Use of remote sensing data to examine spatial pattern measurement for improved forest management practices

2003 ◽  
Author(s):  
H.G. Wilson ◽  
P.J. Howarth
Keyword(s):  
Remote Sensing ◽  
Forest Management ◽  
Spatial Pattern ◽  
Management Practices ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Forest Management Practices

scholarly journals Spatial pattern evaluation of a calibrated national hydrological model – a remote sensing based diagnostic approach

2017 ◽  
Author(s):  
Gorka Mendiguren ◽  
Julian Koch ◽  
Simon Stisen
Keyword(s):  
Remote Sensing ◽  
Spatial Pattern ◽  
Spatial Patterns ◽  
Hydrological Model ◽  
Remote Sensing Data ◽  
Driving Mechanism ◽  
Distributed Hydrological Model ◽  
Eof Analysis ◽  
Sensing Data ◽  
Spatio Temporal

Abstract. Distributed hydrological models are traditionally evaluated against discharge stations, emphasizing the temporal and neglecting the spatial component of a model. The present study widens the traditional paradigm by highlighting spatial patterns of evapotranspiration (ET), a key variable at the land-atmosphere interface, obtained from two different approaches at the national scale of Denmark. The first approach is based on a national water resources model (DK-model), using the MIKE-SHE model code, and the second approach utilizes a two source energy balance model (TSEB) driven mainly by satellite remote sensing data. The main hypothesis of the study is that while both approaches are essentially estimates, the spatial patterns of the remote sensing based approach are explicitly driven by observed land surface temperature and therefore represent the most direct spatial pattern information of ET; enabling its use for distributed hydrological model evaluation. Ideally the hydrological model simulation and remote sensing based approach should present similar spatial patterns and driving mechanism of ET. However, the spatial comparison showed that the differences are significant and indicating insufficient spatial pattern performance of the hydrological model. The differences in spatial patterns can partly be explained by the fact that the hydrological model is configured to run in 6 domains that are calibrated independently from each other, as it is often the case for large scale multi-basin calibrations. Furthermore, the model incorporates predefined temporal dynamics of Leaf Area Index (LAI), root depth (RD) and Crop coefficient (Kc) for each land cover type. This zonal approach of model parametrization ignores the spatio-temporal complexity of the natural system. To overcome this limitation, the study features a modified version of the DK-Model in which LAI, RD, and KC are empirically derived using remote sensing data and detailed soil property maps in order to generate a higher degree of spatio-temporal variability and spatial consistency between the 6 domains. The effects of these changes are analyzed by using the empirical orthogonal functions (EOF) analysis to evaluate spatial patterns. The EOF-analysis shows that including remote sensing derived LAI, RD and KC in the distributed hydrological model adds spatial features found in the spatial pattern of remote sensing based ET.

scholarly journals Using Farmer Decision Rules for Mapping Historical Land Use Change Patterns from 1954 to 2007 in Rural Northwestern Vietnam

Land ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 130
Author(s):  
Thanh Thi Nguyen ◽  
Melvin Lippe ◽  
Carsten Marohn ◽  
Tran Duc Vien ◽  
Georg Cadisch
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Use Change ◽  
Management Practices ◽  
Anthropogenic Activities ◽  
Decision Rules ◽  
Remote Sensing Data ◽  
Rural Landscapes ◽  
Sensing Data ◽  
Assessment Period

The present study revealed how local socioecological knowledge elucidated during participatory rural appraisals and historical remote sensing data can be combined for analyzing land use change patterns from 1954 to 2007 in northwestern Vietnam. The developed approach integrated farmer decision rules on cropping preferences and location, visual and supervised classification methods, and qualitative information obtained during various forms of participatory appraisals. The integration of historical remote sensing data (aerial photo, Landsat, LISS III) with farmer decision rules showed the feasibility of the proposed method to explain crop distribution patterns for the assessment period of 53 years. Our approach is beneficial for data-limited environments, which is a prevalent situation for many developing regions. The derived land use and crop type dataset was used for understanding how anthropogenic activities altered the study area of the Chieng Khoi commune during the assessment period of five decades, and what potential impact this can have on the natural resource base. The newly developed approach offers a methodological pathway that can be easily transferred to local government authorities for a better understanding of cropping transitions and agricultural expansion trends in data-limited rural landscapes. The detected land use change patterns and upland cropping expansion of more than two hundred percent in 53 years not only revealed the consequences of the interactions and feedback between farmers and their land, but further highlighted the urgent need for implementing sustainable land management practices in the case study watershed of the Chieng Khoi commune and northwestern Vietnam in general.

