecohydrological dynamics of small seasonally-flooded wetlands in semi-arid South Africa

• Semi-arid Southern African wetlands are lost due to unsustainable use and poor management. • Ramsar Convention on wetlands encourages development of frameworks for wetlands conservation. • The implementation of these frameworks is hindered by a dearth of high-resolution data. • Google Earth Engine (GEE) offers opportunities to address data challenges. • Findings underscores the relevance of GEE in evaluating wetland ecohydrological dynamics. Wetlands in drylands have high inter- and intra-annual ecohydrological variations that are driven to a great extent by climate variability and anthropogenic influences. The Ramsar Convention on Wetlands encourages the development of frameworks for national action and international cooperation for ensuring conservation and wise use of wetlands and their resources at local, national and regional scales. However, the implementation of these frameworks remains a challenge. This is mainly due to limited availability of high-resolution data and suitable big data processing techniques for assessing and monitoring wetland ecohydrological dynamics at large spatial scales, particularly in the sub-Saharan African region. The availability of cloud computing platforms such as Google Earth Engine (GEE) offers unique big data handling and processing opportunities to address some of these challenges. In this study, we applied the GEE cloud computing platform to monitor the long-term ecohydrological dynamics of a seasonally flooded part of the Nylsvley floodplain wetland complex in north-eastern South Africa over a 20-year period (2000–2020). The specific objectives of the study were 1) to evaluate wetland ecohydrological dynamics using the 20-year multi-date Landsat composite data coupled with the Random Forest machine learning algorithm, and 2) to establish the major drivers of wetland ecohydrological changes, using selected spectral indices (i.e. Normalised Difference Vegetation Index (NDVI), Normalised Difference Water Index (NDWI) and Normalised Difference Phenology Index (NDPI)) coupled with climate data. The ecohydrology of the wetland changed over time, with some classes increasing twice when compared to the previous measurement, while others decreasing significantly during the study period. Notably, the bare surface class increased at rates of 230% and 350% between 2006–2010 and 2016–2020, respectively. Moreover, the indices showed similar trends throughout the 20-year period, with NDWI having minimum values less than zero in all cases. This implied no surface inundation, although the presence of some wetland vegetation indicated seasonal to semi-permanent soil saturation conditions. A comparative analysis of climate data and remotely sensed indices showed that annual changes of precipitation and evapotranspiration were the main drivers of wetland ecohydrological variations. The findings of the study underscore the relevance of cloud computing artificial intelligence techniques, and particularly the GEE platform, in evaluating wetland ecohydrological dynamics for semi-arid southern African systems which are deteriorating due to the unsustainable use and poor management resulting from limited knowledge about their changes over time.

Modeling the Leaf Area Index of Inner Mongolia Grassland Based on Machine Learning Regression Algorithms Incorporating Empirical Knowledge

Leaf area index (LAI) is one of the key biophysical indicators for characterizing the growth and status of vegetation and is also used in modeling earth system processes. Machine learning algorithms (MLAs) such as random forest regression (RFR), artificial neural network regression (ANNR) and support vector regression (SVR) based on satellite data have been widely used for the estimation of LAI. However, the selection of input variables has a great impact on the estimation performance of MLAs. In this study, we aimed to improve the LAI inversion model of Inner Mongolia grassland based on MLAs incorporating empirical knowledge. Firstly, we used the ANNR, SVR and RFR approaches, respectively, to rank the input variables including vegetation indices, climate factors, soil factors and topography factors and found that Normalized Difference Phenology Index (NDPI) contributed the most to LAI estimation. Secondly, we selected four sets of input variables, namely, all variables—A, model selected variables—B, overlapping variables—C and self-defined variables—D, respectively. Subsequently, we built twelve LAI estimation models (RFR-A, RFR-B, RFR-C, etc.) based on three MLAs and four sets of input variables. The evaluation of them showed the RFR produced higher prediction accuracy, followed by ANNR and SVR. Furthermore, the RFR-D presented the highest accuracy in predicting LAI (R2 = 0.55, RMSE = 0.37 m2/m2, MAE = 0.29 m2/m2). Finally, we compared our results with MODIS LAI and GEOV2 LAI products and found that all of them showed a similar spatial distribution of grassland LAI in the four sub-regions covering all grassland types, but our model exhibited larger LAI values in the desert steppe and smaller LAI values in the others. These findings demonstrated that MLAs incorporating empirical knowledge could improve the accuracy of modelling LAI and further study is necessary to reduce the uncertainty in LAI mapping in grassland.