Impacts of mining on vegetation phenology and sensitivity assessment of spectral vegetation indices to mining activities in arid/semi-arid areas

Measuring the impact of mining activities on vegetation phenology and assessing the sensitivity of vegetation indices (VIs) to it are crucial for understanding land degradation in mining areas and enhancing the carbon sink capacity following the ecological restoration of mines. To this end, we have developed a novel technical framework to quantify the impact of mining activities on vegetation, and applied it to the Bainaimiao copper mining area in Inner Mongolia. Phenological indices are extracted based on the VI time series data of Sentinel-2, and changes in phenological differences in various directions are used to quantify the impact of mining activities on vegetation. Finally, indicators such as mean difference, standard deviation, index value distribution interval, and concentration of index value distribution were selected to assess the sensitivity of the Enhanced Vegetation Index (EVI), Green Chlorophyll Index (GCI), Global Environmental Monitoring Index (GEMI), Green Normalized Difference Vegetation Index (GNDVI), Normalized Difference Vegetation Index (NDVI), Renormalized Difference Vegetation Index (RDVI), Red-Edge Chlorophyll Index (RECI), and Soil-Adjusted Vegetation Index (SAVI) to mining activities. The results of the study show that the impact of mining activities on surrounding vegetation extends to an area three times larger than the actual mining activity area. When compared with the reference and unaffected areas, the affected area experienced a delay of approximately 10 days in seasonal vegetation development. Environmental pollution caused by the tailings pond was identified as the primary factor influencing this delay. Significant variations in the sensitivity of each VI to assess mining activities in arid/semi-arid areas were observed. Notably, GCI, GNDVI and RDVI displayed relatively high sensitivity to discrepancies in the spectral attributes of vegetation within the affected area, while SAVI reflected the overall spectral stability of the vegetation in the affected area. The research findings have the potential to provide valuable technical guidance for holistic environmental management in mining areas and hold great significance in preventing further land degradation and supporting ecological restoration in mining areas.

Understanding carbon sequestration trends using model and satellite data under different ecosystems in India

This study discusses carbon sequestration variability in different ecosystems of India. Four different biosphere regions, each over 0.5° × 0.5° area, have been selected considering the geospatial and climatic variability of these regions expanding from Central India (CI), the Northeast region (NER), the Western Ghats (WG), and the Western Himalayan region (WHNI). The climatic conditions of these four regions are different so are the biosphere constituents of these regions. We expect the Gross Primary Productivity (GPP) to enhance during the all India summer monsoon rainfall season but in varied magnitudes suggesting a role of climatic parameters and flora in these regions. The GPP from FLUXCOM for the duration of 2001 to 2019 (19 years) and satellite-derived vegetation indices like the Normalized Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Leaf Area Index (LAI) are used in this study to understand the response of regional vegetation to this variability. EVI seems to be better related to GPP in comparison to NDVI in the preliminary analysis. Further analysis suggests LAI correlates better to GPP than EVI and NDVI in different seasons in these four regions. Also, meteorological parameters like surface temperature, rainfall, soil water, and other derived parameters like Vapor Pressure Deficit (VPD) are studied. It is also observed that the year-to-year variability in the climatic conditions could also have a role to play in the observed features. It is proven that the climate around the world is experiencing changes. Vegetation is one of the potent markers to monitor the impact of climate change. These long-term data and trends were studied to understand if there is any significant impact of the changing climatic conditions on the vegetation in these regions. Our study shows that there is an increasing (positive) trend in GPP at these locations though at different rates. WG and WHNI have shown a significant high rate of increase (6.44 and 5.36 gCm-2 y-1, respectively) in GPP over the last two decades.

Assessing how irrigation practices and soil moisture affect crop growth through monitoring Sentinel-1 and Sentinel-2 data

This study authorizes processes and approaches using optical and microwave data to determine the availability of water in the study area at any given moment. This will aid in identifying the optimal time and location for irrigation to enhance crop growth. For this purpose, a set of spectral vegetation parameters (from Sentinel-2), soil moisture (from Sentinel-1), evapotranspiration, and surface temperature (from Landsat-8) were used, along with field data on water content and irrigation timing. The results showed that both NDVI and NDMI are highly sensitive to moisture, making them the best indices for determining the timing and location of irrigation. This research contributes to sustainable agricultural development. It has implications for farmers, policymakers, and researchers in optimizing irrigation schedules, developing policies for sustainable agriculture, and enhancing crop productivity while conserving water resources. This approach can be particularly useful in regions facing water scarcity, where the efficient use of water resources is crucial for sustainable agricultural development.

Accurate estimation of sorghum crop water content under different water stress levels using machine learning and hyperspectral data

This study investigates the effects of different water stress levels on spectral information, leaf area index (LAI), and the performance of three machine learning (ML) algorithms in estimating crop water content (CWC) of sorghum. The results show that the spectral reflectance of sorghum varies with growth stage and irrigation treatment, but consistent patterns are observed for each treatment. The LAI of sorghum gradually increased throughout the growth stages, with the most significant variation observed during the flowering stage. In this study, three machine learning-based regression models, namely, extreme gradient boosting (XGBoost), random forest (RF), and support vector machine (SVM), were utilized to estimate sorghum CWC using hyperspectral measurements. Recursive feature elimination (RFE) method was used to select the optimal spectral reflectance wavelengths for the ML models, and principal component analysis (PCA) was used to reduce the dimensionality of the hyperspectral data. The results indicated that the RF model achieved the highest R² (0.90) and lowest of RMSE (56.05) value using selected wavelengths, while the XGBoost model demonstrated superior accuracy and reliability in estimating CWC using dimensionality-reduced hyperspectral data (r = 0.96, RMSE = 45.77). Also, the study highlights the importance of vegetation index (VI) in CWC estimate. Some VIs, such as NDVI and MSAVI, performed poorly, while others, such as CL_Rededge and EVI, performed better. The study provides valuable insights into the effects of water stress levels on spectral information, LAI, and the performance of ML algorithms in estimating the CWC of sorghum. The findings have significant implications for precision agriculture, as accurate and reliable estimates of CWC can help farmers optimize irrigation and fertilizer applications, leading to improved crop yields and resource efficiency.

Evaluating ecosystem service changes in a frame of transportation development in Istanbul

Rapid urbanization and growing transportation infrastructure in cities negatively affect ecosystems and their functions. Quantifying these effects is a prerequisite for integrating environmental considerations into all phases of transportation planning. However, in many developing or newly developed countries, research is lacking that helps to understand and manage the ecological impacts of transportation construction under local conditions. Presented research contributed to filling this gap by investigating the implications of growing transportation infrastructure on three ecosystem services: local climate regulation, erosion control, and photosynthesis potential. As a case study, we used spatial indicators to quantify changes in the supply of ecosystem services caused by the development of the 3^rd Bosporus Bridge and its connecting highway in Istanbul, Turkiye. Our results indicate a substantial decrease in ecosystem services close to the transportation infrastructure, including a decrease in local climate regulation (- 5.4%), an increase in erosion (+ 9.4%), and a decline in photosynthesis potential or vegetation health (- 28%). Additionally, hotspots of ES supply change were detected. This study provides a blueprint for planning and impact mitigation studies.

Evaluating gross primary productivity over 9 ChinaFlux sites based on random forest regression models, remote sensing, and eddy covariance data

Accurate modeling of Gross Primary Productivity (GPP) in terrestrial ecosystems is a major challenge in quantifying the carbon cycle. Many light use efficiency (LUE) models have been developed, but the variables and algorithms used for environmental constraints in different models vary importantly. It is still unclear whether the models can be further improved by machine learning methods and the combination of different variables. Here, we have developed a series of RFR-LUE models, which used the random forest regression (RFR) algorithm based on variables of LUE models, to explore the potential of estimating site-level GPP. Based on remote sensing indices, eddy covariance and meteorological data, we applied RFR-LUE models to evaluate the effects of different variables combined on GPP on daily, 8-day, 16-day and monthly scales, respectively. Cross-validation analyses revealed performances of RFR-LUE models varied significantly among sites with R² of 0.52-0.97. Slopes of the regression relationship between simulated and observed GPP ranged from 0.59 to 0.95. Most models performed better in capturing the temporal changes and magnitude of GPP in mixed forests and evergreen needle-leaf forests than in evergreen broadleaf forests and grasslands. Performances were improved at the longer temporal scale, with the average R² for four-time resolutions of 0.81, 0.87, 0.88, and 0.90, respectively. Additionally, the importance of the variables showed that temperature and vegetation indices were critical variables for RFR-LUE models, followed by radiation and moisture variables. The importance of moisture variables was higher in non-forests than in forests. A comparison with four GPP products indicated that RFR-LUE model predicted GPP better matcher observed GPP across sites. The study provided an approach to deriving GPP fluxes and evaluating the extent to which variables affect GPP estimation. It may be used for predicting vegetation GPP at the regional scales and for calibration and evaluation of land surface process models.

TSWIFT: Tower Spectrometer on Wheels for Investigating Frequent Timeseries for high-throughput phenotyping of vegetation physiology

BACKGROUND

Remote sensing instruments enable high-throughput phenotyping of plant traits and stress resilience across scale. Spatial (handheld devices, towers, drones, airborne, and satellites) and temporal (continuous or intermittent) tradeoffs can enable or constrain plant science applications. Here, we describe the technical details of TSWIFT (Tower Spectrometer on Wheels for Investigating Frequent Timeseries), a mobile tower-based hyperspectral remote sensing system for continuous monitoring of spectral reflectance across visible-near infrared regions with the capacity to resolve solar-induced fluorescence (SIF).

RESULTS

We demonstrate potential applications for monitoring short-term (diurnal) and long-term (seasonal) variation of vegetation for high-throughput phenotyping applications. We deployed TSWIFT in a field experiment of 300 common bean genotypes in two treatments: control (irrigated) and drought (terminal drought). We evaluated the normalized difference vegetation index (NDVI), photochemical reflectance index (PRI), and SIF, as well as the coefficient of variation (CV) across the visible-near infrared spectral range (400 to 900 nm). NDVI tracked structural variation early in the growing season, following initial plant growth and development. PRI and SIF were more dynamic, exhibiting variation diurnally and seasonally, enabling quantification of genotypic variation in physiological response to drought conditions. Beyond vegetation indices, CV of hyperspectral reflectance showed the most variability across genotypes, treatment, and time in the visible and red-edge spectral regions.

