Canopy Cover Data Research Articles

Distribution of Fallen Tree Cases on Vegetation Canopy Cover and Land Use in Surakarta City

Abstract Fallen trees are a common phenomenon during the rainy season in Surakarta City. This condition poses a threat to urban communities while losing crucial ecosystem services to urban vegetation. The study aims to map the distribution of fallen trees and compare them with canoe vegetation coverage as well as land use. Analysis of the fallen trees was carried out with administrative territory limits in 2023 until the three first month of 2024. Data collection uses a combination of bottom-up and top-down approaches. The canopy coverage data is estimated from the Sentinel 2A image using the Normalized Difference Vegetation Index (NDVI). A comparison of three variables (fallen tree, NDVI and land use) is implemented through a spatial overlay, regression analysis and descriptive analysis. The results show that there were 113 cases of fallen trees in the 2023-three-month period in early 2024. Fallen trees occur mainly on yards (35.40%) and roadside (29.20%). Surveys based on canopy coverage showed that fallen trees occurred more frequently in the very low (32.74%) and low (31.86%) NDVI categories. This study concludes that changes in the urban environment pose a risk of tree falls due to reduced supportive capacity. Fallen trees cause urban ecological loss to urban area because they also occur in parks (12.38%).

Mapping canopy cover for municipal forestry monitoring: Using free Landsat imagery and machine learning

Trees across the urban-rural continuum are recognized for their ecological importance and ecosystem services. Municipalities often utilize spatial canopy cover data for monitoring this resource. Monitoring frameworks typically rely on fine-scale maps derived from very high spatial resolution sensors, which are high quality but expensive and unwieldy for consistent wide-area monitoring. In this paper, we explore how free Landsat imagery, supported by very high-resolution imagery interpretation and/or digital hemispherical photographs, can be used to effectively map canopy cover at a scale appropriate for municipal monitoring. We compare linear models and random forest machine learning for predicting canopy cover across a landscape (general) and within specific land covers (specialized). We create 2018 canopy cover maps and track progress towards forestry objectives in a region of southern Ontario, Canada. Random forest models using all reference data perform best for general use (R2: 0.90, RMSE: 10.1 %), separating non-canopy vegetation (e.g., agricultural fields) from tree canopy. Specialized models are useful in forest land cover patches, where hemispherical photographs relate with Landsat at a moderate strength (R2: 0.67, RMSE: 2.73 %), and in residential areas, capturing the totality of canopy cover variation (R2: 0.85, RMSE: 5.66 %). Accuracy was assessed with standard cross-validation, which is useful given limited resources. However, following best practice, an independent reference sample was also leveraged to assess the best general model (R2: 0.86, RMSE: 11.4 %), indicating that cross-validation was slightly overoptimistic. Results show that Caledon, a rural-dominant municipality within the study area, is the greenest (34 % canopy cover). The two cities (Brampton and Mississauga) have 15.9 % and 17.5 % canopy cover. Residential canopy criteria indicate “Good” performance in Caledon, “Moderate” in Mississauga, and “Low” in Brampton based on our 2018 assessment. The methods described here can provide municipalities with a low-cost approach for tree canopy monitoring across complex landscapes. Data availabilityThe data used for this paper are available at: https://zenodo.org/records/12549244. The code created for this paper are available at: https://github.com/ZZMitch/PredictTreeCC_Landsat_1972to2020.

Site-Level Modelling Comparison of Carbon Capture by Mixed-Species Forest and Woodland Reforestation in Australia

