This article analyzed the application of Sentinel-2 and Sentinel-1 for monitoring and controlling irrigation in sugarcane production by vinasse (residue of industrial ethanol-sugar manufacturing), a sub-product highly rich in organic matter and minerals, which is used as fertilizer after harvesting, during the months of drought for the plant to regrow. Irrigation with vinasse is a complex process and its lack of use or excess can causes important losses in tonnage of sugarcane. The insufficient spraying of vinasse was identified in the Sentinel-2 image of April 2017 in a farm plantation as the principal cause of stress in the development of the plant in an area representing 21% of the plantation (100 hectares). This represented a total loss of 4335 tons of cane (≈$90000). The identification of the cause of the reduced growth was made possible through analyzing a Sentinel-2 image of July 2016 that showed clearly that no vinasse was spread in the same 100 hectares with cane growing problem. The presence/lack of vinasse was easily detectable using the visible bands of the S-2 image but the difference in growth/stress was better related to the red edge and near infrared bands in the January image. To thoroughly complete our investigation we also acquired a Sentinel-1 radar image of April 2017 where the lack of vinasse could not directly be identified but the effect on the growth can be readily interpreted. This evaluation was extended to other farming facilities and a coefficient of determination of -0.7 was obtained between the production rate per hectare and the plantation where lack of vinasse could be identified in 41 fields. We proposed a systematic approach to monitor the spreading of vinasse with Sentinel-2 images and Sentinel-1 radar images in an effort to increase efficiency.
This study is a pilot project for the “San Francisco Flood Plain Project” (SFFPP), meant to delimit flood plain areas owned by the Brazilian federal government. The objective is to determine the attainable accuracy in river water surface delineation using satellite imagery from Landsat, Sentinel-1 and -2. We prioritize the evaluation of Landsat data due to its long systematical time series, allowing hydrological analysis requiring observations of at least 40 to 60 years and data from the Sentinel missions with their high frequency of revisit, improved spatial resolution (compared with Landsat) and possibility of observation in wet season (S-1). In our approach, we evaluated the accuracy by spectral bands individually and in combination, as well as polarization. We also tested a number of thematic information extraction techniques unsupervised (K-means and EM Cluster Analysis) and supervisioned (Random Forest - RF, k-nearest neighbors - KNN, Maximum Likelihood Classification – ML, Support Vector Machine – SVM, Mahalanobis). To validate our results, we used a PlanetScope mosaic (3 m). Results indicate that shortwave infrared bands have a higher capacity to separate water surface from other classes. For SAR data, the best separation was obtained by VV polarization (compared with VH). Techniques all reached agreement values >94% for the Sentinel-2 image, >93% for the Sentinel-1 image and >;86% for the Landsat-8. We consider both methodologies effectives to extract the water surface and appropriate for the real estate issues of the SFFPP project.
This article describes the “Sao Francisco Floodplain Project” (SFFPP) aiming at defining a Mean Ordinary Flood Line (MOFL) on the banks of the Sao Francisco River, in East Central Brazil. Land inserted within the MOFL of large rivers in Brazil are characterized as government-owned and are ruled by a special legislation. The lack of consensus for an effective method for this delimitation has raised much conflicts between dwellers, land owners and the federal government. To solve this, the SFFPP first aims to determine the mean flood plain level using historical water level data and then find all the dates since the launch of the first satellite sensor with a 30 m resolution (Landsat-4, 1982) corresponding to these particular water levels with a small margin of error. All Landsat images corresponding to these dates were acquired to produce a delineation of the MOFL. In a thorough series of tests to extract the water surface, the K-means segmentation using the shortwave infrared band of Landsat yielded the best results. A first refining of the MOFL was performed by interpolating the Landsat bands to improve the smoothness of the waterline. This refinement reduced the average distance error between the Landsat water edge and the true water edge from 9 to 7.5 meters. Then, some sections of the MOFL was further completed or refined using high-resolution multi-source satellite images where available. These first results were very encouraging and we were able to acquire Landsat images for each section of the river corresponding to the mean flood water level. Because Brazil has been suffering a significant reduction in rainfall since 2013, no recent SAR or optical images such as Sentinel-1 and -2 could be used. Analysis of the water level time series confirmed an alarming decreasing trend in the water discharge of the Sao Francisco River.
