Normalized Difference Vegetation Index Threshold Research Articles

A 40-Year Time Series of Land Surface Emissivity Derived from AVHRR Sensors: A Fennoscandian Perspective

Accurate land surface temperature (LST) retrieval depends on precise knowledge of the land surface emissivity (LSE). Neglecting or inaccurately estimating the emissivity introduces substantial errors and uncertainty in LST measurements. The emissivity, which varies across different surfaces and land uses, reflects material composition and surface roughness. Satellite data offer a robust means to determine LSE at large scales. This study utilises the Normalised Difference Vegetation Index Threshold Method (NDVITHM) to produce a novel emissivity dataset spanning the last 40 years, specifically tailored for the Fennoscandian region, including Norway, Sweden, and Finland. Leveraging the long and continuous data series from the Advanced Very High Resolution Radiometer (AVHRR) sensors aboard the NOAA and MetOp satellites, an emissivity dataset is generated for 1981–2022. This dataset incorporates snow-cover information, enabling the creation of annual emissivity time series that account for winter conditions. LSE time series were extracted for six 15 × 15 km study sites and compared against the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD11A2 LSE product. The intercomparison reveals that, while both datasets generally align, significant seasonal differences exist. These disparities are attributable to differences in spectral response functions and temporal resolutions, as well as the method considering fixed values employed to calculate the emissivity. This study presents, for the first time, a 40-year time series of the emissivity for AVHRR channels 4 and 5 in Fennoscandia, highlighting the seasonal variability, land-cover influences, and wavelength-dependent emissivity differences. This dataset provides a valuable resource for future research on long-term land surface temperature and emissivity (LST&E) trends, as well as land-cover changes in the region, particularly with the use of Sentinel-3 data and upcoming missions such as EUMETSAT’s MetOp Second Generation, scheduled for launch in 2025.

English

Crop mapping and its health monitoring are of key importance for a country like Pakistan where a considerable share of the GDP originates from agricultural production. Currently, extensive capital and human resource are utilized to record the crop types and monitor crop health due to variations in crop pattern and intercropping practices being observed in many parts of the country. Satellite Remote Sensing, however, can be utilized very efficiently to identify crop types and estimate acreage by using unique phenological cycles derived from NDVI thresholds. In this research, we proposed a semi-automated, efficient and cost-effective state-of-the-art technique for crop classification using satellite remote sensing datasets and GIS techniques to replace outdated conventional survey-based crop estimations procedures. This research was conducted in Sahiwal district, Pakistan to categorise several crop types using two major inputs: satellite (Sentinel-2, 10m) imagery and local crop calendar. The research adopted a semi-automatic approach to find the main crops of Rabi (winter) and Kharif (summer) using orthorectified and atmospherically corrected Level-1C images to extract phenological information based on Normalized Difference Vegetation Index (NDVI). The complete phenological life cycle of crops including sowing, growing, and harvesting stages were deeply analyzed with the help of crop calendar and NDVI profiles. Then this information passed through iterative selforganizing (ISO Data Model) of unsupervised classification technique in the form of temporal stacked images. The NDVI profiles of each crop were synchronized with the crop calendar of the study area to develop the crop categorization procedure. The results were validated with the statistics of the agriculture department Lahore and field samples gave about 85% and 88% accuracy for Rabi and Kharif cropping seasons respectively. It is concluded from the research that the tested methodology is capable of providing a fast, cost-efficient, and reliable solution for crop mapping instead of conventional survey-based crop area assessment techniques (conventional Girdawri) which requires a vast number of resources and time, and it can be replicated in different areas with minor changes

Temporal and Spatial Evolution of Herbivorous Waterbird Habitat in Floodplain Wetland Driven by Hydrology