Large areas of Australia’s natural woodlands have been cleared over the last two centuries, and remaining woodlands have experienced degradation from human interventions and anthropogenic climate change. Restoration of woodlands is thus of high priority both for government and society. Revegetation of deforested woodlands is increasingly funded by carbon markets, with accurate predictions of site-level carbon capture an essential step in the decision making to restore. We compared predictions of carbon in above-ground biomass using both the IPCC Tier 2 modelling approach and Australia’s carbon accounting model, FullCAM, to independent validation data from ground-based measurements. The IPCC Tier 2 approach, here referred to as the FastTrack model, was adjusted to simulate carbon capture by mixed-species forests for three planting configurations: direct seeding, tubestock planting, and a mix thereof. For model validation, we collected data on above-ground biomass, crown radius, and canopy cover covering an age range of 9–35 years from 20 plantings (n = 6044 trees). Across the three planting configurations, the FastTrack model showed a bias of 2.4 tC/ha (+4.2% of the observed mean AGB), whilst FullCAM had a bias of −24.6 tC/ha (−42.9% of the observed mean AGB). About two-thirds of the error was partitioned to unsystematic error in FastTrack and about one-quarter in FullCAM, depending on the goodness-of-fit metric assessed. Model bias differed strongly between planting configurations. For the FastTrack model, we found that additional canopy cover data estimated from satellite images obtained at different years can improve the carbon capture projections. To attain the highest accuracy of carbon projection at the site level, we recommend using a model with parameters calibrated for the specific planting configuration using local representative data.

Testing the Performance of LSTM and ARIMA Models for In-Season Forecasting of Canopy Cover (CC) in Cotton Crops

Cotton (Gossypium spp.), a crucial cash crop in the United States, requires the constant monitoring of growth parameters for informed decision-making. Recently, forecasting models have gained prominence for predicting canopy indicators, aiding in-season planning and management decisions to optimize cotton production. This study employed unmanned aerial system (UAS) technology to collect canopy cover (CC) data from a 40-hectare cotton field in Driscoll, Texas, in 2020 and 2021. Long short-term memory (LSTM) models, trained using 2020 data, were subsequently applied to forecast the CC values for 2021. These models were compared with real-time auto-regressive integrated moving average (ARIMA) models to assess their effectiveness in predicting the CC values up to 14 days in advance, starting from the 28th day after crop emergence. The results showed that multiple-input multi-step output LSTM models achieved higher accuracy in predicting the in-season CC values during the early growth stages (up to the 56th day), with an average testing RMSE of 3.86, significantly lower than other single-input LSTM models. Conversely, when sufficient testing data are available, single-input stacked-LSTM models demonstrated precision in CC predictions for later stages, achieving an average RMSE of 3.06. These findings highlight the potential of LSTM models for in-season CC forecasting, facilitating effective management strategies in cotton production.

An Advanced Terrain Vegetation Signal Detection Approach for Forest Structural Parameters Estimation Using ICESat-2 Data

Accurate forest structural parameters (such as forest height and canopy cover) support forest carbon monitoring, sustainable forest management, and the implementation of silvicultural practices. The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), which is a spaceborne Light Detection and Ranging (LiDAR) satellite, offers significant potential for acquiring precise and extensive information on forest structural parameters. However, the ICESat-2 ATL08 product is significantly influenced by the geographical environment and forest characteristics, maintaining considerable potential for enhancing the accuracy of forest height estimation. Meanwhile, it does not focus on providing canopy cover data. To acquire accurate forest structural parameters, the Terrain Signal Neural Network (TSNN) framework was proposed, integrating Computer Vision (CV), Ordering Points to Identify the Clustering Structure (OPTICS), and deep learning. It encompassed an advanced approach for detecting terrain vegetation signals and constructing deep learning models for estimating forest structural parameters using ICESat-2 ATL03 raw data. First, the ATL03 footprints were visualized as Profile Raster Images of Footprints (PRIF), implementing image binarization through adaptive thresholding and median filtering denoising to detect the terrain. Second, the rough denoising buffers were created based on the terrain, combining with the OPTICS clustering and Gaussian denoising algorithms to recognize the terrain vegetation signal footprints. Finally, deep learning models (convolutional neural network (CNN), ResNet50, and EfficientNetB3) were constructed, training standardized PRIF to estimate forest structural parameters (including forest height and canopy cover). The results indicated that the TSNN achieved high accuracy in terrain detection (coefficient of determination (R2) = 0.97) and terrain vegetation signal recognition (F-score = 0.72). The EfficientNetB3 model achieved the highest accuracy in forest height estimation (R2 = 0.88, relative Root Mean Squared Error (rRMSE) = 13.5%), while the CNN model achieved the highest accuracy in canopy cover estimation (R2 = 0.80, rRMSE = 18.5%). Our results have significantly enhanced the accuracy of acquiring ICESat-2 forest structural parameters, while also proposing an original approach combining CV and deep learning for utilizing spaceborne LiDAR data.

