Estimating Fractional Vegetation Cover of Row Crops from High Spatial Resolution Image

With high spatial resolution remote sensing images being increasingly used in precision agriculture, more details of the row structure of row crops are captured in the corresponding images. This phenomenon is a challenge for the estimation of the fractional vegetation cover (FVC) of row crops. Previous studies have found that there is an overestimation of FVC for the early growth stage of vegetation in the current algorithms. When the row crops are a form in the early stage of vegetation, their FVC may also have overestimation. Therefore, developing an algorithm to address this problem is necessary. This study used World-View 3 images as data sources and attempted to use the canopy reflectance model of row crops, coupling backward propagation neural networks (BPNNs) to estimate the FVC of row crops. Compared to the prevailing algorithms, i.e., empirical method, spectral mixture analysis, and continuous crop model coupling BPNNs, the results showed that the calculated accuracy of the canopy reflectance model of row crops coupling with BPNNs is the highest performing (RMSE = 0.0305). Moreover, when the structure is obvious, we found that the FVC of row crops was about 0.5–0.6, and the relationship between estimated FVC of row crops and NDVI presented a strong exponential relationship. The results reinforced the conclusion that the canopy reflectance model of row crops coupled with BPNNs is more suitable for estimating the FVC of row crops in high-resolution images.

Using marker-controlled watershed transform to detect baker’s cyst in MRI images: a pilot study

Introduction: Nowadays, Magnetic resonance imaging (MRI) has a high ability to distinguish between soft tissues because of high spatial resolution. Image processing is extensively used to extract clinical data from imaging modalities. In the medical image processing field, the knee’s cyst (especially baker) segmentation is one of the novel research areas.Material and Method: There are different methods for image segmentation. In this paper, the mathematical operation of the watershed algorithm is utilized by MATLAB software based on marker-controlled watershed segmentation for the detection of baker’s cyst in the knee’s joint MRI sagittal and axial T2-weighted images.Results: The performance of this algorithm was investigated, and the results showed that in a short time baker’s cyst can be clearly extracted from original images in axial and sagittal planes.Conclusion: The marker-controlled watershed segmentation was able to detect baker’s cyst reliable and can save time and current cost, especially in the absence of specialists it can help us for the easier diagnosis of MR images.

Efficient Building Extraction for High Spatial Resolution Images Based on Dual Attention Network

Building extraction with high spatial resolution images becomes an important research in the field of computer vision for urban-related applications. Due to the rich detailed information and complex texture features presented in high spatial resolution images, the distribution of buildings is non-proportional and their difference of scales is obvious. General methods often provide confusion results with other ground objects. In this paper, a building extraction framework based on deep residual neural network with a self-attention mechanism is proposed. This mechanism contains two parts: one is the spatial attention module, which is used to aggregate and relate the local and global features at each position (short and long distance context information) of buildings; the other is channel attention module, in which the representation of comprehensive features (includes color, texture, geometric and high-level semantic feature) are improved. The combination of the dual attention modules makes buildings can be extracted from the complex backgrounds. The effectiveness of our method is validated by the experiments counted on a wide range high spatial resolution image, i.e., Jilin-1 Gaofen 02A imagery. Compared with some state-of-the-art segmentation methods, i.e., DeepLab-v3+, PSPNet, and PSANet algorithms, the proposed dual attention network-based method achieved high accuracy and intersection-over-union for extraction performance and show finest recognition integrity of buildings.

Tree above-ground biomass dynamics in the Northeast Siberian tundra-taiga from nowadays to 3000 AD

<p>Changes in future above-ground biomass (AGB) of terrestrial ecosystems is one of the current interests in the light of climate change and essential to forecast for predicting potential climate feedbacks such as influence on the carbon balance. The tundra-taiga ecotone is a region that is prone to notable above-ground biomass changes, in the first instance due to the treeline advance. Forest is expected to occupy non-polygonal tundra. Our study region in central Chukotka (Northeastern Siberia) is a mountainous area on the northern border of the tundra-taiga ecotone that covers a wide range of vegetation types on a density gradient starting with lichen communities via open graminoid tundra to forest tundra. There is only one tree species – a deciduous conifer Larix cajanderi. We applied the individual-based spatially explicit model LAVESI to simulate larch AGB change from nowadays to 3000 AD under different climate scenarios, depending on Representative Concentration Pathways (RCPs) RCP 2.6, RCP 4.5 and RCP 8.5. We implemented in the model topographical parameters, as well as region-specific individual larch AGB equations, biological parameters of the tree growth and climate variables. We validated the new version of the model against field and Landsat satellite-based data, as well as a high spatial resolution image with distinctive trees visible, provided by ESRI (ArcGIS/World_imagery). Our first results are indicating mostly densification of existing tree stands before 2200 AD and forest expansion in the study region after 2200 AD even under the mildest RCP 2.6 scenario. First evaluations of the average tree AGB increase rates from present to 2200 AD are ranges from 0.007 (RCP 2.6) to 0.01 (RCP 8.5) kg*m<sup>-2</sup>*yr<sup>-1</sup>. Obtained rates of tree AGB change and its future distribution on the landscape can be particularly useful for conservation measures and modelling of future above-ground carbon stock dynamics.</p>