AbstractThe hydrological rhythm of floodplain wetlands affected by natural climate variability and human activities is the driving factor affecting the distribution of herbivorous geese habitats. It has always been challenging to determine its long‐term variation, due to the lack of data on species distribution and broad wetland observation. In this study, the key normalized difference vegetation index threshold range suitable for herbivorous geese, greater white‐fronted geese (Anser albifrons, GWG) and bean geese (Anser fabalis, BG), was discovered by combining GPS tracking data with Landsat satellite data. The suitable habitat area and distribution variation characteristics of GWG and BG from 1982 to 2020 were extracted based on the threshold range. The results showed that the suitable habitat area had a declining trend after 2003, that the distribution had moved toward the lake's center, and that its average elevation had decreased by around 1 m, particularly during the arrival period. The change of herbivorous geese habitat is caused by the continuous drying of Poyang Lake. The study can offer a rational scientific foundation for wetland waterbird protection and watershed hydrological management.

Deep learning combined with Balance Mixup for the detection of pine wilt disease using multispectral imagery

Pine wilt disease (PWD) is one of the most destructive infectious diseases affecting coniferous forests. This research proposes a detection method based on deep learning combined with Balance Mixup. At the beginning, regions of interest (ROIs) on multispectral images are automatically generated using normalized difference vegetation index threshold segmentation algorithm. Multispectral data of these ROIs are extracted and labeled as positive (represented by 1) or negative (represented by 0) depending on whether they contained infected trees. During training, the Balance Mixup algorithm selects two multispectral data from a batch of training data according to a certain random strategy. The two data will be mixed into a new multispectral data whose label is the maximum value of the labels (0 or 1) of the original data. The above process will be repeated many times until a new batch of data is generated. A one-dimensional convolutional neural network called pine wilt disease net (PWDNet) is trained by the mixed data and output feature vectors, which are used as input data by a logistic model to determine whether there are PWD in mixed data. By combining Balance Mixup and PWDNet, our method achieved a recall of 1.00 and a precision of 0.90 in testing set. The satisfactory results indicates that the method can provide technical support for the prevention and control of PWD.

Macroalgae monitoring from satellite optical images using Context-sensitive level set (CSLS) model

Automatically detecting macroalgae from optical remote sensing images has been a long-standing problem in ocean monitoring. Existing operational spectral-based techniques tend to assign a threshold to an indicator such as Normalized Difference Vegetation Index (NDVI) to determine whether or not macroalgae exists in a pixel. This thresholding scheme relies on individual pixel features and cannot address the environment variability among different images or even across one single image. To address these limitations, we develop the unsupervised classification method taking the ecological indicator of NDVI as input by exploiting a region-based level set driven by context-sensitive energy function for macroalgae detection from optical remote sensing images. Quantitative and qualitative evaluations based on satellite optical images with various imaging time and spatial resolutions (16–250 m) from GF-1, GF-4, Landsat-8, and Aqua demonstrate the effectiveness and robustness for Ulva prolifera detection. The model is proved to be superior to the traditional NDVI thresholding method and state-of-the-art level set methods in terms of higher accuracy (Kappa coefficients 0.87–0.92) and automation (without artificial threshold/parameter adjustment). Moreover, the model has the advantage of accommodating complex environmental conditions (such as different water turbidity, thin clouds and sun glint), and can be utilized for detection of Sargassum bloom. The model could facilitate the accurate extraction of macroalgae information and thus serve disaster prevention and control. It may also be used to assist high-accuracy training data preparation for supervised classification and detect the objects with obvious texture and spectral characteristics (such as oil spills) based on customized bands combination.

