Crop type classification and spatial mapping in River Nile and Northern State, Sudan, using Sentinel-2 satellite data and field observation

Maintaining productive farmland necessitates precise crop mapping and identification. While satellite remote sensing makes it possible to generate such maps, there are still issues to resolve, such as how to choose input data and the best classifier algorithm, especially in areas with scarce field data. Accurate assessments of the land used for farming are a crucial part of national food supply and production accounting in many African countries, and to this end, remote sensing tools are being increasingly put to use. The aim of this study was to assess the potentiality of Sentinel-2 to distinguish and discriminate crop species in the study area and constraints on accurately mapping cropping patterns in the winter season in River Nile and Northern State, Sudan. The research utilized Sentinel-2 Normalized Different Vegetation Index (NDVI) at 10 m resolution, unsupervised and supervised classification method with ground sample and accuracy assessment. The results of the study found that the signatures of grain sorghum, wheat, okra, Vicia faba, alfalfa, corn, haricot, onion, potato, tomato, lupine, tree cover, and garlic have clear distinctions, permitting an overall accuracy of 87.38%, with trees cover, onion, wheat, potato, garlic, alfalfa, tomato, lupine and Vicia faba achieving more than 87% accuracy. Major mislabeling problems occurred primarily in irrigated areas for grain sorghum, okra, corn, and haricot, in wooded areas comprised of small parcels of land. The research found that high-resolution temporal images combined with ground data had potential and utility for mapping cropland at the field scale in the winter

A comparison of machine learning models for the mapping of groundwater spring potential

Groundwater resources are vitally important in arid and semi-arid areas meaning that spatial planning tools are required for their exploration and mapping. Accordingly, this research compared the predictive powers of five machine learning models for groundwater potential spatial mapping in Wadi az-Zarqa watershed in Jordan. The five models were random forest (RF), boosted regression tree (BRT), support vector machine (SVM), mixture discriminant analysis (MDA), and multivariate adaptive regression spline (MARS). These algorithms explored spatial distributions of 12 hydrological-geological-physiographical (HGP) conditioning factors (slope, altitude, profile curvature, plan curvature, slope aspect, slope length (SL), lithology, soil texture, average annual rainfall, topographic wetness index (TWI), distance to drainage network, and distance to faults) that determine where groundwater springs are located. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was employed to evaluate the prediction accuracies of the five individual models. Here the results were ranked in descending order as MDA (83.2%), RF (80.6%), SVM (80.2%), BRT (78.0%), and MARS (75.5%).The results show good potential for further use of machine learning techniques for mapping groundwater spring potential in other places where the use and management of groundwater resources is essential for sustaining rural or urban life

Augmenting Land Cover/Land Use Classification by Incorporating Information from Land Surface Phenology: An Application to Quantify Recent Cropland Expansion in South Dakota

Understanding rapid land change in the U.S. NGP region is not only critical for management and conservation of prairie habitats and ecosystem services, but also for projecting production of crops and biofuels and the impacts of land conversion on water quality and rural transportation infrastructure. Hence, it raises the need for an LCLU dataset with good spatiotemporal coverage as well as consistent accuracy through time to enable change analysis. This dissertation aims (1) to develop a novel classification method, which utilizes time series images from comparable sensors, from the perspective of land surface phenology, and (2) to apply the land cover/land use dataset generated from the phenometrically-based classification approach to quantify crop expansion in South Dakota. A novel classification approach from the perspective of land surface phenology (LSP) uses rich time series datasets. First, surface reflectance products at 30 m spatial resolution from Landsat Collection-1, its newer structure—Landsat Analysis Ready Data, and the Harmonized Landsat Sentinel-2 (HLS) data are used to construct vegetation index time series, including the Enhanced Vegetation Index (EVI), and the 2-band EVI (EVI2), and various spectral variables (spectral band and normalized ratio composites). MODIS Level-3 Land Surface Temperature & Emissivity 8-day composite products at 1 km spatial resolution from both the Aqua and Terra satellites are used to compute accumulated growing degree-days (AGDD) time series. The EVI/EVI2 and AGDD time series are then fitted by two different land surface phenology models: the Convex Quadratic model and the Hybrid Piecewise Logistic Model. Suites of phenometrics are derived from the two LSP models and spectral variables and input to Random Forest Classifiers (RFC) to map land cover of sample areas in South Dakota. The results indicate that classifications using only phenometrics can accurately map major crops in the study area but show limited accuracy for non-vegetated land covers. RFC models using the combined spectralphenological variables can achieve higher accuracies than those using either spectral variables or phenometrics alone, especially for the barren/developed class. Among all sampling designs, the “same distribution” models—proportional distribution of the sample is like proportional distribution of the population—tends to yield best land cover prediction. A “same distribution” random sample dataset covering approximately 0.25% or more of the study area appears to achieve an accurate land cover map. To characterize crop expansion in South Dakota, a trajectory-based analysis, which considers the entire land cover dataset generated from the LSP-based classifications, is proposed to improve change detection. An estimated cropland expansion of 5,447 km2 (equivalent to 14% of the existing cropland area) occurred between 2007 and 2015, which matches more closely the reports from the National Agriculture Statistics Service—NASS (5,921 km2) and the National Resources Inventory—NRI (5,034 km2) than an estimation from a bi-temporal change approach (8,018 km2). Cropland gains were mostly concentrated in 10 counties in northern and central South Dakota. An evaluation of land suitability for crops using the Soil Survey Geographic Database—SSURGO indicates a scarcity in high-quality arable land available for cropland expansion