CONCLUSIONS

TSWIFT enables continuous and automated monitoring of hyperspectral reflectance for assessing variation in plant structure and function at high spatial and temporal resolutions for high-throughput phenotyping. Mobile, tower-based systems like this can provide short- and long-term datasets to assess genotypic and/or management responses to the environment, and ultimately enable the spectral prediction of resource-use efficiency, stress resilience, productivity and yield.

Biophysical and biochemical features' feedback associated with a flood episode in a tropical river basin model

Global climate change scenarios such as frequent and extreme floods disturb the river basins by destructing the vegetation resulting in rehabilitation procedures being more costly. Thus, understanding the recovery and regeneration of vegetation followed by extreme flood events is critical for a successful rehabilitation process. Spatial and temporal variation of biochemical and biophysical features derived from remote sensing technology in vegetation can be incorporated to understand the recovery and regeneration of vegetation. The present study explores the flood impact on vegetation caused by major river basins in Sri Lanka (a model tropical river basin) by comparing pre-flood and post-flood cases. The study utilized enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), the fraction of photosynthetically active radiation (FPAR), and gross primary productivity (GPP) of the Moderate Resolution Imaging Spectroradiometer (MODIS) platform. A remarkable decline in EVI, LAI, FPAR, GPP, and vegetation condition index was observed in the post-flood case. Notably, coupled GPP-EVI and GPP-LAI portrayed dependency of features and showed a significant impact triggered by the flood episode by narrowing the feature in post-flood events. EVI depicted the highest regeneration (0.333) while GPP presented the lowest regeneration (0.093) after the flood event. Further, it was revealed that 1.18 years have been on the regeneration. The regeneration of GPP and LAI remained low comparatively justifying the magnitude and impact of the flood event. The study revealed successful implications of vegetation indices on flood basin management of small to large tropical river basins.

Monitoring impacts of soil bund on spatial variation of teff and finger millet yield with Sentinel-2 and spectroradiometric data in Ethiopia

Data from remote sensing devices are essential for monitoring environmental protection practices and estimating crop yields. However, yield estimates in Ethiopia are based on time-consuming surveys. We used Sentinel-2, spectroradiometeric, and ground-truthing data to estimate the grain yield (GY) of two major crops, teff, and finger millet, in Ethiopia's Aba Gerima catchment in 2020 and 2021. At the flowering stage, we performed supervised classification on October Sentinel-2 images and spectral reflectance measurement. We used regression models to identify and predict crop yields, as evaluated by the coefficient of determination (adjusted R²) and root mean square error (RMSE). The enhanced vegetation index (EVI) and normalized-difference vegetation index (NDVI) provided the best fit to the data among the vegetation indices used to predict teff and finger millet GY. Soil bund construction increased the majority of vegetation indices and GY of both crops. We discovered a strong correlation between GY and the satellite EVI and NDVI. However, NDVI and EVI had the greatest influence on teff GY (adjusted R² = 0.83; RMSE = 0.14 ton/ha), while NDVI had the greatest influence on finger millet GY (adjusted R² = 0.85; RMSE = 0.24 ton/ha). Teff GY ranged from 0.64 to 2.16 ton/ha for bunded plots and 0.60 to 1.85 ton/ha for non-bunded plots using Sentinel-2 data. Besides, finger millet GY ranged from 1.92 to 2.57 ton/ha for bunded plots and 1.81 to 2.38 ton/ha for non-bunded plots using spectroradiometric data. Our findings show that Sentinel-2- and spectroradiometeric-based monitoring can help farmers manage teff and finger millet to achieve higher yields, more sustainable food production, and better environmental quality in the area. The study's findings revealed a link between VIs and soil management practices in soil ecological systems. Model extrapolation to other areas will necessitate local validation.

Karst vegetation coverage detection using UAV multispectral vegetation indices and machine learning algorithm

BACKGROUND

Karst vegetation is of great significance for ecological restoration in karst areas. Vegetation Indices (VIs) are mainly related to plant yield which is helpful to understand the status of ecological restoration in karst areas. Recently, karst vegetation surveys have gradually shifted from field surveys to remote sensing-based methods. Coupled with the machine learning methods, the Unmanned Aerial Vehicle (UAV) multispectral remote sensing data can effectively improve the detection accuracy of vegetation and extract the important spectrum features.

RESULTS

In this study, UAV multispectral image data at flight altitudes of 100 m, 200 m, and 400 m were collected to be applied for vegetation detection in a karst area. The resulting ground resolutions of the 100 m, 200 m, and 400 m data are 5.29, 10.58, and 21.16 cm/pixel, respectively. Four machine learning models, including Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), and Deep Learning (DL), were compared to test the performance of vegetation coverage detection. 5 spectral values (Red, Green, Blue, NIR, Red edge) and 16 VIs were selected to perform variable importance analysis on the best detection models. The results show that the best model for each flight altitude has the highest accuracy in detecting its training data (over 90%), and the GBM model constructed based on all data at all flight altitudes yields the best detection performance covering all data, with an overall accuracy of 95.66%. The variables that were significantly correlated and not correlated with the best model were the Modified Soil Adjusted Vegetation Index (MSAVI) and the Modified Anthocyanin Content Index (MACI), respectively. Finally, the best model was used to invert the complete UAV images at different flight altitudes.

CONCLUSIONS

In general, the GBM_all model constructed based on UAV imaging with all flight altitudes was feasible to accurately detect karst vegetation coverage. The prediction models constructed based on data from different flight altitudes had a certain similarity in the distribution of vegetation index importance. Combined with the method of visual interpretation, the karst green vegetation predicted by the best model was in good agreement with the ground truth, and other land types including hay, rock, and soil were well predicted. This study provided a methodological reference for the detection of karst vegetation coverage in eastern China.

Assessment of remote sensing-based indices for drought monitoring in the north-western region of Bangladesh

Drought is a widespread hazard that can tremendously affect the biodiversity, habitat of wild species, and ecosystem functioning and stability, especially in the dry region. Due to its geographic location, the north-western region of Bangladesh has a comparatively arid climate which is very much susceptible to drought occurrence and is marked as a red zone. Despite the growing evidence of the impact of drought on food security and ecosystem functioning, little effort has been paid to mitigate the drought in this region. The present study aimed to assess the drought condition of the north-western region of Bangladesh using earth observation techniques. For this purpose, Landsat data from 1990 to 2020 was used to determine various vegetation indices such as Normalized Difference Vegetation Index (NDVI), Water Index (NDWI), Moisture Index (NDMI) and Soil Adjusted Vegetation Index (SAVI), along with Land Surface Temperature (LST). Results show that the depletion of forests (2832 km²) and water bodies (6773 km²) resulted from the expansion of settlement (6563 km²) and agricultural land (1802 km²) for the period 1990-2020. Examination of the temporal changes of vegetation indices and LST showed that the values of all indices decreased while the LST increased. The negative correlation between NDVI value and LST indicates that the vegetation in our study was subject to drought-induced shocks. This study reveals the current situation of the vegetation health in the north-western region of Bangladesh in relation to the drought conditions. The findings of this study have practical implications for the policymakers in implementing necessary measures for agriculture, forests, water development, and economic zone planning.

Deforestation and fires in the Brazilian Amazon from 2001 to 2020: Impacts on rainfall variability and land surface temperature

Deforestation and fires in the Amazon are serious problems affecting climate, and land use and land cover (LULC) changes. In recent decades, the Amazon biome area has suffered constant fires and deforestation, causing severe environmental problems that considerably impact the land surface temperature (LST) and hydrological cycle. The Amazon biome lost a large forest area during this period. Thus, this study aims to analyze the deforestation and burned areas in the Amazon from 2001 to 2020, considering their impacts on rainfall variability and LST. This study used methods and procedures based on Google Earth Engine for analysis: (a) LULC evolution mapping, (b) vegetation cover change analysis using vegetation indices, (c) mapping of fires, (d) rainfall and LST analyses, and (e) analysis of climate influence and land cover on hydrological processes using the geographically weighted regression method. The results showed significant LULC changes and the main locations where fires occurred from 2001 to 2020. The years 2007 and 2010 had the most significant areas of fires in the Brazilian Amazon (233,401 km² and 247,562 km², respectively). The Pará and Mato Grosso states had the region's largest deforested areas (172,314 km² and 144,128 km², respectively). Deforestation accumulated in the 2016-2020 period is the greatest in the period analyzed (254,465 km²), 92% higher than in the 2005-2010 period and 82% higher than in the 2001-2005 period. The study also showed that deforested areas have been increasing in recent decades, and the precipitation decreased, while an increase is observed in the LST. It was also concluded that indigenous protection areas have suffered from anthropic actions.

Assessment of mangrove cover dynamics and its health status in the Gulf of Khambhat, Western India, using high-resolution multi-temporal satellite data and Google Earth Engine

Anthropogenic activity is a major driving factor of greenhouse gas emission, leading to climate change worldwide. So, the best natural approach to lowering the carbon from the atmosphere is mangroves which have more potential to sequestrate carbon. But mangroves are under threat due to land use land cover change. This research has been carried out on the mangroves of Gulf of Khambhat, Gujarat, India, where anthropic activity is affecting the mangrove forest cover with spatiotemporal heterogeneity. In the present study, multi-temporal high-resolution satellite data AVNIR-2 (Advanced Visible and Near Infrared Radiometer type-2) and LISS-4 (Linear Imaging Self-Scanning Sensors-4) were used for the demarcation of various land use/land cover class (LULC), and change analysis and assessment of mangroves health for the years 2009, 2014, and 2019. The impact of saltpan/aquaculture on mangroves growth and its health status has been calculated by various MODIS (Moderate Resolution Imaging Spectroradiometer) satellite data products such as gross primary productivity (GPP), enhanced vegetation index (EVI), and leaf area index (LAI) in Google Earth Engine (GEE), and field-based method was also considered. This study suggests that there is a marginal increase (17.11 km²) in mangrove cover during the assessment period 2009-2019; on other side, 65.42 km² was degraded also. However, increase in saltpan/aquaculture is imposing an adverse effect on mangroves' basal area, plant density, and productivity. Change analysis also suggests a reduction in healthy mangrove area (from 25.20 to 2.84 km²), which will have an impact on ecosystem services.

