Cambios en la cobertura del matorral seco montano del valle Chota (Imbabura y Carchi, Ecuador) entre 1990 y 2020

El cambio en el uso del suelo es uno de los principales conductores del cambio global, así como una causa de la pérdida de biodiversidad. En el norte del Ecuador, el matorral seco montano es un ecosistema característico de los valles interandinos y que se encuentra amenazado por la intervención antrópica. El presente trabajo estudió el cambio de la cobertura del matorral seco montano en el valle del río Chota en un periodo de 30 años y evaluó su estado de conservación. Se aplicó el método de clasificación supervisada en las imágenes satelitales LANDSAT de los años 1990, 2007 y 2020, para analizar las tasas de variación de las coberturas. El estado de conservación se determinó con una matriz de evaluación que consideró siete variables y 25 indicadores y la sobreposición de capas temáticas con SIG. Los resultados denotaron una pérdida del 20% de la cobertura del matorral seco montano, a un promedio anual de 231.83 ha/año (-0.75%) por causas antrópicas. Estas causas fueron responsables del cambio de cobertura de más de la mitad del 8.34% del área que ocupaba, principalmente la expansión de la frontera agrícola con un 3.96%. La presión y efecto de los factores antrópicos identificados causaron que el estado actual de conservación sea Regular. Se proponen tres estrategias de conservación: buenas prácticas agroecológicas, una gestión ambiental integral y la educación ambiental.

Land use change is one of the main drivers of global change, as well as a cause of biodiversity loss. In northern Ecuador, the montane dry scrub is a characteristic ecosystem of the inter-Andean valleys and is threatened by anthropogenic intervention. This study examined the change in montane dry scrub coverage in the Chota River Valley over a 30-year period and evaluated its conservation status. The supervised classification method was applied to LANDSAT satellite images from 1990, 2007, and 2020 to analyze the rates of coverage variation. The conservation status was determined using an evaluation matrix that considered seven variables and 25 indicators and the overlap of thematic layers with GIS. The results showed a loss of 20% of montane dry scrub coverage, at an annual average of 231.83 ha/year (-0.75%) due to anthropogenic causes. These causes were responsible for the coverage change of more than half of the 8.34% of the area it occupied, mainly the expansion of the agricultural frontier with 3.96%. The pressure and effect of the identified anthropogenic factors caused the current conservation status to be Regular. Three conservation strategies are proposed: good agroecological practices, comprehensive environmental management, and environmental education.

Contrastive analysis of extraction of Polygonatum cyrtonema planting area based on data of "Resource 3" / 中国中药杂志

Polygonatum cyrtonema is a famous bulk medicinal material which is the medicinal and edible homologous. With the implementation of the traditional Chinese medicine industry to promote precise poverty alleviation, the planting area of P. cyrtonema in Jinzhai is becoming larger and larger in recent years. Jinzhai is located in the Dabie Mountainous area, which is the largest mountain area and county in Anhui Province. The cultivation of P. cyrtonema is scattered, and the traditional Chinese medicine resources investigation is not only inefficient and accurate. In this study,the "Resource 3"(ZY-3) remote sensing image was used as the best observation phase,and the method of support vector machine classification was used. The method of parallelepiped, minimum distance, mahalanob is distance, maximum likelihood classification and neural net were used to classify and recognize the P. cyrtonema in the whole region. In order to determine the accuracy and reliability of classification results, the accuracy of six supervised classification results was evaluated by confusion matrix method, and the advantages and disadvantages of six supervised classification methods for extracting P. cyrtonema field planting area were compared and analyzed. The results showed that the method of support vector machine classification was more appropriate than that using other classification methods. It provides a scientific basis for monitoring the planting area of P. cyrtonemain field.

Extracting Paeonia lactiflora planted area in Dangshan of Anhui province based on ZY-3 remote sensing image / 中国中药杂志

In order to comprehensively monitor the dynamic change of Paeonia lactiflora planting area,the investigation of P. lactiflora planting area in Dangshan was carried out. It can provide reference for the planting detection of P. lactiflora in Huaibei Plain.Based on remote sensing technology,this paper extracts the planting area of P. lactiflora in Dangshan in 2018 by using the minimum distance method,maximum likelihood method,parallel hexahedron method and Mahalanobis distance method,using the remote sensing image of ZY-3 Satellite as the data source,and makes a comparative analysis with the results. The results show that the maximum likelihood method is better than the other three methods. This method can provide reference for remote sensing monitoring of P. lactiflora planting area in China.

Avaliação de técnicas de segmentação e classificação automática de imagens landsat-tm no mapeamento do uso do solo na Amazônia1

Land use mapping is essential for the understanding of global change processes, especially in regions which are experiencing great pressure for development such as the Amazon. Traditionally, these mappings have been done using visual interpretation techniques of satellite imagery, that provide satisfactory results but are time-consuming and highly cost. In this paper, a technique of image segmentation based on region growing algorithm, followed by a per-field non-supervised classification, is proposed. Thus, the thematic classification is based on a set of image elements (pixels), benefiting from contextinformation, therefore minimizing the limitations of the digital processing techniques based on single pixels (per-pixel classification). This approach was evaluated in a typical test site of the Amazon region located to the north of Manaus, AM, using both original Landsat Thematic Mapper images and their decomposition into endmembers such as green vegetation, wood material, shade and soil, named mixture image in this paper. The results were validated by a reference map obtained from proved visual interpretation techniques of satellite imagery and by field check and indicated that automatic classification is feasible to map land use in Amazonia. Statistics tests indicated that there was significant agreement between the automated digital classifications and the reference map (at 95% confidence level).

O mapeamento do uso da terra é fundamental para o entendimento dos processos de mudanças globais, especialmente em regiões como a Amazônia que estão sofrendo grande pressão de desenvolvimento. Tradicionalmente estes mapeamentos têm sido feitos utilizando técnicas de interpretação visual de imagens de satélites, que, embora de resultados satisfatórios, demandam muito tempo e alto custo. Neste trabalho é proposta uma técnica de segmentação da imagens com base em um algoritmo de crescimento de regiões, seguida de uma classificação não-supervisionada por regiões. Desta forma, a classificação temática se refere a um conjunto de elementos (pixels da imagem), beneficiando-se portanto da informação contextual e minimizando as limitações das técnicas de processamento digital baseadas em análise pontual (pixel-a-pixel). Esta técnica foi avaliada numa área típica da Amazônia, situada ao norte de Manaus, AM, utilizando imagens do sensor "Thematic Mapper" - TM do satélite Landsat, tanto na sua forma original quanto decomposta em elementos puros como vegetação verde, vegetação seca (madeira), sombra e solo, aqui denominada imagem misturas. Os resultados foram validados por um mapa de referência gerado a partir de técnicas consagradas de interpretação visual, com verificação de campo, e indicaram que a classificação automática é viável para o mapeamento de uso da terra na Amazônia. Testes estatísticos indicaram que houve concordância significativa entre as classificações automáticas digitais e o mapa de referência (em tomo de 95% de confiança).