Long short term memory deep net performance on fused Planet-Scope and Sentinel-2 imagery for detection of agricultural crop

In view of the challenges faced by organizations and departments concerned with agricultural capacity observations, we collected In-Situ data consisting of diverse crops (More than 11 consumable vegetation types) in our pilot region of Harichand Charsadda, Khyber Pakhtunkhwa (KP), Pakistan. Our proposed Long Short-Term Memory based Deep Neural network model was trained for land cover land use statistics generation using the acquired ground truth data, for a synergy between Planet-Scope Dove and European Space Agency’s Sentinel-2. Total of 4 bands from both sentinel-2 and planet scope including Red, Green, Near-Infrared (NIR) and Normalised Difference Vegetation Index (NDVI) were used for classification purpose. Using short temporal frame of Sentinel-2 comprising 5 date images, we propose an realistic and implementable procedure for generating accurate crop statistics using remote sensing. Our self collected data-set consists of a total number of 107,899 pixels which was further split into 70% and 30% for training and testing purpose of the model respectively. The collected data is in the shape of field parcels, which has been further split for training, validation and test sets, to avoid spatial auto-correlation. To ensure the quality and accuracy 15% of the training data was left out for validation purpose, and 15% for testing. Prediction was also performed on our trained model and visual analysis of the area from the image showed significant results. Further more a comparison between Sentinel-2 time series is performed separately from the fused Planet-Scope and Sentinel-2 time-series data sets. The results achieved shows a weighted average of 93% for Sentinel-2 time series and 97% for fused Planet-Scope and Sentinel-2 time series.Web of Science182art. no. e027189

Forest Landscape Restoration and Ecosystem Services in A Luoi District, Thua Thien Hue Province, Vietnam