Monitoring detailed mangrove hurricane damage and early recovery using multisource remote sensing data

Due to their location in tropical latitudes, mangrove forests are susceptible to the impact of hurricanes and can be vastly damaged by their high-speed winds. Given the logistic difficulties regarding field surveys in mangroves, remote sensing approaches have been considered a reliable alternative. We quantified trends in damage and early signs of canopy recovery in a fringe Rhizophora mangle area of Marismas Nacionales, Mexico, following the landfall of Hurricane Willa in October 2018. We monitored (2016-2021) broad canopy defoliation using 21 vegetation indices (VI) from the Google Earth Engine tool (GEE). We also mapped a detailed canopy fragmentation and developed digital surface models (DSM) during five study periods (2018-2021) with a consumer-grade unmanned aerial vehicle (UAV) over an area of 100 ha. Based on optical data from the GEE time series, results indicated an abrupt decline in the overall mangrove canopy. The VARI index was the most reliable VI for the mangrove canopy classification from a standard RGB sensor. The impact of the hurricane caused an overall canopy defoliation of 79%. The series of UAV orthomosaics indicate a gradual recovery in the mangrove canopy, while the linear model predicts at least 8.5 years to reach pre-impact mangrove cover conditions. However, the sequence of DSM estimates that the vertical canopy configuration will require a longer time to achieve its original structure.

Vegetation detection using vegetation indices algorithm supported by statistical machine learning

In precision agriculture (PA), the usage of image processing, artificial intelligence, data analysis, and internet of things provides an increase in efficiency, energy, and time saving. In image processing-based applications, vegetation detection, in other words, segmentation that allows monitoring of plant growth and health as well as identification of weeds has a great importance. Vegetation indices (VIs) are widely used algorithms for segmentation. Their advantages include low computational cost and easy implementation and handling compared to the other algorithms. Nevertheless, they require a manual threshold detection that customizes the process and prevents generalization. In this study, a novel automatic segmentation method, which does not require a manual threshold detection by combining VIs with a classification algorithm, is proposed. It deals with the segmentation process as a two class classification problem (vegetation and background). As the classification algorithm, Discriminative Common Vector Approach (DCVA) that has a high discrimination power is used. Each image pixel is represented with a 3 × 1 dimensional vector whose elements correspond to Excess Green (ExG), Green minus Blue (GB), and Color Index of Vegetation (CIVE); VI values are obtained. Then, on the sample space accepting this pixel vector as a sample, DCVA is applied and a discriminative common vector for each class which is unique and describes that class in the best way possible is obtained and it is used for classification. Proposed segmentation method's performance is compared with Convolutional Neural Networks (CNN) and Random Forest (RF) algorithm. The proposed segmentation algorithm outperformed both CNN's and RF's performance.

Estimation of the net primary productivity of winter wheat based on the near-infrared radiance of vegetation

Quantifying net primary productivity (NPP) is important for understanding the global carbon cycle and for assessing ecosystem carbon dynamics. However, uncertainties remain in NPP estimation. Using winter wheat data obtained from an experimental station in 2019, this study evaluated the ability of the near-infrared radiance of vegetation (NIR_V,Rad) to estimate NPP at different time scales and established an estimation model based on NIR_V,Rad, where NIR_V,Rad was defined as the product of the normalized difference vegetation index (NDVI) and the near-infrared radiance. The results showed that the linear relationship between NIR_V,Rad and NPP was superior to the relationship between NPP and NDVI, enhanced vegetation index-2 (EVI2), and near-infrared reflectance of vegetation (NIR_V,Ref) at each time scale (hourly, daily, and growth period). The advantage of NIR_V,Rad was more evident on the hourly scale, in which the R² of NIR_V,Rad and NPP reached 0.77, whereas the R² values of the correlation of NDVI, EVI2, and NIR_V,Ref with NPP were 0.30, 0.16, and 0.14, respectively. There existed a strong linear relationship between absorbed photosynthetically active radiation, net photosynthetic rate, leaf area index, and NIR_V,Rad, which explained the good relationship between NIR_V,Rad and NPP. Through a comparative analysis of the various models, the NIR_V,Rad model was found to have the strongest ability to estimate NPP and the R², with the measured NPP reaching 0.81. The accuracy of NIR_V,Rad provides a new method for estimating NPP and a scientific basis for estimating NPP using high-resolution satellite remote sensing data on a regional scale.

Quantifying the contributions of climate change and human activities to vegetation dynamic in China based on multiple indices

Distinguishing the respective roles of climate change and anthropogenic activities can provide crucial information for sustainable management of the environment. Here, using the residual trend method (RESTREND), which measures the residue of the actual and potential trends of vegetation, we quantified the relative contributions of human activities (e.g., ecological restoration, overgrazing, and urbanization) and climate change (the warmer and wetter trend) to vegetation dynamics in China during 1988-2018 based on multiple vegetation indices, including the vegetation optical depth (Ku-VOD, C-VOD), normalized difference vegetation index (NDVI), and gross primary productivity (GPP). The results showed that the VOD, NDVI, and GPP exhibited overall increasing trends during 1988-2018. Human activities contributed >70% to the increases in NDVI and GPP in China, whereas a counterbalanced contribution of human activities and climate change was identified for the VOD dynamics (51% vs. 49%). Regions with high contributions from human activities to NDVI, GPP, and VOD were located in northeastern, southern, central, and northwestern China. In northern China, the positive impacts of human activities on NDVI (78%) and BEPS-GPP (83%) were greater than those of climate change. In contrast, human activities contributed 96% to the decrease in Ku-VOD over the same period. Before 2000, climate change promoted increases in GPP and NDVI in most regions of southern China. The increasing rates of GPP and NDVI accelerated after 2000 due to afforestation. However, human activities like overgrazing and urbanization have led to decreases in Ku-VOD in northern and southwestern China, and in C-VOD in northeastern, eastern, central, southwestern, and southern China. In all, the relative roles of climate and human factors varied in different regions when NDVI, GPP, or VOD were individually considered. Our results highlighted that the regional-scale vegetation conditions should be taken into full account to achieve sustainable management of ecosystems.

Soybean cyst nematode detection and management: a review

Soybeans play a key role in global food security. U.S. soybean yields, which comprise [Formula: see text] of the total soybeans planted in the world, continue to experience unprecedented grain loss due to the soybean cyst nematode (SCN) plant pathogen. SCN remains one of the primary disruptive pests despite the existence of advanced management techniques such as crop rotation and SCN-resistant varieties. SCN detection is a key step in managing this disease; however, early detection is challenging because soybeans do not show any above ground symptoms unless they  are significantly damaged. Direct soil sampling remains the most common method for SCN detection, however, this method has several problems. For example, the threshold damage methods-adopted by most of the laboratories to make recommendations-is not reliable as it does not consider soil pH, N, P, and K values and relies solely on the egg count instead of assessment of the root infection. To overcome the challenges of manual soil sampling methods, deep learning and hyperspectral imaging are important current topics in precision agriculture for plant disease detection and have been proposed as cost-effective and efficient detection methods that can work at scale. We have reviewed more than 150 research papers focusing on soybean cyst nematodes with an emphasis on deep learning techniques for detection and management. First: we describe soybean vegetation and reproduction stages, SCN life cycles, and factors influencing this disease. Second: we highlight the impact of SCN on soybean yield loss and the challenges associated with its detection. Third: we describe direct sampling methods in which the soil samples are procured and analyzed to evaluate SCN egg counts. Fourth: we highlight the advantages and limitations of these direct methods, then review computer vision- and remote sensing-based detection methods: data collection using ground, aerial, and satellite approaches followed by a review of machine learning methods for image analysis-based soybean cyst nematode detection. We highlight the evaluation approaches and the advantages of overall detection workflow in high-performance and big data environments. Lastly, we discuss various management approaches, such as crop rotation, fertilization, SCN resistant varieties such as PI 88788, and SCN's increasing resistance to these strategies. We review machine learning approaches for soybean crop yield forecasting as well as the influence of pesticides, herbicides, and fertilizers on SCN infestation reduction. We provide recommendations for soybean research using deep learning and hyperspectral imaging to accommodate the lack of the ground truth data and training and testing methodologies, such as data augmentation and transfer learning, to achieve a high level of detection accuracy while keeping costs as low as possible.

Improving the remote estimation of soil organic carbon in complex ecosystems with Sentinel-2 and GIS using Gaussian processes regression

BACKGROUND AND AIMS

The quantitative retrieval of soil organic carbon (SOC) storage, particularly for soils with a large potential for carbon sequestration, is of global interest due to its link with the carbon cycle and the mitigation of climate change. However, complex ecosystems with good soil qualities for SOC storage are poorly studied.

METHODS

The interrelation between SOC and various vegetation remote sensing drivers is understood to demonstrate the link between the carbon stored in the vegetation layer and SOC of the top soil layers. Based on the mapping of SOC in two horizons (0-30 cm and 30-60 cm) we predict SOC with high accuracy in the complex and mountainous heterogeneous páramo system in Ecuador. A large SOC database (in weight % and in Mg/ha) of 493 and 494 SOC sampling data points from 0-30 cm and 30-60 cm soil profiles, respectively, were used to calibrate GPR models using Sentinel-2 and GIS predictors (i.e., Temperature, Elevation, Soil Taxonomy, Geological Unit, Slope Length and Steepness (LS Factor), Orientation and Precipitation).

RESULTS

In the 0-30 cm soil profile, the models achieved a R² of 0.85 (SOC%) and a R² of 0.79 (SOC Mg/ha). In the 30-60 cm soil profile, models achieved a R² of 0.86 (SOC%), and a R² of 0.79 (SOC Mg/ha).