Decline of a North American rocky intertidal foundation species linked to extreme dry, downslope Santa Ana winds

Foundation species are essential to ecosystem function, but their role as habitat providers is predicated on their spatial dominance. Worldwide, kelps, seagrasses, corals, and other marine foundation species have declined. This is true also for rockweeds, the canopy-forming analog of subtidal kelp forests in temperate rocky intertidal ecosystems. On the west coast of North America, dense beds of the rockweed Silvetia compressa occur across large biogeographic regions, benefitting numerous species by ameliorating physical stress caused by sun exposure, desiccation, heat, and wave disturbance. Like many rockweed species, Silvetia is long-lived, slow-growing, and short-dispersing – characteristics that reduce its resilience to disturbance. Using a generalized additive mixed-effects model with explicit spatial effects, we analyzed canopy cover data from 30 sites spanning 18 years, and we tested the hypothesis that Silvetia population trends are tightly linked to atmospheric climate conditions, particularly Santa Ana wind events (SAWs): strong, hot, and dry downslope winds that originate inland and move offshore. We found that the rockweed had declined markedly, particularly at sites south of the major biogeographic break, Point Conception (PC), including the California Channel Islands and southern California mainland, and a highly significant negative effect of dewpoint depression, a measure of moisture content in the atmosphere, on Silvetia cover across all three regions in this study. Our results suggest that any increases in the frequency or intensity of SAWs are likely to lead to large declines and possible extirpation of Silvetia, as well as the important ecological services the species provides.

Using the plant height and canopy coverage to estimation maize aboveground biomass with UAV digital images

The rapid and efficient estimation of aboveground biomass (AGB) in maize proves advantageous for growth assessment, grain quality and yield prediction, and timely field management. The unmanned aerial vehicle (UAV) imaging system's adaptable operation and its capability to capture images with comparatively high temporal and spatial resolutions underscore its potential for supplanting conventional techniques in crop AGB measurement. However, previous studies have predominantly focused on utilizing spectral, structural, textural, and vegetation indices extracted from UAV imagery to forecast crop AGB through linear models or machine learning methods. Frequently, these approaches lack essential mechanistic elucidation. This study seeks to formulate a three-dimensional (3D) AGB estimation model for maize inbred lines by utilizing plant height and canopy coverage data obtained from UAV digital images. The results indicated that: (1) The amalgamation of UAV digital imagery with the canopy height model (CHM) proficiently enabled the estimation of maize plant height. The coefficient of determination (R2) and root mean square error (RMSE) values for measured and estimated plant height across various growth stages were 0.93 and 0.17 m, respectively. (2) The maize AGB estimation models employing canopy coverage exhibited higher accuracy in comparison to those relying on plant height within the testing set, with the R2 value for the measured and estimated maize AGB across four genotypes all equal to or greater than 0.91. (3) The accuracy of the maize AGB estimation model, developed by incorporating both comprehensive plant height and canopy coverage, surpassed that achieved by solely utilizing plant height or canopy coverage. In applying different genotypes across multiple years, the AGB estimation model presented in the study demonstrated both high accuracy and versatility. The utilization of UAV digital imaging technology offers an expedient and cost-effective approach for AGB in maize breeding.

Rapid and Non-Destructive Methodology for Measuring Canopy Coverage at an Early Stage and Its Correlation with Physiological and Morphological Traits and Yield in Sugarcane