A number of methods have been developed for the automatic identification and delineation of individual tree crowns from high spatial resolution satellite image to provide support for the management and maintenance of forests both in natural and urban environments. In this paper we present a method that integrates a Marked Point Processes (MPP) model and Template Matching (TM) to extract individual tree crowns in two tropical environments. The MPP is an extension of Markov random fields in which objects are defined by their position within a space of possible positions and their marks (e.g. shape). The MPP has been increasingly used for the recognition of objects but most implementation use an oversimplified model as mark. We argue that the MPP could take better advantage of the geometry of trees by incorporating a three-dimensional model as a mark. Conversely, TM is an approach to pattern recognition that takes the characteristics of the objects into account. Our method uses cross-correlation for determining which objects have been correctly targeted by the MPP. The correlation between the illuminated 3D crown model and the image is an inheritance from TM. The methodology was applied in synthetic images and sub-images of the WorldView satellite in two different contexts in Brazil. The results are validated by counting the correctly identified trees and by comparing their size with our interpreted version. Results are encouraging with 65 to 90% of correctly identified trees. The most difficult cases are mostly related to the existence of clustered tree crowns.
Different satellite missions have instruments to measure the water level variation of oceans and some of these instruments are being used in continental water applications with satisfying results. Altimeters on-board the Envisat and SARAL(Altika) satellites are consistently used to measure the water level in continental water bodies. Recent studies on satellite altimetry combined with satellite imagery have shown the great potential of this technique to estimate the water volume of rivers, lakes, wetlands and reservoirs and its temporal variation in response to climate and other environmental variables. A consistent monitoring of water level variations in reservoirs is crucial to the development policies and implementation of actions regarding the distribution and use of the stored water resource. The Trés Marias reservoir is located within the São Francisco river basin, known as the national integration" river, which provides water flow to the semi-arid region of Brazil. This study presents a method to combine satellite altimetry and imagery of the lake's surface to estimate volume changes and create a model from which volume changes could be computed from either the altimetry or the lake's surface area. Our intention with this study is to evaluate the method and its precision, and the possibility to apply it in other areas, such as wetlands and other lakes where in situ measurements are not available. Moreover, data of monitoring stations usually have an arbitrary altitude reference and are not available for the general public; the data from the satellite altimetry has the advantage of being of global reference (geoid) and compatible with the establishment of a worldwide lake and reservoir database. We combined Envisat and SARAL/Altika altimetry data from 2007-2014 period with Landsat imagery from the same time frame. The data was corrected using a novel processing technique resulting in a relative precision of 0.24 m (RMSE).
Satellite altimeters on-board Envisat and SARAL (Altika) are routinely used to create virtual monitoring stations from the satellite path crossing with any river of significant width. These virtual stations have the advantage of having a low operational cost and providing near real-time absolute measurement of water level. However, many shortcomings still remain open questions and the precision of measurements can vary widely depending on a number of factors such as the river width and environmental conditions surrounding the water course. In this article we have concentrated our efforts on the relation between land cover classes, the shape of waveforms produced by the backscatter response and the separability among different land cover classes and water. Seven land cover classes often encountered nearby large river banks were analyzed: agriculture, native forest, planted forest, savanna, pasture, urban and open water. Waveforms of these classes were sampled to build a waveform library. They were compared among themselves using cross-correlation, cumulative difference and Kolmogorov-Smirnov distance. Average waveforms for each class were calculated and compared. The results show that only the open water" and forest" classes could be characterized as having a typical behavior, probably caused by the limitations of the measurements used. Furthermore, these two classes have very similar responses and could easily be confused. The other classes generally showed chaotic behavior which can mostly be attributed to variations in their cover characteristics. We expect that a better understanding of the influence of land cover on waveform shapes will increase accuracy of water level measurements.