CONCLUSIONS

The used Sentinel-2 variables (FVC, CWC, LCC/C_ab, band 5 (705 nm) and SeLI index) were able to improve the estimation accuracy between 3-21% compared to previous results of the same study area. CWC emerged as the most relevant biophysical variable for SOC prediction.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11104-022-05506-1.

Radiative transfer model inversion using high-resolution hyperspectral airborne imagery - Retrieving maize LAI to access biomass and grain yield

Mapping crop within-field yield variability provide an essential piece of information for precision agriculture applications. Leaf Area Index (LAI) is an important parameter that describes maize growth, vegetation structure, light absorption and subsequently maize biomass and grain yield (GY). The main goal for this study was to estimate maize biomass and GY through LAI retrieved from hyperspectral aerial images using a PROSAIL model inversion and compare its performance with biomass and GY estimations through simple vegetation index approaches. This study was conducted in two separate maize fields of 12 and 20 ha located in north-west Mexico. Both fields were cultivated with the same hybrid. One field was irrigated by a linear pivot and the other by a furrow irrigation system. Ground LAI data were collected at different crop growth stages followed by maize biomass and GY at the harvesting time. Through a weekly/biweekly airborne flight campaign, a total of 19 mosaics were acquired between both fields with a micro-hyperspectral Vis-NIR imaging sensor ranging from 400 to 850 nanometres (nm) at different crop growth stages. The PROSAIL model was calibrated and validated for retrieving maize LAI by simulating maize canopy spectral reflectance based on crop-specific parameters. The model was used to retrieve LAI from both fields and to subsequently estimate maize biomass and GY. Additionally, different vegetation indices were calculated from the aerial images to also estimate maize yield and compare the indices with PROSAIL based estimations. The PROSAIL validation to retrieve LAI from hyperspectral imagery showed a R² value of 0.5 against ground LAI with RMSE of 0.8 m²/m². Maize biomass and GY estimation based on NDRE showed the highest accuracies, followed by retrieved LAI, GNDVI and NDVI with R² value of 0.81, 0.73, 0.73 and 0.65 for biomass, and 0.83, 0.69, 0.73 and 0.62 for GY estimation, respectively. Furthermore, the late vegetative growth stage at V16 was found to be the best stage for maize yield prediction for all studied indices.

Modelling soil organic carbon using vegetation indices across large catchments in eastern Australia

Soil organic carbon (SOC) is an important soil component. However, examining SOC at the large catchment scale is difficult due to the intensive labour requirements. This study examines SOC distribution at large (>500 km²) catchment scales using field-sampled SOC data and remote sensed vegetation indices located in eastern Australia (Krui River catchment - 562 km²; Merriwa River catchment - 808 km²) on grazing land-use basalt soil. The SOC data obtained was compared to digital elevation model (DEM) derived elevation and insolation data, as well as Normalised Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) values corresponding to each sample site. These indices were obtained from the MODIS sensor (Terra/Aqua) and Landsat series satellites. Vegetation Indices (VI) captured immediately prior to sampling demonstrated a poor correlation with SOC. The use of multiple, aggregated, prior VI data sets provided a good match with SOC. The strongest match occurred for Landsat 8 EVI, indicating that VIs with higher spatial and spectral resolution, which can account for atmospheric interference, have the potential to produce more accurate SOC mapping (Krui samples in 2006, R² = 0.31, P < 0.01; Krui sampled in 2014, R² = 0.41, P < 0.01; Merriwa samples in 2015, R² = 0.37, P < 0.01). A sensitivity test for both remote sensing platforms demonstrated that the findings were robust. The results demonstrate that VIs are a reliable surrogate for historical vegetation growth in pasture dominated landscapes and therefore soil carbon inputs allowing for mapping of SOC across large catchment scales. Both Landsat and MODIS produced similar results and demonstrate that SOC can be reliably predicted at the large catchment scale and for different catchments in this environment with RMSE range of 0.79 to 1.06. The method and data can be applied globally and provides a new method for environmental assessment.

Rapid Estimation of Water Stress in Choy Sum (Brassica chinensis var. parachinensis) Using Integrative Approach

To optimize crop water consumption and adopt water-saving measures such as precision irrigation, early identification of plant water status is critical. This study explores the effectiveness of estimating water stress in choy sum (Brassica chinensis var. parachinensis) grown in pots in greenhouse conditions using Crop Water Stress Index (CWSI) and crop vegetation indicators to improve irrigation water management. Data on CWSI and Spectral reflectance were collected from choy sum plants growing in sandy loam soil with four different soil field capacities (FC): 90–100% FC as no water stress (NWS); 80–90% FC for light water stress (LWS); 70–80% FC for moderate water stress (MWS); and 60–70% FC for severe water stress (SWS). With four treatments and three replications, the experiment was set up as a completely randomized design (CRD). Throughout the growing season, plant water stress tracers such as leaf area index (LAI), canopy temperature (Tc), leaf relative water content (LRWC), leaf chlorophyll content, and yield were measured. Furthermore, CWSI estimated from the Workswell Wiris Agro R Infrared Camera (CWSI_W) and spectral data acquisition from the Analytical Spectral Device on choy sum plants were studied at each growth stage. NDVI, Photochemical Reflectance Index positioned at 570 nm (PRI570), normalized PRI (PRInorm), Water Index (WI), and NDWI were the Vegetation indices (VIs) used in this study. At each growth stage, the connections between these CWSI_W, VIs, and water stress indicators were statistically analyzed with R² greater than 0.5. The results revealed that all VIs were valuable guides for diagnosing water stress in choy sum. CWSI_W obtained from this study showed that Workswell Wiris Agro R Infrared Camera mounted on proximal remote sensing platform for assessing water stress in choy sum plant was rapid, non-destructive, and user friendly. Therefore, integrating CWSI_W and VIs approach gives a more rapid and accurate approach for detecting water stress in choy sum grown under greenhouse conditions to optimize yield by reducing water loss and enhancing food security and sustainability.

High-throughput phenotyping allows the selection of soybean genotypes for earliness and high grain yield

BACKGROUND

Precision agriculture techniques are widely used to optimize fertilizer and soil applications. Furthermore, these techniques could also be combined with new statistical tools to assist in phenotyping in breeding programs. In this study, the research hypothesis was that soybean cultivars show phenotypic differences concerning wavelength and vegetation index measurements.

RESULTS

In this research, we associate variables obtained via high-throughput phenotyping with the grain yield and cycle of soybean genotypes. The experiment was carried out during the 2018/2019 and 2019/2020 crop seasons, under a randomized block design with four replications. The evaluated soybean genotypes included 7067, 7110, 7739, 8372, Bonus, Desafio, Maracai, Foco, Pop, and Soyouro. The phenotypic traits evaluated were: first pod height (FPH), plant height (PH), number of branches (NB), stem diameter (SD), days to maturity (DM), and grain yield (YIE). The spectral variables evaluated were wavelengths and vegetation indices (NDVI, SAVI, GNDVI, NDRE, SCCCI, EVI, and MSAVI). The genotypes Maracai and Foco showed the highest grain yields throughout the crop seasons, in addition to belonging to the groups with the highest means for all VIs. YIE was positively correlated with the NDVI and certain wavelengths (735 and 790 nm), indicating that genotypes with higher values for these spectral variables are more productive. By path analyses, GNDVI and NDRE had the highest direct effects on the dependent variable DM, while NDVI had a higher direct effect on YIE.

CONCLUSIONS

Our findings revealed that early and productive genotypes can be selected based on vegetation indices and wavelengths. Soybean genotypes with a high grain yield have higher means for NDVI and certain wavelengths (735 and 790 nm). Early genotypes have higher means for NDRE and GNDVI. These results reinforce the importance of high-throughput phenotyping as an essential tool in soybean breeding programs.

Multi-predictor mapping of soil organic carbon in the alpine tundra: a case study for the central Ecuadorian páramo

BACKGROUND

Soil organic carbon (SOC) affects essential biological, biochemical, and physical soil functions such as nutrient cycling, water retention, water distribution, and soil structure stability. The Andean páramo known as such a high carbon and water storage capacity ecosystem is a complex, heterogeneous and remote ecosystem complicating field studies to collect SOC data. Here, we propose a multi-predictor remote quantification of SOC using Random Forest Regression to map SOC stock in the herbaceous páramo of the Chimborazo province, Ecuador.

RESULTS

Spectral indices derived from the Landsat-8 (L8) sensors, OLI and TIRS, topographic, geological, soil taxonomy and climate variables were used in combination with 500 in situ SOC sampling data for training and calibrating a suitable predictive SOC model. The final predictive model selected uses nine predictors with a RMSE of 1.72% and a R² of 0.82 for SOC expressed in weight %, a RMSE of 25.8 Mg/ha and a R² of 0.77 for the model in units of Mg/ha. Satellite-derived indices such as VARIG, SLP, NDVI, NDWI, SAVI, EVI2, WDRVI, NDSI, NDMI, NBR and NBR2 were not found to be strong SOC predictors. Relevant predictors instead were in order of importance: geological unit, soil taxonomy, precipitation, elevation, orientation, slope length and steepness (LS Factor), Bare Soil Index (BI), average annual temperature and TOA Brightness Temperature.

CONCLUSIONS

Variables such as the BI index derived from satellite images and the LS factor from the DEM increase the SOC mapping accuracy. The mapping results show that over 57% of the study area contains high concentrations of SOC, between 150 and 205 Mg/ha, positioning the herbaceous páramo as an ecosystem of global importance. The results obtained with this study can be used to extent the SOC mapping in the whole herbaceous ecosystem of Ecuador offering an efficient and accurate methodology without the need for intensive in situ sampling.

Is satellite Sun-Induced Chlorophyll Fluorescence more indicative than vegetation indices under drought condition?

