Evaluating the relationship between biomass, percent groundcover and remote sensing indices across six winter cover crop fields in Maryland, United States

AbstractWinter cover crops are an essential part of managing nutrient and sediment losses from agricultural lands. Cover crops lessen sedimentation by reducing erosion, and the accumulation of nitrogen in aboveground biomass results in reduced nutrient runoff. Winter cover crops are planted in the fall and are usually terminated in early spring, making them susceptible to senescence, frost burn, and leaf yellowing due to wintertime conditions. This study sought to determine to what extent remote sensing indices are capable of accurately estimating the percent groundcover and biomass of winter cover crops, and to analyze under what critical ranges these relationships are strong and under which conditions they break down. Cover crop growth on six fields planted to barley, rye, ryegrass, triticale or wheat was measured over the 2012–2013 winter growing season. Data collection included spectral reflectance measurements, aboveground biomass, and percent groundcover. Ten vegetation indices were evaluated using surface reflectance data from a 16-band CROPSCAN sensor. Restricting analysis to sampling dates before the onset of prolonged freezing temperatures and leaf yellowing resulted in increased estimation accuracy. There was a strong relationship between the normalized difference vegetation index (NDVI) and percent groundcover (r2=0.93) suggesting that date restrictions effectively eliminate yellowing vegetation from analysis. The triangular vegetation index (TVI) was most accurate in estimating high ranges of biomass (r2=0.86), while NDVI did not experience a clustering of values in the low and medium biomass ranges but saturated in the higher range (>1500kg/ha). The results of this study show that accounting for index saturation, senescence, and frost burn on leaves can greatly increase the accuracy of estimates of percent groundcover and biomass for winter cover crops

Perbandingan Pengolahan DAS Bengkulu Menggunakan NDVI dan Maximum Likelihood

DAS adalah daerah daratan yang merupakan bagian integral dari sungai dan anak-anak sungainya. DAS berfungsi untuk menampung, menyimpan dan mengalirkan air yang berasal dari curah hujan ke danau atau ke laut secara alami, batas di darat adalah pemisah topografi dan batas di laut ke perairan yang masih dipengaruhi oleh aktivitas darat. Pemetaan tutupan lahan di daerah aliran sungai penting untuk memahami masalah yang terjadi di daerah aliran sungai seperti kualitas air yang menurun dan rentan terhadap tanah longsor atau banjir. Dalam studi ini, pemetaan area penutup DAS Rindu Hati dilakukan dari 2014 hingga 2018 dengan menggunakan citra satelit Landsat 8 menggunakan metode Maximum Likelihood dan NDVI. Peta tutupan lahan kemudian diproses dan ditampilkan menggunakan media webGIS. Penelitian ini diharapkan dapat menjadi bahan pertimbangan untuk pengambilan keputusan dalam pengelolaan DAS Rindu Hati di masa depan untuk mendukung lingkungan yang berkelanjutan. Selain itu, penelitian ini menunjukkan bahwa kinerja algoritme kemungkinan maksimum menghasilkan akurasi yang lebih baik (95,81%) daripada hasil yang dihasilkan oleh NDVI (92,85%) untuk proses klasifikasi di DAS Rindu Hati. Pengujian dilakukan ke dalam 100 titik data acak dari hasil klasifikasi dalam kegiatan pemeriksaan lapangan. Kemungkinan maksimum juga menunjukkan waktu pemrosesan yang lebih baik untuk klasifikasi 5 kelas pada nilai rata-rata 0,023 detik daripada algoritme NDVI yang menunjukkan nilai rata-rata 0,031 detik.Kata Kunci: DAS, maximum likelihood, NDVI, remote sensing, webGIS.

The course of drying and colour changes of alfalfa under different drying conditions

ArticleOne of the conditions for successful livestock breeding and efficient livestock production is to ensure quality feed. High quality feed for livestock is alfalfa, which has a very high nutritional value and its cultivation is also important for crop production in terms of improving the soil structure and nitrogen enrichment. The aim of this paper is to inform about the experimental investigations of alfalfa drying and colour changes under different drying conditions. The results of natural convection at 27.5 °C and 40% relative air humidity are compared with forced convection at 1.2 m s -1 air flow velocity at the same air temperature and with results of drying by natural convection at 50 °C. The dry matter content was measured gravimetrically after drying in a hot air dryer at 105 °C. Higher drying rates shorten the time required for drying and earlier preservation and storage in the hayloft or in the hay bales. This reduces the risk of wetting of feed such by rain and degradation by fungi, etc. A shorter drying time is also important in terms of energy savings. The precise knowledge of the drying process and drying curves allows also to determine the appropriate time for storage and conservation for production of another type of fodder e.g. haylage or silage. The measurement results show a positive effect of higher drying speeds as well as increased air temperature. Higher drying air temperature during convection led to the partial lightening and greater yellowing of the feed

