Improved Fine-Scale Tropical Forest Cover Mapping for Southeast Asia Using Planet-NICFI and Sentinel-1 Imagery

The accuracy of existing forest cover products typically suffers from “rounding” errors arising from classifications that estimate the fractional cover of forest in each pixel, which often exclude the presence of large, isolated trees and small or narrow forest clearings, and is primarily attributable to the moderate resolution of the imagery used to make maps. However, the degree to which such high-resolution imagery can mitigate this problem, and thereby improve large-area forest cover maps, is largely unexplored. Here, we developed an approach to map tropical forest cover at a fine scale using Planet and Sentinel-1 synthetic aperture radar (SAR) imagery in the Google Earth Engine platform and used it to map all of Southeastern Asia’s forest cover. The machine learning approach, based on the Random Forests models and trained and validated using a total of 37,345 labels collected from Planet imagery across the entire region, had an accuracy of 0.937 and an F1 score of 0.942, while a version based only on Planet imagery had an accuracy of 0.908 and F1 of 0.923. We compared the accuracy of our resulting maps with 5 existing forest cover products derived from medium-resolution optical-only or combined optical-SAR approaches at 3,000 randomly selected locations. We found that our approach overall achieved higher accuracy and helped minimize the rounding errors commonly found along small or narrow forest clearings and deforestation frontiers where isolated trees are common. However, the forest area estimates varied depending on topographic location and showed smaller differences in highlands (areas \u3e300 m above sea level) but obvious differences in complex lowland landscapes. Overall, the proposed method shows promise for monitoring forest changes, particularly those caused by deforestation frontiers. Our study also represents one of the most extensive applications of Planet imagery to date, resulting in an open, high-resolution map of forest cover for the entire Southeastern Asia region. © 2023 Feng Yang et al