Droughts represent one of the most severe abiotic stress factors that could result in great crop yield loss. Numerous vegetation indices have been proposed for monitoring the vegetation condition under stress and assessing drought impacts on yield loss. However, the understanding and comparison between traditional vegetation indices (VIs) and the newly emerging satellite Sun-Induced Chlorophyll Fluorescence (SIF) for monitoring vegetation condition is still limited especially under drought stress and at multiple spatial scales. In this study, the potential of satellite observation SIF for monitoring corn response to drought was investigated based on the 2012 drought in the US Corn Belt. The standardized precipitation evapotranspiration index (SPEI) was used here to quantify drought. We found that all SPEI were above -1, except for July (-1.27), August (-1.39) and September (-1.14) in 2012, indicating the severity of this drought. We examined the relationship between satellite measurements of SIF, SIF_yield, VIs (e.g., NDVI and EVI) and SPEI. Results indicated that SIF_yield was sensitive to drought and SIF captured the stress more accurately both at the regional and state scales for the US Corn Belt. Quantitatively, SIF_yield had a high correlation with SPEI (r = 0.987, p < 0.05) over the entire Corn Belt, and it indicated losses in response to drought approximately one month earlier than SIF/NDVI/EVI. Furthermore, our results demonstrated that SIF could be trusted as an effective indicator to study the relationship between GPP (R² ≥ 0.8664, p < 0.01) under drought conditions across the Corn Belt. This study highlighted the advantage of using satellite SIF observations to monitor the drought stress on crop growth especially GPP at regional scale.

A random forest model for the classification of wheat and rye leaf rust symptoms based on pure spectra at leaf scale

The pure spectra acquisition of plant disease symptoms is essential to improving the reliability of remote sensing methods in crop protection. The reflectance values read from the pure spectra can be used as valuable training data for development of algorithms designed for plant disease detection at leaf and canopy scale. The aim of this paper is to identify and distinguish spectrally the leaf rust symptoms caused by two closely related special forms (f. sp.) of Puccinia recondita f. sp. tritici on wheat and Puccinia recondita f. sp. recondita on rye at leaf scale. Spectral measurements were made with FieldSpec 3 spectrometer in the wavelength range of 350-2500 nm. The spectrometer was connected to a microscope by optical fiber. Raw spectra of uredinia, chlorotic discoloration, green leaves, senescent inoculated leaves and senescent uninoculated leaves of wheat and rye, all of which obtained for this study, were investigated with a view towards making an automized classification of plant species and their phases. The created Random Forest models were tested separately using pure spectra, and from these vegetation indices were derived as predictors. Three vegetation indices, namely CRI, PRI and GNDVI, appeared to be the most robust in terms of distinguishing uredinia from other symptoms on rye and wheat leaves. PRI, EVI, NDVI₇₀₅, and GNDVI were the most suitable for distinguishing uredinia, chlorotic discoloration, and green leaf stages on rye. That tusk on wheat leaves can be recognized if seven indices (PRI, MSAWI, SAVI, NDVI, NDVI₇₀₅, GNDVI and RVI) are used together. For the classification of all disease symptoms for both plant species, the most useful were wavelengths in the VIS range: 431-436, 696-703 and 646-686 nm. However, the ranges of SWIR wavelengths (1938, 1955) and NIR wavelengths (1099-1104) also have a high contribution to the discrimination accuracy of the model. In the classification of all disease symptoms, the most important vegetation indices were CRI, OSAVI, and GNDVI. Analysis of the results revealed the advantage of the model based on the selected spectral wavelengths (Hit Rate of 96.6%) in comparison with predictions based on vegetation indices alone (Hit Rate of 91.7%). Both approaches show the highly applicable character of utilizing high quality spectral products such as satellite images in reducing operational costs of crop protection.

High-resolution spectral information enables phenotyping of leaf epicuticular wax in wheat

BACKGROUND

Epicuticular wax (EW) is the first line of defense in plants for protection against biotic and abiotic factors in the environment. In wheat, EW is associated with resilience to heat and drought stress, however, the current limitations on phenotyping EW restrict the integration of this secondary trait into wheat breeding pipelines. In this study we evaluated the use of light reflectance as a proxy for EW load and developed an efficient indirect method for the selection of genotypes with high EW density.

RESULTS

Cuticular waxes affect the light that is reflected, absorbed and transmitted by plants. The narrow spectral regions statistically associated with EW overlap with bands linked to photosynthetic radiation (500 nm), carotenoid absorbance (400 nm) and water content (~ 900 nm) in plants. The narrow spectral indices developed predicted 65% (EWI-13) and 44% (EWI-1) of the variation in this trait utilizing single-leaf reflectance. However, the normalized difference indices EWI-4 and EWI-9 improved the phenotyping efficiency with canopy reflectance across all field experimental trials. Indirect selection for EW with EWI-4 and EWI-9 led to a selection efficiency of 70% compared to phenotyping with the chemical method. The regression model EWM-7 integrated eight narrow wavelengths and accurately predicted 71% of the variation in the EW load (mg·dm-2) with leaf reflectance, but under field conditions, a single-wavelength model consistently estimated EW with an average RMSE of 1.24 mg·dm-2 utilizing ground and aerial canopy reflectance.

CONCLUSIONS

Overall, the indices EWI-1, EWI-13 and the model EWM-7 are reliable tools for indirect selection for EW based on leaf reflectance, and the indices EWI-4, EWI-9 and the model EWM-1 are reliable for selection based on canopy reflectance. However, further research is needed to define how the background effects and geometry of the canopy impact the accuracy of these phenotyping methods.

Satellite imagery and machine learning for identification of aridity risk in central Java Indonesia

This study aims to develop a software framework for predicting aridity using vegetation indices (VI) from LANDSAT 8 OLI images. VI data are predicted using machine learning (ml): Random Forest (RF) and Correlation and Regression Trees (CART). Comparison of prediction using Artificial Neural Network (ANN), Support Vector Machine (SVM), k-nearest neighbors (k-nn) and Multivariate Adaptive Regression Spline (MARS). Prediction results are interpolated using Inverse Distance Weight (IDW). This study was conducted in stages: (1) Image preprocessing; (2) calculating numerical data extracted from the LANDSAT band imagery using vegetation indices; (3) analyzing correlation coefficients between VI; (4) prediction using RF and CART; (5) comparing performances between RF and CART using ANN, SVM, k-nn, and MARS; (6) testing the accuracy of prediction using Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE); (7) interpolating with IDW. Correlation coefficient of VI data shows a positive correlation, the lowest r (0.07) and the highest r (0.98). The experiments show that the RF and CART algorithms have efficiency and effectivity in determining the aridity areas better than the ANN, SVM, k-nn, and MARS algorithm. RF has a difference between the predicted results and 1.04% survey data MAPE and the smallest value close to zero is 0.05 MSE. CART has a difference between the predicted results and 1.05% survey data MAPE and the smallest value approaching to zero which is 0.05 MSE. The prediction results of VI show that in 2020 most of the study areas were low vegetation areas with the Normalized Difference Vegetation Index (NDVI) < 0.21, had an indication of drought with the Vegetation Health Index (VHI) < 31.10, had a Vegetation Condition Index (VCI) in some areas between 35%-50% (moderate drought) and < 35% (high drought). The Burn Area Index (dBAI) values are between -3, 971 and -2,376 that show the areas have a low fire risk, and index values are between -0, 208 and -0,412 that show the areas are starting vegetation growth. The result of this study shows that the machine learning algorithms is an accurate and stable algorithm in predicting the risks of drought and land fire based on the VI data extracted from the LANDSAT 8 OLL imagery. The VI data contain the record of vegetation condition and its environment, including humidity, temperatures, and the environmental vegetation health.

Estimating canopy nitrogen concentration of sugarcane crop using in situ spectroscopy

Estimating nitrogen (N) concentration in situ is fundamental for managing the fertilization of the sugarcane crop. The purpose of this work was to develop estimation models that explain how N varies over time as a function of three spectral data transformations in two stages (plant cane and first ratoon) under variable rates of N application. A randomized complete-block experimental design was applied, with four levels of N fertilization: 0, 80, 160, and 240 kg N ha⁻¹. Six sampling events were carried out during the rapid growth stage, where the canopy reflectance spectra with a hyperspectral sensor were measured, and tissue samples for N determination in plant cane and first ratoon were taken, from 60 days after emergence (DAE) and 60 days after harvest (DAH), respectively, until days 210 DAE and 210 DAH. To build the models, partial least squares regression analysis was used and was trained by three transformations of the spectral data: (i) average reflectance spectrum (R), (ii) multiple scatter correction and Savitzky-Golay filter MSC-SG) reflectance spectrum, and (iii) calculated vegetation indices (VIs).

Temporal record and spatial distribution of fire foci in State of Minas Gerais, Brazil

The objectives of this study are: (i) to evaluate the space-temporal variability of fire foci by environmental satellites, CHIRPS and remote sensing products based on applied statistics, and (ii) to identify the relational pattern between the distribution of fire foci and the environmental, meteorological, and socioeconomic variables in the mesoregions of Minas Gerais (MG) - Brazil. This study used a time series of fire foci from 1998 to 2015 via BDQueimadas. The temporal record of fire foci was evaluated by Mann-Kendall (MK), Pettitt (P), Shapiro-Wilk (SW), and Bartlett (B) tests. The spatial distribution by burned area (MCD64A1-MODIS) and the Kernel density - (radius 20 km) were estimated. The environmental variables analyzed were: rainfall (mm) and maximum temperature (°C), besides proxies to vegetation canopy: NDVI, SAVI, and EVI. PCA was applied to explain the interaction between fire foci and demographic, environmental, and geographical variables for MG. The MK test indicated a significant increasing trend in fire foci in MG. The SW and B tests were significant for non-normality and homogeneity of data. The P test pointed to abrupt changes in the 2001 and 2002 cycles (El Niño and La Niña moderated), which contributes to the annual increase and in winter and spring, which is identified by the Kernel density maps. Burned areas highlighted the northern and northwestern regions of MG, Triângulo Mineiro, Jequitinhonha, and South/Southwest MG, in the 3rd quarter (increased 17%) and the 4th quarter (increased 88%). The PCA resulted in three PCs that explained 71.49% of the total variation. The SAVI was the variable that stood out, with 11.12% of the total variation, followed by Belo Horizonte, the most representative in MG. We emphasize that the applied conceptual theoretical model defined here can act in the environmental management of fire risk. However, public policies should follow the technical-scientific guidelines in the mitigation of the resulting socioeconomic - environmental damages.