Efficacy of the association of cover crops with maize and direct sowing short-term effect on crops? yields in maize-cotton cropping system in Western Burkina Faso.

To improve the productivity and sustainability of cotton and cereals based system, direct sow ing under mulch was tested for its efficacy on cotton and maize yields on the research station of Farako - Bâ, in Western Burkina Faso. The experimental design was a complete randomized blocks of Fisher with four replications. Conventional tillage by annual moldboard plowing (T7) was compared with direct sowing under mulch -based cropping system (DMC) using maize association with cover crop s defined as: maize without cover crop (T1), maize +Brachiaria ruziziensis(T2), maize + B. ruziziensis+ Mucuna cochinchinensis (T3), maize + B. ruziziensis+ Panicum maximum (T4), maize + B. ruziziensis + Stylosantes hamata (T5), and maize + Crotalaria juncea (T6). Cover crops were planted 21 days after maize emergence between the rows of this main crop. The biomass produced by the cover crops and maize straws were evaluated as well as maize and cotton yields, during the first 6 years of the study, from 2010 to 2015. Results showed that among cover crops, the biomass production was significantly lower with C. juncea. The associations of cover crops with maize increased significantly the production of total dry matter compared to plots without cover crops, in the conventional tillage. Association with cover crops did not influence significantly nitrogen, phosphorus and potassium contents of maize and the maize’s yields even if the depressive effects were recorded. Compared to the conventional tillage, the DMC appeared also effective on seed cotton yields even without a significant improvement during the 6 first years of the study . These promising results, confirm the feasibility in tropical conditions of DMC which must be continued to better analyze its long-term effects on soil properties

Web-GIS mapping for watershed and land cover area in Bengkulu

Naturally, watershed has a function to accomodate and store water from rainfall to the lake or to the sea. It is affected by land activities and shaped as a boundary of topographic land and as a separator from the sea. Managing the area is one of essential steps for further environment assessement, i.e. decreasing water quality and vulnerability to landslides or floods. However, the ability of web-GIS technology to help the management of watershed area in Bengkulu is still unknown. Here, we showed how the process of Web GIS development for managing the watershade in Bengkulu could be achived. This study mapped the 2018 land cover area of Rindu Hati Sub-watershed by utilizing Landsat 8 satellite images using the Maximum Likelihood method. The land cover map was then processed and displayed using webGIS media. The results showed that the system accuracy for ground truth land use model result was 77.4% which could be accepted as a good result. Further assessment of pixel validation could be one of the future research. We anticipated that the results could be a starting point for more sophisticated area cover of Sumatra and Indonesia. Furthermore, this could be a major development on knowledge discovery in environment and ecology

Exploring Spectral Index Band and Vegetation Indices for Estimating Vegetation Area

Visual analysis and transformation of vegetation indices have been widely applied in studies of vegetation density using remote sensing data. However, visual analysis is time intensive compared to index transformation. On the other hand, the index transformation from medium resolution imagery is not fully representative for urban vegetation studies. Meanwhile, the spectral range of high-resolution imagery is usually limited to visible wavelengths for the image transformation. Worldview-2 imagery provides a new breakthrough with a high spatial resolution and supports various spectral resolutions. This study aims to explore the spectral value of the Worldview-2 image index for estimation of vegetation density. Normalized indices were made for 56 band combinations and Otsu thresholding was implemented for the threshold selection to separate vegetation and non-vegetation areas. This thresholding was done by minimizing classes’ variances between two groups of pixels which are distinguished by system or classification. The image binarization process was performed to differentiate between vegetation and non-vegetation. For the accuracy testing, a total of 250 samples was produced by a stratified random sampling method. Our results show that the combination of indices from red channel, red-edge, NIR-1, and NIR-2 provides the best accuracy for semantic accuracy. Vegetation area extracted from the index was then compared with the results of the visual analysis. Although the index results in area difference of 2.32 m2 compared to visual analysis, the combination of NIR-2 and red bands can give an accuracy of 96.29 %