Mapping global maximum irrigation extent at 30m resolution using the irrigation performances under drought stress

Accurate global irrigation information is essential for managing water scarcity and improving food security. However, the mapping of high-resolution irrigation at the global scale is challenging due to the wide range of climate conditions, crop types and phenology, ambiguous and heterogeneous spectral features, and farming practices. Here, a robust method is proposed using irrigation performance under drought stress as a proxy for crop productivity stabilization and crop water consumption. For each irrigation mapping zone (IMZ), dry months in the 2017–2019 period and the driest months in the 2010–2019 period were identified over the growing season. The thresholds of the normalized difference vegetation index (NDVI) in the dry months from 2017 to 2019 and the NDVI deviation (NDVIdev) in the driest month were identified to separate irrigated and rainfed cropland with samples. The final threshold from either the NDVI or the NDVIdev of the IMZ was determined with a higher overall accuracy in separating irrigated and non-irrigated areas. The results show that the global maximum irrigation extent (GMIE) at a 30-m resolution was 23.38% of global cropland in 2010–2019, with an overall accuracy of 83.6% globally and significant regional differences in irrigation proportions ranging from 1.1% in western Africa to 100% in Old World deserts among the 110 IMZs and from 0.4% in Belarus to 80.2% in Pakistan and 100% in Egypt among 45 countries. The study quantitatively distinguished annually and intermittently irrigated regions, which had values of 42% and 58% of global cropland, respectively, by applying indicators. This method, using the NDVI and NDVIdev thresholds, is simple, concrete and reproducible and better for zones with homogeneous weather conditions. The study offers independent, consistent and comparable information for defining the baseline, tracking changes in irrigation infrastructure, and leading future changes in how stakeholders plan and design irrigation systems.

Use of time series normalized difference vegetation index (NDVI) to monitor fall armyworm (Spodoptera frugiperda) damage on maize production systems in Africa

Fall armyworm (FAW) Spodoptera frugiperda (J.E. Smith), damage was monitored at a regional scale using time series data in Western and Southern African countries. The study employed the normalized difference vegetation index (NDVI) computed from Landsat 8 imagery using the Google Earth Engine (GEE) using image composites for the years 2013 to 2020 for the study areas. The index was then reclassified based on the NDVI threshold values into low, sparse, moderate, and dense classes. FAW prevalence data were then utilized to validate the correlation between the FAW infestation and NDVI values. FAW was associated with a decrease in vegetation productivity between the years 2016, 2017, and 2018 when the pest infestation was reported in the study areas. The validation results showed that there is a correlation between FAW infestation and NDVI (R20.83). Our study highlighted that NDVI can be used as a proxy to quantify pest damage to vegetation productivity.

Estimation of a suitable NDVI oriented for ecological water savings and phytoremediation in Baiyangdian Lake, North China

To maintain the ecosystem services of wetlands, this study developed an estimation method for suitable normalized difference vegetation index (NDVI) values by theintegration of ecological water savings and phytoremediation requirements based on experiments and literature surveys in Baiyangdian Lake, North China. In terms of ecological water savings (namely, reducing wetland water consumption by reducing vegetation evapotranspiration), the upper limit of the reed community area was determined by the water replenishment plan of the lake to achieve water balance. In terms of phytoremediation, Grade III of the Chinese water quality grade scale, the target water quality of the lake, was considered to calculate the lower limit of the reed community area for the purpose of good water quality. Considering the two aspects of the requirements of ecological water savings and phytoremediation comprehensively, the suitable reed planning area and NDVI threshold were determined. The results indicated that the suitable reed planting area was 138.05 ∼ 146.43 km2, and the NDVI threshold was 0.155 ∼ 0.316 in the high-water-lever situation (6.8 ∼ 7.0 m). Concurrently, the appropriate reed planting area was 122.39 ∼ 128.24 km2, and the NDVI threshold was 0.137 ∼ 0.277 in the low-water-lever situation (6.5 ∼ 6.8 m). These findings expanded the use of NDVI in the relationship research between wetland vegetation patterns and water resource utilisations, providing support for making vegetation pattern adjustment decisions for wetland protection and restoration in water-scarce regions.

Surface energy partitioning and evapotranspiration in a Pinus tabuliformis plantation in Northeast China.