Framework for Accounting Reference Levels for REDD+ in Tropical Forests: Case Study from Xishuangbanna, China

The United Nations’ expanded program for Reducing Emissions from Deforestation and Forest Degradation (REDD+) aims to mobilize capital from developed countries in order to reduce emissions from these sources while enhancing the removal of greenhouse gases (GHGs) by forests. To achieve this goal, an agreement between the Parties on reference levels (RLs) is critical. RLs have profound implications for the effectiveness of the program, its cost efficiency, and the distribution of REDD+ financing among countries. In this paper, we introduce a methodological framework for setting RLs for REDD+ applications in tropical forests in Xishuangbanna, China, by coupling the Good Practice Guidance on Land Use, Land Use Change, and Forestry of the Intergovernmental Panel on Climate Change and land use scenario modeling. We used two methods to verify the accuracy for the reliability of land classification. Firstly the accuracy reached 84.43%, 85.35%, and 82.68% in 1990, 2000, and 2010, respectively, based on high spatial resolution image by building a hybrid matrix. Then especially, the 2010 Globeland30 data was used as the standard to verify the forest land accuracy and the extraction accuracy reached 86.92% and 83.66% for area and location, respectively. Based on the historical land use maps, we identified that rubber plantations are the main contributor to forest loss in the region. Furthermore, in the business-as-usual scenario for the RLs, Xishuangbanna will lose 158,535 ha (158,535 × 104 m2) of forest area in next 20 years, resulting in approximately 0.23 million t (0.23 × 109 kg) CO2e emissions per year. Our framework can potentially increase the effectiveness of the REDD+ program in Xishuangbanna by accounting for a wider range of forest-controlled GHGs.

Plume Height Time-Series Retrieval Using Shadow in Single Spatial Resolution Satellite Images

Volcanic plume height is a key parameter in retrieving plume ascent and dispersal dynamics, as well as eruption intensity; all of which are crucial for assessing hazards to aircraft operations. One way to retrieve cloud height is the shadow technique. This uses shadows cast on the ground and the sun geometry to calculate cloud height. This technique has, however, not been frequently used, especially not with high-spatial resolution (30 m pixel) satellite data. On 26 October 2013, Mt Etna (Sicily, Italy) produced a lava fountain feeding an ash plume that drifted SW and through the approach routes to Catania international airport. We compared the proximal plume height time-series obtained from fixed monitoring cameras with data retrieved from a Landsat-8 Operational Land Imager image, with results being in good agreement. The application of the shadow technique to a single high-spatial resolution image allowed us to fully document the ascent and dispersion history of the plume–cloud system. We managed to do this over a distance of 60 km and a time period of 50 min, with a precision of a few seconds and vertical error on plume altitude of ±200 m. We converted height with distance to height with time using the plume dispersion velocity, defining a bent-over plume that settled to a neutral buoyancy level with distance. Potentially, the shadow technique defined here allows downwind plume height profiles and mass discharge rate time series to be built over distances of up to 260 km and periods of 24 h, depending on vent location in the image, wind speed, and direction.

Hyperspectral Image Super-Resolution Based on Spatial Group Sparsity Regularization Unmixing

A hyperspectral image (HSI) contains many narrow spectral channels, thus containing efficient information in the spectral domain. However, high spectral resolution usually leads to lower spatial resolution as a result of the limitations of sensors. Hyperspectral super-resolution aims to fuse a low spatial resolution HSI with a conventional high spatial resolution image, producing an HSI with high resolution in both the spectral and spatial dimensions. In this paper, we propose a spatial group sparsity regularization unmixing-based method for hyperspectral super-resolution. The hyperspectral image (HSI) is pre-clustered using an improved Simple Linear Iterative Clustering (SLIC) superpixel algorithm to make full use of the spatial information. A robust sparse hyperspectral unmixing method is then used to unmix the input images. Then, the endmembers extracted from the HSI and the abundances extracted from the conventional image are fused. This ensures that the method makes full use of the spatial structure and the spectra of the images. The proposed method is compared with several related methods on public HSI data sets. The results demonstrate that the proposed method has superior performance when compared to the existing state-of-the-art.