Extrapolating canopy phenology information using Sentinel-2 data and the Google Earth Engine platform to identify the optimal dates for remotely sensed image acquisition of semiarid mangroves

Continuum monitoring of mangrove ecosystems is required to maintain and improve upon national mangrove conservation strategies. In particular, mangrove canopy assessments using remote sensing methods can be undertaken rapidly and, if freely available, optimize costs. Although such spaceborne data have been used for such purposes, their application to map mangroves at the species level has been limited by the capacity to provide continuous data. The objective of this study was to assess mangrove seasonal patterns using seven multispectral vegetation indices based on a Sentinel-2 (S2) time series (July 2018 to October 2019) to assess phenological trajectories of various semiarid mangrove classes in the Google Earth Engine platform using Fourier analysis for an area located in Western Mexico. The results indicate that the months from November through December and from May through July were critical in mangrove species discrimination using the EVI2, NDVI, and VARI series. The Random Forest classification accuracy for the S2 image was calculated at 79% during the optimal acquisition period (June 25, 2019), whereas only 55% accuracy was calculated for the non-optimal image acquired date (March 2, 2019). Although mangroves are considered evergreen forests, the phenological pattern of various mangrove canopies, based on these indices, were shown to be very similar to the surrounding land-based semiarid deciduous forest. Consequently, it is believed that the rainfall pattern is likely to be the key environmental factor driving mangrove phenology in this semiarid coastal system and thus the degree of success in mangrove remote sensing classification endeavors. Identifying the optimal dates when canopy spectral conditions are ideal in achieving mangrove species discrimination could be of utmost importance when purchasing more expensive very-high spatial resolution satellite images or collecting spatial data from UAVs.

Combined MODIS land surface temperature and greenness data for modeling vegetation phenology, physiology, and gross primary production in terrestrial ecosystems

Vegetation phenology such as the start (SOS) and end (EOS) of the growing season, physiology (represented by seasonal maximum capacity of carbon uptake, GPPmax), and gross primary production (GPP) are sensitive indicators for monitoring ecosystem response to environmental change. However, uncertainty and disagreement between models limit the use phenology metrics and GPP derived from remote sensing data. Statistical models for estimating phenology and physiology were constructed based on key predictor variables derived from enhanced vegetation index (EVI) and land surface temperature (LST) data. Then, a statistical model that integrated remote sensing-based phenology and physiology (RS-SMIPP) data was constructed to estimate seasonal and annual GPP. These models were calibrated and validated with GPP observations from 512 site-years of FLUXNET data covering four plant functional types (PFTs) in the northern hemisphere: deciduous broadleaf forest, evergreen needle-leaf forest, mixed forest, and grassland. Our results showed that phenology and physiology were accurately estimated with relative root mean squared error (RMSEr) <20%, and the errors varied among the PFTs. Spring EVI was an important factor in explaining variation of GPPmax. The RS-SMIPP model outperformed the MOD17 algorithm in accurately estimating seasonal and annual GPP and reduced RMSEr from 25.34%-43.44% to 9.53%-26.19% for annual GPP of the different PFTs. These findings demonstrate that remote sensing-based phenological and physiological indicators could be used to explain the variations of seasonal and annual GPP, and provide an efficient way for improving GPP estimations at a global scale.

Estimation of leaf area index using PROSAIL based LUT inversion, MLRA-GPR and empirical models: Case study of tropical deciduous forest plantation, North India

Forests play a vital role in biological cycles and environmental regulation. To understand the key processes of forest canopies (e.g., photosynthesis, respiration and transpiration), reliable and accurate information on spatial variability of Leaf Area Index (LAI), and its seasonal dynamics is essential. In the present study, we assessed the performance of biophysical parameter (LAI) retrieval methods viz. Look-Up Table (LUT)-inversion, MLRA-GPR (Machine Learning Regression Algorithm-Gaussian Processes Regression) and empirical models, for estimating the LAI of tropical deciduous plantation using ARTMO (Automated Radiative Transfer Models Operator) tool and Sentinel-2 satellite images. The study was conducted in Central Tarai Forest Division, Haldwani, located in the Uttarakhand state, India. A total of 49 ESUs (Elementary Sampling Unit) of 30m×30m size were established based on variability in composition and age of plantation stands. In-situ LAI was recorded using plant canopy imager during the leaf growing, peak and senescence seasons. The PROSAIL model was calibrated with site-specific biophysical and biochemical parameters before used to the predicted LAI. The plantation LAI was also predicted by an empirical approach using optimally chosen Sentinel-2 vegetation indices. In addition, Sentinel-2 and MODIS LAI products were evaluated with respect to LAI measurements. MLRA-GPR offered best results for predicting LAI of leaf growing (R2 = 0.9, RMSE = 0.14), peak (R2 = 0.87, RMSE = 0.21) and senescence (R2 = 0.86, RMSE = 0.31) seasons while LUT inverted model outperformed VI's based parametric regression model. Vegetation indices (VIs) derived from 740 nm, 783 nm and 2190 nm band combinations of Sentinel-2 offered the best prediction of LAI.

Detection of oil pollution impacts on vegetation using multifrequency SAR, multispectral images with fuzzy forest and random forest methods

Oil pollution harms terrestrial ecosystems. There is an urgent requirement to improve on existing methods for detecting, mapping and establishing the precise extent of oil-impacted and oil-free vegetation. This is needed to quantify existing spill extents, formulate effective remediation strategies and to enable effective pipeline monitoring strategies to identify leakages at an early stage. An effective oil spill detection algorithm based on optical image spectral responses can benefit immensely from the inclusion of multi-frequency Synthetic Aperture Radar (SAR) data, especially when the effect of multi-collinearity is sufficiently reduced. This study compared the Fuzzy Forest (FF) and Random Forest (RF) methods in detecting and mapping oil-impacted vegetation from a post spill multispectral optical sentinel 2 image and multifrequency C and X Band Sentinel - 1, COSMO Skymed and TanDEM-X SAR images. FF and RF classifiers were employed to discriminate oil-spill impacted and oil-free vegetation in a study area in Nigeria. Fuzzy Forest uses specific functions for the selection and use of uncorrelated variables in the classification process to yield an improved result. This method proved an efficient variable selection technique addressing the effects of high dimensionality and multi-collinearity, as the optimization and use of different SAR and optical image variables generated more accurate results than the RF algorithm in densely vegetated areas. An Overall Accuracy (OA) of 75% was obtained for the dense (Tree Cover Area) vegetation, while cropland and grassland areas had 59.4% and 65% OA respectively. However, RF performed better in Cropland areas with OA = 75% when SAR-optical image variables were used for classification, while both methods performed equally well in Grassland areas with OA = 65%. Similarly, significant backscatter differences (P < 0.005) were observed in the C-Band backscatter sample mean of polluted and oil-free TCA, while strong linear associations existed between LAI and backscatter in grassland and TCA. This study demonstrates that SAR based monitoring of petroleum hydrocarbon impacts on vegetation is feasible and has high potential for establishing oil-impacted areas and oil pipeline monitoring.

Estimating persistent oil contamination in tropical region using vegetation indices and random forest regression

The persistence of soil contamination after cessation of oil activities remains a major environmental issue in tropical regions. The assessment of the contamination is particularly difficult on vegetated sites, but promising advances in reflectance spectroscopy have recently emerged for this purpose. This study aimed to exploit vegetation reflectance for estimating low concentrations of Total Petroleum Hydrocarbons (TPH) in soils. A greenhouse experiment was carried out for 42 days on Cenchrus alopecuroides (L.) under realistic tropical conditions. The species was grown on oil-contaminated mud pit soils from industrial sites, with various concentrations of TPH. After 42 days, a significant decrease in plant growth and leaf chlorophyll and carotenoid contents was observed for plants exposed to 5-19 g kg^-1 TPH in comparison to the controls (p < 0.05). Conversely, pigment contents were higher for plants exposed to 1 g kg^-1 TPH (hormesis phenomenon). These modifications proportionally affected the reflectance of C. alopecuroides at leaf and plant scales, especially in the visible region around 550 and 700 nm. 33 vegetation indices were used for linking the biochemical and spectral responses of the species to oil using elastic net regressions. The established models indicated that chlorophylls a and b and β-carotene were the main pigments involved in the modifications of reflectance (R² > 0.7). The same indices also succeeded in estimating the concentrations of TPH using random forest regression, at leaf and plant scales (RMSE = 1.46 and 1.63 g kg^-1 and RPD = 5.09 and 4.44, respectively). Four out of the 33 indices contributed the most to the models (>75%). This study opens up encouraging perspectives for monitoring the cessation of oil activities in tropical regions. Further researches will focus on the application of our approach at larger scale, on airborne and satellite imagery.

Investigating the response of soil and vegetable crops to poultry and cow manure using ground and satellite data

Based on the massive production of cow and poultry manures, farmers in Saudi Arabia are moving towards the application of organic fertilizers in their farms. Therefore, the present work was conducted to study the response of soil and selected vegetable crops to poultry and cow manures, using ground data and Landsat-8 and Hyperion images. The studied vegetable crops are cabbage, cauliflower, broccoli, and lettuce. A total of 100 t ha⁻¹ organic manures were applied as a pre-planting treatment. A 12.5 ha field in Tawdeehiya Farms, 200 km southeast of Riyadh, was earmarked for this study. The field was divided into sectors cultivated with the above-mentioned vegetable crops. Soil characteristics, including the soil pH, the electric conductivity (EC), the nitrogen (N), the phosphorus (P) and the potassium (K), were examined before the application of manures and 25 days after the transplanting process. Observations on crops chlorophyll content, number of leaves, the diameter of merchantable products and yield were also investigated. Furthermore, the relationship between the crop performance and yield was investigated through the satellite images generated vegetation indices (VIs). This study revealed the better performance of poultry manure compared to cow manure in terms of development and production parameters of the experimental crops. Dynamics of the chlorophyll content across the crop growth period revealed that all the tested crops responded significantly (R² = 0.69; P = 0.001) to the poultry manure treatments. Among the tested crops, the chlorophyll content, curd or head sizes and crop yields were quite better in poultry manure applied plots. The investigation of crop yield was significant with poultry manure (R² = 0.64; P = 0.001) than cow manure (R² = 0.57; P = 0.001) using the OSAVI and mNDVI, respectively.