Examining the land-atmosphere interaction in vegetation rehabilitation areas is important for better understanding of land surface processes affected by human activities. In this study, energy flux observations were used to investigate surface energy partitioning and evapotranspiration (ET) in a Pinus tabuliformis plantation in Northeast China in 2020 and 2021. The sensible heat flux (H) was the dominant component of Rn, and the ratio of H to the latent heat flux was higher than 1 at all growth stages. The two most important factors influencing the midday evaporative fraction and daily ET were the normalized difference vegetation index (NDVI) and soil water content at 10cm depth (SWC10). Cumulative precipitation (P) minus ET was 62.83 and 239.90mm in 2020 (annual P of 435.2mm) and 2021 (annual P of 632.8mm), respectively. The midday Priestley-Taylor coefficient (α), surface conductance (gs), and decoupling coefficient increased gradually from the onset of the mid-growing stage and decreased from the later growing stage. Midday α and gs increased with NDVI and SWC10 increasing until the NDVI (0.5) and SWC10 (0.17 mm3 mm-3) thresholds were reached, respectively. Midday α and gs were significantly influenced by vapor pressure deficit below 3 kPa, and the threshold value of midday gs was approximately 12mm s-1. In conclusion, this Pinus tabuliformis plantation regulated surface energy partitioning properly, and left a part of P for surface runoff and groundwater recharge in the semiarid region of Northeast China.

Developing Turfgrass Water Response Function and Assessing Visual Quality, Soil Moisture and NDVI Dynamics of Tall Fescue Under Varying Irrigation Scenarios in Inland Southern California

Highlights A turfgrass water response function was developed for 'Westcoaster' tall fescue. The Weathermatic smart controller demonstrated reliable estimations of reference evapotranspiration. Restricting irrigation frequency did not yield water savings or lead to improvements in turfgrass quality. Abstract. Efficient landscape irrigation is an important water conservation strategy and reduces strain on limited water resources in inland Southern California. A three-year field irrigation trial was conducted in Riverside, California, to evaluate the response of 'Westcoaster' tall fescue (Festuca arundinacea Schreb.) to a wide range of autonomous irrigation scenarios implemented using a Weathermatic smart controller. The response of the turfgrass was assessed using visual rating (VR) and normalized difference vegetation index (NDVI) data collected with a handheld sensor. These experimental data were then used to develop a regression-based turfgrass water response function (TWRF), which was further employed to estimate the response of tall fescue to different irrigation levels during years with varying atmospheric evaporation demands, including extreme conditions. A strong correlation was observed between NDVI and VR (r = 0.88). A minimum NDVI threshold of 0.7 was identified to maintain an acceptable level of tall fescue quality for residential areas. During the trial periods, irrigation application rates below 100% of reference evapotranspiration (ETo) were insufficient to sustain the desired quality of tall fescue. The TWRF estimations suggested that 60% ETo can be applied for short periods (7 to 43 days, depending on the atmospheric evaporative demands) during the summer before the visual quality drops below the minimum threshold. Comparing different irrigation strategies, it was observed that on-demand irrigation, which adjusts irrigation frequency based on ETo demand, resulted in higher NDVI and VR values compared to a limited watering schedule of only a few days per week. Analysis of soil moisture data revealed that the primary root water uptake occurred mainly in the topsoil layer (<33 cm), and the applied irrigation water primarily replenished the shallow soil layer (<20 cm) due to the coarse sandy loam soil characteristics. The Weathermatic controller, equipped with on-site air temperature measurements, provided reliable ETo estimations and can be used for autonomous landscape irrigation scheduling in semiarid regions. Keywords: Keywords., Evapotranspiration, Remote sensing, Smart irrigation controller, Urban irrigation, Water conservation.