Screening for elite sugarcane genotypes for canopy cover in a rapid and non-destructive way is important to accelerate varietal/clonal selection, and little information is available regarding canopy cover and leaf production, leaf area, biomass production, and cane yield in sugarcane crop. In the present investigation, the digital images of sugarcane crop by using Canopeo software was assessed for their correlation with the physiological and morphological parameters and cane yield production. The results revealed that among the studied parameters, canopy coverage has shown a significantly better correlation with the plant height (0.581 **), leaf length (0.853 **), leaf width (0.587 **), and leaf area (0.770 **) in commercial sugarcane clones. Two-way cluster analysis has led to the identification of Co 0238, Co 86249, Co 10026, Co 99004, Co 94008, and Co 95020 with better physiological traits for higher sugarcane yield under changing climate. Additionally, in another field experiment with pre-breeding, germplasm, and interspecific hybrid sugarcane clones, the canopy coverage showed a significantly better correlation with germination, shoot count, leaf weight, leaf area index, and plant height, and finally with biomass (r = 0.612 **) and cane yield (r = 0.458 **). It has been found that the plant height, total dry matter (TDM), and leaf area index (LAI) had significant correlation with the cane yield, and the canopy cover data from digital images act as a surrogate for these traits, and further it has been observed that CC had better correlation with cane yield compared to the other physiological traits viz., SPAD, total chlorophyll (TC), and canopy temperature (CT) under ambient conditions. Light interception determined using a line quantum sensor had a significant positive correlation (r = 0.764 **) with canopy coverage, signifying the importance of determining the latter in a non-destructive way in a rapid manner and low cost.

Mangrove Distribution and Estimation of Canopy Closure Condition: Employing the Simple Hemispherical Photography Method within the Mangrove Community of Waiheru Village

The canopy closure within Waiheru Village's mangrove community is crucial in shielding residential areas from robust winds and providing shade for associated aquatic organisms, thereby mitigating the effects of direct sunlight. Furthermore, the shading provided by the mangrove canopy contributes to the area's carrying capacity for developing mangrove-based ecotourism. This study aims to investigate the distribution of mangrove species, substrate distribution, and the condition of the mangrove canopy closure. Stratified Random Sampling was employed for the data collection on mangroves and substrates, while the Hemispherical Photography method was utilized to gather canopy cover data. The analysis revealed the presence of 11 mangrove species from seven different families, with Rhizophora mucronata exhibiting the most extensive growth distribution compared to other species. Two substrate types, sandy and sandy mud, were also identified. The mangrove canopy closure in Waiheru Village remains in favorable condition, as most observation stations demonstrate a high proportion of dense and moderate canopy closure.

Improved Model Estimates of Tree Debris Following Ice Storms

Planning to prepare for storms should involve the estimation of tree debris. This paper tested an improvement of a rapid estimation model of tree debris following ice storms. An initial model found using 30-m resolution National Land Cover Database (NLCD) tree canopy cover (TCC) data did not significantly (P ≈ 0.20) improve estimation of tree debris within a community right-of-way (ROW) following an ice storm. We tested if finer resolution National Agriculture Imagery Program (NAIP) TCC imagery (2-m resolution or better) could more accurately predict tree debris after an ice storm. Tree canopy cover was estimated with NAIP across the entire community (TCCCITY) and also the area that only covered the ROW plus a 15.24-m (50-foot) buffer on each side (TCCROW). The TCCCITY (P = 0.08) estimate marginally improved tree debris prediction in the overall multiple regression model (R2 adj = 0.917; F = 133.8; df = 3,33), but this was not the case with the TCCROW (P = 0.66) estimate. The TCCCITY estimate was 34.7% (SEM = 2.0) and significantly (P < 0.001) 2 times greater than in the 16.2% (SEM = 2.2) TCC estimate from NLCD imagery. We found the TCCROW was 32.6% (SEM = 1.6) and significantly lower (P = 0.003) than in TCCCITY. Results from this study may improve the overall ability to predict tree debris following ice storms from the regional models currently used to a more local estimate for a city. Future investigations are needed to determine if this is the case.

Habitat Usage, Dietary Niche Overlap, and Potential Partitioning between the Endangered Spotted Turtle (Clemmys guttata) and Other Turtle Species