Estimativa de biomassa acima do solo de caatinga através de imagens SAR

A Caatinga é um bioma de ocorrência do semiárido do Brasil, tendo uma das maiores ocupações populacionais em terras secas no mundo. Porém, ainda há carência da aplicação de novas técnicas de estimativa de sua biomassa a partir de dados remotos. Sendo assim, o objetivo da tese foi avaliar a acurácia das imagens do Sentinel-1 na estimativa da biomassa acima do solo (BAS) da Caatinga no Alto Sertão do estado de Sergipe. A distribuição espacial e fenológica da Caatinga na região estudada foi analisada utilizando o Normalized Difference Vegetation Index (NDVI). A análise florística e fitossociológica foi realizada por meio do inventário florestal, utilizado também para calcular a BAS nos fragmentos de Caatinga. Foram testados diferentes métodos de filtragem para avaliar a eficácia na redução do speckle na imagem do Sentinel-1 analisando o número equivalente de looks (NEL). A estimativa da BAS utilizando imagens do Sentinel-1 utilizou dados do inventário em campo, comparando as acurácias das respostas de filtros a partir da decomposição polarimétrica e, posteriormente, testando os atributos: VV, VH, VH/VV, Radar Vegetation Index (RVI), Dual Polarization SAR Vegetation Index (DPSVI), Entropia (H), Ângulo Alpha (α), por meio de regressões lineares simples e múltiplas, na Caatinga Verde, Intermediária e Seca. A Caatinga estudada não é influenciada pelos fatores fisiográficos: declividade, altimetria, proximidade ao rio e tipo de solo. A Caatinga densa apresenta curvas fenológicas com maior condição de verdor que a aberta. A espécie Cenostigma pyramidale é a mais abundante entre as 25 identificadas. O filtro Gamma apresentou melhor desempenho na redução do speckle. A comparação da BAS estimada e observada indicou que a regressão múltipla fornece melhor acurácia nos períodos de Verdor (R2: 0,72) e Intermediário (R2: 0,73) da vegetação, com a contribuição de atributos coerentes e incoerentes. Portanto, o estudo permitiu analisar espacialmente a Caatinga estudada, caracterizando-a fenologicamente bem como sua composição e fitossociologia. Também foi possível verificar as diferentes atenuações do speckle no pré- processamento das imagens. Por fim, constatou-se que as imagens do Sentinel-1 podem ser utilizadas para a estimar a BAS.The Caatinga is a biome occurring in the semiarid region of Brazil, having one of the largest population occupations in dry lands in the world. However, there is still a lack of application of new techniques for estimating its biomass from remote data. Therefore, the objective of the thesis was to evaluate the accuracy of Sentinel-1 images in estimating the aboveground biomass (BAS) of the Caatinga in the Alto Sertão of the state of Sergipe. The spatial and phenological distribution of the Caatinga in the studied region was analyzed using the Normalized Difference Vegetation Index (NDVI). The floristic and phytosociological analysis was carried out through the forest inventory, also used to calculate the BAS in the Caatinga fragments. Different filtering methods were tested to evaluate the effectiveness of speckle reduction in the Sentinel-1 image by analyzing the equivalent number of looks (NEL). The BAS estimate using Sentinel-1 images used field inventory data comparing the accuracy of filter responses from the polarimetric decomposition and, later, testing the attributes: VV, VH, VH/VV, Radar Vegetation Index (RVI), Dual Polarization SAR Vegetation Index (DPSVI), Entropy (H), Alpha Angle (α), through simple and multiple linear regressions, in the Greenness, Intermediate and Dry Caatinga. The studied Caatinga is not influenced by physiographic factors: slope, altimetry, proximity to the river and type of soil. Dense Caatinga has phenological curves with greater greenness than open one. The Cenostigma pyramidale species is the most abundant among the 25 identified. The Gamma filter showed better performance in speckle reduction. The comparison of the estimated and observed BAS indicated that the multiple regression provides better accuracy in the Greenness (R2: 0.72) and Intermediate (R2: 0.73) periods of the vegetation, with the contribution of coherent and incoherent attributes. Therefore, the study allowed the spatial analysis of the studied Caatinga, characterizing it phenologically as well as its composition and phytosociology. It was also possible to verify the different attenuations of the speckle in the pre-processing of the images. Finally, it was found that Sentinel-1 images can be used to estimate BAS

FIREX mission requirements document for renewable resources

The initial experimental program and mission requirements for a satellite synthetic aperture radar (SAR) system FIREX (Free-Flying Imaging Radar Experiment) for renewable resources is described. The spacecraft SAR is a C-band and L-band VV polarized system operating at two angles of incidence which is designated as a research instrument for crop identification, crop canopy condition assessments, soil moisture condition estimation, forestry type and condition assessments, snow water equivalent and snow wetness assessments, wetland and coastal land type identification and mapping, flood extent mapping, and assessment of drainage characteristics of watersheds for water resources applications. Specific mission design issues such as the preferred incidence angles for vegetation canopy measurements and the utility of a dual frequency (L and C-band) or dual polarization system as compared to the baseline system are addressed

Earth Resources: A continuing bibliography with indexes, issue 29, April 1981

This bibliography lists 308 reports, articles, and other documents introduced into the NASA scientific and technical information system between January 1, 1981 and March 31, 1981. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

Earth Resources: A continuing bibliography, with indexes, issue 31

This bibliography lists 505 reports, articles, and other documents introduced into the NASA scientific and technical information system. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

Earth Resources: A continuing bibliography (issue 32)

This bibliography list 580 reports, articles and other documents introduced into the NASA scientific and technical information system. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

Applications of satellite ‘hyper-sensing’ in Chinese agriculture:Challenges and opportunities