Remote Sensing Monitoring of Changes in Lake Aquatic Vegetation before and after the Removal of the Fence Based on Sentinel-2: A Case Study in Lake Futou, Hubei Province

Aquatic vegetation is an important component and main primary producer of lake ecosystems and plays an important role in improving water quality and maintaining biodiversity, which is critical to diagnosing the health of aquatic ecosystems in shallow lakes. It is therefore important to accurately obtain information on dynamic changes and spatial-temporal distribution of aquatic vegetation. Based on the Sentinel-2 satellite remote sensing images from 2016–2022, we studied the feasibility of using remote sensing technology to monitor the spatial-temporal changes of aquatic vegetation before and after the removal of the fence, taking Futou Lake in Hubei Province as a case study. Two vegetation indices, Normalized Difference Vegetation Index (NDVI) and Submerged Aquatic Vegetation Index (SAVI) were applied to identify the open water and the aquatic vegetation through two threshold determination methods, Otsu algorithm and manual division threshold method. The results show that: (1) the classification based on the NDVI and manual division threshold method performs the best, with the overall classification accuracy of 94.44% and the Kappa coefficient of 85.23%. (2) The growth of aquatic vegetation is divided into stages, the first stage is enclosing culture, and the distribution of aquatic vegetation is less in 2016–2017, all around 10 km2. The second stage is after the removal of the fence, the distribution area of aquatic vegetation in 2018 is on an upward trend, and in 2019–2022 it is growing rapidly. (3) Spatially, the aquatic vegetation was mainly distributed at the former fence, specifically in the northeastern and southwestern waters of the Futou Lake and it spread to the core area of the lake, probably due to the elevation of the siltation of the lake bottom. (4) Potamogeton crispus and Trapa are the dominant species, the peak of the distribution range in Futou Lake occurs in 2021 with an area of about 50.89 km2, which needs to be controlled moderately. (5) The area covered by Potamogeton crispus in the Futou Lake has increased significantly, probably due to the siltation and accumulation of nutrients in the Futou Lake caused by the history of purse seine farming.

Where are the trees? Extent, configuration, and drivers of poor forest recovery 30 years after the 1988 Yellowstone fires

Postfire recovery of fire-adapted forests remains uncertain as climate and fire regimes continue to change. Areas of poor postfire tree regeneration following late-20th-century fires may reveal characteristics associated with increased vulnerability to forest decline. However, sufficient time must have elapsed and pre- and postfire forest cover must be compared to distinguish areas that have not recovered. We used remotely sensed data and the Normalized Difference Vegetation Index (NDVI) to detect areas of poor forest recovery across > 250,000 ha of area burned as stand-replacing fire 30 years after the 1988 Yellowstone fires. We asked three questions: (1) What is the extent and configuration of sparse and reduced forest recovery? (2) How do vegetation characteristics compare between areas of sparse and reduced recovery vs. recovered forest? (3) What environmental characteristics explain the distribution and patch size of sparse and reduced recovery? We related postfire (2013–14) NDVI to field-measured stem density to establish an NDVI threshold of sparse tree regeneration, and we contrasted pre- (1986–87) and postfire (2018–19) NDVI as a proxy for pre- and postfire forest cover. Sparse and reduced forest recovery occupied ∼ 41,000 ha across the burned area, about half of which was ≥ 150 m from ex situ seed sources. Patches of poor recovery were generally large, with ∼ 13,400 ha in patches ≥ 50 ha and an area-weighted mean patch size of 97 ha. Vegetation was short (<2 m) in areas of sparse and reduced recovery and non-evergreen biomass was three times greater than in recovered forest. Sparse and reduced recovery was more likely at high elevations, on steep slopes, and far from ex situ seed sources, and patches were larger at high elevations and far from seed sources. It took 20 years for sparse and reduced recovery to be distinguishable from recovered forest using NDVI, suggesting a time lag before remotely sensed data can detect alternative pathways of postfire forest recovery. Now, 30 years after the 1988 fires, ∼16 % of the forest burned as stand-replacing fire has failed to recover. The extent and configuration of these areas suggest some may persist as sparse or non-forest for the foreseeable future, with implications for biodiversity and ecosystem processes.