Abstract The Government of Vietnam has invested efforts to increase the forest cover, and to conserve biodiversity through different forest development projects and programs. Losing natural forests and landscapes in the context of the “exhaust” of ecosystem services has been seen as burden in many mountainous areas. The Decision No.16 on ecosystem restoration, which was adopted by the Conference of the Parties to the Convention on Biological Diversity (CBD) at the 11th meeting (December 5th, 2012) stated that ecosystem restoration requires the application of suitable technologies and the fully-effective participation of local entities. This serves to identify obstacles while attempting to restore, regenerate ecosystem services and biodiversity, which have been degraded and lost in the recent decades. Furthermore, Vietnam’s National Forest Development Strategy targeted to achieve a forest area of 16.2 million hectares by the year 2020. Local people living adjacent to forests depend on the forest ecosystem services supplied from various natural forest landscapes in the area. This holds true especially for the people of Central Vietnam where the terrestrial area is narrow due to the country shape. In this area, agriculture practices play an essential role although the agricultural land is very limited due to the topographic conditions. The distinct land-uses reflect the natural distribution of plant and animal species as well as human interventions. In Vietnam, the forest ecosystems have been classified into three categories according to their main functions: special-use forest for nature conservation; protection forest for the watershed and protective measures; and production forest for commercial operations. This study was conducted in the A Luoi District, Thua Thien Hue Province. Ground truth samples were inventoried in three forest types from 150 m to 1162 m above sea level (a.s.l.) and steep slopes from 5 to 48 degrees. The elevation range was divided into the lower elevation level H1 ranging from 150 m – 699 m and into the higher elevation level H2 from 700 m-1162 m a.s.l.. The slopes were stratified into level S1 from 5-20 degrees, and into S2 from 21-48 degrees. The forest cover was classified into the types: undisturbed forest (UF), low disturbed forest (LF), and heavily disturbed forest (DF). To strengthen the classification of forest types, a t-test of extracted vegetation indices between ground truth plots and training sample plots was done. Up to date, no remote sensing-based work on ecological stratification of the natural forest landscapes has been conducted. Finding the tree species distribution, species diversity, and species composition over the sub-stratification of the elevations, slopes, and the forest types - by applying remote sensing - are necessary to classify the land-use types and to map out the availability of natural resources, especially the ecosystem services supply and demand of local people. Land-use and forest type classification may contribute remarkably to long-term planning, which has been assigned to local authorities, and which should include local communities. The entire study consists of four main parts. The first part aimed at evaluating the influence of topography on tree species diversity, distribution, and composition of the forests in Central Vietnam. A significant difference of species richness and species diversity was found in shallower and steeper slopes (p < 0.05) and a relatively high correlation of the species distribution, the number of stems, and the number of tree families with the elevation factor was found. The lower elevation and shallower slope showed higher species richness (p < 0.05) but not a significant difference between the number of families and the evenness. The dominance and the abundance of tree species among the topographic attributes were significantly different (p < 0.05). Lower elevation and shallower slope showed higher species richness and species diversity than the higher elevation and steeper slope. The most dominant and abundant tree families from different elevations and slopes included the Myrtaceae, Dipterocarpaceae, Burseraceae, Fagaceae, Moraceae, Cornaceae, Apocynaceae, Sapindaceae, Cannabaceae, Juglandaceae, Lauraceae, Myristicaeae, Annonaceae, Ebenaceae, Meliaceae, Rubiaceae, and the Rosaceae. The second part aimed at assessing the soil qualities, which belong to the most essential elements for land-use planning and agricultural production. 155 soil samples from different land-use types and topographic aspects were collected in order to compare information on soil organic carbon (SOC), soil total nitrogen (STN), and soil acidity (pH) at two soil depths. The SOC of arable land and forest plantation land was found to be higher than those of grassland and of natural forests (p < 0.05). The total nitrogen in the natural forests was significantly less, compared to the other land-use types. No significant differences in the total nitrogen content (p < 0.05) were found among arable land, plantation forest, and grassland. The soil organic carbon and the total nitrogen were high in the upper soil and less downwards, within all land-use types. The soil pH in the plantation forest and the arable land-use types showed no significant change among soil depth categories. Significant differences were not found in topographic aspects and the soil organic carbon content; however, differing trends of soil organic carbon and land-use types and aspects were found. The impact of the slope, elevation, farming system and soil texture accounted for the main differences of soil indicators under varying land-use types in the A Luoi District. The third part of this study was designed to apply remote sensing data from Landsat-8 and Sentinel-2 sources in order to classify land-cover and land-use classes (including three forest types UF, LF, and DF) in the study area by using machine learning algorithms. Further, vegetation indices were applied to find possible correlations and regressions of both, vertical and horizontal structures of the dominant forest tree species within different forest types. It was found that the vegetation indices between the ground-truth plots and the training sample plots were significantly different (p<0.05). The most dominant and abundant tree families in the context of the vertical structure were the Dipterocaparceae, Combretaceae, Moraceae, Leguminosae, Burseraceae, and the Polygalaceae. These, in the context of the horizontal structure were the Fagaceae, Lauraceae, Leguminosae, Dipterocaparceae, Myrtaceae, Myristicaceae, Euphorbiaceae, and the Clusiaceae. The results of the land cover and the land-use classification of Sentinel-2 were found to be more precise than those of Landsat-8 with the Random Forest algorithm: (Sentinel-2 with out-of-bag error of 14.3%, overall accuracy of 85.7%, kappa of 83% and Landsat-8 with out-of-bag error 31.6%, overall accuracy of 68%, kappa of 67.5%). The study found relationships (from 43% up to 66%) between four (out of ten) vegetation indices within horizontal and vertical structures of the forest stands: the Enhanced Vegetation Index (EVI), the Difference Vegetation Index (DVI), the Perpendicular Vegetation Index (PVI), and the Transformed Normalized Difference Vegetation Index (TNDVI). The fourth part evaluated potential provisioning services of the current natural forests - apart from wood and timber supply. It (i) assessed and compared the amount of non-timber forest tree species (NTFP species) in the different investigated forest types and elevations as potential resources; explored (ii) the respective demands of local people and (iii) their personal views concerning the importance of natural forests and the satisfaction with their provisioning services; and finally (iv) gathered their awareness of limited consequences of former forest development and requirements for forest landscape restoration. Thirty-nine NTFP tree species were found for various uses such as food, medicine, and resin or oil. Random on-site interviews of 120 out of 627 local households were conducted in a commune with high dependency on local natural forest products. Their importance and satisfaction ranking of natural forests - considering different target groups with respect to gender, income, age-class, and education - was commenced. Multiple methods were used to assess an array of gathering information, which are related to (a) the forest resources importance and (b) the local people satisfaction. These were set into context with the involvement of non-timber forest goods extraction, landslides, goods declination, and the perception for natural forest landscapes restoration, in order to clarify perspectives on forest provisioning services. The results revealed remarkable differences among target groups, adjustment, perceptions. The insufficient supply of NTFPs, particularly profitable natural medicine provision, urges for adapted silvicultural measures. The results imply that NTFPs from natural forests are not only very important to the local communities, but also contribute to the enrichment of biodiversity. The participation of local people in practical forest management and forest improvement should be considered in the decision-making process for natural forest landscape restoration of remote mountainous areas. The findings of this study can support sustainable forest management; natural forest landscape restoration with the involvement of local communities; conservation practices of biodiversity, based on topographic conditions; land-use planning; identification of dominant tree species using vegetation indices’ values, and land cover and land-use classification using open source satellite images. This final component will be aided by application of machine learning algorithms in the current study area and in the central mountainous area of Vietnam.2021-07-2