Habitat loss and degradation affect populations in isolation while also modifying the intensity of interspecific interactions, which may be especially relevant for species of conservation concern coexisting with more common species. We explored habitat preferences and potential interspecific resource competition in the endangered Spotted Turtle (Clemmys guttata). For habitat data, water quality measurements were recorded once per trapping session, while canopy cover and vegetation data were recorded once per field season. We also investigated niche overlap in habitat and food resources between C. guttata and other, more common, turtle species. Our data indicated that the abundance of C. guttata was negatively correlated with dissolved O2 and pH levels and positively correlated with body of water depth and canopy cover. The investigation of niche overlap revealed that Mud Turtles (Kinosternon subrubrum) and Painted Turtles (Chrysemys picta), but not Snapping Turtles (Chelydra serpentina), were negatively correlated with the abundance of C. guttata despite differences in characteristics of bodies of water used by each species. We used structural equation modeling to better understand if the relationships between C. guttata and other turtles were due to direct interactions or simply reflected environmental influences. We then used stable isotope analyses to compare similarities in δ13C and δ15N isotopes as proxies for diet overlap between C. guttata and other turtles and ultimately found that all species had similar, broad diets. The inverse relationship between abundance of C. guttata and other species, paired with the overlap in dietary niche space, suggests there is potential for interspecific interactions to negatively impact the abundance of C. guttata within turtle communities on the Atlantic Coastal Plain. The results of this study highlight that management efforts may need to consider that species of conservation concern coexist with more common species, especially as habitat loss decreases the breadth of habitat available.

Comparison of mangrove canopy covering accuracy using landsat 8 and landsat 9 imagery based on several vegetation indices in West Bali National Park

The remote sensing implementation is beneficial as a means of monitoring the ecosystem. Landsat imagery is a remote sensing (open access) based data source with a long and wide monitoring period with good image quality. This study compares the accuracy of Landsat 8 and Landsat 9 satellite images in detecting mangrove canopy cover using 13 different remote sensing vegetation indices in the West Bali National Park, Indonesia. The mangrove canopy cover data was collected with the hemispherical photography method. A linear regression test was conducted to determine the relationship between the remote sensing vegetation indices and the field's percentage of mangrove canopy cover. The result indicated that Landsat 8 was more accurate in detecting mangrove canopy cover than Landsat 9. Of the 13 remote sensing indices evaluated, the Chlorophyll Vegetation Index (CVI) had the highest accuracy, with R2 values of 0.86 and 0.75 for Landsat 8 and 9, respectively.*

Ecological Niche Modelling and Potential Distribution of Artemisia sieberi in the Iranian Steppe Vegetation

Artemisia sieberi Besser occurs in many parts of the Irano-Turanian floristic region, which is mostly distributed throughout the Iranian plateau, especially in Iran. This study aimed to identify the effect of the soil and topography variables on A. sieberi distribution. We used canopy cover data to fit models using generalized additive models (GAMs). The results showed that the response pattern of A. sieberi along with the gradient of soil clay, soil saturation moisture, soil nitrogen and soil acidity followed the monotonic increase model, and its canopy cover percentage augmented by increasing the values of the factors. Conversely, the A. sieberi canopy cover percentage decreased by increasing the amount of soil sand, bare soil and the geographic aspect. The A. sieberi responses are in contrast to the niche theory. The relationship between the species response pattern and the gradients of soil silt, soil salinity, lime percentage, organic carbon, altitude, land slope, litter, gravel percentage, stone percentage, mean annual precipitation and mean annual temperature followed a unimodal model (consistent with the niche theory). The optimal growth limits for these factors were 32%, 1.75 ds/m, 35%, 1.3%, 2000 m, 43%, 10%, 32%, 250 mm and 15 °C, respectively. Our results highlight that environmental factors, such as soil texture, amount of soil lime, mean annual precipitation, altitude and land slope, had quantifiable effects on the performance of A. sieberi. Our findings could provide useful information for improvement, restoration and conservation programs. However, a further comprehension of the species–environment relationship is needed to predict the effects of climate change on the species habitat.