Representation of crop phenology and associated management practices in the SWAT+ model using global datasets for large scale hydrological applications

2021 ◽  
Author(s):  
Albert Nkwasa ◽  
Celray James Chawanda ◽  
Ann van Griensven ◽  
Jonas Jägermeyr
Keyword(s):  
Remote Sensing ◽  
Management Practices ◽  
Swat Model ◽  
Remote Sensing Data ◽  
Hydrological Models ◽  
Multiple Cropping ◽  
Crop Phenology ◽  
Sensing Data ◽  
Cropping Seasons ◽  
Global Datasets

<p>Even though cropland cultivation covers over 40% of the planet’s ice free land surface, most regional and global hydrological models either ignore the representation of cropland or consider crop cultivation in a simplistic way or in abstract terms without any management practices. Yet, the water balance of cultivated areas is strongly influenced by applied management practices (e.g. planting, irrigation, fertilization, harvesting). For instance, the SWAT+ model represents agricultural land by default in a generic way where the timing of the cropping season is driven by accumulated heat units. However, this approach does not work for tropical and sub-tropical regions such as the sub-Saharan Africa where crop growth dynamics are mainly controlled by rainfall rather than temperature.</p><p>In this study, we present an approach on how to reasonably incorporate crop phenology using decision tables and global datasets of rainfed and irrigated croplands with the associated cropping calendar and fertilizer applications in a SWAT+ model for North Eastern Africa. We evaluate the influence of the crop phenology representation on simulations of Leaf Area Index (LAI) and Evapotranspiration (ET) using LAI remote sensing data derived from Proba-V satellite and WaPOR ET data respectively. Results show that a representation of crop phenology using global datasets leads to improved temporal patterns of LAI and ET simulations especially for regions with a single cropping cycle.  However, for regions with multiple cropping seasons, global phenology datasets need to be complemented with local data or remote sensing data to capture additional cropping seasons. We conclude that regional and global hydrological models can benefit from improved representations of crop phenology and the associated management practices. Future work regarding the incorporation of multiple cropping seasons in global phenology data is needed to better represent cropping cycles in global hydrological models.</p>

Rice water requirements: local assessment based on remote sensing data in the Lower Mondego (Portugal)

2021 ◽  
Author(s):  
Isabel P. de Lima ◽  
Romeu G. Jorge ◽  
João L.M.P. de Lima
Keyword(s):  
Remote Sensing ◽  
Irrigation Water ◽  
Management Practices ◽  
Remote Sensing Data ◽  
Weather Data ◽  
Practical Consideration ◽  
Water Requirements ◽  
Rice Agriculture ◽  
Sensing Data ◽  
The Impact

<p>Irrigated rice agriculture, which is traditionally conducted applying continuous flooding, requires much more irrigation water than non-ponded crops. This can be a constraint in areas facing water scarcity issues, where the pursue for water resources optimization requires that water use efficiency is increased. Therefore, main local challenges for rice production are often to identify and apply more favorable and efficient irrigation and crop management practices, while guaranteeing high crop yields. For this purpose, the knowledge of rice crop water requirements is an important practical consideration. However, there are usually several limiting factors to obtain relevant data for the local conditions. Several recent approaches and methodologies based on remote sensing data, such as images obtained from satellites and Unmanned Aerial Systems (UAS), are offering attractive alternative routes to estimate crop evapotranspiration in a fast and easy way, including in rice fields.</p><p>For the rice producing area of the Lower Mondego region (Portugal), we report on exploring the usefulness of remote sensing tools for the local rice agriculture monitoring and management. Data include 25 land surface images of rice cultivated areas obtained from satellite Sentinel-2A during 2020, which together with weather data and crop parameters permits to calculate biophysical indicators and indices of vegetation water stress. Although remote sensing data available from satellite imagery presents some practical constraints (e.g. cloud cover, resolution), preliminary results from this study show that they allow to improve the characterization of the rice local cultivation conditions, therefore contributing to evaluate the impact of applying different irrigation and agriculture management practices, in particular those that have the potential to lead to significant savings of irrigation water.</p><p>This work was conducted under the umbrella of the international project MEDWATERICE (www.medwaterice.org) that focus on improving the sustainable use of water in the Mediterranean rice agro-ecosystem and aims to exploring the opportunity to apply water-saving, alternative, rice irrigation methods.</p>