System Engineering Analyses for the Study of Future Multispectral Land Imaging Satellite Sensors for Vegetation Monitoring

Vegetation monitoring is one of the key applications of earth observing systems. Landsat data have spatial resolution of 30 meters, moderate temporal coverage, and reasonable spectral sampling to capture key vegetation features. These characteristics of Landsat make it a good candidate for generating vegetation monitoring products. Recently, the next satellite in the Landsat series has been under consideration and different concepts have been proposed. In this research, we studied the impact on vegetation monitoring of two proposed potential design concepts: a wider field-of-view (FOV) instrument and the addition of red-edge spectral band(s). Three aspects were studied in this thesis: First, inspired by the potential wider FOV design, the impacts of a detector relative spectral response (RSR) central wavelength shift effect at high angles of incidence (AOI) on the radiance signal were studied and quantified. Results indicate: 1) the RSR shift effect is band-dependent and more significant in the green, red and SWIR 2 bands; 2) At high AOI, the impact of the RSR shift effect will exceed sensor noise specifications in all bands except the SWIR 1 band; and 3) The RSR shift will cause SWIR2 band more to be sensitive to atmospheric conditions. Second, also inspired by the potential wider FOV design, the impacts of the potential new wider angular observations on vegetation monitoring scientific products were studied. Both crop classification and biophysical quantity retrieval applications were studied using the simulation code DIRSIG and the canopy radiative transfer model PROSAIL. It should be noted that the RSR shift effect was also considered. Results show that for single view observation based analysis, the higher view angular observations have limited influence on both applications. However, for situations where two different angular observations are available potentially from two platforms, up to 4% improvement for crop classification and 2.9% improvement for leaf chlorophyll content retrieval were found. Third, to quantify the benefits of a potential new design with red-edge band(s), the impact of adding red-edge spectral band(s) in future Landsat instruments on agroecosystem leaf area index (LAI) and canopy chlorophyll content (CCC) retrieval were studied using a real dataset. Three major retrieval approaches were tested, results show that a potential new spectral band located between the Landsat-8 Operational Land Imager (OLI) red and NIR bands slightly improved the retrieval accuracy (LAI: R2 of 0.787 vs. 0.810 for empirical vegetation index regression approach, 0.806 vs. 0.828 for look-up-table inversion approach, and 0.925 vs. 0.933 for machine learning approach; CCC: R2 of 0.853 vs. 0.875 for empirical vegetation index regression approach, 0.500 vs. 0.570 for look-up-table inversion approach, and 0.854 vs. 0.887 for machine learning approach). In general, for the potential wider FOV design, the RSR shift effect was found to cause noticable radiance signal difference that is higher than detector noise in all OLI bands except SWIR1 band, which is not observed in the current OLI design with its 15 degree FOV. Also both the new wider angular observations and potential red-edge band(s) were found to slightly improve the vegetation monitoring product accuracy. In the future, the RSR shift effect in other optical designs should be evaluated since this study assumed the angle reaching the filter array is the same as the angle reaching the sensor. In addition to improve the accuracy of the off angle imaging study, a 3D vegetation geometry model should be explored for vegetation monitoring related studies instead of the 2D PROSAIL model used in this thesis