Examining the Integration of Landsat Operational Land Imager with Sentinel-1 and Vegetation Indices in Mapping Southern Yellow Pines (Loblolly, Shortleaf, and Virginia Pines)

The mapping of southern yellow pines (loblolly, shortleaf, and Virginia pines) is important to supporting forest inventory and the management of forest resources. The overall aim of this study was to examine the integration of Landsat Operational Land Imager (OLI ) optical data with Sentinel-1 microwave C-band satellite data and vegetation indices in mapping the canopy cover of southern yellow pines. Specifically, this study assessed the overall mapping accuracies of the canopy cover classification of southern yellow pines derived using four data-integration scenarios: Landsat OLI alone; Landsat OLI and Sentinel-1; Landsat OLI with vegetation indices derived from satellite data—normalized difference vegetation index, soil-adjusted vegetation index, modified soil-adjusted vegetation index, transformed soil-adjusted vegetation index, and infrared percentage vegetation index; and 4) Landsat OLI with Sentinel-1 and vegetation indices. The results showed that the integration of Landsat OLI reflectance bands with Sentinel-1 backscattering coefficients and vegetation indices yielded the best overall classification accuracy, about 77%, and standalone Landsat OLI the weakest accuracy, approximately 67%. The findings in this study demonstrate that the addition of backscattering coefficients from Sentinel-1 and vegetation indices positively contributed to the mapping of southern yellow pines

Analyzing the Adoption, Cropping Rotation, and Impact of Winter Cover Crops in the Mississippi Alluvial Plain (MAP) Region through Remote Sensing Technologies

This dissertation explores the application of remote sensing technologies in conservation agriculture, specifically focusing on identifying and mapping winter cover crops and assessing voluntary cover crop adoption and cropping patterns in the Arkansas portion of the Mississippi Alluvial Plain (MAP). In the first chapter, a systematic review using the PRISMA methodology examines the last 30 years of thematic research, development, and trends in remote sensing applied to conservation agriculture from a global perspective. The review uncovers a growing interest in remote sensing-based research in conservation agriculture and emphasizes the necessity for further studies dedicated to conservation practices. Among the 68 articles examined, 94% of studies utilized a pixel-based classification method, while only 6% employed an object-based approach. The analysis also revealed a thematic shift over time, with tillage practices being extensively studied before 2005, followed by a focus on crop residue from 2004 to 2012. From 2012 to 2020, there was a renewed emphasis on cover crops research. These findings highlight the evolving research landscape and provide insights into the trends within remote sensing-based conservation agriculture studies. The second chapter presents a methodological framework for identifying and mapping winter cover crops. The framework utilizes the Google Earth Engine (GEE) and a Random Forest (RF) classifier with time series data from Landsat 8 satellite. Results demonstrate a high classification accuracy (97.7%) and a significant increase (34%) in model-predicted cover crop adoption over the study period between 2013 and 2019. Additionally, the study showcases the use of multi-year datasets to efficiently map the growing season\u27s length and cover crops\u27 phenological characteristics. The third chapter assesses the voluntary adoption of winter cover crops and cropping patterns in the MAP region. Remote sensing technologies, USDA-NRCS government cover crop data sources, and the USDA Cropland Data Layer (CDL) are employed to identify cover crop locations, analyze county-wide voluntary adoption, and cropping rotations. The result showed a 5.33% increase in the overall voluntary adoption of cover crops in the study region between 2013 and 2019. The findings also indicate a growing trend in cover crop adoption, with soybean-cover crop rotations being prominent. This dissertation enhances our understanding of the role of remote sensing in conservation agriculture with a particular focus on winter cover crops. These insights are valuable for policymakers, stakeholders, and researchers seeking to promote sustainable agricultural practices and increased cover crop adoption. The study also underscores the significance of integrating remote sensing technologies into agricultural decision-making processes and highlights the importance of collaboration among policymakers, researchers, and producers. By leveraging the capabilities of remote sensing, it will enhance conservation agriculture contribution to long-term environmental sustainability and agricultural resilience. Keywords: Remote sensing technologies, Conservation agriculture, Winter cover crops, Voluntary adoption, Cropping patterns, Sustainable agricultural practice