Analysis of habitat canopy cover for bird’s family bucerotidae with canopy capture in pocut meurah intan forest park, aceh besar

Pocut Meurah Intan Grand Forest Park (Tahura) is a form of nature conservation area which is overgrown by various types of plants that are widely used by bird species such as forage trees and nesting trees. Bird species from the Bucerotidae family are one of the fauna that take advantage of the presence of various types of plants in the area. When the existence of plants that function as feeding trees or nesting trees is threatened, the impact will be experienced by various bird species due to habitat changes such as a shortage of life-supporting plants. One of the factors that influence the use of trees in their habitat by birds is the density and canopy cover. The purpose of this study was to analyze the percentage of canopy cover for forage trees and bird nesting trees in the Bucerotidae family in Tahura Pocut Meurah Intan. Descriptive method is the method chosen in this study. The research location stations were determined using purposive sampling. To determine the canopy cover of forage trees and nesting trees, Canopycapture ver.1.0.2 software was used with the Hemispherical Photography method. Hemispherical Photography is a method by calculating the area of the canopy cover using a camera from under the tree canopy. This method produces more accurate data because the area captured by the camera is more specific so that it describes the actual tree canopy cover. Tree canopy cover data collection using Hemispherical Photography using the front camera of the XIAOMI Redmi Note 6 Pro mobile phone with a dual camera resolution of 20 and 2 megapixels with HD resolution (1,280 x 720) at a point of taking photos. This technique is used in a 10 mx 10 m plot and analyzed using Microsoft office 2021 software. This research was conducted in January – February 2021 with a total of 2 stations. The results of this study found 14 species of forage trees and 13 species of nesting trees. The Moraceae family is more dominant in the use of birds from the Bucerotidae family as feed trees, while the Lauraceae family and also the Moraceae family are dominant as nesting trees. The value of tree canopy cover in Tahura Pocut Meurah Intan is 84.00% which belongs to the solid category.

Evaluating Statewide NAIP Photogrammetric Point Clouds for Operational Improvement of National Forest Inventory Estimates in Mixed Hardwood Forests of the Southeastern U.S.

The U.S. Forest Service, Forest Inventory and Analysis (FIA) program is tasked with making and reporting estimates of various forest attributes using a design-based network of permanent sampling plots. To make its estimates more precise, FIA uses a technique known as post-stratification to group plots into more homogenous classes, which helps lower variance when deriving population means. Currently FIA uses a nationally available map of tree canopy cover for post-stratification, which tends to work well for forest area estimates but less so for structural attributes like volume. Here we explore the use of new statewide digital aerial photogrammetric (DAP) point clouds developed from stereo imagery collected by the National Agricultural Imagery Program (NAIP) to improve these estimates in the southeastern mixed hardwood forests of Tennessee and Virginia, United States (U.S.). Our objectives are to 1. evaluate the relative quality of NAIP DAP point clouds using airborne LiDAR and FIA tree height measurements, and 2. assess the ability of NAIP digital height models (DHMs) to improve operational forest inventory estimates above the gains already achieved from FIA’s current post-stratification approach. Our results show the NAIP point clouds were moderately to strongly correlated with FIA field measured maximum tree heights (average Pearson’s r = 0.74) with a slight negative bias (−1.56 m) and an RMSE error of ~4.0 m. The NAIP point cloud heights were also more accurate for softwoods (R2s = 0.60–0.79) than hardwoods (R2s = 0.33–0.50) with an error structure that was consistent across multiple years of FIA measurements. Several factors served to degrade the relationship between the NAIP point clouds and FIA data, including a lack of 3D points in areas of advanced hardwood senescence, spurious height values in deep shadows and imprecision of FIA plot locations (which were estimated to be off the true locations by +/− 8 m). Using NAIP strata maps for post-stratification yielded forest volume estimates that were 31% more precise on average than estimates stratified with tree canopy cover data. Combining NAIP DHMs with forest type information from national map products helped improve stratification performance, especially for softwoods. The monetary value of using NAIP height maps to post-stratify FIA survey unit total volume estimates was USD 1.8 million vs. the costs of installing more field plots to achieve similar precision gains. Overall, our results show the benefit and growing feasibility of using NAIP point clouds to improve FIA’s operational forest inventory estimates.