Applications of satellite ‘hyper-sensing’ in Chinese agriculture:Challenges and opportunities

Ensuring adequate food supplies to a large and increasing population continues to be the key challenge for China. Given the increasing integration of China within global markets for agricultural products, this issue is of considerable significance for global food security. Over the last 50 years, China has increased the production of its staple crops mainly by increasing yield per unit land area. However, this has largely been achieved through inappropriate agricultural practices, which have caused environmental degradation, with deleterious consequences for future agricultural productivity. Hence, there is now a pressing need to intensify agriculture in China using practices that are environmentally and economically sustainable. Given the dynamic nature of crops over space and time, the use of remote sensing technology has proven to be a valuable asset providing end-users in many countries with information to guide sustainable agricultural practices. Recently, the field has experienced considerable technological advancements reflected in the availability of ‘hyper-sensing’ (high spectral, spatial and temporal) satellite imagery useful for monitoring, modelling and mapping of agricultural crops. However, there still remains a significant challenge in fully exploiting such technologies for addressing agricultural problems in China. This review paper evaluates the potential contributions of satellite ‘hyper-sensing’ to agriculture in China and identifies the opportunities and challenges for future work. We perform a critical evaluation of current capabilities in satellite ‘hyper-sensing’ in agriculture with an emphasis on Chinese sensors. Our analysis draws on a series of in-depth examples based on recent and on-going projects in China that are developing ‘hyper-sensing’ approaches for (i) measuring crop phenology parameters and predicting yields; (ii) specifying crop fertiliser requirements; (iii) optimising management responses to abiotic and biotic stress in crops; (iv) maximising yields while minimising water use in arid regions; (v) large-scale crop/cropland mapping; and (vi) management zone delineation. The paper concludes with a synthesis of these application areas in order to define the requirements for future research, technological innovation and knowledge exchange in order to deliver yield sustainability in China

Drones in Vegetable Crops: A Systematic Literature Review

In the context of increasing global population and climate change, modern agriculture must enhance production efficiency. Vegetables production is crucial for human nutrition and has a significant environmental impact. To address this challenge, the agricultural sector needs to modernize and utilize advanced technologies such as drones to increase productivity, improve quality, and reduce resource consumption. These devices, known as Unmanned Aerial Vehicles (UAV), with their agility and versatility play a crucial role in monitoring and spraying operations. They significantly contribute to enhancing the efficacy of precision farming. The aim of this review is to examine the critical role of drones as innovative tools to enhance management and yield of vegetable crops cultivation. This review was carried out using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) framework and involved the analysis of a wide range of research published from 2018 to 2023. According to the phases of Identification, Screening, and Eligibility, 132 papers were selected and analysed. These papers were categorized based on the types of drone applications in vegetable crop production, providing an overview of how these tools fit into the field of Precision Farming. Technological developments of these tools and data processing methods were then explored, examining the contributions of Machine and Deep Learning and Artificial Intelligence. Final considerations were presented regarding practical implementation and future technical and scientific challenges to fully harness the potential of drones in precision agriculture and vegetable crop production. The review pointed out the significance of drone applications in vegetable crops and the immense potential of these tools in enhancing cultivation efficiency. Drone utilization enables the reduction of input quantities such as herbicides, fertilizers, pesticides, and water but also the prevention of damages through early diagnosis of various stress types. These input savings can yield environmental benefits, positioning these technologies as potential solutions for the environmental sustainability of vegetable crops