Urban land use land cover mapping in tropical savannah using Landsat-8 derived normalized difference vegetation index (NDVI) threshold

Generation of land use/land cover map at different spatial scales using satellite remote sensing data has been in practice as far back as early 1970s. Since then, research focus has been on the development of classification steps and improving the quality of the resulting maps. In recent times, the demand for detailed high accuracy land-use and land-cover (LULC) data has been on the increase due to the growing complexity of earth processes, while, at the same time, processing step is becoming more complex. This paper explores Landsat 8 derived normalized difference vegetation index (NDVI) threshold for the purpose of simplifying land cover classification process. NDVI images of January, May and December, 2018, representing dry, wet and harmattan seasons were generated. Thereafter, NDVI values corresponding to the location of a set of training data representing the target urban land covers (water, built-up area, soil, grassland and shrub) were extracted. Using the statistics of the extracted values, NDVI threshold for the respective land cover type were determined for the classification process. Finally, the classification accuracy was evaluated using the unbiased matrix coefficient technique which produced overall accuracy of 71.3%, 46.4% and 75.6% at 95% confidence limit for the months of January, May and December of the year review respectively. The result has shown that NDVI threshold is a simple and practical alternative to obtain LULC map at a reasonable time with a few data.

Evaluating the accuracy of satellite-based methods to estimate residential proximity to agricultural crops.

Epidemiologic investigations increasingly employ remote sensing data to estimate residential proximity to agriculture as a means of approximating individual-level pesticide exposure. Few studies have examined the accuracy of these methods and the implications for exposure misclassification. Compare metrics of residential proximity to agricultural land between a groundtruth approach and commonly-used satellite-based estimates. We inspected 349 fields and identified crops in current production within a 0.5 km radius of 40 residences in Idaho. We calculated the distance from each home to the nearest agricultural field and the total acreage of agricultural fields within a 0.5 km buffer. We compared these groundtruth estimates to satellite-derived estimates from three widely used datasets: CropScape, the National Land Cover Database (NLCD), and Landsat imagery (using Normalized Difference Vegetation Index thresholds). We found poor to moderate agreement between the classification of individuals living within 0.5 km of an agricultural field between the groundtruth method and the comparison datasets (53.1-77.6%). All satellite-derived estimates overestimated the acreage of agricultural land within 0.5 km of each home (average = 82.8-148.9%). Using two satellite-derived datasets in conjunction resulted in substantial improvements; specifically, combining CropScape or NLCD with Landsat imagery had the highest percent agreement with the groundtruth data (92.8-93.8% agreement). Residential proximity to agriculture is frequently used as a proxy for pesticide exposure in epidemiologic investigations, and remote sensing-derived datasets are often the only practical means of identifying cultivated land. We found that estimates of agricultural proximity obtained from commonly-used satellite-based datasets are likely to result in exposure misclassification. We propose a novel approach that capitalizes on the complementary strengths of different sources of satellite imagery, and suggest the combined use of one dataset with high temporal resolution (e.g., Landsat imagery) in conjunction with a second dataset that delineates agricultural land use (e.g., CropScape or NLCD).

Climatic and edaphic‐based predictors of normalized difference vegetation index in tropical dry landscapes: A pantropical analysis

AbstractAimSpatial patterns in resource supply drive variability in vegetation structure and function, yet quantification of this variability for tropical dry forests (TDFs) remains rudimentary. Several climate‐driven indices have been developed to classify and delineate TDFs globally, but there has not been a climo‐edaphic synthesis of these indices to assess and delineate the extent of TDFs. A statistical climo‐edaphic synthesis of these indices is therefore required.LocationPantropical.Time periodModern.Major taxa studiedVascular plants.MethodsWe assembled most known prior descriptions of TDFs into a single data layer and assessed statistically how the TDF biome, which we call tropical dry landscapes (TDLs) composed of forest and non‐forest vegetation, varied with respect to the normalized difference vegetation index (NDVI) sensed by MODIS (250 m pixel resolution). We examined how the NDVI varied with respect to mean annual temperature (MAT) and rainfall (MAR), precipitation regime, evapotranspiration and the physical, chemical and biological properties of TDL soils.ResultsOverall, the NDVI varied widely across TDLs, and we were able to identify five principal NDVI categories. A regression tree model captured 90% of NDVI variation across TDLs, with 14 climate and soil metrics as predictors. The model was then pruned to use only the three strongest metrics. These included the Lang aridity index, total evapotranspiration (ET) and MAT, which aligned with identified NDVI thresholds and accounted for 70% of the variation in NDVI. We found that across a global TDL distribution, ET was the strongest positive predictor and MAT the strongest negative predictor of the NDVI.Main conclusionsThe remote sensing‐based approach described here provides a comprehensive and quantitative biogeographical characterization of global TDL occurrence and the climatic and edaphic drivers of these landscapes.