Association between unmanned aerial vehicle high‐throughput canopy phenotyping and soybean yield

AbstractIdentifying agronomic traits correlated to grain yield can be very useful for soybean [Glycine max (L.) Merr.] breeding, especially if these traits can be measured through unmanned aerial vehicle high‐throughput phenotyping rather than through manual measurements. The objective of the present study was to assess the association between canopy coverage and soybean grain yield through different statistical methodologies. A panel with 97 soybean genotypes was evaluated in two field experiments conducted in Paraná State, Brazil. Canopy coverage was determined by using an RGB camera coupled to a drone. Images taken during flights at phenological stages V3‐V4, V5‐V6, V7‐V8, and V9‐R1 were used to calculate canopy coverage based on the green pixel ratio in each experimental unit. There were significant genotype × environment interactions in all evaluated traits. Selective accuracy values (0.73–0.96) revealed indirect yield selection efficiency based on canopy coverage. High genetic correlation estimates (0.76) were observed between grain yield and canopy coverage at flowering in one of the assessed environments. These results were confirmed through genetic correlation coefficient decomposition in direct and indirect effects and of gain estimates presenting indirect selection. Thus, canopy coverage data remotely collected using drones to soybean indirect selection for grain yield can be a promising strategy to accelerate genetic gains in soybean breeding programs.

Functional Evaluation of Digital Soil Hydraulic Property Maps through Comparison of Simulated and Remotely Sensed Maize Canopy Cover

Soil maps can usefully serve in data scarce regions, for example for yield (gap) assessments using a crop simulation model. The soil property estimates’ contribution to inaccuracy and uncertainty can be functionally evaluated by comparing model results using the estimates as input against independent observations. We conducted a functional evaluation of digital maps of soil hydraulic properties of the Zambezi River Basin using a crop growth model AquaCrop. AquaCrop was run, alimented with local meteorological data, and with soil hydraulic properties derived from the digital maps of digital soil mapping (DSM) techniques, as opposed to estimations from the widely used Saxton and Rawls pedotransfer functions. The two simulated time series of canopy cover (CC) (AquaCrop-CC-DSM and AquaCrop-CC-Saxton), which were compared against canopy cover data derived from the remotely sensed Leaf Area Index (LAI) from the MODIS archive (MODIS-CC). A pairwise comparison of the time series resulted in a root mean squared error (RMSE) of 0.07 and a co-efficient of determination (R2) of 0.93 for AquaCrop-CC-DSM versus MODIS-CC, and an RMSE of 0.08 and R2 of 0.88 for AquaCrop-CC-Saxton versus MODIS-CC. In dry years, the AquaCrop-CC-DSM deviated less from the MODIS-CC than the AquaCrop-CC-Saxton (p < 0.001), although this difference was not significant in wet years. The functional evaluation showed that soil hydraulic property estimates based on digital soil mapping outperformed those based on Saxton and Rawls when used for simulating crop growth in dry years in the Zambezi River Basin. This study also shows the value of conducting a functional evaluation of estimated (static) soil hydraulic properties in terms of dynamic model output.

Model for Predicting Rice Yield from Reflectance Index and Weather Variables in Lowland Rice Fields

Smallholder rice farmers need a multi-purpose model to forecast yield and manage limited resources such as fertiliser, irrigation water supply in-season, thus optimising inputs and increasing rice yield. Active sensing tools like Canopeo and GreenSeeker-NDVI have provided the opportunity to monitor crop health and development at different growth stages. In this study, we assessed the effectiveness of in-season estimation of rice yield in lowland fields of northwest Cambodia using weather data and vegetation cover information measured with; (1) the mobile app-Canopeo, and (2) the conventional GreenSeeker hand-held device that measures the normalised difference vegetative index (NDVI). We collected data from a series of on-farm field experiments in the rice-growing regions in 2018 and 2019. Average temperature and cumulative rainfall were calculated at panicle initiation and pre-heading stages when the crop cover index was measured. A generalised additive model (GAM) was generated using log-transformed data for grain yield, with the combined predictors of canopy cover and weather data during panicle initiation and pre-heading stages. The pre-heading stage was the best stage for grain yield prediction with the Canopeo-derived vegetation index and weather data. Overall, the Canopeo index model explained 65% of the variability in rice yield and Canopeo index, average temperature and cumulative rainfall explained 5, 65 and 56% of the yield variability in rice yield, respectively, at the pre-heading stage. The model (Canopeo index and weather data) evaluation for the training set between the observed and the predicted yield indicated an R2 value of 0.53 and root mean square error (RMSE) was 0.116 kg ha−1 at the pre-heading stage. When the model was tested on a validation set, the R2 value was 0.51 (RMSE = 925.533 kg ha−1) between the observed and the predicted yield. The NDVI-weather model explained 62% of the variability in yield, NDVI, average temperature and cumulative rainfall explained 3, 62 and 54%, respectively, of the variability in yield for the training set. The NDVI-weather model evaluation for the training set showed a slightly lower fit with R2 value of 0.51 (RMSE = 0.119 kg ha−1) between the observed and the predicted yield at pre-heading stage. The accuracy performance of the model indicated an R2 value of 0.46 (RMSE = 979.283 kg ha−1) at the same growth stage for validation set. The vegetation-derived information from Canopeo index-weather data increasingly correlated with rice yield than NDVI-weather data. Therefore, the Canopeo index-weather model is a flexible and effective tool for the prediction of rice yield in smallholder fields and can potentially be used to identify and manage fertiliser and water supply to maximise productivity in rice production systems. Data availability from more field experiments are needed to test the model’s accuracy and improve its robustness.