Ensuring adequate food supplies to a large and increasing population continues to be the key challenge for China. Given the increasing integration of China within global markets for agricultural products, this issue is of considerable significance for global food security. Over the last 50 years, China has increased the production of its staple crops mainly by increasing yield per unit land area. However, this has largely been achieved through inappropriate agricultural practices, which have caused environmental degradation, with deleterious consequences for future agricultural productivity. Hence, there is now a pressing need to intensify agriculture in China using practices that are environmentally and economically sustainable. Given the dynamic nature of crops over space and time, the use of remote sensing technology has proven to be a valuable asset providing end-users in many countries with information to guide sustainable agricultural practices. Recently, the field has experienced considerable technological advancements reflected in the availability of ‘hyper-sensing’ (high spectral, spatial and temporal) satellite imagery useful for monitoring, modelling and mapping of agricultural crops. However, there still remains a significant challenge in fully exploiting such technologies for addressing agricultural problems in China. This review paper evaluates the potential contributions of satellite ‘hyper-sensing’ to agriculture in China and identifies the opportunities and challenges for future work. We perform a critical evaluation of current capabilities in satellite ‘hyper-sensing’ in agriculture with an emphasis on Chinese sensors. Our analysis draws on a series of in-depth examples based on recent and on-going projects in China that are developing ‘hyper-sensing’ approaches for (i) measuring crop phenology parameters and predicting yields; (ii) specifying crop fertiliser requirements; (iii) optimising management responses to abiotic and biotic stress in crops; (iv) maximising yields while minimising water use in arid regions; (v) large-scale crop/cropland mapping; and (vi) management zone delineation. The paper concludes with a synthesis of these application areas in order to define the requirements for future research, technological innovation and knowledge exchange in order to deliver yield sustainability in China

Earth resources: A continuing bibliography (issue 26)

This bibliography lists 480 reports, articles, and other documents introduced into the NASA scientific and technical information system between April 1, 1980 and June 30, 1980. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

Remote Sensing in Agriculture: State-of-the-Art

The Special Issue on “Remote Sensing in Agriculture: State-of-the-Art” gives an exhaustive overview of the ongoing remote sensing technology transfer into the agricultural sector. It consists of 10 high-quality papers focusing on a wide range of remote sensing models and techniques to forecast crop production and yield, to map agricultural landscape and to evaluate plant and soil biophysical features. Satellite, RPAS, and SAR data were involved. This preface describes shortly each contribution published in such Special Issue

Crop monitoring and yield estimation using polarimetric SAR and optical satellite data in southwestern Ontario

Optical satellite data have been proven as an efficient source to extract crop information and monitor crop growth conditions over large areas. In local- to subfield-scale crop monitoring studies, both high spatial resolution and high temporal resolution of the image data are important. However, the acquisition of optical data is limited by the constant contamination of clouds in cloudy areas. This thesis explores the potential of polarimetric Synthetic Aperture Radar (SAR) satellite data and the spatio-temporal data fusion approach in crop monitoring and yield estimation applications in southwestern Ontario. Firstly, the sensitivity of 16 parameters derived from C-band Radarsat-2 polarimetric SAR data to crop height and fractional vegetation cover (FVC) was investigated. The results show that the SAR backscatters are affected by many factors unrelated to the crop canopy such as the incidence angle and the soil background and the degree of sensitivity varies with the crop types, growing stages, and the polarimetric SAR parameters. Secondly, the Minimum Noise Fraction (MNF) transformation, for the first time, was applied to multitemporal Radarsat-2 polarimetric SAR data in cropland area mapping based on the random forest classifier. An overall classification accuracy of 95.89% was achieved using the MNF transformation of the multi-temporal coherency matrix acquired from July to November. Then, a spatio-temporal data fusion method was developed to generate Normalized Difference Vegetation Index (NDVI) time series with both high spatial and high temporal resolution in heterogeneous regions using Landsat and MODIS imagery. The proposed method outperforms two other widely used methods. Finally, an improved crop phenology detection method was proposed, and the phenology information was then forced into the Simple Algorithm for Yield Estimation (SAFY) model to estimate crop biomass and yield. Compared with the SAFY model without forcing the remotely sensed phenology and a simple light use efficiency (LUE) model, the SAFY incorporating the remotely sensed phenology can improve the accuracy of biomass estimation by about 4% in relative Root Mean Square Error (RRMSE). The studies in this thesis improve the ability to monitor crop growth status and production at subfield scale