ANÁLISE COMPARATIVA ENTRE TÉCNICAS DE SENSORIAMENTO REMOTO PARA MENSURAÇÃO DA VEGETAÇÃO URBANA NO MUNICÍPIO DE ALEGRE, ES

Objetivou-se com este estudo, comparar os resultados obtidos por meio de técnicas de sensoriamento remoto orbital, no intuito de mensurar a vegetação arbórea no município de Alegre, ES. Utilizou-se uma imagem de alta resolução espacial do satélite GeoEye-1 e determinou-se a fotointerpretação da vegetação como técnica modelo a ser comparada perante os índices de vegetação NDVI, SAVI e classificadores de imagens por Distância Euclidiana e Isoseg. Os Índices de Vegetação e os classificadores foram fatiados em três classes; vegetação urbana, pastagem e áreas urbanas. Por meio da fotointerpretação a vegetação urbana foi mensurada em 68 ha. Já por meio do índice de vegetação SAVI com fator de ajuste L 0,25 obteve 66,46 ha, correspondendo a 11,73% do perímetro urbano, entretanto, o índice NDVI subestimou a vegetação urbana em 19,13 ha quando comparado à área mapeada com o SAVI 0,25. Para a região em estudo o índice SAVI com fator de ajuste ao solo 0,25 e o classificador Isoseg podem ser usados para substituir a fotointerpretação, pois apresentaram áreas de vegetação urbana mensurada com valores aproximados, além de serem menos onerosos para obtenção do mapeamento da vegetação. Palavras-chave: Geoprocessamento; fotointerpretação; mapeamento urbano. COMPARATIVE ANALYSIS BETWEEN TECHNIQUES OF REMOTE SENSING IN MEASUREMENT OF VEGETATION URBAN IN MUNICIPALITY OF ALEGRE, ES Abstract The objective of the study was to compare the results obtained by means of orbital remote sensing techniques, in order to measure the arboreal vegetation in municipality of Alegre, ES. A high spatial resolution image of the GeoEye-1 satellite was used and the vegetation photointerpretation was determined as a model technique to be compared to NDVI, SAVI vegetation indexes and Euclidian Distance and Isoseg image classifiers.The Vegetation Indexes and the classifiers were sliced ​​into three classes; Urban vegetation, pasture and urban areas. Through the photointerpretation the urban vegetation was measured in 68 ha. However, the SAVI vegetation index with adjustment factor L 0.25 obtained 66.46 ha, corresponding to 11.73% of the urban perimeter, however, the NDVI index underestimated the urban vegetation by 19.13 ha when compared to the area Mapped with SAVI 0.25. For the study area, the SAVI index with soil adjustment factor 0.25 and the Isoseg classifier can be used to replace the photointerpretation, since they presented areas of urban vegetation measured with approximate values, besides being less expensive to obtain the mapping of the vegetation. Keywords: Geoprocessing; photointerpretation; urban mapping. ANÁLISIS COMPARATIVO ENTRE LAS TÉCNICAS DE TELEDETECCIÓN PARA LA MEDICIÓN DE LA VEGETACIÓN EN URBAN ALEGRE, ES Resumen El objetivo de este estudio fue comparar los resultados obtenidos por medio de técnicas de teledetección con el fin de medir la vegetación arbórea en la ciudad de Alegre, ES. Se utilizó una imagen de alta resolución espacial GeoEye-1 vía satélite y determinó la fotointerpretación de técnica de modelado de la vegetación que se compara con las imágenes de NDVI, SAVI y clasificadores de distancia euclídea y Isoseg. El índice de vegetación y clasificadores se cortaron en tres clases; la vegetación urbana, pastos y áreas urbanas. A través de la interpretación de fotografías vegetación urbana se midió en 68 ha. Ya través del índice de vegetación SAVI con factor de ajuste L obtenido 66,46 0,25 ha, que corresponde al 11,73% de la zona urbana, sin embargo, el índice NDVI subestimar la vegetación urbana en 19.13 ha, frente a la zona mapeada con el SAVI 0,25. Para la región en estudio el factor de ajuste del índice suelo SAVI 0,25 y clasificador Isoseg se pueden utilizar para reemplazar la interpretación de fotografías, como áreas presentados de la vegetación urbana medidos con valores aproximados, y son menos costosos de obtener el mapeo la vegetación. Palavras clave: Geoprocesamiento; fotointerpretación; la cartografía urbana.