Assessing City Greenness using Tree Canopy Cover: The Case of Yogyakarta, Indonesia

The study aims to measure the greenness of an Indonesia city using tree canopy cover data. Rapid physical development brings impacts to the loss of urban trees, which leads to the increase of flooding risk, local temperature and pollution level. To address the issues, a baseline assessment of urban tree canopy existence is necessary as inputs for effective urban environmental management policies. The methods used in this research include 1) remote sensing and spatial analysis, and 2) simple quantitative analysis. Furthermore, three indicators are used in assessing the greenness, including 1) size of the canopy, 2) canopy cover percentage, and 3) canopy per capita. The results found that the city of Yogyakarta has a low level of greenness based on the canopy size in which covers only 467.37 ha or 14.38% of the total area. The second finding is Yogyakarta has an unequal distribution of canopy cover percentage in each district (kecamatan). The third finding is Yogyakarta City has a canopy per capita rate of 10.93 sq m/person. This number is below the UN recommendation of 15sq m / person. It indicates that residents have poor access to urban greenery. Additionally, the article discusses that the three indicators used have strength and weakness in measuring the level of greenness. Therefore, the assessment objectives must be taken into account. We recommend the use of each indicator as follows: 1) the canopy size is used as an initial inventory of the existence and distribution of the canopy, 2) the canopy cover percentage canopy percentage for measuring and comparing the level of greenness spatially and visually between areas, 3) the canopy per capita is used to measure the possibility of access and interaction of residents with the presence of a tree canopy. Cities’ authority can use the information to measure the achievement of SDGs number 11, 13, or 15.

Assessing City Greenness using Tree Canopy Cover: The Case of Yogyakarta, Indonesia

The study aims to measure the greenness of an Indonesia city using tree canopy cover data. Rapid physical development brings impacts to the loss of urban trees, which leads to the increase of flooding risk, local temperature and pollution level. To address the issues, a baseline assessment of urban tree canopy existence is necessary as inputs for effective urban environmental management policies. The methods used in this research include 1) remote sensing and spatial analysis, and 2) simple quantitative analysis. Furthermore, three indicators are used in assessing the greenness, including 1) size of the canopy, 2) canopy cover percentage, and 3) canopy per capita. The results found that the city of Yogyakarta has a low level of greenness based on the canopy size in which covers only 467.37 ha or 14.38% of the total area. The second finding is Yogyakarta has an unequal distribution of canopy cover percentage in each district (kecamatan). The third finding is Yogyakarta City has a canopy per capita rate of 10.93 sq m/person. This number is below the UN recommendation of 15sq m / person. It indicates that residents have poor access to urban greenery. Additionally, the article discusses that the three indicators used have strength and weakness in measuring the level of greenness. Therefore, the assessment objectives must be taken into account. We recommend the use of each indicator as follows: 1) the canopy size is used as an initial inventory of the existence and distribution of the canopy, 2) the canopy cover percentage canopy percentage for measuring and comparing the level of greenness spatially and visually between areas, 3) the canopy per capita is used to measure the possibility of access and interaction of residents with the presence of a tree canopy. Cities’ authority can use the information to measure the achievement of SDGs number 11, 13, or 15.