A Comparison of Model-Assisted Estimators to Infer Land Cover/Use Class Area Using Satellite Imagery

Remote sensing provides timely, economic, and objective data over a large area and has become the main data source for land cover/use area estimation. However, the classification results cannot be directly used to calculate the area of a given land cover/use type because of classification errors. The main purpose of this study is to explore the performance and stability of several model-assisted estimators in various overall accuracies of classification and sampling fractions. In this study, the confusion matrix calibration direct estimator, confusion matrix calibration inverse estimator, ratio estimator, and simple regression estimator were implemented to infer the areas of several land cover classes using simple random sampling without replacement in two experiments: a case study using simulation data based on RapidEye images and that using actual RapidEye and Huan Jing (HJ)-1A images. In addition, the simple estimator using a simple random sample without replacement was regarded as a basic estimator. The comparison results suggested that the confusion matrix calibration estimators, ratio estimator, and simple regression estimator could provide more accurate and stable estimates than the simple random sampling estimator. In addition, high-quality classification data played a positive role in the estimation, and the confusion matrix inverse estimators were more sensitive to the classification accuracy. In the simulated experiment, the average deviation of a confusion matrix calibration inverse estimator decreased by about 0.195 with the increasing overall accuracy of classification; otherwise, the variation of the other three model-assisted estimators was 0.102. Moreover, the simple regression estimator was slightly superior to the confusion matrix calibration estimators and required fewer sample units under acceptable classification accuracy levels of 70%–90%

Dinámica espacial de la cobertura de ribera de la zona de influencia del río Chambo

El objetivo del presente trabajo es evaluar la dinámica espacial de la cobertura de ribera de la zona de influencia del río Chambo en la zona del nacimiento del río (cuenca media alta), entre los 2500 a 3000 m.s.n.m. Para su ejecución, se emplean imágenes Landsat 7 del año 2000, RapidEye del año 2009 y Spot 6 del año 2019, en dos períodos: 2000-2009 y 2009-2019, las cuales se someten a clasificación supervisada aplicando el algoritmo de máxima verosimilitud, identificándose cinco clases de cobertura de suelo: pasto, cultivos, suelo-remanentes de páramo, bosque, y antrópica; los resultados de la clasificación se validan mediante el cálculo de medidas de precisión y el índice kappa. Con el empleo de matrices de tabulación cruzada se identifican las ganancias, pérdidas y persistencias en los dos períodos estudiados; donde, se determina que, en el primer período de estudio la cobertura de suelo-remanentes de páramo presenta el mayor porcentaje de pérdida (26.70%), la cobertura de cultivo el mayor porcentaje de ganancia (28.91%), y en el segundo período la clase de cultivo presenta los mayores porcentajes de pérdidas (18.94%) y ganancias (17.29%). La proyección cartográfica de la zona, para el año 2030, aplicando la metodología MOLUSCE, predice que las áreas: categoría antrópica incrementará en un 1.27%, la de bosque disminuirá en 1.19%, la de suelo-remanentes de páramo ganará un 0.79%, las coberturas de cultivo y pasto disminuirán en 0.45% y 0.43% respectivamente. Los resultados obtenidos permiten atribuir las transiciones entre coberturas al crecimiento poblacional, actividades de forestación, reforestación, deforestación y agropecuarias, erupciones volcánicas, colonización de tierras y expansión de la actividad agrícola. Se recomiendan estudios complementarios que involucren medios de vida y calidad de agua, que faciliten la identificación de zonas vulnerables para proponer medidas de adaptación, prevención y/o restauración.The aim of the research is to evaluate the spatial dynamics of the river bank coverage in the influence area of Chambo River, specifically in the area of the river source (upper middle basin), from 2,500 to 3,000 meters above sea level. For its implementation, Landsat 7 images 2000, RapidEye 2009 and Spot 6 2019 are used in two periods: 2000-2009 and 2009-2019, which are exposed to supervised classification applying the maximum likelihood algorithm, identifying five types of ground cover like grass, crops, soil-moorland remnant, forest, and anthropic; The results of the classification are validated by calculating precision measures and kappa index. With the use of cross-tabulation matrices, the gains, losses and persistence are identified in the two periods studied. It is evidenced that during the first study period, the soil-moorland remnant coverage presents the highest loss percentage (26.70%), the crop cover represents the highest percentage of gain (28.91%), and during the second period the crop type presents the highest loss percentages (18.94%) and gains (17.29%). The map projection of the area for 2030 applying the MOLUSCE methodology, predicts that the areas with anthropic category will increase by 1.27%, the forest will decrease by 1.19%, the soil-moorland remnant will gain 0.79%, crop and pasture coverage will decrease by 0.45% and 0.43% respectively. The results obtained allow the transitions between coverage to be attributed to population growth, afforestation, reforestation, deforestation and agricultural activities, volcanic eruptions, land colonization and expansion of agricultural activity. Complementary studies involving livelihoods and water quality are recommended in order to facilitate the identification of vulnerable areas to propose adaptation, prevention and/or restoration measures