Spatial and temporal variation of the vegetation of the semiarid Puna in a pastoral system in the Pozuelos Biosphere Reserve

This study aimed to analyze the spatial and temporal variation of the vegetation in the northern Argentine Puna, utilizing both field sampling and remote-sensing tools. The study was performed within the Pozuelos Biosphere Reserve (Jujuy province, Argentina), which aims to generate socio-economic development compatible with biodiversity conservation. Our study was designed to analyze the dynamics of the Puna vegetation at local scale and assess and monitor the seasonal (dry and wet seasons), interannual, and spatial variation of the vegetation cover, biomass, dominant species, and vegetation indices. Ten vegetation units (with differences in composition, cover, and high and low stratum biomass) were identified at our study site. The diversity of these vegetation units correlated with geomorphology and soil type. In the dry season, the vegetation unit with greatest vegetation cover and biomass was the Festuca chrysophylla grassland, whereas in the wet season, the units with greatest cover and biomass were vegas (peatlands) and short grasslands. The Festuca chrysophylla grasslands and short grasslands were located in areas with clay soils, except peatlands, associated with valleys and coarse-texture soils. The vegetation indices used (NDVI, SAVI, and MSAVI2) were able to differentiate functional types of vegetation and showed a good statistical fit with cover values. Our results suggest that the integrated utilization of remote-sensing tools and field surveys improves the assessment of the Puna vegetation and would allow a periodic monitoring at production unit scale taking into account its spatial and temporal variation.

Evaluation of long-term estuarine vegetation changes through Landsat imagery

Salt marshes support estuarine biodiversity and provide ecosystem services; however, their general decay is being observed worldwide, in large part due to land reclamation. Accordingly, there is a growing concern about salt marsh preservation status having in mind the promotion of effective management decisions towards their conservation and restoration. Satellite imagery offers the opportunity to monitor land surface dynamics, constituting a fundamental information source for wetland monitoring. This study analyses spatial and temporal vegetation changes within Ria de Aveiro coastal lagoon between 1984 and 2017, by processing and analyzing TM and ETM+ Landsat imagery. A database consisting of 264 cloud-free images was collected and analyzed. The Normalized Difference Water Index was computed using the remote surface reflectance and was then used to distinguish land from water and to estimate the flooded lagoon area. Moreover, the tidal state was determined for each image from a tidal elevation record monitored at the lagoon entrance. Subsequently, four vegetation indices (VI) were computed and their spatial variability in the lagoon area uncovered by water was assessed. Spatially averaged spectral indices were also statistically analyzed and seasonal variations and interannual trends evaluated. Results show that the intertidal area increased, and VI values decreased indicating a possible reduction in the Chlorophyll content and suggesting that the new intertidal regions are mostly covered by mud. The spatially averaged VI values show seasonal patterns, with peaks in spring and summer, coinciding with high biomass productivity periods. The largest flooded area and VI modifications occurred after 1999, suggesting that changes are associated with dredging activities performed in the main lagoon channels. This study reinforced the potential of Landsat archives to monitor coastal wetlands, highlighting their importance for coastal managers of threatened systems, and therefore helping to define management strategies about the ecological conservation of estuarine systems.

High-throughput analysis of leaf physiological and chemical traits with VIS-NIR-SWIR spectroscopy: a case study with a maize diversity panel

BACKGROUND

Hyperspectral reflectance data in the visible, near infrared and shortwave infrared range (VIS-NIR-SWIR, 400-2500 nm) are commonly used to nondestructively measure plant leaf properties. We investigated the usefulness of VIS-NIR-SWIR as a high-throughput tool to measure six leaf properties of maize plants including chlorophyll content (CHL), leaf water content (LWC), specific leaf area (SLA), nitrogen (N), phosphorus (P), and potassium (K). This assessment was performed using the lines of the maize diversity panel. Data were collected from plants grown in greenhouse condition, as well as in the field under two nitrogen application regimes. Leaf-level hyperspectral data were collected with a VIS-NIR-SWIR spectroradiometer at tasseling. Two multivariate modeling approaches, partial least squares regression (PLSR) and support vector regression (SVR), were employed to estimate the leaf properties from hyperspectral data. Several common vegetation indices (VIs: GNDVI, RENDVI, and NDWI), which were calculated from hyperspectral data, were also assessed to estimate these leaf properties.

RESULTS

Some VIs were able to estimate CHL and N (R² > 0.68), but failed to estimate the other four leaf properties. Models developed with PLSR and SVR exhibited comparable performance to each other, and provided improved accuracy relative to VI models. CHL were estimated most successfully, with R² (coefficient of determination) > 0.94 and ratio of performance to deviation (RPD) > 4.0. N was also predicted satisfactorily (R² > 0.85 and RPD > 2.6). LWC, SLA and K were predicted moderately well, with R² ranging from 0.54 to 0.70 and RPD from 1.5 to 1.8. The lowest prediction accuracy was for P, with R² < 0.5 and RPD < 1.4.

CONCLUSION

This study showed that VIS-NIR-SWIR reflectance spectroscopy is a promising tool for low-cost, nondestructive, and high-throughput analysis of a number of leaf physiological and biochemical properties. Full-spectrum based modeling approaches (PLSR and SVR) led to more accurate prediction models compared to VI-based methods. We called for the construction of a leaf VIS-NIR-SWIR spectral library that would greatly benefit the plant phenotyping community for the research of plant leaf traits.

Specalyzer-an interactive online tool to analyze spectral reflectance measurements

Low-cost phenotyping using proximal sensors is increasingly becoming popular in plant breeding. As these techniques generate a large amount of data, analysis pipelines that do not require expertise in computer programming can benefit a broader user base. In this work, a new online tool Specalyzer is presented that allows interactive analysis of the spectral reflectance data generated by proximal spectroradiometers. Specalyzer can be operated from any web browser allowing data uploading, analysis, interactive plots and exporting by point and click using a simple graphical user interface. Specalyzer is evaluated with case study data from a winter wheat fertilizer trial with two fertilizer treatments. Specalyzer can be accessed online at http://www.specalyzer.org.

Combination of lumped hydrological and remote-sensing models to evaluate water resources in a semi-arid high altitude ungauged watershed of Sierra Nevada (Southern Spain)

Assessing water resources in high mountain semi-arid zones is essential to be able to manage and plan the use of these resources downstream where they are used. However, it is not easy to manage an unknown resource, a situation that is common in the vast majority of high mountain hydrological basins. In the present work, the discharge flow in an ungauged basin is estimated using the hydrological parameters of an HBV (Hydrologiska Byråns Vattenbalansavdelning) model calibrated in a "neighboring gauged basin". The results of the hydrological simulation obtained in terms of average annual discharge are validated using the VI-ETo model. This model relates a simple hydrological balance to the discharge of the basin with the evaporation of the vegetal cover of the soil, and this to the SAVI index, which is obtained remotely by means of satellite images. The results of the modeling for both basins underscore the role of the underground discharge in the total discharge of the hydrological system. This is the result of the deglaciation process suffered by the high mountain areas of the Mediterranean arc. This process increases the infiltration capacity of the terrain, the recharge and therefore the discharge of the aquifers that make up the glacial and periglacial sediments that remain exposed on the surface as witnesses of what was the last glaciation.

Estimating and mapping forest biomass using regression models and Spot-6 images (case study: Hyrcanian forests of north of Iran)

Hyrcanian forests of North of Iran are of great importance in terms of various economic and environmental aspects. In this study, Spot-6 satellite images and regression models were applied to estimate above-ground biomass in these forests. This research was carried out in six compartments in three climatic (semi-arid to humid) types and two altitude classes. In the first step, ground sampling methods at the compartment level were used to estimate aboveground biomass (Mg/ha). Then, by reviewing the results of other studies, the most appropriate vegetation indices were selected. In this study, three indices of NDVI, RVI, and TVI were calculated. We investigated the relationship between the vegetation indices and aboveground biomass measured at sample-plot level. Based on the results, the relationship between aboveground biomass values and vegetation indices was a linear regression with the highest level of significance for NDVI in all compartments. Since at the compartment level the correlation coefficient between NDVI and aboveground biomass was the highest, NDVI was used for mapping aboveground biomass. According to the results of this study, biomass values were highly different in various climatic and altitudinal classes with the highest biomass value observed in humid climate and high-altitude class.

Retrieval of canopy water content of different crop types with two new hyperspectral indices: Water Absorption Area Index and Depth Water Index

Crop canopy water content (CWC) is an essential indicator of the crop's physiological state. While a diverse range of vegetation indices have earlier been developed for the remote estimation of CWC, most of them are defined for specific crop types and areas, making them less universally applicable. We propose two new water content indices applicable to a wide variety of crop types, allowing to derive CWC maps at a large spatial scale. These indices were developed based on PROSAIL simulations and then optimized with an experimental dataset (SPARC03; Barrax, Spain). This dataset consists of water content and other biophysical variables for five common crop types (lucerne, corn, potato, sugar beet and onion) and corresponding top-of-canopy (TOC) reflectance spectra acquired by the hyperspectral HyMap airborne sensor. First, commonly used water content index formulations were analysed and validated for the variety of crops, overall resulting in a R² lower than 0.6. In an attempt to move towards more generically applicable indices, the two new CWC indices exploit the principal water absorption features in the near-infrared by using multiple bands sensitive to water content. We propose the Water Absorption Area Index (WAAI) as the difference between the area under the null water content of TOC reflectance (reference line) simulated with PROSAIL and the area under measured TOC reflectance between 911 and 1271 nm. We also propose the Depth Water Index (DWI), a simplified four-band index based on the spectral depths produced by the water absorption at 970 and 1200 nm and two reference bands. Both the WAAI and DWI outperform established indices in predicting CWC when applied to heterogeneous croplands, with a R² of 0.8 and 0.7, respectively, using an exponential fit. However, these indices did not perform well for species with a low fractional vegetation cover (< 30%). HyMap CWC maps calculated with both indices are shown for the Barrax region. The results confirmed the potential of using generically applicable indices for calculating CWC over a great variety of crops.