Applying Machine Learning for Threshold Selection in Drought Early Warning System

This study investigates the relationship between the Normalized Difference Vegetation Index (NDVI) and meteorological drought category to identify NDVI thresholds that correspond to varying drought categories. The gridded evaluation was performed across a 34-year period from 1982 to 2016 on a monthly time scale for Grassland and Temperate regions in Australia. To label the drought category for each grid inside the climate zone, we use the Australian Gridded Climate Dataset (AGCD) across a 120-year period from 1900 to 2020 on a monthly scale and calculate percentiles corresponding to drought categories. The drought category classification model takes NDVI data as the input and outputs of drought categories. Then, we propose a threshold selection algorithm to distinguish the NDVI threshold to indicate the boundary between two adjacent drought categories. The performance of the drought category classification model is evaluated using the accuracy metric, and visual interpretation is performed using the heat map. The drought classification model provides a concept to evaluate drought severity, as well as the relationship between NDVI data and drought severity. The results of this study demonstrate the potential application of this concept toward early drought warning systems.

Assessment of vegetation dynamics and forest loss using google earth engine and multi-temporal sentinel-2 imagery

This study evaluated regional vegetation dynamics and changes between 2015 and 2020 using Google earth engine (GEE) platform and normalized difference vegetation index (NDVI) derived from the multi-petabyte catalogue of sentinel-2 imageries. Using the computational capability of GEE, yearly mean NDVI from 2015 to 2020 were computed using level C-1 product. Subsequently, each of the NDVI images was classified into four land cover classes; water bodies, non-vegetated, grassland /cropland /shrubs, and forest using NDVI threshold values of &lt; 0.01, 0.01-0.20, 0.20-0.30 and &gt; 0.30, respectively. The classified maps allowed for the assessment of yearly variation in vegetation and changes between 2015 and 2020. Result showed that non-vegetated area increased from 18.53% in 2015 to 42.56% in 2020 (~ 25.00% gain), the forest area reduced to 6.78% in 2020 compared to 23.76% measured in 2015 (~ 17.00% loss in forest); whereas water bodies and grassland/cropland/shrubs remained relatively constant (0.21 and ~ 50.00%, respectively) across the years studied. Presently, the forest land was estimated to be about 2, 371.131 km2 (~ 6.70%) of the total land mass, grassland/cropland/shrubs occupied 17, 770.79 km2 (~ 50.07%), non-vegetated area was slightly less than half with 15, 274.85 km2 (~ 43.04%) and water bodies occupied 75.68 km2 (~ 0.21%).

Mapping Fine-Scale Crown Scorch in 3D with Remotely Piloted Aircraft Systems

Remotely piloted aircraft systems (RPAS) are providing fresh perspectives for the remote sensing of fire. One opportunity is mapping tree crown scorch following fires, which can support science and management. This proof-of-concept shows that crown scorch is distinguishable from uninjured canopy in point clouds derived from low-cost RGB and calibrated RGB-NIR cameras at fine resolutions (centimeter level). The Normalized Difference Vegetation Index (NDVI) provided the most discriminatory spectral data, but a low-cost RGB camera provided useful data as well. Scorch heights from the point cloud closely matched field measurements with a mean absolute error of 0.52 m (n = 29). Voxelization of the point cloud, using a simple threshold NDVI classification as an example, provides a suitable dataset worthy of application and further research. Field-measured scorch heights also showed a relationship to RPAS-thermal-camera-derived fire radiative energy density (FRED) estimates with a Spearman rank correlation of 0.43, but there are many issues still to resolve before robust inference is possible. Mapping fine-scale scorch in 3D with RPAS and SfM photogrammetry is a viable, low-cost option that can support related science and management.