From comprehensive field inventories to remotely sensed wall-to-wall stand attribute data - a brief history of management inventories in the Nordic countries

2020 ◽  
Author(s):  
Matti Maltamo ◽  
Petteri Packalen ◽  
Annika Kangas
Keyword(s):  
Remote Sensing ◽  
Forest Management ◽  
Laser Scanning ◽  
Remote Sensing Data ◽  
Nordic Countries ◽  
Optical Remote Sensing ◽  
Mixed Stands ◽  
Forest Management Planning ◽  
Sensing Data ◽  
Visual Assessments

Forest Management Inventories (FMIs) provide critical information, usually at the stand level, for forest management planning. A typical FMI includes i) the delineation of the inventory area to stands by applying auxiliary information, ii) the classification of the stands according to categorical attributes, such as age, site fertility, main tree species, stand development, and iii) measurement, modelling and prediction of stand attributes of interest. The emergence of wall-to-wall remote-sensing data has enabled a paradigm change in FMIs from highly subjective, visual assessments to objective, model-based inferences. Previously, optical remote-sensing data were used to complement visual assessments, especially in stand delineation and height measurements. The evolution of airborne laser scanning (ALS) has made objective estimation of forest characteristics with known accuracy possible. New optical and Lidar-based sensors and platforms will allow further improvements of accuracy. However, there are still bottlenecks related to species-specific stand attribute information in mixed stands and assessments of tree quality. Here we concentrate on approaches and methods that have been applied in the Nordic countries in particular.

scholarly journals Mapping Mature Post-Agricultural Forests in the Polish Eastern Carpathians with Archival Remote Sensing Data

2021 ◽  
Vol 13 (10) ◽  
pp. 2018
Author(s):  
Zofia Jabs-Sobocińska ◽  
Andrzej N. Affek ◽  
Ireneusz Ewiak ◽  
Mihai Daniel Nita
Keyword(s):  
Remote Sensing ◽  
Satellite Images ◽  
Management Practices ◽  
Forest Cover ◽  
Remote Sensing Data ◽  
Aerial Photographs ◽  
Secondary Forests ◽  
Agricultural Lands ◽  
Sensing Data ◽  
Eastern Carpathians

Post-WWII displacements in the Polish Carpathians resulted in widespread land abandonment. Most of the pre-war agricultural areas are now covered with secondary forests, which will soon reach the felling age. Mapping their exact cover is crucial to investigate succession–regeneration processes and to determine their role in the landscape, before making management decisions. Our goal was to map post-agricultural forests in the Polish Eastern Carpathians using archival remote sensing data, and to assess their connectivity with pre-displacement forests. We used German Flown Aerial Photography from 1944 to map agricultural lands and forests from before displacements, and Corona satellite images to map agricultural lands which converted into the forest as a result of this event. We classified archival images using Object-Based Image Analysis (OBIA) and compared the output with the current forest cover derived from Sentinel-2. Our results showed that mature (60–70 years old) post-agricultural forests comprise 27.6% of the total forest area, while younger post-agricultural forests comprise 9%. We also demonstrated that the secondary forests fill forest gaps more often than form isolated patches: 77.5% of patches are connected with the old-woods (forests that most likely have never been cleared for agriculture). Orthorectification and OBIA classification of German Flown Aerial Photographs and Corona satellite images made it possible to accurately determine the spatial extent of post-agricultural forest. This, in turn, paves the way for the implementation of site-specific forest management practices to support the regeneration of secondary forests and their biodiversity.

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