Potential for using remote sensing to estimate carbon fluxes across northern peatlands - A review

Peatlands store large amounts of terrestrial carbon and any changes to their carbon balance could cause large changes in the greenhouse gas (GHG) balance of the Earth's atmosphere. There is still much uncertainty about how the GHG dynamics of peatlands are affected by climate and land use change. Current field-based methods of estimating annual carbon exchange between peatlands and the atmosphere include flux chambers and eddy covariance towers. However, remote sensing has several advantages over these traditional approaches in terms of cost, spatial coverage and accessibility to remote locations. In this paper, we outline the basic principles of using remote sensing to estimate ecosystem carbon fluxes and explain the range of satellite data available for such estimations, considering the indices and models developed to make use of the data. Past studies, which have used remote sensing data in comparison with ground-based calculations of carbon fluxes over Northern peatland landscapes, are discussed, as well as the challenges of working with remote sensing on peatlands. Finally, we suggest areas in need of future work on this topic. We conclude that the application of remote sensing to models of carbon fluxes is a viable research method over Northern peatlands but further work is needed to develop more comprehensive carbon cycle models and to improve the long-term reliability of models, particularly on peatland sites undergoing restoration.

Agricultural water policy reforms in China: a representative look at Zhangye City, Gansu Province, China

Water resources are essential for agricultural production in the grain-producing region of China, and water shortage could significantly affect the production and international trade of agricultural products. China is placing effort in new policies to effectively respond to changes in water resources due to changes in land use/land cover as well as climatic variations. This research investigates the changes in land, water, and the awareness of farmer vis-à-vis the implementation of water-saving policies in Zhangye City, an experimental site for pilot programs of water resources management in China. This research indicates that the water saved through water-saving programs and changes in cropping structure (2.2 × 10⁸ m³ a^-1) is perhaps lower than the newly increased water withdrawal through corporate-led land reclamation (3.7 × 10⁸ m³ a^-1). Most critically, the groundwater withdrawal has increased. In addition, our survey suggests that local government is facing a dilemma of water conservation and agricultural development. Therefore, the enforcement of the ban on farmland reclamation and irrigation water quotas in our study area is revealed to be relatively loose. In this vein, the engagement of local stakeholders in water governance is essential for the future sustainable management of water resources.

Automatic and adaptive paddy rice mapping using Landsat images: Case study in Songnen Plain in Northeast China

Spatiotemporal explicit information on paddy rice distribution is essential for ensuring food security and sustainable environmental management. Paddy rice mapping algorithm through the Combined Consideration of Vegetation phenology and Surface water variations (CCVS) has been efficiently applied based on the 8day composites time series datasets. However, the great challenge for phenology-based algorithms introduced by unpromising data availability in middle/high spatial resolution imagery, such as frequent cloud cover and coarse temporal resolution, remained unsolved. This study addressed this challenge through developing an automatic and Adaptive paddy Rice Mapping Method (ARMM) based on the cloud frequency and spectral separability. The proposed ARMM method was tested on the Landsat 8 Operational Land Imager (OLI) image (path/row 118/028) in the Songnen Plain in Northeast China in 2015. First, the whole study region was automatically and adaptively subdivided into undisturbed and disturbed regions through a per-pixel strategy based on Landsat image data availability during key phenological stage. Second, image objects were extracted from approximately cloud-free images in disturbed and undisturbed regions, respectively. Third, phenological metrics and other feature images from individual or multiple images were developed. Finally, a flexible automatic paddy rice mapping strategy was implemented. For undisturbed region, an object-oriented CCVS method was utilized to take the full advantages of phenology-based method. For disturbed region, Random Forest (RF) classifier was exploited using training data from CCVS-derived results in undisturbed region and feature images adaptively selected with full considerations of spectral separability and the spatiotemporal coverage. The ARMM method was verified by 473 reference sites, with an overall accuracy of 95.77% and kappa index of 0.9107. This study provided an efficient strategy to accommodate the challenges of phenology-based approaches through transferring knowledge in parts of a satellite scene with finer time series to targets (other parts) with deficit data availability.

NDVI, scale invariance and the modifiable areal unit problem: An assessment of vegetation in the Adelaide Parklands

This research addresses the question as to whether or not the Normalised Difference Vegetation Index (NDVI) is scale invariant (i.e. constant over spatial aggregation) for pure pixels of urban vegetation. It has been long recognized that there are issues related to the modifiable areal unit problem (MAUP) pertaining to indices such as NDVI and images at varying spatial resolutions. These issues are relevant to using NDVI values in spatial analyses. We compare two different methods of calculation of a mean NDVI: 1) using pixel values of NDVI within feature/object boundaries and 2) first calculating the mean red and mean near-infrared across all feature pixels and then calculating NDVI. We explore the nature and magnitude of these differences for images taken from two sensors, a 1.24m resolution WorldView-3 and a 0.1m resolution digital aerial image. We apply these methods over an urban park located in the Adelaide Parklands of South Australia. We demonstrate that the MAUP is not an issue for calculation of NDVI within a sensor for pure urban vegetation pixels. This may prove useful for future rule-based monitoring of the ecosystem functioning of green infrastructure.

Genomic Bayesian functional regression models with interactions for predicting wheat grain yield using hyper-spectral image data

BACKGROUND

Modern agriculture uses hyperspectral cameras that provide hundreds of reflectance data at discrete narrow bands in many environments. These bands often cover the whole visible light spectrum and part of the infrared and ultraviolet light spectra. With the bands, vegetation indices are constructed for predicting agronomically important traits such as grain yield and biomass. However, since vegetation indices only use some wavelengths (referred to as bands), we propose using all bands simultaneously as predictor variables for the primary trait grain yield; results of several multi-environment maize (Aguate et al. in Crop Sci 57(5):1-8, 2017) and wheat (Montesinos-López et al. in Plant Methods 13(4):1-23, 2017) breeding trials indicated that using all bands produced better prediction accuracy than vegetation indices. However, until now, these prediction models have not accounted for the effects of genotype × environment (G × E) and band × environment (B × E) interactions incorporating genomic or pedigree information.

RESULTS

In this study, we propose Bayesian functional regression models that take into account all available bands, genomic or pedigree information, the main effects of lines and environments, as well as G × E and B × E interaction effects. The data set used is comprised of 976 wheat lines evaluated for grain yield in three environments (Drought, Irrigated and Reduced Irrigation). The reflectance data were measured in 250 discrete narrow bands ranging from 392 to 851 nm (nm). The proposed Bayesian functional regression models were implemented using two types of basis: B-splines and Fourier. Results of the proposed Bayesian functional regression models, including all the wavelengths for predicting grain yield, were compared with results from conventional models with and without bands.

CONCLUSIONS

We observed that the models with B × E interaction terms were the most accurate models, whereas the functional regression models (with B-splines and Fourier basis) and the conventional models performed similarly in terms of prediction accuracy. However, the functional regression models are more parsimonious and computationally more efficient because the number of beta coefficients to be estimated is 21 (number of basis), rather than estimating the 250 regression coefficients for all bands. In this study adding pedigree or genomic information did not increase prediction accuracy.

Studying bio-thermal effects at and around MSW dumps using Satellite Remote Sensing and GIS

Estimating negative impacts of MSW dumps on its surrounding environment is the key requirement for any remedial measures. This study has been undertaken to map bio-thermal effects of MSW dumping at and around dumping facilities (non-engineered) using satellite imagery for Faisalabad, Pakistan. Thirty images of Landsat 8 have been selected after validation for the accuracy of their observational details from April 2013 to October 2015. Land Surface Temperature (LST), NDVI, SAVI and MSAVI have been derived from these images through Digital Image Processing (DIP) and have been subjected to spatio-temporal analysis in GIS environment. MSW dump has been found with average temperature elevation of 4.3K and 2.78K from nearby agriculture land and urban settlement respectively. Vegetation health has been used as the bio-indicator of MSW effects and is implemented through NDVI, SAVI, MSAVI. Spatial analyses have been used to mark boundary of bio-thermally affected zone around dumped MSW and measure 700m. Seasonal fluctuations of elevated temperatures and boundary of the bio-thermally affected zones have also been discussed. Based on the direct relation found between vegetation vigor and the level of deterioration within the bio-thermally affected region, use of crops with heavy vigor is recommended to study MSW hazard influence using bio-indicators of vegetation health.

Statistical analysis of land surface temperature-vegetation indexes relationship through thermal remote sensing

Vegetation coverage has a significant influence on the land surface temperature (LST) distribution. In the field of urban heat islands (UHIs) based on remote sensing, vegetation indexes are widely used to estimate the LST-vegetation relationship. This paper devises two objectives. The first analyzes the correlation between vegetation parameters/indicators and LST. The subsequent computes the occurrence of vegetation parameter, which defines the distribution of LST (for quantitative analysis of urban heat island) in Kalaburagi (formerly Gulbarga) City. However, estimation work has been done on the valuation of the relationship between different vegetation indexes and LST. In addition to the correlation between LST and the normalized difference vegetation index (NDVI), the normalized difference build-up index (NDBI) is attempted to explore the impacts of the green land to the build-up land on the urban heat island by calculating the evaluation index of sub-urban areas. The results indicated that the effect of urban heat island in Kalaburagi city is mainly located in the sub-urban areas or Rurban area especially in the South-Eastern and North-Western part of the city. The correlation between LST and NDVI, indicates the negative correlation. The NDVI suggests that the green land can weaken the effect on urban heat island, while we perceived the positive correlation between LST and NDBI, which infers that the built-up land can strengthen the effect of urban heat island in our case study. Although satellite data (e.g., Landsat TM thermal bands data) has been applied to test the distribution of urban heat islands, but the method still needs to be refined with in situ measurements of LST in future studies.