NDVI Threshold-Based Urban Green Space Mapping from Sentinel-2A at the Local Governmental Area (LGA) Level of Victoria, Australia

Obtaining accurate, precise and timely spatial information on the distribution and dynamics of urban green space is crucial in understanding livability of the cities and urban dwellers. Inspired from the importance of spatial information in planning urban lives, and availability of state-of-the-art remote sensing data and technologies in open access forms, in this work, we develop a simple three-level hierarchical mapping of urban green space with multiple usability to various stakeholders. We utilize the established Normalized Difference Vegetation Index (NDVI) threshold on Sentinel-2A Earth Observation image data to classify the urban vegetation of each Victorian Local Government Area (LGA). Firstly, we categorize each LGA region into two broad classes as vegetation and non-vegetation; secondly, we further categorize the vegetation regions of each LGA into two sub-classes as shrub (including grassland) and trees; thirdly, for both shrub and trees classes, we further classify them as stressed and healthy. We not only map the urban vegetation in hierarchy but also develop Urban Green Space Index (UGSI) and Per Capita Green Space (PCGS) for the Victorian Local Government Areas (LGAs) to provide insights on the association of demography with urban green infrastructure using urban spatial analytics. To show the efficacy of the applied method, we evaluate our results using a Google Earth Engine (GEE) platform across different NDVI threshold ranges. The evaluation result shows that our method produces excellent performance metrics such as mean precision, recall, f-score and accuracy. In addition to this, we also prepare a recent Sentinel-2A dataset and derived products of urban green space coverage of the Victorian LGAs that are useful for multiple stakeholders ranging from bushfire modellers to biodiversity conservationists in contributing to sustainable and resilient urban lives.

USA Crop Yield Estimation with MODIS NDVI: Are Remotely Sensed Models Better than Simple Trend Analyses?

Crop yield forecasting is performed monthly during the growing season by the United States Department of Agriculture’s National Agricultural Statistics Service. The underpinnings are long-established probability surveys reliant on farmers’ feedback in parallel with biophysical measurements. Over the last decade though, satellite imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) has been used to corroborate the survey information. This is facilitated through the Global Inventory Modeling and Mapping Studies/Global Agricultural Monitoring system, which provides open access to pertinent real-time normalized difference vegetation index (NDVI) data. Hence, two relatively straightforward MODIS-based modeling methods are employed operationally. The first model constitutes mid-season timing based on the maximum peak NDVI value, while the second is reflective of late-season timing by integrating accumulated NDVI over a threshold value. Corn model results nationally show the peak NDVI method provides a R2 of 0.88 and a coefficient of variation (CV) of 3.5%. The accumulated method, using an optimally derived 0.58 NDVI threshold, improves the performance to 0.93 and 2.7%, respectively. Both these models outperform simple trend analysis, which is 0.48 and 7.4%, correspondingly. For soybeans the R2 results of the peak NDVI model are 0.62, and 0.73 for the accumulated using a 0.56 threshold. CVs are 6.8% and 5.7%, respectively. Spring wheat’s R2 performance with the accumulated NDVI model is 0.60 but just 0.40 with peak NDVI. The soybean and spring wheat models perform similarly to trend analysis. Winter wheat and upland cotton show poor model performance, regardless of method. Ultimately, corn yield forecasting derived from MODIS imagery is robust, and there are circumstances when forecasts for soybeans and spring wheat have merit too.