With the availability of high-resolution satellite data, much research has been focused on the automatic detection and classi cation of individual tree crowns. Most of these studies were applied to temperate climates of the northern hemisphere, especially for forests of coniferous. Very few studies have been applied to the detection of trees in the tropical regions, least of all in the urban environment. Urban trees play a major role in maintaining or even improving the quality of life in cities by their contribution to the quality of the air, by absorbing rain water, by refreshing the air through transpiration and providing shadow. In this study we explored the potential of high-resolution WorldView-2 satellite data for the identi cation of urban individual tree crowns in the city of Belo Horizonte, Minas Gerais, Brazil, through an object-oriented approach. Irrelevant areas were masked (e.g. buildings, asphalt, shadows, exposed soil) using a threshold of NDVI. Three di erent approaches were tested to isolate and delineate individual tree crowns: region growing, watershed and template matching. For the rst two approaches several parameters were tested to nd the best result for the isolation of the individual tree crowns. An in-house program has been developed for template matching using a set of seven di erent templates of di erent species. A set of 300 individual tree crowns were visually interpreted in the WorldView-2 image to serve as validation and to compare the performance of the three di erent approaches. Then, the comparison was performed between the visual interpretation and the results of each approach by calculating the di erence between the areas as a ratio of the validated area. Our results show that the region growing approach provided the best results, with an accuracy of over 80%.
Non-point source pollution (NPSP) is perhaps the leading cause of water quality problems and one of the most challenging environmental issues given the difficulty of modeling and controlling it. In this article, we applied the Manning equation, a hydraulic concept, to improve models of non-point source pollution and determine its influence as a function of slope - land cover roughness for runoff to reach the stream. In our study the equation is somewhat taken out of its usual context to be applies to the flow of an entire watershed. Here a digital elevation model (DEM) from the SRTM satellite was used to compute the slope and data from the RapidEye satellite constellation was used to produce a land cover map later transformed into a roughness surface. The methodology is applied to a 1433 km2 watershed in Southeast Brazil mostly covered by forest, pasture, urban and wetlands. The model was used to create slope buffer of varying width in which the proportions of land cover and roughness coefficient were obtained. Next we correlated these data, through regression, with four water quality parameters measured in situ: nitrate, phosphorous, faecal coliform and turbidity. We compare our results with the ones obtained by fixed buffer. It was found that slope buffer outperformed fixed buffer with higher coefficients of determination up to 15%.
Radar-based satellite altimetry is a well recognized measuring technique with good precision for oceanographic applications. For continental hydrology, its use is complicated by a number of factors such as river width, satellite crossing angle and noise from the river banks or islands. These factors make precision vary significantly. The satellite crossing points can be made into virtual gauging stations that can complement the existing network of in situ stations. This article describes a series of spatially explicit processing to correct or exclude altimetry measurements not related to the water level. While some processing take advantage of a priori information such as the centerline of the river, other processing are based on pattern recognition to characterize the shape described by the sequence of points. These problems are dealt with by fitting a second degree polynomial curve to the sequence of points and characterizing its shape. The correction is applied by determining a weight for each point in the crossing sequence of measurements. These processing approaches have been combined into a single tool called VHSTOOL. The method is tested on a 1000 km stretch of the S˜ao Francisco River in Brazil. Data from Envisat cover the 2003-2010 period while the recently launched Altika sensor provided data for a few months in 2013. Results show that the average accuracy of 60 cm obtained (45 cm by removing outliers) is comparable to that of completely manual methods. Altika measurements could not be validated since no recent in situ data was available but initial evaluation suggests increased details should bring some improvements over Envisat data.
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